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.
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Directional Indicator Crossovers [JopAlgo]Directional Indicator Crossovers — read trend intent at a glance, on any timeframe
Most traders ask two questions before they click: who’s in control right now and is control getting stronger or weaker?
The Directional Indicator (DI) answers the first one cleanly. +DI tracks upward directional movement; –DI tracks downward directional movement. When +DI crosses above –DI, buyers have the initiative; when –DI crosses above +DI, sellers do. DI Xover focuses on that simple, tradeable signal—the crossover—and keeps the pane uncluttered so you can layer it with your location/flow tools.
(If you add screenshots: image #1 can label +DI, –DI and a bullish crossover; image #2 can show a failed crossover in chop next to a successful one at a strong level.)
What you’re seeing (and how it’s built)
This indicator plots two lines in a separate pane:
+DI (green): smoothed positive directional movement.
–DI (red): smoothed negative directional movement.
Under the hood (length = 14 by default):
It measures how much today’s high exceeded yesterday’s high (up move) and how much today’s low fell below yesterday’s low (down move).
It keeps only the dominant side each bar (if up > down and up > 0 → up counts; vice-versa for down).
It normalizes by True Range (so moves are scaled by volatility) and smooths with RMA (so you don’t get jitter).
It raises alerts when +DI crosses above –DI (bullish) or –DI crosses above +DI (bearish).
How to read it, fast:
Cross up = buyers just took initiative.
Cross down = sellers just took initiative.
Wider distance between the lines = stronger control.
Lines braided/tight = balance/chop → expect more fake crosses.
DI is about directional control. It doesn’t tell you where to trade—that’s your location (e.g., Volume Profile, AVWAP). Use DI as a timing/confirmation layer, not as a standalone level generator.
Using DI Crossovers on any timeframe
The framework doesn’t change; only your expectations do as you zoom.
Scalping (1–5m)
Treat crossovers as triggers at levels. If price is tagging VAL/VAH/LVN (from Volume Profile v3.2) or Anchored VWAP, a fresh +DI cross up is your green light for a quick long; –DI cross up flips that logic for shorts.
Avoid taking every crossover mid-range—wait for location first.
In fast tape, require the lines to separate for 1–2 bars after the cross before you click.
Intraday (15m–1H)
In trend days, the first pullback into your level (POC/VA boundary/AVWAP) that prints a fresh +DI cross up is often the cleanest add/entry.
In balance days, fade DI crosses at edges back to POC—only if your flow tool isn’t screaming absorption against you.
Swing (2H–4H)
Look for confluence: at Weekly AVWAP or composite VAL/VAH, a DI crossover that stays separated for several bars is a solid momentum confirmation.
Failed crossover (lines recross quickly) near a level is a useful fail signal—expect a move back into value.
Position (1D–1W)
Use fewer, bigger signals: a weekly DI cross at Monthly/Quarterly AVWAP or at composite value edges marks a regime change.
Add on pullbacks when the controlling DI stays dominant (distance holds or widens).
Entries, exits, and risk (simple rules)
Entry (with level): wait for price to reach your level (e.g., VAL/VAH or AVWAP), then take the trade with the DI cross in that direction.
Filter: skip crosses when the two lines are braided (tiny separation) unless you’re trading a tight scalp with strict risk.
Exit / reduce: if your trade was based on a bullish cross, consider reducing when –DI recaptures +DI or the lines flatten at your target HVN/POC.
Stops: put them beyond the level (not just on a DI recross), but treat a fast recross as a warning to tighten.
Settings that actually matter (and how to tune them)
DI Length (default 14):
Shorter (7–10) = faster signals, more noise (good for scalps with filters).
Longer (20–30) = fewer but stronger signals (good for swing/position).
If you often see flip-flops, lengthen the setting or take crosses only at VP/AVWAP levels.
Pro tip: Define a minimum separation rule for yourself (e.g., after a cross, require the gap between +DI and –DI to increase on the next bar). You don’t need extra code for this—just enforce it visually.
What to look for (pattern cheatsheet)
Cross + hold at a level: The lines cross at your level and keep separating → high-quality entry in that direction.
Sneaky fail: Cross, then immediate recross back → treat it as a fade signal back into value (especially near VAH/VAL).
Strength confirmation: After a breakout, +DI stays above –DI on pullbacks → trend is healthy; buy dips at AVWAP/POC.
Pre-move tell: DI lines unbraid and begin diverging before price leaves a range; wait for location + trigger.
Combining DI Xover with other tools
Cumulative Volume Delta v1 (CVDv1):
Use DI for direction, and CVDv1 for quality. A bullish DI cross with ALIGN OK + Imbalance strong + no Absorption is a far better long than DI alone.
If DI crosses up but CVDv1 flags Absorption (red), don’t chase—look for the fail/reclaim instead.
Volume Profile v3.2 :
Let VP choose the battleground (POC/VAH/VAL/LVNs). Take the DI crossover at those references.
Classic: bearish DI cross at VAH → fade toward POC; bullish DI cross at VAL → rotate to POC—assuming CVDv1 isn’t vetoing with Absorption.
Anchored VWAP :
Treat reclaims/rejections of AVWAP as the location and DI cross as the trigger.
Example: price reclaims Weekly AVWAP, then on the next pullback, a +DI cross up confirms the add.
Common pitfalls this helps you avoid
Trading crosses in the middle of nowhere. DI is a trigger, not a level; wait for VP/AVWAP.
Chasing every wiggle. When the lines are braided, you’re likely in balance—expect fake crosses.
Ignoring flow. A DI cross against CVDv1 Absorption is often a trap; quality > quantity.
Practical defaults to start with
Length: 14
Timeframes: Works out of the box on 15m–4H. For 1–5m scalps try 10–12; for daily/weekly swings try 20–30.
Process: Only act on crosses at levels (VP v3.2 / Anchored VWAP), and prefer those where CVDv1 says ALIGN OK and no Absorption.
Alerts (what they tell you)
Bullish DI Crossover: +DI crossed above –DI → buyers just took initiative. Look to your chart for location and CVDv1 quality before entering.
Bearish DI Crossover: –DI crossed above +DI → sellers took initiative. Same rule: confirm at a level with flow.
Open source & disclaimer
This indicator is published open source so traders can learn, adapt, and build rules they trust. No tool guarantees outcomes; risk management remains essential.
Disclaimer — Not Financial Advice.
The “Directional Indicator Crossovers ” 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.
Keltner Channels v1 [JopAlgo]Keltner Channels v1 — a clean volatility envelope for timing pullbacks, breakouts, and risk
Keltner Channels are a moving-average centerline with volatility-based bands above and below. They give you a live “speed limit” for price: when the market is calm, bands are tight (expect mean reversion); when volatility expands, bands widen (trend moves can breathe). KC v1 keeps the classic idea but adds a small twist that traders appreciate in crypto: an adaptive centerline that switches between EMA and SMA based on trendiness, plus a choice of how you measure volatility for the bands.
This makes KC v1 useful for any timeframe—from fast scalps to multi-day swings—because it answers three practical questions on every chart:
Where’s the “middle” of price right now? (the centerline)
How far is “far” for current volatility? (the bands)
Should I fade back to the middle or ride with the expansion? (context from band width + slope)
If you attach screenshots to your script page, show one image labeling Upper / Middle / Lower bands with a classic pullback-to-middle entry, and another showing a band expansion where price hugs the outer band in trend.
What you’re seeing (and how it’s computed)
Middle band (MA):
KC v5 computes both an EMA and an SMA of your source (default close) with the same length, then auto-selects the middle band:
If ATR > SMA(ATR) over length, KC marks the market as trending and uses the EMA (faster, responsive).
Otherwise, it uses the SMA (steadier) in balance.
Result: you get a centerline that’s calm in chop and snappier in trend, without touching settings.
Upper / Lower bands:
upper = middle + (mult × volatility)
lower = middle - (mult × volatility)
You choose the volatility measure via Bands Style:
Average True Range (default): smooth, robust; uses ATR(atrlength). Best all-around choice.
True Range: raw TR each bar (more jumpy; reacts to gaps and spikes quickly).
Range: RMA of (high - low) over length (gentler; good for tight mean-reversion regimes).
Colors & fill:
Upper = red, Lower = green, Middle = white, with muted fill between bands so you can still read candles.
How to use Keltner Channels on any timeframe
Same framework everywhere: trade with the envelope when expanding, fade back to the middle when contracting—but only at objective locations and with healthy flow.
Scalping (1–5m)
Pullback-to-middle entry: In a micro-trend, wait for price to retrace to the middle band and print a hold. Enter with the trend, stop just beyond the opposite side of the middle or below minor structure; first target is the near band.
Band tap fades (only in contraction): When bands are tightening and the middle is flat, quick fades from upper → middle or lower → middle are high-probability if your volume/flow read doesn’t show aggressive pressure against you.
Avoid: Fading when bands expand and middle slopes—expect continuation instead.
Intraday (15m–1H)
Continuation rides: When bands open up (volatility expansion) and the middle slopes, price often walks the outer band. Enter on minor pullbacks that hold above the middle (for longs) and trail using the middle band or a structure stop.
Squeeze to break: A period of narrowing bands often precedes a move. Let price close outside the channel with good flow, then buy the retest toward the middle that holds.
Swing (2H–4H)
Trend participation: In established trends, treat pullbacks to the middle band as your primary entry. The upper/lower band is not a take-profit by itself—use it with Volume Profile targets (POC/HVNs) or key swing levels.
Mean reversion in balance: When the middle is flat and bands are tight over many bars, fade outer band → middle at Volume Profile edges, provided your flow read isn’t showing absorption against your idea.
Position (1D–1W)
Context: Use KC to judge regime (wide bands + slope = trend; tight/flat = balance). Position entries come from pullbacks to middle that coincide with Weekly AVWAP / VP value edges.
Entries, exits, and risk (simple rules)
Trend entry (with expansion):
Wait for band expansion + sloping middle in your direction. Enter on the first clean pullback to middle (or shallow pullback that can’t even tag middle).
Stop: below the middle band or just beyond local swing.
Trail: by the middle band in trend, or step-trail under pivots.
Targets: next Volume Profile HVN/POC or structural levels; the far Keltner band is a context line, not a hard TP.
Mean-reversion entry (in contraction):
Bands tight + flat middle → fade outer band back to middle at a Volume Profile VA edge.
Stop: just beyond the band.
Target: middle band (first), opposite band if flow remains weak.
Breakout confirmation:
A strong close outside the band by itself can be a trap. Treat it as signal only when your flow read confirms (see “Combining with other tools”).
Settings that actually matter (and how to tune them)
MA Length (default 20): controls both middle smoothness and the trending test (ATR vs SMA(ATR)).
Shorter (10–14) reacts faster, more whips in chop.
Longer (30–50) steadier middle, better for swings/position.
Multiplier (default 2.0): scales band distance.
Crypto majors: 1.8–2.2 is a good starting range on 15m–4H.
Volatile alts: 2.2–2.6 to avoid over-triggering.
If you keep getting faked out on fades: increase the multiplier.
If the channel rarely contains price for long stretches: decrease slightly.
Bands Style:
ATR for most use cases;
TR when you want maximum responsiveness to spikes;
Range for calmer envelopes in slow, balanced markets.
ATR Length (default 10): only applies if you choose ATR for band style.
Shorter = quicker band changes, good for scalps;
Longer = steadier bands for swings.
Note: KC v1 auto-selects EMA vs SMA for the middle band using the ATR trend test. That’s intentional, so you don’t have to toggle it manually.
What to look for (pattern cheatsheet)
Walk-the-band: In expansion, price hugs the outer band and barely returns to the middle—ride, don’t fade.
First touch of middle in trend: Often the cleanest add or first entry after a breakout.
Band pinch (“squeeze”): A long, narrow channel with flat middle sets up a breakout. Wait for acceptance (close outside + hold on retest).
False break tell: Price pokes outside band but closes back inside quickly—watch for reversion to middle, especially if your flow read shows Absorption against the poke.
Combining KC v1 with other tools
like the Cumulative Volume Delta v1 (CVDv1):
Do not chase an outside-band move if CVDv1 shows Absorption—that’s a classic failed break.
Prefer pullbacks to the middle band when Alignment = OK and Imbalance % is strong in your direction.
Reclaim setups: after a poke outside the band, a CVD divergence on the return through the middle often precedes a mean-reversion run.
Volume Profile v3.2 :
Use VAH/VAL/LVNs for location. A pullback-to-middle that coincides with VA boundary is A-tier.
Breakouts through LVNs with expanding bands tend to travel fast toward the next HVN/POC—good for continuation targets.
(A great screenshot: KC middle kiss at VAL with CVDv1 Efficient, then a move to POC.)
Common pitfalls KC v1 helps you avoid
Fading expansion: Trying to short the upper band when bands are widening and middle slopes up is how you get steamrolled. KC tells you it’s not that kind of day.
Chasing inside contraction: Buying every tiny outside poke while bands are pinched leads to whips. Let acceptance form; buy the retest to middle that holds.
Stops too tight: In trend, volatility is elevated; stops need to live beyond the middle or behind structure, not right at the band.
Practical defaults to start with
Length: 20
Multiplier: 2.0 (adjust ±0.2–0.4 per asset)
Bands Style: ATR
ATR Length: 10
Timeframes: works out of the box on 15m–4H; for 1–5m scalps, consider length=14; for daily swings, length=30.
Open source & disclaimer
This indicator is provided open source so traders can study, test, and adapt it to their workflow. No tool guarantees outcomes; risk management is essential.
Disclaimer — Not Financial Advice.
The “Keltner Channels v1 ” indicator and this description are provided for educational purposes only and do not constitute financial or investment advice. Trading involves risk, including possible loss of capital. makes no warranties and assumes no responsibility for any trading decisions or outcomes resulting from the use of this script. Past performance is not indicative of future results.
DTM 444 BANDS 🚀DTM 444 BANDS 🚀:
The DTM 444 BANDS 🚀 is a powerful, multi-purpose trading indicator combining Supertrend, Dynamic Band Levels, Breakout Signals, and Volume Confirmation to help traders identify high-probability trade setups across different timeframes.
🔧 Key Features
✅ Multi-Timeframe Support
Analyze price action across any timeframe using the Timeframe input.
All band calculations (High, Low, Midline, and Supertrend) are pulled from a higher timeframe for clearer context.
✅ Dynamic Bands Based on Supertrend
High Band: Rolling highest of Supertrend over hiLen period.
Low Band: Rolling lowest of Supertrend over loLen period.
Midline: Midpoint of the above.
Acts like dynamic support/resistance, ideal for trend-following and breakout strategies.
✅ Dual Signal System
Breakout Signals (Buy and Sell): Triggered when price breaks the bands with volume confirmation.
Supertrend Crossover Signals (Buy1 and Sell1): Classic momentum entries with a confirmation twist.
Exit Signals: Optional take-profit/neutral indicators when price reverses.
✅ Volume Confirmation Filter (Optional)
Only triggers signals if the volume exceeds its 20-period SMA.
Helps filter out false breakouts and weak trends in low-liquidity periods.
✅ Visual Enhancements
Color-coded candles based on band positioning (e.g., red = weak, green = strong, etc.)
On-chart labels for each signal for quick reference.
Real-time Signal Dashboard using Pine Script tables showing:
Current signal
Volume filter status
Live volume vs volume SMA
🧪 Practical Use Cases
Trend Traders: Use the Supertrend cross and band breakouts to ride trends early.
Breakout Traders: Catch high-probability moves outside established ranges.
Swing Traders: Time entries and exits using color-coded bars and exit labels.
Volume-Sensitive Traders: Focus on trades with strong volume backing.
📊 Backtest Snapshot
Based on the example chart for Reliance Industries (RELIANCE.NS) on the weekly timeframe:
Several profitable buy and breakout signals during uptrends.
Timely exits and breakdown alerts before reversals.
Volume filter keeps trades clean and avoids noise.
⚙️ Customizable Parameters
High Length and Low Length (default: 19)
Supertrend Multiplier and ATR Length
Volume Filter: Toggle ON/OFF
Volume SMA Length: Default 20
Custom Timeframe: Choose any higher timeframe for multi-timeframe analysis
📢 Alerts Ready
Fully integrated with TradingView alerts:
Breakout & Breakdown
Supertrend crossovers
All alerts respect the volume filter setting
🏁 Final Thoughts
DTM 444 BANDS 🚀 is a versatile and adaptive trading system that blends trend analysis, volatility bands, and volume validation. Whether you're a trend trader, breakout hunter, or swing trader — this tool gives you a structured edge with clear visual cues and real-time alerts.
PineConnectorLibrary "PineConnector"
This library is a comprehensive alert webhook text generator for PineConnector. It contains every possible alert syntax variation from the documentation, along with some debugging functions.
To use it, just import the library (eg. "import ZenAndTheArtOfTrading/PineConnector/1 as pc") and use pc.buy(licenseID) to send an alert off to PineConnector - assuming all your webhooks etc are set up correctly.
View the PineConnector documentation for more information on how to send the commands you're looking to send (all of this library's function names match the documentation).
all()
Usage: pc.buy(pc_id, freq=pc.all())
Returns: "all"
once_per_bar()
Usage: pc.buy(pc_id, freq=pc.once_per_bar())
Returns: "once_per_bar"
once_per_bar_close()
Usage: pc.buy(pc_id, freq=pc.once_per_bar_close())
Returns: "once_per_bar_close"
na0(value)
Checks if given value is either 'na' or 0. Useful for streamlining scripts with float user setting inputs which default values to 0 since na is unavailable as a user input default.
Parameters:
value (float) : The value to check
Returns: True if the given value is 0 or na
getDecimals()
Calculates how many decimals are on the quote price of the current market.
Returns: The current decimal places on the market quote price
truncate(number, decimals)
Truncates the given number. Required params: mumber.
