Trend Continuation Filter - 🚀 Trend Continuation Filter — Multi-Factor Overlay
This overlay plots bullish / bearish continuation labels & arrows only when the market has enough confluence behind the move. Think of it as your “trend gatekeeper” — cutting out weak setups and highlighting only those with real momentum + structure.
🔍 Built-in Filters
✔ Ichimoku Cloud → trend bias + Tenkan/Kijun confirmation
✔ MACD (12/26/9) → acceleration via histogram slope
✔ RSI / MFI (14) → momentum quality (≥60 bullish / ≤40 bearish)
✔ ADX (14) → strength check (≥20 and rising)
➕ EMA Alignment (9/21/55/233) (optional)
➕ ATR Slope (14) (optional)
🎯 How it works
✅ Prints a Bull Continuation label/arrow when ≥4 filters align to the upside
✅ Prints a Bear Continuation label/arrow when ≥4 filters align to the downside
⚙️ minChecks input lets you adjust the strictness:
• Normal Days → set to 4 (more frequent, flexible)
• Trend Days → raise to 5–6 (fewer, high-conviction setups)
📈 Best Practices
⏰ Focus on London & New York sessions for clean expectancy
🧩 Pair with a HUD/Dashboard panel to see exactly which filters are active
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Daryl Guppy's Multiple Moving Averages - GMMAThe Guppy EMAs indicator (Daryl Guppy’s method) displays two groups of exponential moving averages (EMAs) on the chart:
Fast EMA group: 3, 5, 8, 10, 12, 15 periods (thinner, more responsive lines)
Slow EMA group: 30, 35, 40, 45, 50, 60 periods (thicker, smoother lines)
Color Logic:
Fast EMAs turn AQUA if all fast EMAs are in bullish alignment and slow EMAs are in bullish alignment.
Fast EMAs turn ORANGE if all fast EMAs are in bearish alignment and slow EMAs are in bearish alignment.
Otherwise, fast EMAs appear GRAY.
Slow EMAs turn LIME when in bullish order, RED when bearish, and remain GRAY otherwise.
The area between the outermost fast EMAs and slow EMAs is filled with a semi-transparent silver color for visual emphasis.
Volume % of Diluted Shares OutstandingIndicator does what it says - shows the volume traded per time frame as percentage of shares outstanding.
There are three scaling modes, see below.
Absolute (0–100%+) → The line values are the true % of diluted shares traded.
If the plot is at 12, that means 12% of all diluted shares traded that day.
Auto-range (absolute) → The line values are still the true % of shares traded (the y-axis is in real percentages).
But the reference lines (25/50/75/100) are not literal percentages anymore; they are markers at fractions of the local min-to-max range.
So your blue bars are real (e.g., 12% really is 12%), but the dotted lines are relative.
Normalize to 100 → The line values are not the true % anymore.
Everything is re-expressed as a fraction of the recent maximum, so 100 = “highest in the lookback window,” not “100% of shares.”
If the true max was 30% of shares traded, and today is 15%, then the plot will show 50 (because 15 is half of 30).
Advanced Trend Momentum [Alpha Extract]The Advanced Trend Momentum indicator provides traders with deep insights into market dynamics by combining exponential moving average analysis with RSI momentum assessment and dynamic support/resistance detection. This sophisticated multi-dimensional tool helps identify trend changes, momentum divergences, and key structural levels, offering actionable buy and sell signals based on trend strength and momentum convergence.
🔶 CALCULATION
The indicator processes market data through multiple analytical methods:
Dual EMA Analysis: Calculates fast and slow exponential moving averages with dynamic trend direction assessment and ATR-normalized strength measurement.
RSI Momentum Engine: Implements RSI-based momentum analysis with enhanced overbought/oversold detection and momentum velocity calculations.
Pivot-Based Structure: Identifies and tracks dynamic support and resistance levels using pivot point analysis with configurable level management.