Parameters:
number (float) : Number to truncate
decimals (int) : Decimal places to cut down to
Returns: The input number, but as a string truncated to X decimals
getPipSize(multiplier)
Calculates the pip size of the current market.
Parameters:
multiplier (int) : The mintick point multiplier (1 by default, 10 for FX/Crypto/CFD but can be used to override when certain markets require)
Returns: The pip size for the current market
toWhole(number)
Converts pips into whole numbers. Required params: number.
Parameters:
number (float) : The pip number to convert into a whole number
Returns: The converted number
toPips(number)
Converts whole numbers back into pips. Required params: number.
Parameters:
number (float) : The whole number to convert into pips
Returns: The converted number
debug(txt, tooltip, displayLabel)
Prints to console and generates a debug label with the given text. Required params: txt.
Parameters:
txt (string) : Text to display
tooltip (string) : Tooltip to display (optional)
displayLabel (bool) : Turns on/off chart label (default: off)
Returns: Nothing
order(licenseID, command, symbol, parameters, accfilter, comment, secret, freq, debug)
Generates an alert string. Required params: licenseID, command.
Parameters:
licenseID (string) : Your PC license ID
command (string) : Command to send
symbol (string) : The symbol to trigger this order on
parameters (string) : Other optional parameters to include
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: An alert string with valid PC syntax based on supplied parameters
market_order(licenseID, buy, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)
Generates a market entry alert with relevant syntax commands. Required params: licenseID, buy, risk.
Parameters:
licenseID (string) : Your PC license ID
buy (bool) : true=buy/long, false=sell/short
risk (float) : Risk quantity (according to EA settings)
sl (float) : Stop loss distance in pips or price
tp (float) : Take profit distance in pips or price
betrigger (float) : Breakeven will be activated after the position gains this number of pips
beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect
spread (float) : Enter the position only if the spread is equal or less than the specified value in pips
trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips
traildist (float) : Distance of the trailing stop-loss from current price
trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips
atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440
atrperiod (int) : ATR averaging period
atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1
atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0
atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)
symbol (string) : The symbol to trigger this order on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A market order alert string with valid PC syntax based on supplied parameters
buy(licenseID, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)
Generates a market buy alert with relevant syntax commands. Required params: licenseID, risk.
Parameters:
licenseID (string) : Your PC license ID
risk (float) : Risk quantity (according to EA settings)
sl (float) : Stop loss distance in pips or price
tp (float) : Take profit distance in pips or price
betrigger (float) : Breakeven will be activated after the position gains this number of pips
beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect
spread (float) : Enter the position only if the spread is equal or less than the specified value in pips
trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips
traildist (float) : Distance of the trailing stop-loss from current price
trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips
atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440
atrperiod (int) : ATR averaging period
atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1
atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0
atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)
symbol (string) : The symbol to trigger this order on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A market order alert string with valid PC syntax based on supplied parameters
sell(licenseID, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)
Generates a market sell alert with relevant syntax commands. Required params: licenseID, risk.
Parameters:
licenseID (string) : Your PC license ID
risk (float) : Risk quantity (according to EA settings)
sl (float) : Stop loss distance in pips or price
tp (float) : Take profit distance in pips or price
betrigger (float) : Breakeven will be activated after the position gains this number of pips
beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect
spread (float) : Enter the position only if the spread is equal or less than the specified value in pips
trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips
traildist (float) : Distance of the trailing stop-loss from current price
trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips
atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440
atrperiod (int) : ATR averaging period
atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1
atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0
atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)
symbol (string) : The symbol to trigger this order on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A market order alert string with valid PC syntax based on supplied parameters
closeall(licenseID, comment, secret, freq, debug)
Closes all open trades at market regardless of symbol. Required params: licenseID.
Parameters:
licenseID (string) : Your PC license ID
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
closealleaoff(licenseID, comment, secret, freq, debug)
Closes all open trades at market regardless of symbol, and turns the EA off. Required params: licenseID.
Parameters:
licenseID (string) : Your PC license ID
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
closelong(licenseID, symbol, comment, secret, freq, debug)
Closes all long trades at market for the given symbol. Required params: licenseID.
Parameters:
licenseID (string) : Your PC license ID
symbol (string) : Symbol to act on (defaults to current symbol)
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
closeshort(licenseID, symbol, comment, secret, freq, debug)
Closes all open short trades at market for the given symbol. Required params: licenseID.
Parameters:
licenseID (string) : Your PC license ID
symbol (string) : Symbol to act on (defaults to current symbol)
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
closelongshort(licenseID, symbol, comment, secret, freq, debug)
Closes all open trades at market for the given symbol. Required params: licenseID.
Parameters:
licenseID (string) : Your PC license ID
symbol (string) : Symbol to act on (defaults to current symbol)
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
closelongbuy(licenseID, risk, symbol, comment, secret, freq, debug)
Close all long positions and open a new long at market for the given symbol with given risk/contracts. Required params: licenseID.
Parameters:
licenseID (string) : Your PC license ID
risk (float) : Risk or contracts (according to EA settings)
symbol (string) : Symbol to act on (defaults to current symbol)
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
closeshortsell(licenseID, risk, symbol, comment, secret, freq, debug)
Close all short positions and open a new short at market for the given symbol with given risk/contracts. Required params: licenseID, risk.
Parameters:
licenseID (string) : Your PC license ID
risk (float) : Risk or contracts (according to EA settings)
symbol (string) : Symbol to act on (defaults to current symbol)
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
newsltplong(licenseID, sl, tp, symbol, accfilter, comment, secret, freq, debug)
Updates the stop loss and/or take profit of any open long trades on the given symbol with the given values. Required params: licenseID, sl and/or tp.
Parameters:
licenseID (string) : Your PC license ID
sl (float) : Stop loss pips or price (according to EA settings)
tp (float) : Take profit pips or price (according to EA settings)
symbol (string) : Symbol to act on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
newsltpshort(licenseID, sl, tp, symbol, accfilter, comment, secret, freq, debug)
Updates the stop loss and/or take profit of any open short trades on the given symbol with the given values. Required params: licenseID, sl and/or tp.
Parameters:
licenseID (string) : Your PC license ID
sl (float) : Stop loss pips or price (according to EA settings)
tp (float) : Take profit pips or price (according to EA settings)
symbol (string) : Symbol to act on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
closelongpct(licenseID, symbol, comment, secret, freq, debug)
Close a percentage of open long positions (according to EA settings). Required params: licenseID.
Parameters:
licenseID (string) : Your PC license ID
symbol (string) : Symbol to act on (defaults to current symbol)
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
closeshortpct(licenseID, symbol, comment, secret, freq, debug)
Close a percentage of open short positions (according to EA settings). Required params: licenseID.
Parameters:
licenseID (string) : Your PC license ID
symbol (string) : Symbol to act on (defaults to current symbol)
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
closelongvol(licenseID, risk, symbol, comment, secret, freq, debug)
Close all open long contracts on the current symbol until the given risk value is remaining. Required params: licenseID, risk.
Parameters:
licenseID (string) : Your PC license ID
risk (float) : The quantity to leave remaining
symbol (string) : Symbol to act on (defaults to current symbol)
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
closeshortvol(licenseID, risk, symbol, comment, secret, freq, debug)
Close all open short contracts on the current symbol until the given risk value is remaining. Required params: licenseID, risk.
Parameters:
licenseID (string) : Your PC license ID
risk (float) : The quantity to leave remaining
symbol (string) : Symbol to act on (defaults to current symbol)
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
limit_order(licenseID, buy, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)
Generates a limit order alert with relevant syntax commands. Required params: licenseID, buy, price, risk.
Parameters:
licenseID (string) : Your PC license ID
buy (bool) : true=buy/long, false=sell/short
price (float) : Price or pips to set limit order (according to EA settings)
risk (float) : Risk quantity (according to EA settings)
sl (float) : Stop loss distance in pips or price
tp (float) : Take profit distance in pips or price
betrigger (float) : Breakeven will be activated after the position gains this number of pips
beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect
spread (float) : Enter the position only if the spread is equal or less than the specified value in pips
trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips
traildist (float) : Distance of the trailing stop-loss from current price
trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips
atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440
atrperiod (int) : ATR averaging period
atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1
atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0
atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)
symbol (string) : The symbol to trigger this order on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A limit order alert string with valid PC syntax based on supplied parameters
buylimit(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)
Generates a buylimit order alert with relevant syntax commands. Required params: licenseID, price, risk.
Parameters:
licenseID (string) : Your PC license ID
price (float) : Price or pips to set limit order (according to EA settings)
risk (float) : Risk quantity (according to EA settings)
sl (float) : Stop loss distance in pips or price
tp (float) : Take profit distance in pips or price
betrigger (float) : Breakeven will be activated after the position gains this number of pips
beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect
spread (float) : Enter the position only if the spread is equal or less than the specified value in pips
trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips
traildist (float) : Distance of the trailing stop-loss from current price
trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips
atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440
atrperiod (int) : ATR averaging period
atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1
atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0
atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)
symbol (string) : The symbol to trigger this order on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A limit order alert string with valid PC syntax based on supplied parameters
selllimit(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)
Generates a selllimit order alert with relevant syntax commands. Required params: licenseID, price, risk.
Parameters:
licenseID (string) : Your PC license ID
price (float) : Price or pips to set limit order (according to EA settings)
risk (float) : Risk quantity (according to EA settings)
sl (float) : Stop loss distance in pips or price
tp (float) : Take profit distance in pips or price
betrigger (float) : Breakeven will be activated after the position gains this number of pips
beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect
spread (float) : Enter the position only if the spread is equal or less than the specified value in pips
trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips
traildist (float) : Distance of the trailing stop-loss from current price
trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips
atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440
atrperiod (int) : ATR averaging period
atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1
atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0
atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)
symbol (string) : The symbol to trigger this order on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A limit order alert string with valid PC syntax based on supplied parameters
stop_order(licenseID, buy, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)
Generates a stop order alert with relevant syntax commands. Required params: licenseID, buy, price, risk.
Parameters:
licenseID (string) : Your PC license ID
buy (bool) : true=buy/long, false=sell/short
price (float) : Price or pips to set limit order (according to EA settings)
risk (float) : Risk quantity (according to EA settings)
sl (float) : Stop loss distance in pips or price
tp (float) : Take profit distance in pips or price
betrigger (float) : Breakeven will be activated after the position gains this number of pips
beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect
spread (float) : Enter the position only if the spread is equal or less than the specified value in pips
trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips
traildist (float) : Distance of the trailing stop-loss from current price
trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips
atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440
atrperiod (int) : ATR averaging period
atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1
atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0
atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)
symbol (string) : The symbol to trigger this order on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A stop order alert string with valid PC syntax based on supplied parameters
buystop(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)
Generates a buystop order alert with relevant syntax commands. Required params: licenseID, price, risk.
Parameters:
licenseID (string) : Your PC license ID
price (float) : Price or pips to set limit order (according to EA settings)
risk (float) : Risk quantity (according to EA settings)
sl (float) : Stop loss distance in pips or price
tp (float) : Take profit distance in pips or price
betrigger (float) : Breakeven will be activated after the position gains this number of pips
beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect
spread (float) : Enter the position only if the spread is equal or less than the specified value in pips
trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips
traildist (float) : Distance of the trailing stop-loss from current price
trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips
atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440
atrperiod (int) : ATR averaging period
atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1
atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0
atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)
symbol (string) : The symbol to trigger this order on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A stop order alert string with valid PC syntax based on supplied parameters
sellstop(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)
Generates a sellstop order alert with relevant syntax commands. Required params: licenseID, price, risk.
Parameters:
licenseID (string) : Your PC license ID
price (float) : Price or pips to set limit order (according to EA settings)
risk (float) : Risk quantity (according to EA settings)
sl (float) : Stop loss distance in pips or price
tp (float) : Take profit distance in pips or price
betrigger (float) : Breakeven will be activated after the position gains this number of pips
beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect
spread (float) : Enter the position only if the spread is equal or less than the specified value in pips
trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips
traildist (float) : Distance of the trailing stop-loss from current price
trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips
atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440
atrperiod (int) : ATR averaging period
atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1
atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0
atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)
symbol (string) : The symbol to trigger this order on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A stop order alert string with valid PC syntax based on supplied parameters
cancel_neworder(licenseID, order, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)
Cancel + place new order template function.
Parameters:
licenseID (string) : Your PC license ID
order (string) : Cancel order type
price (float) : Price or pips to set limit order (according to EA settings)
risk (float) : Risk quantity (according to EA settings)
sl (float) : Stop loss distance in pips or price
tp (float) : Take profit distance in pips or price
betrigger (float) : Breakeven will be activated after the position gains this number of pips
beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect
spread (float) : Enter the position only if the spread is equal or less than the specified value in pips
trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips
traildist (float) : Distance of the trailing stop-loss from current price
trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips
atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440
atrperiod (int) : ATR averaging period
atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1
atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0
atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)
symbol (string) : The symbol to trigger this order on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A stop order alert string with valid PC syntax based on supplied parameters
cancellongbuystop(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)
Cancels all long orders with the specified symbol and places a new buystop order. Required params: licenseID, price, risk.
Parameters:
licenseID (string) : Your PC license ID
price (float) : Price or pips to set limit order (according to EA settings)
risk (float) : Risk quantity (according to EA settings)
sl (float) : Stop loss distance in pips or price
tp (float) : Take profit distance in pips or price
betrigger (float) : Breakeven will be activated after the position gains this number of pips
beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect
spread (float) : Enter the position only if the spread is equal or less than the specified value in pips
trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips
traildist (float) : Distance of the trailing stop-loss from current price
trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips
atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440
atrperiod (int) : ATR averaging period
atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1
atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0
atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)
symbol (string) : The symbol to trigger this order on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A stop order alert string with valid PC syntax based on supplied parameters
cancellongbuylimit(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)
Cancels all long orders with the specified symbol and places a new buylimit order. Required params: licenseID, price, risk.
Parameters:
licenseID (string) : Your PC license ID
price (float) : Price or pips to set limit order (according to EA settings)
risk (float) : Risk quantity (according to EA settings)
sl (float) : Stop loss distance in pips or price
tp (float) : Take profit distance in pips or price
betrigger (float) : Breakeven will be activated after the position gains this number of pips
beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect
spread (float) : Enter the position only if the spread is equal or less than the specified value in pips
trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips
traildist (float) : Distance of the trailing stop-loss from current price
trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips
atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440
atrperiod (int) : ATR averaging period
atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1
atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0
atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)
symbol (string) : The symbol to trigger this order on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A stop order alert string with valid PC syntax based on supplied parameters
cancelshortsellstop(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)
Cancels all short orders with the specified symbol and places a sellstop order. Required params: licenseID, price, risk.
Parameters:
licenseID (string) : Your PC license ID
price (float) : Price or pips to set limit order (according to EA settings)
risk (float) : Risk quantity (according to EA settings)
sl (float) : Stop loss distance in pips or price
tp (float) : Take profit distance in pips or price
betrigger (float) : Breakeven will be activated after the position gains this number of pips
beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect
spread (float) : Enter the position only if the spread is equal or less than the specified value in pips
trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips
traildist (float) : Distance of the trailing stop-loss from current price
trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips
atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440
atrperiod (int) : ATR averaging period
atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1
atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0
atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)
symbol (string) : The symbol to trigger this order on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A stop order alert string with valid PC syntax based on supplied parameters
cancelshortselllimit(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)
Cancels all short orders with the specified symbol and places a selllimit order. Required params: licenseID, price, risk.
Parameters:
licenseID (string) : Your PC license ID
price (float) : Price or pips to set limit order (according to EA settings)
risk (float) : Risk quantity (according to EA settings)
sl (float) : Stop loss distance in pips or price
tp (float) : Take profit distance in pips or price
betrigger (float) : Breakeven will be activated after the position gains this number of pips
beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect
spread (float) : Enter the position only if the spread is equal or less than the specified value in pips
trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips
traildist (float) : Distance of the trailing stop-loss from current price
trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips
atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440
atrperiod (int) : ATR averaging period
atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1
atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0
atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)
symbol (string) : The symbol to trigger this order on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment (maximum 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A stop order alert string with valid PC syntax based on supplied parameters
cancellong(licenseID, symbol, accfilter, comment, secret, freq, debug)
Cancels all pending long orders with the specified symbol. Required params: licenseID.
Parameters:
licenseID (string) : Your PC license ID
symbol (string) : Symbol to act on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A cancel long alert command
cancelshort(licenseID, symbol, accfilter, comment, secret, freq, debug)
Cancels all pending short orders with the specified symbol. Required params: licenseID.
Parameters:
licenseID (string) : Your PC license ID
symbol (string) : Symbol to act on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: A cancel short alert command
newsltpbuystop(licenseID, sl, tp, symbol, accfilter, comment, secret, freq, debug)
Updates the stop loss and/or take profit of any pending buy stop orders on the given symbol. Required params: licenseID, sl and/or tp.
Parameters:
licenseID (string) : Your PC license ID
sl (float) : Stop loss pips or price (according to EA settings)
tp (float) : Take profit pips or price (according to EA settings)
symbol (string) : Symbol to act on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
newsltpbuylimit(licenseID, sl, tp, symbol, accfilter, comment, secret, freq, debug)
Updates the stop loss and/or take profit of any pending buy limit orders on the given symbol. Required params: licenseID, sl and/or tp.
Parameters:
licenseID (string) : Your PC license ID
sl (float) : Stop loss pips or price (according to EA settings)
tp (float) : Take profit pips or price (according to EA settings)
symbol (string) : Symbol to act on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
newsltpsellstop(licenseID, sl, tp, symbol, accfilter, comment, secret, freq, debug)
Updates the stop loss and/or take profit of any pending sell stop orders on the given symbol. Required params: licenseID, sl and/or tp.