Signal Integration: Combines trend direction, momentum characteristics, and structural proximity to generate high-probability trading signals.
Formula:
Fast EMA = EMA(Close, Fast Length)
Slow EMA = EMA(Close, Slow Length)
Trend Direction = Fast EMA > Slow EMA ? 1 : -1
Trend Strength = |Fast EMA - Slow EMA| / ATR(Period) × 100
RSI Momentum = RSI(Close, RSI Length)
Momentum Value = Change(Close, 5) / ATR(10) × 100
Pivot Support/Resistance = Dynamic pivot arrays with configurable lookback periods
Bullish Signal = Trend Change + Momentum Confirmation + Strength > 1%
Bearish Signal = Trend Change + Momentum Confirmation + Strength > 1%
🔶 DETAILS
Visual Features:
Trend EMAs: Fast and slow exponential moving averages with dynamic color coding (bullish/bearish)
Enhanced RSI: RSI oscillator with color-coded zones, gradient fills, and reference bands at overbought/oversold levels
Trend Fill: Dynamic gradient between EMAs indicating trend strength and direction
Support/Resistance Lines: Horizontal levels extending from pivot-based calculations with configurable maximum levels
Momentum Candles: Color-coded candlestick overlay reflecting combined trend and momentum conditions
Divergence Markers: Diamond-shaped signals highlighting bullish and bearish momentum divergences
Analysis Table: Real-time summary of trend direction, strength percentage, RSI value, and momentum reading
Interpretation:
Trend Direction: Bullish when Fast EMA crosses above Slow EMA with strength confirmation
Trend Strength > 1%: Strong trending conditions with institutional participation
RSI > 70: Overbought conditions, potential selling opportunity
RSI < 30: Oversold conditions, potential buying opportunity
Momentum Divergence: Price and momentum moving opposite directions signal potential reversals
Support/Resistance Proximity: Dynamic levels provide optimal entry/exit zones
Combined Signals: Trend changes with momentum confirmation generate high-probability opportunities
🔶 EXAMPLES
Trend Confirmation: Fast EMA crossing above Slow EMA with trend strength exceeding 1% and positive momentum confirms strong bullish conditions.
Example: During institutional accumulation phases, EMA crossovers with momentum confirmation have historically preceded significant upward moves, providing optimal long entry points.
15min
4H
Momentum Divergence Detection: RSI reaching overbought levels while momentum decreases despite rising prices signals potential trend exhaustion.
Example: Bearish divergence signals appearing at resistance levels have marked major market tops, allowing traders to secure profits before corrections.
Support/Resistance Integration: Dynamic pivot-based levels combined with trend and momentum signals create high-probability trading zones.
Example: Bullish trend changes occurring near established support levels offer optimal risk-reward entries with clearly defined stop-loss levels.
Multi-Dimensional Confirmation: The indicator's combination of trend, momentum, and structural analysis provides comprehensive market validation.
Example: When trend direction aligns with momentum characteristics near key structural levels, the confluence creates institutional-grade trading opportunities with enhanced probability of success.
🔶 SETTINGS
Customization Options:
Trend Analysis: Fast EMA Length (default: 12), Slow EMA Length (default: 26), Trend Strength Period (default: 14)
Support & Resistance: Pivot Length for level detection (default: 10), Maximum S/R Levels displayed (default: 3), Toggle S/R visibility
Momentum Settings: RSI Length (default: 14), Oversold Level (default: 30), Overbought Level (default: 70)
Visual Configuration: Color schemes for bullish/bearish/neutral conditions, transparency settings for fills, momentum candle overlay toggle
Display Options: Analysis table visibility, divergence marker size, alert system configuration
The Advanced Trend Momentum indicator provides traders with comprehensive insights into market dynamics through its sophisticated integration of trend analysis, momentum assessment, and structural level detection. By combining multiple analytical dimensions into a unified framework, this tool helps identify high-probability opportunities while filtering out market noise through its multi-confirmation approach, enabling traders to make informed decisions across various market cycles and timeframes.