Parameters:
licenseID (string) : Your PC license ID
sl (float) : Stop loss pips or price (according to EA settings)
tp (float) : Take profit pips or price (according to EA settings)
symbol (string) : Symbol to act on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
newsltpselllimit(licenseID, sl, tp, symbol, accfilter, comment, secret, freq, debug)
Updates the stop loss and/or take profit of any pending sell limit orders on the given symbol. Required params: licenseID, sl and/or tp.
Parameters:
licenseID (string) : Your PC license ID
sl (float) : Stop loss pips or price (according to EA settings)
tp (float) : Take profit pips or price (according to EA settings)
symbol (string) : Symbol to act on (defaults to current symbol)
accfilter (float) : Optional minimum account balance filter
comment (string) : Optional comment to include (max 20 characters)
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
eaoff(licenseID, secret, freq, debug)
Turns the EA off. Required params: licenseID.
Parameters:
licenseID (string) : Your PC license ID
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
eaon(licenseID, secret, freq, debug)
Turns the EA on. Required params: licenseID.
Parameters:
licenseID (string) : Your PC license ID
secret (string) : Optional secret key (must be enabled in dashboard)
freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"
debug (bool) : Turns on/off debug label
Returns: The required alert syntax as a string
Rollover LTEThis indicator shows where price needs to be and when in order to cause the 20-sma and 50-sma moving averages to change directions. A change in direction requires the slope of a moving average to change from negative to positive or from positive to negative. When a moving average changes direction, it can be said that it has “rolled over” or “rolled up,” with the latter only applying if slope went from negative to positive.
Theory:
In order to solve for the price of the current bar that will cause the moving average to roll up, the slope from the previous bar’s average to the current bar’s average must be set equal to zero which is to say that the averages must be the same.
For the 20-sma, the equation simply stated in words is as follows:
Current MA as a function of current price and previous 19 values = previous MA which is fixed based on previous 20 values
The denominators which are both 20 cancel and the previous 19 values cancel. What’s left is current price on the left side and the value from 20 bars ago on the right.
Current price = value from 20 bars ago
and since the equation was set up for solving for the price of the current bar that will cause the MA to roll over
Rollover price = value from 20 bars ago
This makes plotting rollover price, both current and forecasted, fairly simple, as it’s merely the closing price plotted with an offset to the right the same distance as the moving average length.
Application:
The 20-sma and 50-sma rollover prices are plotted because they are considered to be the two most important moving averages for rollover analysis. Moving average lengths can be modified in the indicator settings. The 20-sma and 20-sma rollover price are both plotted in white and the 50-sma and 50-sma rollover price are both plotted in blue. There are two rollover prices because the 20-sma rollover price is the price that will cause the 20-sma to roll over and the 50-sma rollover price is the price that will cause the 50-sma to roll over. The one that's vertically furthest away from the current price is the one that will cause both to rollover, as should become clearer upon reading the explanation below.
The distance between the current price and the 20-sma rollover price is referred to as the “rollover strength” of the price relative to the 20-sma. A large disparity between the current price and the rollover price suggests bearishness (negative rollover strength) if the rollover price is overhead because price would need to travel all that distance in order to cause the moving average to roll up. If the rollover price and price are converging, as is often the case, a change in moving average and price direction becomes more plausible. The rollover strengths of the 20-sma and 50-sma are added together to calculate the Rollover Strength and if a negative number is the result then the background color of the plot cloud turns red. If the result is positive, it turns green. Rollover Strength is plotted below price as a separate indicator in this publication for reference only and it's not part of this indicator. It does not look much different from momentum indicators. The code is below if anybody wants to try to use it. The important thing is that the distances between the rollover prices and the price action are kept in mind as having shrinking, growing, or neutral bearish and bullish effects on current and forecasted price direction. Trades should not be entered based on cloud colorization changes alone.
If you are about to crash into a wall of the 20-sma rollover price, as is indicated on the chart by the green arrow, you might consider going long so long as the rollover strength, both current and forecasted, of the 50-sma isn’t questionably bearish. This is subject to analysis and interpretation. There was a 20-sma rollover wall as indicated with yellow arrow, but the bearish rollover strength of the 50-sma was growing and forecasted to remain strong for a while at that time so a long entry would have not been suggested by both rollover prices. If you are about to crash into both the 20-sma and 50-sma rollover prices at the same time (not shown on this chart), that’s a good time to place a trade in anticipation of both slopes changing direction. You may, in the case of this chart, see that a 20-sma rollover wall precedes a 50-sma rollover convergence with price and anticipate a cascade which turned out to be the case with this recent NQ rally.
Price exiting the cloud entirely to either the upside or downside has strong implications. When exiting to the downside, the 20-sma and 50-sma have both rolled over and price is below both of them. The same is true for upside exits. Re-entering the cloud after a rally may indicate a reversal is near, especially if the forecasted rollover prices, particularly the 50-sma, agree.
This indicator should be used in conjunction with other technical analysis tools.
Additional Notes:
The original version of this script which will not be published was much heavier, cluttered, and is not as useful. This is the light version, hence the “LTE” suffix.
LTE stands for “long-term evolution” in telecommunications, not “light.”
Bar colorization (red, yellow, and green bars) was added using the MACD Hybrid BSH script which is another script I’ve published.
If you’re not sure what a bar is, it’s the same thing as a candle or a data point on a line chart. Every vertical line showing price action on the chart above is a bar and it is a bar chart.
sma = simple moving average
Rollover Strength Script:
// This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © Skipper86
//@version=5
indicator(title="Rollover Strength", shorttitle="Rollover Strength", overlay=false)
source = input.source(close)
length1 = input.int(20, "Length 1", minval=1)
length2 = input.int(50, "Length 2", minval=1)
RolloverPrice1 = source
RolloverPrice2 = source
RolloverStrength1 = source-RolloverPrice1
RolloverStrength2 = source-RolloverPrice2
RolloverStrength = RolloverStrength1 + RolloverStrength2
Color1 = color.rgb(155, 155, 155, 0)
Color2 = color.rgb(0, 0, 200, 0)
Color3 = color.rgb(0, 200, 0, 0)
plot(RolloverStrength, title="Rollover Strength", color=Color3)
hline(0, "Middle Band", color=Color1)
//End of Rollover Strength Script
Turtle Trader StrategyTurtle Trader Strategy :
Introduction :
This strategy is based on the well known « Turtle Trader Strategy », that has proven itself over the years. It sends long and short signals with pyramid orders of up to 5, meaning that the strategy can trigger up to 5 orders in the same direction. Good risk and money management.
It's important to note that the strategy combines 2 systems working together (S1 and S2). Let’s describe the specific features of this strategy.
1/ Position size :
Position size is very important for turtle traders to manage risk properly. This position sizing strategy adapts to market volatility and to account (gains and losses). It’s based on ATR (Average True Range) which can also be called « N ». Its length is per default 20.
ATR(20) = (previous_atr(20)*19 + actual_true_range)/20
The number of units to buy is :
Unit = 1% * account/(ATR(20)*dollar_per_point)
where account is the actual account value and dollar_per_point is the variation in dollar of the asset with a 1 point move.
Depending on your risk aversion, you can increase the percentage of your account, but turtle traders default to 1%. If you trade contracts, units must be rounded down by default.
There is also an additional rule to reduce the risk if the value of the account falls below the initial capital : in this case and only in this case, account in the unit formula must be replace by :
account = actual_account*actual_account/initial capital
2/ Open a position :
2 systems are working together :
System 1 : Entering a new 20 day breakout
System 2 : Entering a new 55 day breakout
A breakout is a new high or new low. If it’s a new high, we open long position and vice versa if it’s a new low we enter in short position.
We add an additional rule :
System 1 : Breakout is ignored if last long/short position was a winner
System 2 : All signals are taken
This additional rule allows the trader to be in the major trends if the system 1 signal has been skipped. If a signal for system 1 has been skipped, and next candle is also a new 20 day breakout, S1 doesn’t give a signal. We have to wait S2 signal or wait for a candle that doesn’t make a new breakout to reactivate S1.
3/ Pyramid orders :
Turtle Strategy allows us to add extra units to the position if the price moves in our favor. I've configured the strategy to allow up to 5 orders to be added in the same direction. So if the price varies from 0.5*ATR(20) , we add units with the position size formula. Note that the value of account will be replaced by "remaining_account", i.e. the cash remaining in our account after subtracting the value of open positions.
4/ Stop Loss :
We set a stop loss at 1.5*ATR(20) below the entry price for longs and above the entry price for shorts. If pyramid units are added, the stop is increased/decreased by 0.5*ATR(20). Note that if SL is configured for a loss of more than 10%, we set the SL to 10% for the first entry order to avoid big losses. This configuration does not work for pyramid orders as SL moves by 0.5*ATR(20).
5/ Exit signals :
System 1 :
Exit long on a 10 day low
Exit short on a 10 day high
System 2 :
Exit long on a 20 day low
Exit short on a 20 day high
6/ What types of orders are placed ?
To enter in a position, stop orders are placed meaning that we place orders that will be automatically triggered by the signal at the exact breakout price. Stop loss and exit signals are also stop orders. Pyramid orders are market orders which will be triggered at the opening of the next candle to avoid repainting.
PARAMETERS :
Risk % of capital : Percentage used in the position size formula. Default is 1%
ATR period : ATR length used to calculate ATR. Default is 20
Stop ATR : Parameters used to fix stop loss. Default is 1.5 meaning that stop loss will be set at : buy_price - 1.5*ATR(20) for long and buy_price + 1.5*ATR(20) for short. Turtle traders default is 2 but 1.5 is better for cryptocurrency as there is a huge volatility.
S1 Long : System 1 breakout length for long. Default is 20
S2 Long : System 2 breakout length for long. Default is 55
S1 Long Exit : System 1 breakout length to exit long. Default is 10
S2 Long Exit : System 2 breakout length to exit long. Default is 20
S1 Short : System 1 breakout length for short. Default is 15
S2 Short : System 2 breakout length for short. Default is 55
S1 Short Exit : System 1 breakout length to exit short. Default is 7
S2 Short Exit : System 2 breakout length to exit short. Default is 20
Initial capital : $1000
Fees : Interactive Broker fees apply to this strategy. They are set at 0.18% of the trade value.
Slippage : 3 ticks or $0.03 per trade. Corresponds to the latency time between the moment the signal is received and the moment the order is executed by the broker.
Pyramiding : Number of orders that can be passed in the same direction. Default is 5.
Important : Turtle traders don't trade crypto. For this specific asset type, I modify some parameters such as SL and Short S1 in order to maximize return while limiting drawdown. This strategy is the most optimal on BINANCE:BTCUSD in 1D timeframe with the parameters set per default. If you want to use this strategy for a different crypto please adapt parameters.
NOTE :
It's important to note that the first entry order (long or short) will be the largest. Subsequent pyramid orders will have fewer units than the first order. We've set a maximum SL for the first order of 10%, meaning that you won't lose more than 10% of the value of your first order. However, it is possible to lose more on your pyramid orders, as the SL is increased/decreased by 0.5*ATR(20), which does not secure a loss of more than 10% on your pyramid orders. The risk remains well managed because the value of these orders is less than the value of the first order. Remain vigilant to this small detail and adjust your risk according to your risk aversion.
Enjoy the strategy and don’t forget to take the trade :)
Adv EMA Cloud v6 (ADX, Alerts)Summary:
This indicator provides a multi-faceted view of market trends using Exponential Moving Averages (EMAs) arranged in visually intuitive clouds, enhanced with an optional ADX-based range filter and configurable alerts for key market conditions. It aims to help traders quickly gauge trend alignment across short, medium, and long timeframes while filtering signals during potentially choppy market conditions.
Key Features:
Multiple EMAs: Displays 10-period (Fast), 20-period (Mid), and 50-period (Slow) EMAs.
Long-Term Trend Filter: Includes a 200-period EMA to provide context for the overall dominant trend direction.
Dual EMA Clouds:
Fast/Mid Cloud (10/20 EMA): Fills the area between the 10 and 20 EMAs. Defaults to Green when 10 > 20 (bullish short-term momentum) and Red when 10 < 20 (bearish short-term momentum).
Mid/Slow Cloud (20/50 EMA): Fills the area between the 20 and 50 EMAs. Defaults to Aqua when 20 > 50 (bullish mid-term trend) and Fuchsia when 20 < 50 (bearish mid-term trend).
Optional ADX Range Filter: Uses the Average Directional Index (ADX) to identify potentially non-trending or choppy markets. When enabled and ADX falls below a user-defined threshold, the EMA clouds will turn grey, visually warning that trend-following signals may be less reliable.
Configurable Alerts: Provides several built-in alert conditions using Pine Script's alertcondition function:
Confluence Condition: Triggers when a 10/20 EMA crossover occurs while both EMA clouds show alignment (both bullish/green/aqua or both bearish/red/fuchsia) and price respects the 200 EMA filter and the ADX filter indicates a trend (if filters are enabled).
MA Filter Cross: Triggers when price crosses above or below the 200 EMA filter line.
Full Alignment Start: Triggers on the first bar where full bullish or bearish alignment occurs (both clouds aligned + MA filter respected + ADX trending, if filters are enabled).
How It Works:
EMA Calculation: Standard Exponential Moving Averages are calculated for the 10, 20, 50, and 200 periods based on the closing price.
Cloud Creation: The fill() function visually shades the area between the 10 & 20 EMAs and the 20 & 50 EMAs.
Cloud Coloring: The color of each cloud is determined by the relationship between the two EMAs that define it (e.g., if EMA 10 is above EMA 20, the first cloud is bullish-colored).
ADX Filter Logic: The script calculates the ADX value. If the "Use ADX Trend Filter?" input is checked and the calculated ADX is below the specified "ADX Trend Threshold", the script considers the market potentially ranging.
ADX Visual Effect: During detected ranging periods (if the ADX filter is active), the plotCloud12Color and plotCloud23Color variables are assigned a neutral grey color instead of their normal bullish/bearish colors before being passed to the fill() function.
Alert Logic: Boolean variables track the specific conditions (crossovers, cloud alignment, filter positions, ADX state). The alertcondition() function creates triggerable alerts based on these pre-defined conditions.
Potential Interpretation (Not Financial Advice):
Trend Alignment: When both clouds share the same directional color (e.g., both bullish - Green & Aqua) and price is on the corresponding side of the 200 EMA filter, it may suggest a stronger, more aligned trend. Conversely, conflicting cloud colors may indicate indecision or transition.
Dynamic Support/Resistance: The EMA lines themselves (especially the 20, 50, and 200) can sometimes act as dynamic levels where price might react.
Range Warning: Greyed-out clouds (when ADX filter is enabled) serve as a visual warning that trend-based strategies might face increased difficulty or whipsaws.
Confluence Alerts: The specific confluence alerts signal moments where multiple conditions align (crossover + cloud agreement + filters), which some traders might view as higher-probability setups.
Customization:
All EMA lengths (10, 20, 50, 200) are adjustable via the Inputs menu.
The ADX length and threshold are configurable.
The MA Trend Filter and ADX Trend Filter can be independently enabled or disabled.
Disclaimer:
This indicator is provided for informational and educational purposes only. Trading financial markets involves significant risk. Past performance is not indicative of future results. Always conduct your own thorough analysis and consider your risk tolerance before making any trading decisions. This indicator should be used in conjunction with other analysis methods and tools. Do not trade based solely on the signals or visuals provided by this indicator.
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).
References
Ang, A. (2014) *Asset Management: A Systematic Approach to Factor Investing*. Oxford: Oxford University Press.
Ang, A., Piazzesi, M. and Wei, M. (2006) 'What does the yield curve tell us about GDP growth?', *Journal of Econometrics*, 131(1-2), pp. 359-403.
Asness, C.S. (2003) 'Fight the Fed Model', *The Journal of Portfolio Management*, 30(1), pp. 11-24.
Asness, C.S., Moskowitz, T.J. and Pedersen, L.H. (2013) 'Value and Momentum Everywhere', *The Journal of Finance*, 68(3), pp. 929-985.
Baker, M. and Wurgler, J. (2006) 'Investor Sentiment and the Cross-Section of Stock Returns', *The Journal of Finance*, 61(4), pp. 1645-1680.
Baker, M. and Wurgler, J. (2007) 'Investor Sentiment in the Stock Market', *Journal of Economic Perspectives*, 21(2), pp. 129-152.
Baur, D.G. and Lucey, B.M. (2010) 'Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold', *Financial Review*, 45(2), pp. 217-229.
Bollerslev, T. (1986) 'Generalized Autoregressive Conditional Heteroskedasticity', *Journal of Econometrics*, 31(3), pp. 307-327.
Boudoukh, J., Michaely, R., Richardson, M. and Roberts, M.R. (2007) 'On the Importance of Measuring Payout Yield: Implications for Empirical Asset Pricing', *The Journal of Finance*, 62(2), pp. 877-915.
Brinson, G.P., Hood, L.R. and Beebower, G.L. (1986) 'Determinants of Portfolio Performance', *Financial Analysts Journal*, 42(4), pp. 39-44.
Brock, W., Lakonishok, J. and LeBaron, B. (1992) 'Simple Technical Trading Rules and the Stochastic Properties of Stock Returns', *The Journal of Finance*, 47(5), pp. 1731-1764.
Calmar, T.W. (1991) 'The Calmar Ratio', *Futures*, October issue.
Campbell, J.Y. and Shiller, R.J. (1988) 'The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors', *Review of Financial Studies*, 1(3), pp. 195-228.
Cochrane, J.H. (2011) 'Presidential Address: Discount Rates', *The Journal of Finance*, 66(4), pp. 1047-1108.
Damodaran, A. (2012) *Equity Risk Premiums: Determinants, Estimation and Implications*. Working Paper, Stern School of Business.