SMA+MACD+RSI+Stoch Entry📌 Tools Used:
• SMA 21, SMA 50, SMA 200
• MACD (12, 26, 9)
• Pivot Point Standard
• RSI (length 75)
• Stochastic (14, 3, 3)
Trading Timeframe:
• Usable on all timeframes
Chart Preparation:
• Analyze the overall market trend and the instrument being traded
• Set an appropriate timeframe according to the market
• Apply SMA 21, SMA 50, SMA 200
• Apply MACD and Pivot Point Standard
• Ensure a proper market trend by checking the position of SMA 21, SMA 50, and SMA 200 relative to each other
If there are too many crossings between SMA 21, SMA 50, and SMA 200, do not enter any trades until the market trend stabilizes
Conditions for Entering a Long Trade (Bullish Trigger):
• Candle closes above the pivot line
• Confirm an uptrend by checking that SMA 21 is above SMA 50, and SMA 50 is above SMA 200
• RSI is above the midpoint
• Presence of a suitable corrective step with normal slope, considering the strength of the previous step
• MACD histogram indicates bullish momentum
• Stochastic shows a bullish crossover from below
Conditions for Entering a Short Trade (Bearish Trigger):
• Candle closes below the pivot line
• Confirm a downtrend by checking that SMA 21 is below SMA 50, and SMA 50 is below SMA 200
• RSI is below the midpoint
• Presence of a suitable corrective step with normal slope, considering the strength of the previous step
• MACD histogram indicates bearish momentum
• Stochastic shows a bearish crossover from above
RTH Levels: VWAP + PDH/PDL + ONH/ONL + IBAlgo Index — Levels Pro (ONH/ONL • PDH/PDL • VWAP±Bands • IB • Gaps)
Purpose. A session-aware, non-repainting levels tool for intraday decision-making. Designed for futures and indices, with clean visuals, alerts, and a one-click Minimal Mode for screenshot-ready charts.
What it plots
• PDH/PDL (RTH-only) – Prior Regular Trading Hours high/low, computed intraday and frozen at the RTH close (no 24h mix-ups, no repainting).
• ONH/ONL – Prior Overnight high/low, held throughout RTH.
• RTH VWAP with ±σ bands – Volume-weighted variance, reset each RTH.
• Initial Balance (IB) – First N minutes of RTH, plus 1.5× / 2.0× extensions after IB completes.
• Today’s RTH Open & Prior RTH Close – With gap detection and “gap filled” alert.
• Killzone shading – NY Open (09:30–10:30 ET) and Lunch (11:15–13:30 ET).
• Values panel (top-right) – Each level with live distance in points & ticks.
• Right-edge level tags – With anti-overlap (stagger + vertical jitter).
• Price-scale tags – Native trackprice markers that always “stick” to the axis.
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New in v6.4
• Minimal Mode: one click for a clean look (thinner lines, VWAP bands/IB extensions hidden, on-chart right-edge labels off; price-scale tags remain).
• Theme presets: Dark Hi-Contrast / Light Minimal / Futures Classic / Muted Dark.
• Anti-overlap controls: horizontal staggering, vertical jitter, and baseline offset to keep tags readable even when levels cluster.
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Quick start (2 minutes)
1. Add to chart → keep defaults.
2. Sessions (ET):
• RTH Session default: 09:30–16:00 (US equities cash hours).
• Overnight Session default: 18:00–09:29.
Adjust for your market if you use different “day” hours (e.g., many use 08:20–13:30 ET for COMEX Gold).