Engle, R.F. (1982) 'Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation', *Econometrica*, 50(4), pp. 987-1007.
Estrella, A. and Hardouvelis, G.A. (1991) 'The Term Structure as a Predictor of Real Economic Activity', *The Journal of Finance*, 46(2), pp. 555-576.
Estrella, A. and Mishkin, F.S. (1998) 'Predicting U.S. Recessions: Financial Variables as Leading Indicators', *Review of Economics and Statistics*, 80(1), pp. 45-61.
Faber, M.T. (2007) 'A Quantitative Approach to Tactical Asset Allocation', *The Journal of Wealth Management*, 9(4), pp. 69-79.
Fama, E.F. and French, K.R. (1989) 'Business Conditions and Expected Returns on Stocks and Bonds', *Journal of Financial Economics*, 25(1), pp. 23-49.
Fama, E.F. and French, K.R. (1992) 'The Cross-Section of Expected Stock Returns', *The Journal of Finance*, 47(2), pp. 427-465.
Garman, M.B. and Klass, M.J. (1980) 'On the Estimation of Security Price Volatilities from Historical Data', *Journal of Business*, 53(1), pp. 67-78.
Gilchrist, S. and Zakrajšek, E. (2012) 'Credit Spreads and Business Cycle Fluctuations', *American Economic Review*, 102(4), pp. 1692-1720.
Gordon, M.J. (1962) *The Investment, Financing, and Valuation of the Corporation*. Homewood: Irwin.
Graham, B. and Dodd, D.L. (1934) *Security Analysis*. New York: McGraw-Hill.
Hamilton, J.D. (1989) 'A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle', *Econometrica*, 57(2), pp. 357-384.
Ilmanen, A. (2011) *Expected Returns: An Investor's Guide to Harvesting Market Rewards*. Chichester: Wiley.
Jaconetti, C.M., Kinniry, F.M. and Zilbering, Y. (2010) 'Best Practices for Portfolio Rebalancing', *Vanguard Research Paper*.
Jegadeesh, N. and Titman, S. (1993) 'Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency', *The Journal of Finance*, 48(1), pp. 65-91.
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Korteweg, A. (2010) 'The Net Benefits to Leverage', *The Journal of Finance*, 65(6), pp. 2137-2170.
Lo, A.W. and MacKinlay, A.C. (1990) 'Data-Snooping Biases in Tests of Financial Asset Pricing Models', *Review of Financial Studies*, 3(3), pp. 431-467.
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Mandelbrot, B. (1963) 'The Variation of Certain Speculative Prices', *The Journal of Business*, 36(4), pp. 394-419.
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Moreira, A. and Muir, T. (2017) 'Volatility-Managed Portfolios', *The Journal of Finance*, 72(4), pp. 1611-1644.
Moskowitz, T.J., Ooi, Y.H. and Pedersen, L.H. (2012) 'Time Series Momentum', *Journal of Financial Economics*, 104(2), pp. 228-250.
Parkinson, M. (1980) 'The Extreme Value Method for Estimating the Variance of the Rate of Return', *Journal of Business*, 53(1), pp. 61-65.
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Schwert, G.W. (1989) 'Why Does Stock Market Volatility Change Over Time?', *The Journal of Finance*, 44(5), pp. 1115-1153.
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Candle VolumeScript Based on Volume Based Coloured Bars by KivancOzbilgic
/////////////
This indicator turns the candle into a volume-weighted signal, When the price falls, the candle is red, and when the price rises, the candle is green. In addition, we each have two colors Happening:
Dark red: It is dark red when the downtrend trading volume is greater than 200% of its average price (default 20 days), which indicates that our price action is supported by strong bearish trading volume
Red: When the price drops and the trading volume is between 50% and 200% of its average (default 20 days), in this case, we can think that the trading volume is neither strong nor weak
Light red: When the price drops and VOLUME is less than 50% of its average price (default 20 days), the trading volume is weak and there is not much support for price movements
Dark green: When the price rises and the trading volume is greater than 200% of its average price (default 20 days), it indicates that our price movement is supported by a strong bullish trading volume
Green: When the price rises and the trading volume is between 50% and 200% of its average price (the default is 20 days), in this case, we can think that the trading volume is neither strong nor weak
Light green: When the price rises and the trading volume is less than 50% of its average price (default 20 days), the trading volume is weak and does not support the price trend well
Default Low Volume is 50% (0.5) and High 200% (2), but if those values don't suit you, you can change them according to your trading personality
//////////////////////////
Esse é um indicador que colore a candlera de acordo com o volume baseado na média, quando o volume está acima da média a candlera fica verde, e quando está abaixo, a candlera fica vermelha, e as cores das candleras funcionam dessa forma :
Vermelho escuro: fica vermelho escuro quando o preço cai e o volume de negociação é superior a 200% do preço médio (padrão 20 dias), o que indica que nossa ação de preço é suportada por um forte volume de negociação de baixa
Vermelho: quando o preço cai e o volume de negociação está entre 50% e 200% de sua média (padrão de 20 dias), nesse caso, podemos pensar que o volume de negociação não é forte nem fraco
Vermelho claro: quando o preço cai e VOLUME é inferior a 50% do preço médio (padrão 20 dias), o volume de negociação é fraco e não há muito suporte para movimentos de preço
Verde escuro: quando o preço aumenta e o volume de negociação é superior a 200% do preço médio (padrão 20 dias), isso indica que nosso movimento de preço é suportado por um forte volume de negociação de alta
Verde: quando o preço aumenta e o volume de negociação está entre 50% e 200% do preço médio (o padrão é 20 dias), nesse caso, podemos pensar que o volume de negociação não é forte nem fraco
Verde claro: quando o preço aumenta e o volume de negociação é inferior a 50% do preço médio (padrão 20 dias), o volume de negociação é fraco e não suporta bem a tendência de preço
O volume baixo padrão é 50% (0,5) e alto 200% (2), mas se esses valores não forem adequados para você, você poderá alterá-los de acordo com sua personalidade de trading
Stochastic [Paifc0de]Stochastic — clean stochastic oscillator with visual masking, neutral markers, and basic filters
What it does
This indicator plots a standard stochastic oscillator (%K with smoothing and %D) and adds practical quality-of-life features for lower timeframes: optional visual masking when %K hugs overbought/oversold, neutral K–D cross markers, session-gated edge triangles (K crossing 20/80), and simple filters (minimum %K slope, minimum |K–D| gap, optional %D slope agreement, mid-zone mute, and a cooldown between markers). Display values are clamped to 0–100 to keep the panel scale stable. The tool is for research/education and does not generate entries/exits or financial advice.
Default preset: 20 / 10 / 10
K Length = 20
Classic lookback used in many textbooks. On intraday charts it balances responsiveness and stability: short enough to react to momentum shifts, long enough to avoid constant whipsaws. In practice it captures ~the last 20 bars’ position of close within the high–low range.
K Smoothing = 10
A 10-period SMA applied to the raw %K moderates the “saw-tooth” effect that raw stochastic can exhibit in choppy phases. The smoothing reduces over-reaction to micro spikes while preserving the main rhythm of swings; visually, %K becomes a continuous path that is easier to read.
D Length = 10
%D is the moving average of smoothed %K. With 10, %D becomes a clearly slower guide line. The larger separation between %K(10-SMA) and %D(10-SMA of %K) produces cleaner crosses and fewer spurious toggles than micro settings (e.g., 3/3/3). On M5–M15 this pair often yields readable cross cycles without flooding the chart.
How the 20/10/10 trio behaves
In persistent trends, %K will spend more time near 20 or 80; the 10-period smoothing delays flips slightly and emphasizes only meaningful turn attempts.
In ranges, %K oscillates around mid-zone (40–60). With 10/10 smoothing, cross signals cluster less densely; combining with the |K–D| gap filter helps keep only decisive crosses.
If your symbol is unusually volatile or illiquid, reduce K Length (e.g., 14) or reduce K Smoothing (e.g., 7) to keep responsiveness. If crosses feel late, decrease D Length (e.g., 7). If noise is excessive, increase K Smoothing first, then consider raising D Length.
Visuals
OB/OS lines: default 80/20 reference levels and a midline at 50.
Masking near edges: %K can be temporarily hidden when it is pressing an edge, approaching it with low slope, or going nearly flat near the boundary. This keeps the panel readable during “stuck at the edge” phases.
Soft glow (optional): highlights %K’s active path; can be turned off.
Light/Dark palette: quick toggle to match your chart theme.
Scale safety: all plotted values (lines, fills, markers) are clamped to 0–100 to prevent the axis from expanding beyond the stochastic range.
Markers and filters
Neutral K–D cross markers: circles in the mid-zone when %K crosses %D.
Edge triangles: show when %K crosses 20 or 80; can be restricted to a session window (02:00–12:00 ET).
Filters (optional):
Min %K slope: require a minimum absolute slope so very flat crosses are ignored.
Min |K–D| gap: demand separation between lines at the cross moment.
%D slope agreement: keep crosses that align with %D’s direction.
Mid-zone mute: suppress crosses inside a user-defined 40–60 band (defaults).
Cooldown: minimum bars between successive markers.
Parameters (quick guide)
K Length / K Smoothing / D Length: core stochastic settings. Start with 20/10/10; tune K Smoothing first if you see too much jitter.
Overbought / Oversold (80/20): adjust for assets that tend to trend (raise to 85/15) or mean-revert (lower to 75/25).
Slope & gap filters: increase on very noisy symbols; reduce if you miss too many crosses.
Session window (triangles only): use if you want edge markers only during active hours.
Marker size and offset: cosmetic; they do not affect calculations.
Alerts
K–D Cross Up (filtered) and K–D Cross Down (filtered): fire when a cross passes your filters/cooldown.
Edge Up / Edge Down: fire when %K crosses the 20/80 levels.
All alerts confirm on bar close.
Notes & attribution
Original implementation and integration by Paifc0de; no third-party code is copied.
This indicator is for research/education and does not provide entries/exits or financial advice.
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE)
## Advanced Multi-Dimensional Mathematical Analysis System
*Where Quantum Mathematics Meets Market Structure*
---
## 🎓 THEORETICAL FOUNDATION
The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry.
### 🌊 Multi-Dimensional Volatility with Jump Detection
**Hawkes Process Implementation:**
The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects.
**Mathematical Foundation:**
```
Intensity λ(t) = μ + Σ α(t - Tᵢ)
```
Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99).
**Mahalanobis Distance Calculation:**
The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions:
- **Dimension 1:** Price volatility (standard deviation of returns)
- **Dimension 2:** Volume volatility (normalized volume fluctuations)
- **Dimension 3:** Range volatility (high-low spread variations)
- **Dimension 4:** Correlation volatility (price-volume relationship changes)
- **Dimension 5:** Microstructure volatility (intrabar positioning analysis)
This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches.
### 📐 Hurst Exponent Regime Detection
**Fractal Market Hypothesis Integration:**
The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification:
- **H > 0.6:** Trending (persistent) markets - momentum strategies optimal
- **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal
- **H ≈ 0.5:** Random walk markets - breakout strategies preferred
**Adaptive R/S Analysis:**
Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions:
```
H = log(R/S) / log(n)
```
Where R is the range of cumulative deviations and S is the standard deviation over period n.
**Dynamic Regime Classification:**
The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods.
### 🔄 Transfer Entropy Analysis
**Information Flow Quantification:**
Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements:
```
TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log
```
**Causality Detection:**
- **Volume → Price:** Indicates accumulation/distribution phases
- **Price → Volume:** Suggests retail participation or momentum chasing
- **Balanced Flow:** Market equilibrium or transition periods
The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows.
---
## 🔧 COMPREHENSIVE INPUT SYSTEM
### Core Parameters Group
**Primary Analysis Window (10-100, Default: 50)**
The fundamental lookback period affecting all calculations. Optimization by timeframe:
- **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements)
- **15 minute-1 hour:** 30-50 (balanced responsiveness and stability)
- **4 hour-daily:** 50-100 (smooth signals, reduced noise)
- **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60
**Signal Sensitivity (0.1-2.0, Default: 0.7)**
Master control affecting all threshold calculations:
- **Conservative (0.3-0.6):** High-quality signals only, fewer false positives
- **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles
- **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering
**Signal Generation Mode:**
- **Aggressive:** Any component signals (highest frequency)
- **Confluence:** 2+ components agree (balanced approach)
- **Conservative:** All 3 components align (highest quality)
### Volatility Jump Detection Group
**Volatility Dimensions (2-5, Default: 3)**
Determines the mathematical space complexity:
- **2D:** Price + Volume volatility (suitable for clean markets)
- **3D:** + Range volatility (optimal for most conditions)
- **4D:** + Correlation volatility (advanced multi-asset analysis)
- **5D:** + Microstructure volatility (maximum sensitivity)
**Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)**
Standard deviations required for volatility jump classification:
- **Cryptocurrency:** 2.0-2.5σ (naturally volatile)
- **Stock Indices:** 2.5-3.0σ (moderate volatility)
- **Forex Major Pairs:** 3.0-3.5σ (typically stable)
- **Commodities:** 2.0-3.0σ (varies by commodity)
**Jump Clustering Decay (0.5-0.99, Default: 0.85)**
Hawkes process memory parameter:
- **0.5-0.7:** Fast decay (jumps treated as independent)
- **0.8-0.9:** Moderate clustering (realistic market behavior)
- **0.95-0.99:** Strong clustering (crisis/event-driven markets)
### Hurst Exponent Analysis Group
**Calculation Method Options:**
- **Classic R/S:** Original Rescaled Range (fast, simple)
- **Adaptive R/S:** Dynamic windowing (recommended for trading)
- **DFA:** Detrended Fluctuation Analysis (best for noisy data)
**Trending Threshold (0.55-0.8, Default: 0.60)**
Hurst value defining persistent market behavior:
- **0.55-0.60:** Weak trend persistence
- **0.65-0.70:** Clear trending behavior
- **0.75-0.80:** Strong momentum regimes
**Mean Reversion Threshold (0.2-0.45, Default: 0.40)**
Hurst value defining anti-persistent behavior:
- **0.35-0.45:** Weak mean reversion
- **0.25-0.35:** Clear ranging behavior
- **0.15-0.25:** Strong reversion tendency
### Transfer Entropy Parameters Group
**Information Flow Analysis:**
- **Price-Volume:** Classic flow analysis for accumulation/distribution
- **Price-Volatility:** Risk flow analysis for sentiment shifts
- **Multi-Timeframe:** Cross-timeframe causality detection
**Maximum Lag (2-20, Default: 5)**
Causality detection window:
- **2-5 bars:** Immediate causality (scalping)
- **5-10 bars:** Short-term flow (day trading)
- **10-20 bars:** Structural flow (swing trading)
**Significance Threshold (0.05-0.3, Default: 0.15)**
Minimum entropy for signal generation:
- **0.05-0.10:** Detect subtle information flows
- **0.10-0.20:** Clear causality only
- **0.20-0.30:** Very strong flows only
---
## 🎨 ADVANCED VISUAL SYSTEM
### Tensor Volatility Field Visualization
**Five-Layer Resonance Bands:**
The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength:
- **Core Layer (Purple):** Primary tensor field with highest intensity
- **Layer 2 (Neutral):** Secondary mathematical resonance
- **Layer 3 (Info Blue):** Tertiary harmonic frequencies
- **Layer 4 (Warning Gold):** Outer field boundaries
- **Layer 5 (Success Green):** Maximum field extension
**Field Strength Calculation:**
```
Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity)
```
The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility.
**Radiation Line Network:**
During active tensor states, the system projects directional radiation lines showing field energy distribution:
- **8 Directional Rays:** Complete angular coverage
- **Tapering Segments:** Progressive transparency for natural visual flow
- **Pulse Effects:** Enhanced visualization during volatility jumps
### Dimensional Portal System
**Portal Mathematics:**
Dimensional portals visualize regime transitions using category theory principles:
- **Green Portals (◉):** Trending regime detection (appear below price for support)
- **Red Portals (◎):** Mean-reverting regime (appear above price for resistance)
- **Yellow Portals (○):** Random walk regime (neutral positioning)
**Tensor Trail Effects:**
Each portal generates 8 trailing particles showing mathematical momentum:
- **Large Particles (●):** Strong mathematical signal
- **Medium Particles (◦):** Moderate signal strength
- **Small Particles (·):** Weak signal continuation
- **Micro Particles (˙):** Signal dissipation
### Information Flow Streams
**Particle Stream Visualization:**
Transfer entropy creates flowing particle streams indicating information direction:
- **Upward Streams:** Volume leading price (accumulation phases)
- **Downward Streams:** Price leading volume (distribution phases)
- **Stream Density:** Proportional to information flow strength
**15-Particle Evolution:**
Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent.
### Fractal Matrix Grid System
**Multi-Timeframe Fractal Levels:**
The system calculates and displays fractal highs/lows across five Fibonacci periods:
- **8-Period:** Short-term fractal structure
- **13-Period:** Intermediate-term patterns
- **21-Period:** Primary swing levels
- **34-Period:** Major structural levels
- **55-Period:** Long-term fractal boundaries
**Triple-Layer Visualization:**
Each fractal level uses three-layer rendering:
- **Shadow Layer:** Widest, darkest foundation (width 5)
- **Glow Layer:** Medium white core line (width 3)
- **Tensor Layer:** Dotted mathematical overlay (width 1)
**Intelligent Labeling System:**
Smart spacing prevents label overlap using ATR-based minimum distances. Labels include:
- **Fractal Period:** Time-based identification
- **Topological Class:** Mathematical complexity rating (0, I, II, III)
- **Price Level:** Exact fractal price
- **Mahalanobis Distance:** Current mathematical field strength
- **Hurst Exponent:** Current regime classification
- **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡)
### Wick Pressure Analysis
**Rejection Level Mathematics:**
The system analyzes candle wick patterns to project future pressure zones:
- **Upper Wick Analysis:** Identifies selling pressure and resistance zones
- **Lower Wick Analysis:** Identifies buying pressure and support zones
- **Pressure Projection:** Extends lines forward based on mathematical probability
**Multi-Layer Glow Effects:**
Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart.