3. Theme & Minimal Mode: pick a Theme Preset; enable Minimal Mode for screenshots.
4. Visibility: toggle PD/ON/VWAP/IB/References/Panel to taste.
5. Right-edge labels: turn Show Right-Edge Labels on. If they crowd, tune:
• Anti-overlap: min separation (ticks)
• Horizontal offset per tag (bars)
• Vertical jitter per step (ticks)
• Right-edge baseline offset (bars)
6. Alerts: open Add alert → Condition: and pick the events you want.
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How levels are computed (no repainting)
• PDH/PDL: Intraday H/L are accumulated only while in RTH and saved at RTH close for “yesterday’s” values.
• ONH/ONL: Accumulated across the defined Overnight window and then held during RTH.
• RTH VWAP & ±σ: Volume-weighted mean and standard deviation, reset at the RTH open.
• IB: First N minutes of RTH (default 60). Extensions (1.5×/2.0×) appear after IB completes.
• Gaps: Today’s RTH open vs prior RTH close; “Gap Filled” triggers when price trades back to prior close.
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Practical playbooks (how to trade around the levels)
1) PDH/PDL interactions
• Rejection: Price taps PDH/PDL then closes back inside → mean-reversion toward VWAP/IB.
• Acceptance: Close/hold beyond PDH/PDL with momentum → continuation to next HTF/IB target.
• Alert: PD Touch/Break.
2) ONH/ONL “taken”
• Often one ON extreme is taken during RTH. ONH Taken / ONL Taken → check if it’s a clean break or sweep & reclaim.
• Sweep + reclaim near VWAP can fuel rotations through the ON range.
3) VWAP ±σ framework
• Balanced: First tag of ±1σ often reverts toward VWAP.
• Trend: Persistent trade beyond ±1σ + IB break → target ±2σ/±3σ.
• Alerts: VWAP Cross and VWAP Reject (cross then immediate fail back).
4) IB breaks
• After IB completes, a clean IB break commonly targets 1.5× and sometimes 2.0×.
• Quick return inside IB = possible fade back to the opposite IB edge/VWAP.
• Alerts: IB Break Up / Down.
5) Gaps
• Gap-and-go: Opening drive away from prior close + VWAP support → trend until IB completion.
• Gap-fill: Weak open and VWAP overhead/underfoot → trade toward prior close; manage on Gap Filled alert.
Pro tip: Stack confluences (e.g., ONL sweep + VWAP reclaim + IB hold) and respect your execution rules (e.g., require a 5-minute close in direction, or your order-flow confirmation).
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Inputs you’ll actually touch
• Sessions (ET): Session Timezone, RTH Session, Overnight Session.
• Visibility: toggles for PD/ON/VWAP/IB/Ref/Panel.
• VWAP bands: set σ multipliers (±1/±2/±3).
• IB: duration (minutes) and extension multipliers (1.5× / 2.0×).
• Style & Theme: Theme Preset, Main Line Width, Trackprice, Minimal Mode, and anti-overlap controls.
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Alerts included
• PD Touch/Break — High ≥ PDH or Low ≤ PDL
• ONH Taken / ONL Taken — First in-RTH take of ONH/ONL
• VWAP Cross — Close crosses VWAP
• VWAP Reject — Cross then immediate fail back
• IB Break Up / Down — Break of IB High/Low after IB completes
• Gap Filled — Price trades back to prior RTH close
Setup: Add alert → Condition: Algo Index — Levels Pro → choose event → message → Notify on app/email.
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Panel guide
The top-right panel shows each level plus live distance from last price:
LevelValue (Δpoints | Δticks)
Coloring: green if level is below current price, red if above.
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Styling & screenshot tips
• Use Theme Preset that matches your chart.
• For dark charts, “Dark Hi-Contrast” with Main Line Width = 3 works well.
• Enable Trackprice for crisp axis tags that always stick to the right edge.
• Turn on Minimal Mode for cleaner screenshots (no VWAP bands or IB extensions, on-chart tags off; price-scale tags remain).
• If tags crowd, increase min separation (ticks) to 30–60 and horizontal offset to 3–5; add vertical jitter (4–12 ticks) and/or push tags farther right with baseline offset (bars).
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Behavior & limitations
• Levels are computed incrementally; tables refresh on the last bar for efficiency.
• Right-edge labels are placed at bar_index + offset and do not track extra right-margin scrolling (TradingView limitation). The price-scale tags (from trackprice) do track the axis.
• “RTH” is what you define in inputs. If your market uses different day hours, change the session strings so PDH/PDL reflect your definition of “yesterday’s session.”
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FAQ
Q: My PDH/PDL don’t match the daily chart.
A: By design this uses RTH-only highs/lows, not 24h daily bars. Adjust sessions if you want a different definition.
Q: Right-edge tags overlap or don’t sit at the far right.
A: Increase min separation / horizontal offset / vertical jitter and/or push tags farther with baseline offset. If you want markers that always hug the axis, rely on Trackprice.
Q: Can I change killzones?
A: Yes—edit the session strings in settings or request a version with user inputs for custom windows.
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Disclaimer
Educational use only. This is not financial advice. Always apply your own risk management and confirmation rules.
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Enjoy it? Please ⭐ the script and share screenshots using Minimal Mode + a Theme Preset that fits your style.
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).
Live Trading Metrics DashboardReal-Time Trading Data Table for Chart Analysis
This clean and professional dashboard displays essential trading metrics directly on your chart in an easy-to-read table format. Perfect for traders who need quick access to key volatility and momentum data without cluttering their chart with multiple indicators.
Key Metrics Displayed:
IBD Relative Strength (RS):
Professional Formula: Uses Investor's Business Daily methodology
Multi-Timeframe Analysis: Weighted calculation across 3, 6, 9, and 12-month periods
Performance Indicator: Shows how the instrument performs relative to its historical price action
Real-Time Updates: Values update with each bar for current market conditions
1.5 ATR (Average True Range):
Volatility Measurement: 14-period ATR multiplied by 1.5 for extended range analysis
Stop-Loss Placement: Ideal for setting dynamic stop-loss levels
Risk Management: Helps determine appropriate position sizing based on volatility
Breakout Targets: Useful for setting profit targets on breakout trades
1.5 ATR Percentage:
Relative Volatility: Shows 1.5 ATR as a percentage of current price
Cross-Asset Comparison: Enables volatility comparison across different instruments
Position Sizing: Helps calculate risk per trade as percentage of price
Market Context: Understand volatility relative to instrument value
How to Interpret:
Positive IBD RS: Instrument showing strength relative to historical performance
Negative IBD RS: Instrument showing weakness relative to historical performance
Higher ATR Values: Increased volatility, wider stops needed
Higher ATR %: Greater relative volatility for the instrument's price level
Perfect For:
Day traders needing quick volatility reference
Swing traders using IBD methodology
Position traders managing risk with ATR-based stops
Any trader wanting clean, organized data display
Trading Macro Windows by BW v2
Trading Macros by BW: Integrating ICT Concepts for Session Analysis
This indicator combines two key Inner Circle Trader (ICT) concepts—Change in State of Delivery (CISD) or Inverted Fair Value Gap (IFVG) signals with Macro Time Windows—to provide a unified tool for analyzing intraday price action, particularly during Pacific Time (PT) sessions. Rather than simply merging existing scripts, this integration creates a cohesive visual framework that highlights how macro consolidation periods interact with potential reversal or continuation signals like CISD or IFVG. By overlaying macro candle styling and borders on the chart alongside selectable signal lines, traders can better contextualize setups within ICT's macro narrative, where price often manipulates liquidity during these windows before displacing toward higher-timeframe objectives.
Core Components and How They Work Together:
Macro Time Windows (Inspired by ICT's Macro Periods):
ICT emphasizes "macro" as 30-minute windows (e.g., 06:45–07:15 PT, 07:45–08:15 PT, up to 11:45–12:15 PT) where price tends to consolidate, sweep liquidity, or form key structures like Fair Value Gaps (FVGs). These periods set the stage for the session's directional bias.