### Enhanced Regime Background
**Dynamic Intensity Mapping:**
Background colors reflect mathematical regime strength:
- **Deep Transparency (98% alpha):** Subtle regime indication
- **Pulse Intensity:** Based on regime strength calculation
- **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random)
**Smoothing Integration:**
Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts.
### Color Scheme System
**Six Professional Themes:**
- **Dark (Default):** Professional trading environment optimization
- **Light:** High ambient light conditions
- **Classic:** Traditional technical analysis appearance
- **Neon:** High-contrast visibility for active trading
- **Neutral:** Minimal distraction focus
- **Bright:** Maximum visibility for complex setups
Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences.
---
## 📊 INSTITUTIONAL-GRADE DASHBOARD
### Tensor Field Status Section
**Field Strength Display:**
Real-time Mahalanobis distance calculation with dynamic emoji indicators:
- **⚡ (Lightning):** Extreme field strength (>1.5× threshold)
- **● (Solid Circle):** Strong field activity (>1.0× threshold)
- **○ (Open Circle):** Normal field state
**Signal Quality Rating:**
Democratic algorithm assessment:
- **ELITE:** All 3 components aligned (highest probability)
- **STRONG:** 2 components aligned (good probability)
- **GOOD:** 1 component active (moderate probability)
- **WEAK:** No clear component signals
**Threshold and Anomaly Monitoring:**
- **Threshold Display:** Current mathematical threshold setting
- **Anomaly Level (0-100%):** Combined volatility and volume spike measurement
- **>70%:** High anomaly (red warning)
- **30-70%:** Moderate anomaly (orange caution)
- **<30%:** Normal conditions (green confirmation)
### Tensor State Analysis Section
**Mathematical State Classification:**
- **↑ BULL (Tensor State +1):** Trending regime with bullish bias
- **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias
- **◈ SUPER (Tensor State 0):** Random walk regime (neutral)
**Visual State Gauge:**
Five-circle progression showing tensor field polarity:
- **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment
- **⚪⚪🟡⚪⚪:** Neutral/transitional state
- **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment
**Trend Direction and Phase Analysis:**
- **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification
- **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength)
- **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength)
- **😴 CALM:** Low information flow (<1.0 flow strength)
### Trading Signals Section
**Real-Time Signal Status:**
- **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability
- **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability
- **Components (X/3):** Active algorithmic components
- **Mode Display:** Current signal generation mode
**Signal Strength Visualization:**
Color-coded component count:
- **Green:** 3/3 components (maximum confidence)
- **Aqua:** 2/3 components (good confidence)
- **Orange:** 1/3 components (moderate confidence)
- **Gray:** 0/3 components (no signals)
### Performance Metrics Section
**Win Rate Monitoring:**
Estimated win rates based on signal quality with emoji indicators:
- **🔥 (Fire):** ≥60% estimated win rate
- **👍 (Thumbs Up):** 45-59% estimated win rate
- **⚠️ (Warning):** <45% estimated win rate
**Mathematical Metrics:**
- **Hurst Exponent:** Real-time fractal dimension (0.000-1.000)
- **Information Flow:** Volume/price leading indicators
- **📊 VOL:** Volume leading price (accumulation/distribution)
- **💰 PRICE:** Price leading volume (momentum/speculation)
- **➖ NONE:** Balanced information flow
- **Volatility Classification:**
- **🔥 HIGH:** Above 1.5× jump threshold
- **📊 NORM:** Normal volatility range
- **😴 LOW:** Below 0.5× jump threshold
### Market Structure Section (Large Dashboard)
**Regime Classification:**
- **📈 TREND:** Hurst >0.6, momentum strategies optimal
- **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal
- **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred
**Mathematical Field Analysis:**
- **Dimensions:** Current volatility space complexity (2D-5D)
- **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00)
- **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal)
### Settings Summary Section (Large Dashboard)
**Active Configuration Display:**
- **Sensitivity:** Current master sensitivity setting
- **Lookback:** Primary analysis window
- **Theme:** Active color scheme
- **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA)
**Dashboard Sizing Options:**
- **Small:** Essential metrics only (mobile/small screens)
- **Normal:** Balanced information density (standard desktop)
- **Large:** Maximum detail (multi-monitor setups)
**Position Options:**
- **Top Right:** Standard placement (avoids price action)
- **Top Left:** Wide chart optimization
- **Bottom Right:** Recent price focus (scalping)
- **Bottom Left:** Maximum price visibility (swing trading)
---
## 🎯 SIGNAL GENERATION LOGIC
### Multi-Component Convergence System
**Component Signal Architecture:**
The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing:
**Volatility Component:**
- **Jump Detection:** Mahalanobis distance threshold breach
- **Hawkes Intensity:** Self-exciting process activation (>0.2)
- **Multi-dimensional:** Considers all volatility dimensions simultaneously
**Hurst Regime Component:**
- **Trending Markets:** Price above SMA-20 with positive momentum
- **Mean-Reverting Markets:** Price at Bollinger Band extremes
- **Random Markets:** Bollinger squeeze breakouts with directional confirmation
**Transfer Entropy Component:**
- **Volume Leadership:** Information flow from volume to price
- **Volume Spike:** Volume 110%+ above 20-period average
- **Flow Significance:** Above entropy threshold with directional bias
### Democratic Signal Weighting
**Signal Mode Implementation:**
- **Aggressive Mode:** Any single component triggers signal
- **Confluence Mode:** Minimum 2 components must agree
- **Conservative Mode:** All 3 components must align
**Momentum Confirmation:**
All signals require momentum confirmation:
- **Long Signals:** RSI >50 AND price >EMA-9
- **Short Signals:** RSI <50 AND price 0.6):**
- **Increase Sensitivity:** Catch momentum continuation
- **Lower Mean Reversion Threshold:** Avoid counter-trend signals
- **Emphasize Volume Leadership:** Institutional accumulation/distribution
- **Tensor Field Focus:** Use expansion for trend continuation
- **Signal Mode:** Aggressive or Confluence for trend following
**Range-Bound Markets (Hurst <0.4):**
- **Decrease Sensitivity:** Avoid false breakouts
- **Lower Trending Threshold:** Quick regime recognition
- **Focus on Price Leadership:** Retail sentiment extremes
- **Fractal Grid Emphasis:** Support/resistance trading
- **Signal Mode:** Conservative for high-probability reversals
**Volatile Markets (High Jump Frequency):**
- **Increase Hawkes Decay:** Recognize event clustering
- **Higher Jump Threshold:** Avoid noise signals
- **Maximum Dimensions:** Capture full volatility complexity
- **Reduce Position Sizing:** Risk management adaptation
- **Enhanced Visuals:** Maximum information for rapid decisions
**Low Volatility Markets (Low Jump Frequency):**
- **Decrease Jump Threshold:** Capture subtle movements
- **Lower Hawkes Decay:** Treat moves as independent
- **Reduce Dimensions:** Simplify analysis
- **Increase Position Sizing:** Capitalize on compressed volatility
- **Minimal Visuals:** Reduce distraction in quiet markets
---
## 🚀 ADVANCED TRADING STRATEGIES
### The Mathematical Convergence Method
**Entry Protocol:**
1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels
2. **Tensor Field Confirmation:** Verify field expansion supporting direction
3. **Portal Signal:** Wait for dimensional portal appearance
4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals
5. **Component Consensus:** Confirm 2+ components agree in Confluence mode
**Example Implementation:**
- Price approaching 21-period fractal high
- Tensor field expanding upward (bullish mathematical alignment)
- Green portal appears below price (trending regime confirmation)
- ELITE quality signal with 3/3 components active
- Enter long position with stop below fractal level
**Risk Management:**
- **Stop Placement:** Below/above fractal level that generated signal
- **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size)
- **Profit Targets:** Next fractal level or tensor field resistance
### The Regime Transition Strategy
**Regime Change Detection:**
1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds
2. **Portal Color Change:** Regime transitions show different portal colors
3. **Background Intensity:** Increasing regime background intensity
4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis)
**Trading Implementation:**
- **Trending Transitions:** Trade momentum breakouts, follow trend
- **Mean Reversion Transitions:** Trade range boundaries, fade extremes
- **Random Transitions:** Trade breakouts with tight stops
**Advanced Techniques:**
- **Multi-Timeframe:** Confirm regime on higher timeframe
- **Early Entry:** Enter on regime transition rather than confirmation
- **Regime Strength:** Larger positions during strong regime signals
### The Information Flow Momentum Strategy
**Flow Detection Protocol:**
1. **Monitor Transfer Entropy:** Watch for significant information flow shifts
2. **Volume Leadership:** Strong edge when volume leads price
3. **Flow Acceleration:** Increasing flow strength indicates momentum
4. **Directional Confirmation:** Ensure flow aligns with intended trade direction
**Entry Signals:**
- **Volume → Price Flow:** Enter during accumulation/distribution phases
- **Price → Volume Flow:** Enter on momentum confirmation breaks
- **Flow Reversal:** Counter-trend entries when flow reverses
**Optimization:**
- **Scalping:** Use immediate flow detection (2-5 bar lag)
- **Swing Trading:** Use structural flow (10-20 bar lag)
- **Multi-Asset:** Compare flow between correlated assets
### The Tensor Field Expansion Strategy
**Field Mathematics:**
The tensor field expansion indicates mathematical pressure building in market structure:
**Expansion Phases:**
1. **Compression:** Field contracts, volatility decreases
2. **Tension Building:** Mathematical pressure accumulates
3. **Expansion:** Field expands rapidly with directional movement
4. **Resolution:** Field stabilizes at new equilibrium
**Trading Applications:**
- **Compression Trading:** Prepare for breakout during field contraction
- **Expansion Following:** Trade direction of field expansion
- **Reversion Trading:** Fade extreme field expansion
- **Multi-Dimensional:** Consider all field layers for confirmation
### The Hawkes Process Event Strategy
**Self-Exciting Jump Trading:**
Understanding that market shocks cluster and create follow-on opportunities:
**Jump Sequence Analysis:**
1. **Initial Jump:** First volatility jump detected
2. **Clustering Phase:** Hawkes intensity remains elevated
3. **Follow-On Opportunities:** Additional jumps more likely
4. **Decay Period:** Intensity gradually decreases
**Implementation:**
- **Jump Confirmation:** Wait for mathematical jump confirmation
- **Direction Assessment:** Use other components for direction
- **Clustering Trades:** Trade subsequent moves during high intensity
- **Decay Exit:** Exit positions as Hawkes intensity decays
### The Fractal Confluence System
**Multi-Timeframe Fractal Analysis:**
Combining fractal levels across different periods for high-probability zones:
**Confluence Zones:**
- **Double Confluence:** 2 fractal levels align
- **Triple Confluence:** 3+ fractal levels cluster
- **Mathematical Confirmation:** Tensor field supports the level
- **Information Flow:** Transfer entropy confirms direction
**Trading Protocol:**
1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR
2. **Mathematical Support:** Verify tensor field alignment
3. **Signal Quality:** Wait for STRONG or ELITE signal
4. **Risk Definition:** Use fractal level for stop placement
5. **Profit Targeting:** Next major fractal confluence zone
---
## ⚠️ COMPREHENSIVE RISK MANAGEMENT
### Mathematical Position Sizing
**Mahalanobis Distance Integration:**
Position size should inversely correlate with mathematical field strength:
```
Position Size = Base Size × (Threshold / Mahalanobis Distance)
```
**Risk Scaling Matrix:**
- **Low Field Strength (<2.0):** Standard position sizing
- **Moderate Field Strength (2.0-3.0):** 75% position sizing
- **High Field Strength (3.0-4.0):** 50% position sizing
- **Extreme Field Strength (>4.0):** 25% position sizing or no trade
### Signal Quality Risk Adjustment
**Quality-Based Position Sizing:**
- **ELITE Signals:** 100% of planned position size
- **STRONG Signals:** 75% of planned position size
- **GOOD Signals:** 50% of planned position size
- **WEAK Signals:** No position or paper trading only
**Component Agreement Scaling:**
- **3/3 Components:** Full position size
- **2/3 Components:** 75% position size
- **1/3 Components:** 50% position size or skip trade
### Regime-Adaptive Risk Management
**Trending Market Risk:**
- **Wider Stops:** Allow for trend continuation
- **Trend Following:** Trade with regime direction
- **Higher Position Size:** Trend probability advantage
- **Momentum Stops:** Trail stops based on momentum indicators
**Mean-Reverting Market Risk:**
- **Tighter Stops:** Quick exits on trend continuation
- **Contrarian Positioning:** Trade against extremes
- **Smaller Position Size:** Higher reversal failure rate
- **Level-Based Stops:** Use fractal levels for stops
**Random Market Risk:**
- **Breakout Focus:** Trade only clear breakouts
- **Tight Initial Stops:** Quick exit if breakout fails
- **Reduced Frequency:** Skip marginal setups
- **Range-Based Targets:** Profit targets at range boundaries
### Volatility-Adaptive Risk Controls
**High Volatility Periods:**
- **Reduced Position Size:** Account for wider price swings
- **Wider Stops:** Avoid noise-based exits
- **Lower Frequency:** Skip marginal setups
- **Faster Exits:** Take profits more quickly
**Low Volatility Periods:**
- **Standard Position Size:** Normal risk parameters
- **Tighter Stops:** Take advantage of compressed ranges
- **Higher Frequency:** Trade more setups
- **Extended Targets:** Allow for compressed volatility expansion
### Multi-Timeframe Risk Alignment
**Higher Timeframe Trend:**
- **With Trend:** Standard or increased position size
- **Against Trend:** Reduced position size or skip
- **Neutral Trend:** Standard position size with tight management
**Risk Hierarchy:**
1. **Primary:** Current timeframe signal quality
2. **Secondary:** Higher timeframe trend alignment
3. **Tertiary:** Mathematical field strength
4. **Quaternary:** Market regime classification
---
## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS
### Advanced Mathematical Concepts
**Tensor Analysis in Markets:**
The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture.
**Information Theory Applications:**
Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior.
**Fractal Geometry in Trading:**
The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure.
**Stochastic Process Theory:**
The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects.
### Learning Progressive Complexity
**Beginner Mathematical Concepts:**
- **Volatility Dimensions:** Understanding multi-dimensional analysis
- **Regime Classification:** Learning market personality types
- **Signal Democracy:** Algorithmic consensus building
- **Visual Mathematics:** Interpreting mathematical concepts visually
**Intermediate Mathematical Applications:**
- **Mahalanobis Distance:** Statistical distance in multi-dimensional space
- **Rescaled Range Analysis:** Fractal dimension measurement
- **Information Entropy:** Quantifying uncertainty and causality
- **Field Theory:** Understanding mathematical fields in market context
**Advanced Mathematical Integration:**
- **Tensor Field Dynamics:** Multi-dimensional market force analysis
- **Stochastic Self-Excitation:** Event clustering and memory effects
- **Categorical Composition:** Mathematical signal combination theory
- **Topological Market Analysis:** Understanding market shape and connectivity
### Practical Mathematical Intuition
**Developing Market Mathematics Intuition:**
The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of:
- **How markets exhibit mathematical structure beneath apparent randomness**
- **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches**
- **How information flows through markets in measurable, predictable ways**
- **Why mathematical models provide probabilistic edges rather than certainties**
---
## 🔬 IMPLEMENTATION AND OPTIMIZATION
### Getting Started Protocol
**Phase 1: Observation (Week 1)**
1. **Apply with defaults:** Use standard settings on your primary trading timeframe
2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams
3. **Monitor dashboard:** Observe how metrics change with market conditions
4. **No trading:** Focus entirely on pattern recognition and understanding
**Phase 2: Pattern Recognition (Week 2-3)**
1. **Identify signal patterns:** Note what market conditions produce different signal qualities
2. **Regime correlation:** Observe how Hurst regimes affect signal performance
3. **Visual confirmation:** Learn to read tensor field expansion and portal signals
4. **Component analysis:** Understand which components drive signals in different markets
**Phase 3: Parameter Optimization (Week 4-5)**
1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument
2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe
3. **Sensitivity adjustment:** Balance signal frequency with quality
4. **Visual customization:** Optimize colors and intensity for your trading environment
**Phase 4: Live Implementation (Week 6+)**
1. **Paper trading:** Test signals with hypothetical trades
2. **Small position sizing:** Begin with minimal risk during learning phase
3. **Performance tracking:** Monitor actual vs. expected signal performance
4. **Continuous optimization:** Refine settings based on real performance data
### Performance Monitoring System
**Signal Quality Tracking:**
- **ELITE Signal Win Rate:** Track highest quality signals separately
- **Component Performance:** Monitor which components provide best signals
- **Regime Performance:** Analyze performance across different market regimes
- **Timeframe Analysis:** Compare performance across different session times
**Mathematical Metric Correlation:**
- **Field Strength vs. Performance:** Higher field strength should correlate with better performance
- **Component Agreement vs. Win Rate:** More component agreement should improve win rates
- **Regime Alignment vs. Success:** Trading with mathematical regime should outperform
### Continuous Optimization Process
**Monthly Review Protocol:**
1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown
2. **Parameter Assessment:** Evaluate if current settings remain optimal
3. **Market Adaptation:** Adjust for changes in market character or volatility
4. **Component Weighting:** Consider if certain components should receive more/less emphasis
**Quarterly Deep Analysis:**
1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid
2. **Regime Distribution:** Analyze time spent in different market regimes
3. **Signal Evolution:** Track how signal characteristics change over time
4. **Correlation Analysis:** Monitor correlations between different mathematical components
---
## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS
### Revolutionary Mathematical Integration
**First-Ever Implementations:**
1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility
2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes
3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation
4. **Democratic Component Voting:** First algorithmic consensus system for signal generation
5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels
### Advanced Visualization Innovations
**Mathematical Visualization Breakthroughs:**
- **Tensor Field Radiation:** Visual representation of mathematical field energy
- **Dimensional Portal System:** Category theory visualization for regime transitions
- **Information Flow Streams:** Real-time visual display of market information transfer
- **Multi-Layer Fractal Grid:** Intelligent spacing and projection system
- **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength
### Practical Trading Innovations
**Trading System Advances:**
- **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence
- **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality
- **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering
- **Component Performance Tracking:** Real-time monitoring of algorithmic component success
- **Field-Strength Position Sizing:** Mathematical volatility integration for risk management
---
## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS
### Mathematical Model Limitations
**Understanding Model Boundaries:**
While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations:
- **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements
- **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely
- **Model Risk:** Complex models can fail during unprecedented market conditions
- **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions
### Proper Implementation Guidelines
**Risk Management Requirements:**
- **Never Risk More Than 2% Per Trade:** Regardless of signal quality
- **Diversification Mandatory:** Don't rely solely on mathematical signals
- **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence
- **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters
**Realistic Expectations:**
- **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes
- **Learning Curve Required:** Complex mathematical concepts require time to master
- **Market Adaptation Necessary:** Parameters must evolve with changing market conditions
- **Continuous Education Important:** Understanding underlying mathematics improves application
### Ethical Trading Considerations
**Market Impact Awareness:**
- **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants
- **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure
- **Sharing Knowledge:** Consider educational contributions to trading community
- **Fair Market Participation:** Use mathematical advantages responsibly within market framework
### Professional Development Path
**Skill Development Sequence:**
1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application
2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals
3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights
4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution
---
## 🔮 CONCLUSION
The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability.
### Mathematical Innovation Legacy
This indicator establishes new paradigms in technical analysis:
- **Tensor analysis for market volatility understanding**
- **Stochastic self-excitation for event clustering prediction**
- **Information theory for causality-based trade generation**
- **Democratic algorithmic consensus for signal quality enhancement**
- **Mathematical field visualization for intuitive market understanding**
### Practical Trading Revolution
Beyond mathematical innovation, the TMAE transforms practical trading:
- **Quality-rated signals replace binary buy/sell decisions**
- **Regime-adaptive strategies automatically optimize for market personality**
- **Multi-dimensional risk management integrates mathematical volatility measures**
- **Visual mathematical concepts make complex analysis immediately interpretable**
- **Educational value creates lasting improvement in trading understanding**
### Future-Proof Design
The mathematical foundations ensure lasting relevance:
- **Universal mathematical principles transcend market evolution**
- **Multi-dimensional analysis adapts to new market structures**
- **Regime detection automatically adjusts to changing market personalities**
- **Component democracy allows for future algorithmic additions**
- **Mathematical visualization scales with increasing market complexity**
### Commitment to Excellence
The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value.
### Trading with Mathematical Precision
In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals.
By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics.
### Final Perspective
Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure.
Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior.
Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE.
*"In mathematics, you don't understand things. You just get used to them." - John von Neumann*
*With the TMAE, mathematical market understanding becomes not just possible, but intuitive.*
— Dskyz, Trade with insight. Trade with anticipation.
Swing EMAWhat is Swing EMA?
Swing EMA is an exponential moving average crossover-based indicator used for low-risk directional trading.
it's used for different types of Ema 20,50,100 and 200, 3 of them are plotted on chat 20,100,200.
100 and 200 Ema is used for showing support and resistance and it contains highlights area between them and its change color according to market crossover condition.
20 moving average is used for knowing Market Behaviour and changing its color according to crossover conditions of 50 and 20 Ema.
How does it work?
It contains 4 different types of moving averages 20,50,100, 200 out of 3 are plotted on the chart.
20 Ema is used for knowing current market behavior. Its changes its color based on the crossover of 50 Ema and 20 Ema, if 20 Ema is higher than 50 Ema then it changes its color to green, and its opposites are changed their color to red when 20 Ema is lower than 50 Ema.
100 and 200 Ema used as a support and resistance and is also contain highlighted areas between them its change their color based on the crossover if 100 Ema is higher than 200 Ema a then both of them are going to change color to Green and as an opposite, if 200 Ema is higher then 100 Ema is going to change its color to red.
So in simple word 100 and 200 Ema is used as support and resistance zone and 20 Ema is used to know current market behavior.
How to use it?
It is very easy to understand by looking at the example I gave where are the two different types of phrases. phrase bull phrase and bear phrase so 100 and 200 Ema is used as a support and resistance and to tell you which phrase is currently on the market on example there is a bull phrase on the left side and bear phrase on the right side by using your technical analysis you can find out a really good spot to buy your stocks on a bull phrase and too short on the bear phrase. 20 Ema is used as a knowing the current market behavior it doesn't make any difference on buying or selling as much as 100 Ema and 200 Ema.
Tips
Don't trade against the market.
Try trade on trending stocks rather than sideways stock.
The higher the area between 100 Ema and 200 Ema is the stronger the phrase.
Do Backtesting before real trading.
Enjoy Trading.
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.
Linh Index Trend & Exhaustion SuitePurpose: One overlay to judge trend, reversal risk, overextension, and volatility squeezes on indexes (built for VNINDEX/VN30, works on any symbol & timeframe).
What it shows
Trend state: Bull / Bear / Transition via 20/50/200 EMAs + slope check.
Overextension heatmap: Background paints when price is stretched vs the 20-EMA by ATR or % (you set the thresholds).
Squeeze detection:
Squeeze ON (yellow dot): Bollinger Bands (20,2) inside Keltner Channels (20,1.5).
Squeeze OFF + Release: White dot; script confirms direction only when close > BB upper (up) or close < BB lower (down).
52-week context: Distance to 52-week high/low (%).
Higher-TF alignment: Optional weekly trend reading shown on the label while you’re on the daily.
Anchored VWAP(s): Two optional AVWAPs from dates you choose (e.g., YTD open, last big gap/earnings).
Plots & labels
EMAs 20/50/200 (toggle on/off).
Optional BB & KC bands for diagnostics.
AVWAP #1 / #2 (optional).
Status label with: Trend, EMAs, Dist to 20-EMA (%, ATR), 52-week distances, HTF state.
Built-in alerts (set “Once per bar close”)
EMA10 ↔ EMA20 cross (early momentum shift)
EMA20 ↔ EMA50 cross (trend confirmation/negation)
Price ↔ EMA200 cross (long-term regime)
Squeeze Release UP / DOWN (BB breakout after squeeze)
Overextension Cool-off UP / DN (stretched vs 20-EMA + momentum rolling)
Near 52-week High (within your % threshold)
How to use (playbook)
Map regime: Prefer trades when Daily = Bull and HTF (Weekly) = Bull (shown on label).
Hunt expansion: Yellow → White dot and close beyond BB = fresh move.
Avoid chasing stretch: If background is painted (overextended vs 20-EMA), wait for a pullback or intraday base.
Locations matter: 52-week proximity + HTF Bull improves breakout quality.
Anchors: Add AVWAP from YTD open or last major gap to frame support/resistance.
Suggested settings
Overextension: ATR = 2.0, % = 4.0 to start; tune per index volatility.
Squeeze bands: BB(20,2) & KC(20,1.5) default are balanced; tighten KC (1.3) for more signals, widen (1.8) for fewer/higher quality.
Timeframes: Daily for signals, Weekly for bias. Optional 65-min for entries.
EMA Crossover Strategy with Take Profit and Candle HighlightingStrategy Overview:
This strategy is based on the Exponential Moving Averages (EMA), specifically the EMA 20 and EMA 50. It takes advantage of EMA crossovers to identify potential trend reversals and uses multiple take-profit levels and a stop-loss for risk management.
Key Components:
EMA Crossover Signals:
Buy Signal (Uptrend): A buy signal is generated when the EMA 20 crosses above the EMA 50, signaling the start of a potential uptrend.
Sell Signal (Downtrend): A sell signal is generated when the EMA 20 crosses below the EMA 50, signaling the start of a potential downtrend.
Take Profit Levels:
Once a buy or sell signal is triggered, the strategy calculates multiple take-profit levels based on the range of the previous candle. The user can define multipliers for each take-profit level.
Take Profit 1 (TP1): 50% of the previous candle's range above or below the entry price.
Take Profit 2 (TP2): 100% of the previous candle's range above or below the entry price.
Take Profit 3 (TP3): 150% of the previous candle's range above or below the entry price.
Take Profit 4 (TP4): 200% of the previous candle's range above or below the entry price.
These levels are adjusted dynamically based on the previous candle's high and low, so they adapt to changing market conditions.
Stop Loss:
A stop-loss is set to manage risk. The default stop-loss is 3% from the entry price, but this can be adjusted in the settings. The stop-loss is triggered if the price moves against the position by this amount.
Trend Direction Highlighting:
The strategy highlights the bars (candles) with colors:
Green bars indicate an uptrend (when EMA 20 crosses above EMA 50).
Red bars indicate a downtrend (when EMA 20 crosses below EMA 50).
These visual cues help users easily identify the market direction.
Strategy Entries and Exits:
Entries: The strategy enters a long (buy) position when the EMA 20 crosses above the EMA 50 and a short (sell) position when the EMA 20 crosses below the EMA 50.
Exits: The strategy exits the positions at any of the defined take-profit levels or the stop-loss. Multiple exit levels provide opportunities to take profit progressively as the price moves in the favorable direction.
Entry and Exit Conditions in Detail:
Buy Entry Condition (Uptrend):
A buy position is opened when EMA 20 crosses above EMA 50, signaling the start of an uptrend.
The strategy calculates take-profit levels above the entry price based on the previous bar's range (high-low) and the multipliers for TP1, TP2, TP3, and TP4.
Sell Entry Condition (Downtrend):
A sell position is opened when EMA 20 crosses below EMA 50, signaling the start of a downtrend.
The strategy calculates take-profit levels below the entry price, similarly based on the previous bar's range.
Exit Conditions:
Take Profit: The strategy attempts to exit the position at one of the take-profit levels (TP1, TP2, TP3, or TP4). If the price reaches any of these levels, the position is closed.
Stop Loss: The strategy also has a stop-loss set at a default value (3% below the entry for long trades, and 3% above for short trades). The stop-loss helps to protect the position from significant losses.
Backtesting and Performance Metrics:
The strategy can be backtested using TradingView's Strategy Tester. The results will show how the strategy would have performed historically, including key metrics like:
Net Profit
Max Drawdown
Win Rate
Profit Factor
Average Trade Duration
These performance metrics can help users assess the strategy's effectiveness over historical periods and optimize the input parameters (e.g., multipliers, stop-loss level).
Customization:
The strategy allows for the adjustment of several key input values via the settings panel:
Take Profit Multipliers: Users can customize the multipliers for each take-profit level (TP1, TP2, TP3, TP4).
Stop Loss Percentage: The user can also adjust the stop-loss percentage to a custom value.
EMA Periods: The default periods for the EMA 50 and EMA 20 are fixed, but they can be adjusted for different market conditions.
Pros of the Strategy:
EMA Crossover Strategy: A classic and well-known strategy used by traders to identify the start of new trends.
Multiple Take Profit Levels: By taking profits progressively at different levels, the strategy locks in gains as the price moves in favor of the position.
Clear Trend Identification: The use of green and red bars makes it visually easier to follow the market's direction.
Risk Management: The stop-loss and take-profit features help to manage risk and optimize profit-taking.
Cons of the Strategy:
Lagging Indicators: The strategy relies on EMAs, which are lagging indicators. This means that the strategy might enter trades after the trend has already started, leading to missed opportunities or less-than-ideal entry prices.
No Confirmation Indicators: The strategy purely depends on the crossover of two EMAs and does not use other confirming indicators (e.g., RSI, MACD), which might lead to false signals in volatile markets.
How to Use in Real-Time Trading:
Use for Backtesting: Initially, use this strategy in backtest mode to understand how it would have performed historically with your preferred settings.
Paper Trading: Once comfortable, you can use paper trading to test the strategy in real-time market conditions without risking real money.
Live Trading: After testing and optimizing the strategy, you can consider using it for live trading with proper risk management in place (e.g., starting with a small position size and adjusting parameters as needed).
Summary:
This strategy is designed to identify trend reversals using EMA crossovers, with customizable take-profit levels and a stop-loss to manage risk. It's well-suited for traders looking for a systematic way to enter and exit trades based on clear market signals, while also providing flexibility to adjust for different risk profiles and trading styles.
Market Analysis Assistant This indicator uniquely maps and interprets key market conditions using Moving Averages, MACD, RSI, and Bollinger Bands. Unlike traditional indicators that only display visual signals, this tool provides written analysis directly on your chart as soon as specific conditions are met. This feature makes it easier to understand the market’s current state and anticipate potential moves.
Why Moving Averages? Moving Averages are essential for identifying the overall trend of the market. By analyzing the 200, 20, and 9-period Moving Averages, this indicator helps traders quickly determine whether the market is in an uptrend, downtrend, or sideways phase. The integration of multiple averages offers a comprehensive view, allowing for more accurate trend identification.
Why MACD? The MACD is a powerful tool for spotting trend reversals and momentum shifts. By monitoring MACD crossovers, divergences, and the position of the MACD line relative to the zero line, this indicator helps you identify potential changes in the trend direction before they fully develop, giving you a critical edge.
Why RSI? RSI is crucial for understanding the market's overbought and oversold conditions. By tracking RSI levels and its crossover with its moving average, this indicator provides early warnings for potential trend reversals or continuations, helping you time your entries and exits more effectively.
Why Bollinger Bands? Bollinger Bands are used to measure market volatility and identify breakout opportunities. By analyzing the price’s relationship with the upper and lower bands, this indicator helps traders spot potential overbought or oversold conditions, as well as possible breakout scenarios, offering a clear view of market dynamics.
Trend Identification (getTrend()): Detects whether the market is in an uptrend, downtrend, or sideways phase by analyzing the position of the price relative to the 200, 20, and 9-period moving averages.
MACD Analysis (analyzeMACD()): Identifies potential trend reversals or continuations through MACD divergence, crossovers, and the MACD signal line's position relative to the zero line.
RSI Monitoring (analyzeRSI()): Detects overbought and oversold conditions and anticipates trend continuation or corrections based on RSI crossings with its moving average.
Trap Zone Detection (analyzeTrapZone()): Highlights areas of potential price consolidation between the 20 and 200-period moving averages, indicating possible breakouts.
Bollinger Bands Analysis (analyzeBollingerBands()): Analyzes the price’s relationship with Bollinger Bands to identify overbought/oversold conditions, breakouts, and potential trend continuations or correction.
Fibonacci retracement will also check the moment the price tests a monthly or daily weekly Fibonacci retracement
What Makes This Indicator Unique?
This indicator stands out by transforming complex technical analysis into clear, written insights directly on your chart. As soon as specific conditions are met—such as a MACD crossover or an RSI overbought/oversold level—this tool immediately displays a written summary of the event, helping traders to quickly understand and act on market developments.
How to Use My Indicator:
The indicator is designed to provide detailed, real-time market condition analysis using Moving Averages, MACD, RSI, and Bollinger Bands. When certain market conditions are met, such as the price testing a specific moving average or the MACD indicating a potential reversal, the indicator displays this information in written form directly on the chart, in both English and Portuguese.
How to Interpret the Displayed Information:
The information displayed by the indicator can be used for:
Identifying Support and Resistance: The indicator can help identify when the price is testing an important support or resistance level, such as a moving average or a Fibonacci level, allowing the user to decide whether to enter or exit a position.
Trend Detection: If the indicator shows that the price is above the 200, 20, and 9-period moving averages, this may be a sign of an uptrend, indicating that the user should consider maintaining or opening buy positions.
Correction Signals: When the MACD indicates a potential correction, the user may decide to protect their profits by adjusting stops or even exiting the position to avoid losses.
Identifying Overbought/Oversold Conditions: Based on the RSI, the indicator can alert to overbought or oversold conditions, helping the user avoid entering a trade at an unfavorable time.
Example of Use:
the indicator shows several important pieces of information, such as:
"US100 Price is at the 50.0% Fibonacci level (Last Monthly)."
This suggests that the price is testing a significant Fibonacci level, which could be a point of reversal or continuation. A trader can use this information to adjust their entry or exit strategy.
"DXY RSI below 30: Indication of oversold condition"
This indicates that the DXY is in an oversold condition, which might suggest an upcoming bullish reversal. A trader could consider this when trading DXY-related assets.
"Bullish Trend: Price is above the 200, 20, and 9-period moving averages."
This confirms an uptrend, giving the user more confidence to hold long positions.
Availability:
This indicator is available in two languages: English and Portuguese. It is ideal for traders who prefer analysis in English as well as those who prefer it in Portuguese, making it a versatile and accessible tool for traders from different backgrounds
Este indicador mapeia e interpreta de forma única as principais condições de mercado utilizando Médias Móveis, MACD, RSI e Bandas de Bollinger. Ao contrário dos indicadores tradicionais que apenas exibem sinais visuais, esta ferramenta oferece uma análise escrita diretamente no seu gráfico assim que determinadas condições são atendidas. Isso facilita o entendimento do estado atual do mercado e a antecipação de possíveis movimentos.
Por que Médias Móveis? As Médias Móveis são essenciais para identificar a tendência geral do mercado. Ao analisar as Médias Móveis de 200, 20 e 9 períodos, este indicador ajuda os traders a determinarem rapidamente se o mercado está em tendência de alta, baixa ou em fase lateral. A integração de múltiplas médias oferece uma visão abrangente, permitindo uma identificação mais precisa das tendências.
Por que MACD? O MACD é uma ferramenta poderosa para identificar reversões de tendência e mudanças de momentum. Monitorando os cruzamentos do MACD, divergências e a posição da linha MACD em relação à linha zero, este indicador ajuda você a identificar possíveis mudanças na direção da tendência antes que elas se desenvolvam completamente, dando-lhe uma vantagem crítica.
Por que RSI? O RSI é crucial para entender as condições de sobrecompra e sobrevenda do mercado. Acompanhando os níveis do RSI e seu cruzamento com sua média móvel, este indicador fornece avisos antecipados para possíveis reversões ou continuações de tendência, ajudando você a cronometrar suas entradas e saídas de forma mais eficaz.
Por que Bandas de Bollinger? As Bandas de Bollinger são usadas para medir a volatilidade do mercado e identificar oportunidades de rompimento. Ao analisar a relação do preço com as bandas superior e inferior, este indicador ajuda os traders a identificar condições de sobrecompra ou sobrevenda, bem como possíveis cenários de rompimento, oferecendo uma visão clara da dinâmica do mercado.
Identificação de Tendências (getTrend()): Detecta se o mercado está em tendência de alta, baixa ou em fase lateral, analisando a posição do preço em relação às médias móveis de 200, 20 e 9 períodos.
Análise de MACD (analyzeMACD()): Identifica possíveis reversões ou continuações de tendência através de divergências do MACD, cruzamentos, e a posição da linha de sinal do MACD em relação à linha zero.
Monitoramento do RSI (analyzeRSI()): Detecta condições de sobrecompra e sobrevenda e antecipa a continuação da tendência ou correções com base nos cruzamentos do RSI com sua média móvel.
Detecção de Zona de Armadilha (analyzeTrapZone()): Destaca áreas de possível consolidação de preços entre as médias móveis de 20 e 200 períodos, indicando possíveis rompimentos.
Análise das Bandas de Bollinger (analyzeBollingerBands()): Analisa a relação do preço com as Bandas de Bollinger para identificar condições de sobrecompra/sobrevenda, rompimentos e possíveis continuações de tendência ou correção.
A retração de Fibonacci também verificará o momento em que o preço testa uma retração de Fibonacci semanal mensal ou diária
O que Torna Este Indicador Único?
Este indicador se destaca por transformar análises técnicas complexas em insights escritos claros diretamente no seu gráfico. Assim que condições específicas são atendidas—como um cruzamento do MACD ou um nível de sobrecompra/sobrevenda do RSI—esta ferramenta exibe imediatamente um resumo escrito do evento, ajudando os traders a entenderem e agirem rapidamente sobre as mudanças do mercado.
Como Utilizar o Meu Indicador:
O indicador foi desenvolvido para oferecer uma análise detalhada e em tempo real das condições de mercado, utilizando os conceitos de Médias Móveis, MACD, RSI e Bandas de Bollinger. Quando certas condições de mercado são atingidas, como o preço testando uma média móvel específica ou o MACD indicando uma possível reversão, o indicador exibe essas informações de forma escrita diretamente no gráfico, em inglês e português.
Como Interpretar as Informações Exibidas:
As informações exibidas pelo indicador podem ser usadas para:
Identificação de Suportes e Resistências: O indicador pode ajudar a identificar quando o preço está testando um nível de suporte ou resistência importante, como uma média móvel ou um nível de Fibonacci, permitindo ao usuário decidir se deve entrar ou sair de uma posição.
Detecção de Tendências: Se o indicador mostra que o preço está acima das médias móveis de 200, 20 e 9 períodos, isso pode ser um sinal de uma tendência de alta, indicando que o usuário deve considerar manter ou abrir posições de compra.
Sinais de Correção: Quando o MACD indica uma possível correção, o usuário pode decidir proteger seus lucros ajustando os stops ou até mesmo saindo da posição para evitar perdas.
Identificação de Condições de Sobrecompra/Sobrevenda: Com base no RSI, o indicador pode alertar sobre condições de sobrecompra ou sobrevenda, ajudando o usuário a evitar entrar em uma operação em um momento desfavorável.
Exemplo de Utilização:
o indicador mostra várias informações importantes, como:
"O preço do US100 está no nível de Fibonacci de 50,0% (mês passado)."
Isso sugere que o preço está testando um nível significativo de Fibonacci, o que pode ser um ponto de reversão ou continuação. Um trader pode usar essa informação para ajustar sua estratégia de entrada ou saída.
DXY RSI abaixo de 30: Indicação de condição de sobrevenda"
Isso indica que o DXY está em uma condição de sobrevenda, o que pode sugerir uma reversão de alta em breve. Um trader pode considerar isso ao fazer operações relacionadas ao DXY.
"Tendência de alta: o preço está acima das médias móveis de 200, 20 e 9 períodos."
Isso confirma uma tendência de alta, dando ao usuário mais confiança para manter posições longas.
Disponibilidade:
Este indicador está disponível em dois idiomas: inglês e português. Ele é ideal tanto para traders que preferem análises em inglês quanto para aqueles que preferem em português. Isso o torna uma ferramenta versátil e acessível para traders de diferentes origens.
Turtle Trade Channels Indicator TUTCILegendary trade system which proved that great traders can be made, not born.
Turtle Trade Experiment made 80% annual return for 4 years and made 150 million $
Turtle Trade trend following system is a complete opposite to the "buy low and sell high" approach.
This trend following system was taught to a group of average and normal individuals, and almost everyone turned into a profitable trader.
They used the basis logic of well known DONCHIAN CHANNELS which developed by Richard Donchian.
The main rule is "Trade an 20-day breakout and take profits when an 10-day high or low is breached ". Examples:
Buy a 20-day breakout and close the trade when price action reaches a 10-day low.
Go short a 20-day breakout and close the trade when price action reaches a 10-day high.
In this indicator,
The red line is the trading line which indicates the trend directio n:
Price bars over the trend line indicates uptrend
Price bars under the trend line means downtrend
The dotted blue line is the exit line.
Original system is:
Go long when the price High is equal to or above previous 20 day Highest price.
Go short when the price Low is equal to or below previous 20 day Lowest price.
Exit long positions when the price touches the exit line
Exit short positions when the price touches the exit line
Recommended initial stop-loss is ATR * 2 from the opening price.
Default system parameters were 20,10 and 55,20.
Original Turtle Rules:
To trade exactly like the turtles did, you need to set up two indicators representing the main and the failsafe system.
Set up the main indicator with EntryPeriod = 20 and ExitPeriod = 10 (A.k.a S1)
Set up the failsafe indicator with EntryPeriod = 55 and ExitPeriod = 20 using a different color. (A.k.a S2)
The entry strategy using S1 is as follows
Buy 20-day breakouts using S1 only if last signaled trade was a loss.
Sell 20-day breakouts using S1 only if last signaled trade was a loss.
If last signaled trade by S1 was a win, you shouldn't trade -Irregardless of the direction or if you traded last signal it or not-
The entry strategy using S2 is as follows:
Buy 55-day breakouts only if you ignored last S1 signal and the market is rallying without you
Sell 55-day breakouts only if you ignored last S1 signal and the market is pluging without you
You can Highlight the chart with provided trade signals:
Green background color when Long
Red background color when Short
No background color when flat
WARNING: TURTLE TRADE STOP or ADDING more UNITS RULES ARE NOT INCLUDED.
Author: Kıvanç Özbilgiç
Also you can show or hide trade signals with the button on the settings menu
S&P Merval Index Volume Indicator (Shares, ARS, U$S CCL GGAL)S&P Merval Index Volume Indicator (Shares, ARS, U$S CCL GGAL)
◾ This indicator reflects a close estimate of the traded volume in the S&P Merval Index BCBA:IMV for nominal shares, traded money in ARS & USD using a financial FX rate.
◾ The constituents of the index "must meet minimum size and liquidity requirements" as it is been declared by S&P Dow Jones Indexes. On this version of the indicator were reflected the current set of stocks for the Index as of Monday, July 27, 2020 for actual and historical sessions.
◾ Eventually, there could be changes in consitutents as per the S&P Dow Jones Indexes classification and re-balance that will be reflected on this script or a new one.
◾ Aggregated volume of nominal shares for each of the stocks constitutents is multiplied by their closing prices to estimates the effective volume in ARS & adjusted by the FX rate with "Contado con Liquidación" FX rate closing session price.
◾ It serves as a dynamical volume indicator available for standard and customized timeframes. Provides an assertive look over trading activity which allows the analyst to measure effectively either resistance or support zones in Bull / Flat or Bear markets.
◾ Output of 10 trading days of effective volume was cross-checked with "IAMC Informe diario" www.iamc.com.ar the official daily report by the exchange ByMA (Bolsas y Mercados de Argentina).
1) Trading Sessions Dates
7/27/20; 7/23/20; 7/22/20; 7/21/20; 7/20/20; 7/16/20; 7/15/20; 7/14/20; 7/13/20
2) IAMC Informe Diario S&P Merval Index Effective volume (ARS) for each of 1)
$1309.4M; $1999.3M; $1691.1M; $1585.6M; $949.7M; $818.6M; $1010.4M; $962.3M; $1515.7M
3) Pine indicator S&P Merval Index Effective volume (ARS) for each 1)
$1294.6M; $1911.7M; $1691.3M; $1526.6M; $901.4M; $796.7M; $961.9M; $939.7M; $1404.7 M
4) Variance 3) | 2)
-1%; -4%; 0%; -4%; -5%; -3%; -5%; -2%; -7%
Average Deviation: -4%
Standard Deviation: 2%
* This quick analysis depicts that effective volume displayed may (or not) have a non significance variance over the real data reported by the National Exchange due to the script calculation.
* Thanks to Alan who helped me a lot with the code!
6 SMA&look back 20SMA strategy6 SMA&look back 20SMA strategy
The moving average is calculated based on historical prices and is a backward indicator,Unable to forcast future prices.
But the moving average still represents the price trend, so we can forcast by moving average.
Each point of the 20-day SMA is calculated based on the closing price of the past 20 days.
Therefore, we can know that the closing price of the first 20 days directly affects the latest point of SMA.
The last 20 days are the prices included in the calculation, which directly affect the trend of the latest points.
For example,if the day before the 20th is a sharp fall, the latest SMA becomes an upward trend;If the day before the 20th is a sharp rise, the latest SMA becomes a downward trend，it showing the opposite trend
Recommended to use the daily trend.
Good Luck
移動平均線是根據歷史價格計算，屬於落後指標
但是移動平均線仍代表價格趨勢，因此我們可以通過移動平均線進行預測。
20天均線的每個點都是基於過去20天的收盤價計算的。
因此，我們可以知道前20天的收盤價直接影響SMA的最新點。
最近20天是計算中包括的價格，它直接影響最新點的趨勢。
例如，如果20日之前的一天是急劇下跌，則最新的SMA成為上升趨勢；如果20日之前的一天是急劇上漲，則最新的SMA成為下降趨勢，呈現相反的趨勢。
參考老王對SMA扣抵值的簡易版應用
使用20MA的扣抵蹺蹺板來判斷今日可能是UP或DOWN
建議使用日線
祝好運
Multi-TF Trend Table (Configurable)1) What this tool does (in one minute)
A compact, multi‑timeframe dashboard that stacks eight timeframes and tells you:
Trend (fast MA vs slow MA)
Where price sits relative to those MAs
How far price is from the fast MA in ATR terms
MA slope (rising, falling, flat)
Stochastic %K (with overbought/oversold heat)
MACD momentum (up or down)
A single score (0%–100%) per timeframe
Alignment tick when trend, structure, slope and momentum all agree
Use it to:
Frame bias top‑down (M→W→D→…→15m)
Time entries on your execution timeframe when the higher‑TF stack is aligned
Avoid counter‑trend traps when the table is mixed
2) Table anatomy (each column explained)
The table renders 9 columns × 8 rows (one row per timeframe label you define).
TF — The label you chose for that row (e.g., Month, Week, 4H). Cosmetic; helps you read the stack.
Trend — Arrow from fast MA vs slow MA: ↑ if fastMA > slowMA (up‑trend), ↓ otherwise (down‑trend). Cell is green for up, red for down.
Price Pos — One‑character structure cue:
🔼 if price is above both fast and slow MAs (bullish structure)
🔽 if price is below both (bearish structure)
– otherwise (between MAs / mixed)
MA Dist — Distance of price from the fast MA measured in ATR multiples:
XS < S < M < L < XL according to your thresholds (see §3.3). Useful for judging stretch/mean‑reversion risk and stop sizing.
MA Slope — The fast MA one‑bar slope:
↑ if fastMA - fastMA > 0
↓ if < 0
→ if = 0
Stoch %K — Rounded %K value (default 14‑1‑3). Background highlights when it aligns with the trend:
Green heat when trend up and %K ≤ oversold
Red heat when trend down and %K ≥ overbought Tooltip shows K and D values precisely.
Trend % — Composite score (0–100%), the dashboard’s confidence for that timeframe:
+20 if trendUp (fast>slow)
+20 if fast MA slope > 0
+20 if MACD up (signal definition in §2.8)
+20 if price above fast MA
+20 if price above slow MA
Background colours:
≥80 lime (strong alignment)
≥60 green (good)
≥40 orange (mixed)
<40 grey (weak/contrary)
MACD — 🟢 if EMA(12)−EMA(26) > its EMA(9), else 🔴. It’s a simple “momentum up/down” proxy.
Align — ✔ when everything is in gear for that trend direction:
For up: trendUp and price above both MAs and slope>0 and MACD up
For down: trendDown and price below both MAs and slope<0 and MACD down Tooltip spells this out.
3) Settings & how to tune them
3.1 Timeframes (TF1–TF8)
Inputs: TF1..TF8 hold the resolution strings used by request.security().
Defaults: M, W, D, 720, 480, 240, 60, 15 with display labels Month, Week, Day, 12H, 8H, 4H, 1H, 15m.
Tips
Keep a top‑down funnel (e.g., Month→Week→Day→H4→H1→M15) so you can cascade bias into entries.
If you scalp, consider D, 240, 120, 60, 30, 15, 5, 1.
Crypto weekends: consider 2D in place of W to reflect continuous trading.
3.2 Moving Average (MA) group
Type: EMA, SMA, WMA, RMA, HMA. Changes both fast & slow MA computations everywhere.
Fast Length: default 20. Shorten for snappier trend/slope & tighter “price above fast” signals.
Slow Length: default 200. Controls the structural trend and part of the score.
When to change
Swing FX/equities: EMA 20/200 is a solid baseline.
Mean‑reversion style: consider SMA 20/100 so trend flips slower.
Crypto/indices momentum: HMA 21 / EMA 200 will read slope more responsively.
3.3 ATR / Distance group
ATR Length: default 14; longer makes distance less jumpy.
XS/S/M/L thresholds: define the labels in column MA Dist. They are compared to |close − fastMA| / ATR.
Defaults: XS 0.25×, S 0.75×, M 1.5×, L 2.5×; anything ≥L is XL.
Usage
Entries late in a move often occur at L/XL; consider waiting for a pullback unless you are trading breakouts.
For stops, an initial SL around 0.75–1.5 ATR from fast MA often sits behind nearby noise; use your plan.
3.4 Stochastic group
%K Length / Smoothing / %D Smoothing: defaults 14 / 1 / 3.
Overbought / Oversold: defaults 70 / 30 (adjust to 80/20 for trendier assets).
Heat logic (column Stoch %K): highlights when a pullback aligns with the dominant trend (oversold in an uptrend, overbought in a downtrend).
3.5 View
Full Screen Table Mode: centers and enlarges the table (position.middle_center). Great for clean screenshots or multi‑monitor setups.
4) Signal logic (how each datapoint is computed)
Per‑TF data (via a single request.security()):
fastMA, slowMA → based on your MA Type and lengths
%K, %D → Stoch(High,Low,Close,kLen) smoothed by kSmooth, then %D smoothed by dSmooth
close, ATR(atrLen) → for structure and distance
MACD up → (EMA12−EMA26) > EMA9(EMA12−EMA26)
fastMA_prev → yesterday/previous‑bar fast MA for slope
TrendUp → fastMA > slowMA
Price Position → compares close to both MAs
MA Distance Label → thresholds on abs(close − fastMA)/ATR
Slope → fastMA − fastMA
Score (0–100) → sum of the five 20‑point checks listed in §2.7
Align tick → conjunction of trend, price vs both MAs, slope and MACD (see §2.9)
Important behaviour
HTF values are sampled at the execution chart’s bar close using Pine v6 defaults (no lookahead). So the daily row updates only when a daily bar actually closes.
5) How to trade with it (playbooks)
The table is a framework. Entries/exits still follow your plan (e.g., S/D zones, price action, risk rules). Use the table to know when to be aggressive vs patient.
Playbook A — Trend continuation (pullback entry)
Look for Align ✔ on your anchor TFs (e.g., Week+Day both ≥80 and green, Trend ↑, MACD 🟢).
On your execution TF (e.g., H1/H4), wait for Stoch heat with the trend (oversold in uptrend or overbought in downtrend), and MA Dist not at XL.
Enter on your trigger (break of pullback high/low, engulfing, retest of fast MA, or S/D first touch per your plan).
Risk: consider ATR‑based SL beyond structure; size so 0.25–0.5% account risk fits your rules.
Trail or scale at M/L distances or when score deteriorates (<60).
Playbook B — Breakout with confirmation
Mixed stack turns into broad green: Trend % jumps to ≥80 on Day and H4; MACD flips 🟢.
Price Pos shows 🔼 across H4/H1 (above both MAs). Slope arrows ↑.
Enter on the first clean base‑break with volume/impulse; avoid if MA Dist already XL.
Playbook C — Mean‑reversion fade (advanced)
Use only when higher TFs are not aligned and the row you trade shows XL distance against the higher‑TF context. Take quick targets back to fast MA. Lower win‑rate, faster management.
Playbook D — Top‑down filter for Supply/Demand strategy
Trade first retests only in the direction where anchor TFs (Week/Day) have Align ✔ and Trend % ≥60. Skip counter‑trend zones when the stack is red/green against you.
6) Reading examples
Strong bullish stack
Week: ↑, 🔼, S/M, slope ↑, %K=32 (green heat), Trend 100%, MACD 🟢, Align ✔
Day: ↑, 🔼, XS/S, slope ↑, %K=45, Trend 80%, MACD 🟢, Align ✔
Action: Look for H4/H1 pullback into demand or fast MA; buy continuation.
Late‑stage thrust
H1: ↑, 🔼, XL, slope ↑, %K=88
Day/H4: only 60–80%
Action: Likely overextended on H1; wait for mean reversion or multi‑TF alignment before chasing.
Bearish transition
Day flips from 60%→40%, Trend ↓, MACD turns 🔴, Price Pos “–” (between MAs)
Action: Stand aside for longs; watch for lower‑high + Align ✔ on H4/H1 to join shorts.
7) Practical tips & pitfalls
HTF closure: Don’t assume a daily row changed mid‑day; it won’t settle until the daily bar closes. For intraday anticipation, watch H4/H1 rows.
MA Type consistency: Changing MA Type changes slope/structure everywhere. If you compare screenshots, keep the same type.
ATR thresholds: Calibrate per asset class. FX may suit defaults; indices/crypto might need wider S/M/L.
Score ≠ signal: 100% does not mean “must buy now.” It means the environment is favourable. Still execute your trigger.
Mixed stacks: When rows disagree, reduce size or skip. The tool is telling you the market lacks consensus.
8) Customisation ideas
Timeframe presets: Save layouts (e.g., Swing, Intraday, Scalper) as indicator templates in TradingView.
Alternative momentum: Replace the MACD condition with RSI(>50/<50) if desired (would require code edit).
Alerts: You can add alert conditions for (a) Align ✔ changes, (b) Trend % crossing 60/80, (c) Stoch heat events. (Not shipped in this script, but easy to add.)
9) FAQ
Q: Why do I sometimes see a dash in Price Pos? A: Price is between fast and slow MAs. Structure is mixed; seek clarity before acting.
Q: Does it repaint? A: No, higher‑TF values update on the close of their own bars (standard request.security behaviour without lookahead). Intra‑bar they can fluctuate; decisions should be made at your bar close per your plan.
Q: Which columns matter most? A: For trend‑following: Trend, Price Pos, Slope, MACD, then Stoch heat for entries. The Score summarises, and Align enforces discipline.
Q: How do I integrate with ATR‑based risk? A: Use the MA Dist label to avoid chasing at extremes and to size stops in ATR terms (e.g., SL behind structure at ~1–1.5 ATR).
Chart-Only Scanner — Pro Table v2.5.1Chart-Only Scanner — Pro Table v2.5
User Manual (Pine Script v6)
What this tool does (in one line)
A compact, on-chart table that scores the current chart symbol (or an optional override) using momentum, volume, trend, volatility, and pattern checks—so you can quickly decide UP, DOWN, or WAIT.
Quick Start (90 seconds)
Add the indicator to any chart and timeframe (1m…1M).
Leave “Override chart symbol” = OFF to auto-use the chart’s symbol.
Choose your layout:
Row (wide horizontal strip), or Grid (title + labeled cells).
Pick a size preset (Micro, Small, Medium, Large, Mobile).
Optional: turn on “Use Higher TF (EMA 20/50)” and set HTF Multiplier (e.g., 4 ⇒ if chart is 15m, HTF is 60m).
Watch the table:
DIR (↑/↓/→), ROC%, MOM, VOL, EMA stack, HTF, REV, SCORE, ACT.
Add an alert if you want: the script fires when |SCORE| ≥ Action threshold.
What to expect
A small table appears on the chart corner you choose, updating each bar (or only at bar close if you keep default smart-update).
The ACT cell shows 🔥 (strong), 👀 (medium), or ⏳ (weak).
Panels & Settings (every option explained)
Core
Momentum Period: Lookback for rate-of-change (ROC%). Shorter = more reactive; longer = smoother.
ROC% Threshold: Minimum absolute ROC% to call direction UP (↑) or DOWN (↓); otherwise →.
Require Volume Confirmation: If ON and VOL ≤ 1.0, the SCORE is forced to 0 (prevents low-volume false positives).
Override chart symbol + Custom symbol: By default, the indicator uses the chart’s symbol. Turn this ON to lock to a specific ticker (e.g., a perpetual).
Higher TF
Use Higher TF (EMA 20/50): Compares EMA20 vs EMA50 on a higher timeframe.
HTF Multiplier: Higher TF = (chart TF × multiplier).
Example: on 3H chart with multiplier 2 ⇒ HTF = 6H.
Volatility & Oscillators
ATR Length: Used to show ATR% (ATR relative to price).
RSI Length: Standard RSI; colors: green ≤30 (oversold), red ≥70 (overbought).
Stoch %K Length: With %D = SMA(%K, 3).
MACD Fast/Slow/Signal: Standard MACD values; we display Line, Signal, Histogram (L/S/H).
ADX Length (Wilder): Wilder’s smoothing (internal derivation); also shows +DI / −DI if you enable the ADX column.
EMAs / Trend
EMA Fast/Mid/Slow: We compute EMA(20/50/200) by default (editable).
EMA Stack: Bull if Fast > Mid > Slow; Bear if Fast < Mid < Slow; Flat otherwise.
Benchmark (optional, OFF by default)
Show Relative Strength vs Benchmark: Displays RS% = ROC(symbol) − ROC(benchmark) over the Momentum Period.
Benchmark Symbol: Ticker used for comparison (e.g., BTCUSDT as a market proxy).
Columns (show/hide)
Toggle which fields appear in the table. Hiding unused fields keeps the layout clean (especially on mobile).
Display
Layout Mode:
Row = a single two-row strip; each column is a metric.
Grid = a title row plus labeled pairs (label/value) arranged in rows.
Size Preset: Micro, Small, Medium, Large, Mobile change text size and the grid density.
Table Corner: Where the panel sits (e.g., Top Right).
Opaque Table Background: ON = dark card; OFF = transparent(ish).
Update Every Bar: ON = update intra-bar; OFF = smart update (last bar / real-time / confirmed history).
Action threshold (|score|): The cutoff for 🔥 and alert firing (default 70).
How to read each field
CHART: The active symbol name (or your custom override).
DIR: ↑ (ROC% > threshold), ↓ (ROC% < −threshold), → otherwise.
ROC%: Rate of change over Momentum Period.
Formula: (Close − Close ) / Close × 100.
MOM: A scaled momentum score: min(100, |ROC%| × 10).
VOL: Volume ratio vs 20-bar SMA: Volume / SMA(Volume,20).
1.5 highlights as yellow (significant participation).
ATR%: (ATR / Close) × 100 (volatility relative to price).
RSI: Colored for extremes: ≤30 green, ≥70 red.
Stoch K/D: %K and %D numbers.
MACD L/S/H: Line, Signal, Histogram. Histogram color reflects sign (green > 0, red < 0).
ADX, +DI, −DI: Trend strength and directional components (Wilder). ADX ≥ 25 is highlighted.
EMA 20/50/200: Current EMA values (editable lengths).
STACK: Bull/Bear/Flat as defined above.
VWAP%: (Close − VWAP) / Close × 100 (premium/discount to VWAP).
HTF: ▲ if HTF EMA20 > EMA50; ▼ if <; · if flat/off.
RS%: Symbol’s ROC% − Benchmark ROC% (positive = outperforming).
REV (reversal):
🟢 Eng/Pin = bullish engulfing or bullish pin detected,
🔴 Eng/Pin = bearish engulfing or bearish pin,
· = none.
SCORE (absolute shown as a number; sign shown via DIR and ACT):
Components:
base = MOM × 0.4
volBonus = VOL > 1.5 ? 20 : VOL × 13.33
htfBonus = use_mtf ? (HTF == DIR ? 30 : HTF == 0 ? 15 : 0) : 0
trendBonus = (STACK == DIR) ? 10 : 0
macdBonus = 0 (placeholder for future versions)
scoreRaw = base + volBonus + htfBonus + trendBonus + macdBonus
SCORE = DIR ≥ 0 ? scoreRaw : −scoreRaw
If Require Volume Confirmation and VOL ≤ 1.0 ⇒ SCORE = 0.
ACT:
🔥 if |SCORE| ≥ threshold
👀 if 50 < |SCORE| < threshold
⏳ otherwise
Practical examples
Strong long (trend + participation)
DIR = ↑, ROC% = +3.2, MOM ≈ 32, VOL = 1.9, STACK = Bull, HTF = ▲, REV = 🟢
SCORE: base(12.8) + volBonus(20) + htfBonus(30) + trend(10) ≈ 73 → ACT = 🔥
Action idea: look for longs on pullbacks; confirm risk with ATR%.
Weak long (no volume)
DIR = ↑, ROC% = +1.0, but VOL = 0.8 and Require Volume Confirmation = ON
SCORE forced to 0 → ACT = ⏳
Action: wait for volume > 1.0 or turn off confirmation knowingly.
Bearish reversal warning
DIR = →, REV = 🔴 (bearish engulfing), RSI = 68, HTF = ▼
SCORE may be mid-range; ACT = 👀
Action: watch for breakdown and rising VOL.
Alerts (how to use)
The script calls alert() whenever |SCORE| ≥ Action threshold.
To receive pop-ups, sounds, or emails: click “⏰ Alerts” in TradingView, choose this indicator, and pick “Any alert() function call.”
The alert message includes: symbol, |SCORE|, DIR.
Layout, Size, and Corner tips
Row is best when you want a compact status ribbon across the top.
Grid is clearer on big screens or when you enable many columns.
Size:
Mobile = one pair per row (tall, readable)
Micro/Small = dense; good for many fields
Large = presentation/screenshots
Corner: If the table overlaps price, change the corner or set Opaque Background = OFF.
Repaint & timeframe behavior
Default smart update prefers stability (last bar / live / confirmed history).
For a stricter, “close-only” behavior (less repaint): turn Update Every Bar = OFF and avoid Heikin Ashi when you want raw market OHLC (HA modifies price inputs).
HTF logic is derived from a clean, integer multiple of your chart timeframe (via multiplier). It works with 3H/4H and any TF.
Performance notes
The script analyzes one symbol (chart or override) with multiple metrics using efficient tuple requests.
If you later want a multi-symbol grid, do it with pages (10–15 per page + rotate) to stay within platform limits (recommended future add-on).
Troubleshooting
No table visible
Ensure the indicator is added and not hidden.
Try toggling Opaque Background or switch Corner (it might be behind other drawings).
Keep Columns count reasonable for the chosen Size.
If you turned ON Override, verify the Custom symbol exists on your data provider.
Numbers look different on HA candles
Heikin Ashi modifies OHLC; switch to regular candles if you need raw price metrics.
3H/4H issues
Use integer HTF Multiplier (e.g., 2, 4). The tool builds the correct string internally; no manual timeframe strings needed.
Power user tips
Volume gating: keeping Require Volume Confirmation = ON filters most fake moves; if you’re a scalper, reduce strictness or turn it off.
Action threshold: 60–80 is typical. Higher = fewer but stronger signals.
Benchmark RS%: great for spotting leaders/laggards; positive RS% = outperformance vs benchmark.
Change policy & safety
This version doesn’t alter your historical logic you tested (no radical changes).
Any future “radical” change (score weights, HTF logic, UI hiding data) will ship with a toggle and an Impact Statement so you can keep old behavior if you prefer.
Glossary (quick)
ROC%: Percent change over N bars.
MOM: Scaled momentum (0–100).
VOL ratio: Volume vs 20-bar average.
ATR%: ATR as % of price.
ADX/DI: Trend strength / direction components (Wilder).
EMA stack: Relationship between EMAs (bullish/bearish/flat).
VWAP%: Premium/discount to VWAP.
RS%: Relative strength vs benchmark.
Liquid Pulse Liquid Pulse by Dskyz (DAFE) Trading Systems
Liquid Pulse is a trading algo built by Dskyz (DAFE) Trading Systems for futures markets like NQ1!, designed to snag high-probability trades with tight risk control. it fuses a confluence system—VWAP, MACD, ADX, volume, and liquidity sweeps—with a trade scoring setup, daily limits, and VIX pauses to dodge wild volatility. visuals include simple signals, VWAP bands, and a dashboard with stats.
Core Components for Liquid Pulse
Volume Sensitivity (volumeSensitivity) controls how much volume spikes matter for entries. options: 'Low', 'Medium', 'High' default: 'High' (catches small spikes, good for active markets) tweak it: 'Low' for calm markets, 'High' for chaos.
MACD Speed (macdSpeed) sets the MACD’s pace for momentum. options: 'Fast', 'Medium', 'Slow' default: 'Medium' (solid balance) tweak it: 'Fast' for scalping, 'Slow' for swings.
Daily Trade Limit (dailyTradeLimit) caps trades per day to keep risk in check. range: 1 to 30 default: 20 tweak it: 5-10 for safety, 20-30 for action.
Number of Contracts (numContracts) sets position size. range: 1 to 20 default: 4 tweak it: up for big accounts, down for small.
VIX Pause Level (vixPauseLevel) stops trading if VIX gets too hot. range: 10 to 80 default: 39.0 tweak it: 30 to avoid volatility, 50 to ride it.
Min Confluence Conditions (minConditions) sets how many signals must align. range: 1 to 5 default: 2 tweak it: 3-4 for strict, 1-2 for more trades.
Min Trade Score (Longs/Shorts) (minTradeScoreLongs/minTradeScoreShorts) filters trade quality. longs range: 0 to 100 default: 73 shorts range: 0 to 100 default: 75 tweak it: 80-90 for quality, 60-70 for volume.
Liquidity Sweep Strength (sweepStrength) gauges breakouts. range: 0.1 to 1.0 default: 0.5 tweak it: 0.7-1.0 for strong moves, 0.3-0.5 for small.
ADX Trend Threshold (adxTrendThreshold) confirms trends. range: 10 to 100 default: 41 tweak it: 40-50 for trends, 30-35 for weak ones.
ADX Chop Threshold (adxChopThreshold) avoids chop. range: 5 to 50 default: 20 tweak it: 15-20 to dodge chop, 25-30 to loosen.
VWAP Timeframe (vwapTimeframe) sets VWAP period. options: '15', '30', '60', '240', 'D' default: '60' (1-hour) tweak it: 60 for day, 240 for swing, D for long.
Take Profit Ticks (Longs/Shorts) (takeProfitTicksLongs/takeProfitTicksShorts) sets profit targets. longs range: 5 to 100 default: 25.0 shorts range: 5 to 100 default: 20.0 tweak it: 30-50 for trends, 10-20 for chop.
Max Profit Ticks (maxProfitTicks) caps max gain. range: 10 to 200 default: 60.0 tweak it: 80-100 for big moves, 40-60 for tight.
Min Profit Ticks to Trail (minProfitTicksTrail) triggers trailing. range: 1 to 50 default: 7.0 tweak it: 10-15 for big gains, 5-7 for quick locks.
Trailing Stop Ticks (trailTicks) sets trail distance. range: 1 to 50 default: 5.0 tweak it: 8-10 for room, 3-5 for fast locks.
Trailing Offset Ticks (trailOffsetTicks) sets trail offset. range: 1 to 20 default: 2.0 tweak it: 1-2 for tight, 5-10 for loose.
ATR Period (atrPeriod) measures volatility. range: 5 to 50 default: 9 tweak it: 14-20 for smooth, 5-9 for reactive.
Hardcoded Settings volLookback: 30 ('Low'), 20 ('Medium'), 11 ('High') volThreshold: 1.5 ('Low'), 1.8 ('Medium'), 2 ('High') swingLen: 5
Execution Logic Overview trades trigger when confluence conditions align, entering long or short with set position sizes. exits use dynamic take-profits, trailing stops after a profit threshold, hard stops via ATR, and a time stop after 100 bars.
Features Multi-Signal Confluence: needs VWAP, MACD, volume, sweeps, and ADX to line up.
Risk Control: ATR-based stops (capped 15 ticks), take-profits (scaled by volatility), and trails.
Market Filters: VIX pause, ADX trend/chop checks, volatility gates. Dashboard: shows scores, VIX, ADX, P/L, win %, streak.
Visuals Simple signals (green up triangles for longs, red down for shorts) and VWAP bands with glow. info table (bottom right) with MACD momentum. dashboard (top right) with stats.
Chart and Backtest:
NQ1! futures, 5-minute chart. works best in trending, volatile conditions. tweak inputs for other markets—test thoroughly.
Backtesting: NQ1! Frame: Jan 19, 2025, 09:00 — May 02, 2025, 16:00 Slippage: 3 Commission: $4.60
Fee Typical Range (per side, per contract)
CME Exchange $1.14 – $1.20
Clearing $0.10 – $0.30
NFA Regulatory $0.02
Firm/Broker Commis. $0.25 – $0.80 (retail prop)
TOTAL $1.60 – $2.30 per side
Round Turn: (enter+exit) = $3.20 – $4.60 per contract
Disclaimer this is for education only. past results don’t predict future wins. trading’s risky—only use money you can lose. backtest and validate before going live. (expect moderators to nitpick some random chart symbol rule—i’ll fix and repost if they pull it.)
About the Author Dskyz (DAFE) Trading Systems crafts killer trading algos. Liquid Pulse is pure research and grit, built for smart, bold trading. Use it with discipline. Use it with clarity. Trade smarter. I’ll keep dropping badass strategies ‘til i build a brand or someone signs me up.
2025 Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