The indicator styles candles within these windows using a user-defined color for wicks, borders, and bodies (translucent for visibility). This visual emphasis helps traders focus on activity inside macros, where reversals or continuations often originate.
Borders are drawn as vertical lines at the start and end of each window (with a +5 minute buffer to capture related activity), using a dotted style by default. This creates a "study zone" that encapsulates macro events, allowing traders to assess if price is respecting or violating these zones in alignment with broader ICT models like the Power of 3 (AMD cycle).
Toggle: "Macro Candles Enabled" (default: true) – Turn off to disable styling and borders if focusing solely on signals.
CISD or IFVG Signals (Selectable Mode):
Mode Selection: Choose between "Change in the State of Delivery" (CISD) or "IFVG" (default: IFVG). Both detect shifts in market delivery during specific 30-minute slices (15–45 or 17–45 minutes past the hour in PT sessions).
CISD Mode: Based on ICT's definition of a sudden directional shift, this identifies aggressive displacements after sweeping recent highs/lows. It uses a rolling reference high/low over 6 bars, checks for sweeps (penetrating by at least 2 ticks in the last 2-3 bars), reclamation (closing beyond the reference with at least 50% body), and displacement (50% of prior range or an immediate FVG of 6+ ticks). Signals plot a horizontal line from the close, extending 24 bars right, labeled "CISD."
IFVG Mode: Focuses on Inverted Fair Value Gaps, where a bullish FVG (low > high by 13+ ticks) forms but is inverted (closed below) in the same slice, signaling bearish intent (or vice versa). This targets violations against opposing liquidity, often leading to raids on external ranges. Signals plot similarly, labeled "IFVG."
Shared Logic: Both modes enforce a 55-bar cooldown to prevent clustering, operate only during PT sessions (06:30–13:00), and use tick-based thresholds for precision across instruments. The integration with macros allows traders to see if signals occur within or at the edges of macro windows, enhancing confirmation—for example, a CISD inside a macro might indicate a manipulated reversal toward the session's true objective.
Toggle: "Signals Enabled" (default: true) – Turn off to hide all signal lines and labels, isolating the macro visualization.
How Components Interact:
Macro windows provide the "narrative context" (consolidation/manipulation), while CISD/IFVG signals detect the "delivery shift" (displacement). Together, they form a mashup that justifies publication: isolated signals can be noisy, but when filtered by macro periods, they align with ICT's session model. For instance, an IFVG inversion during a macro might confirm a liquidity sweep before targeting PD arrays or order blocks.
No external dependencies; all calculations are self-contained using Pine's built-in functions like ta.highest/lowest for references and time-based sessions for windows.
Usage Guidelines:
Apply to intraday charts (e.g., 1-5 min) or stocks during PT hours.
Look for confluence: A bull IFVG signal post-macro low sweep might target the next macro high or daily bias.
Customize colors/styles for signals (solid/dashed/dotted lines) and macros to suit your chart.
Backtest in replay mode to observe how macros frame signals—e.g., price often respects macro borders as S/R.
Limitations: Timezone-fixed to PT (America/Los_Angeles); signals are directional hints, not trade entries. Combine with ICT tools like order blocks or liquidity pools for full setups.
This script draws from community ICT implementations but refines them into a single, purpose-built tool for macro-driven trading, reducing chart clutter while emphasizing interconnected concepts. Feedback welcome!
XMR Divergences vs KrakenSUMMARY
This script finds the percentage difference between Kraken, and multiple other exchanges, for the price of XMRUSD, and then runs a variable length moving average of those differences. Optionally, you can multiply by the reported volume of the exchange in question. Skip to "USAGE" at the bottom for a quick view of the settings. But I recommend reading DETAILED DESCRIPTION as well.
PURPOSE
The purpose of this script is to get a look into the relative funds flows of XMR between Kraken and the other exchanges. So long as an exchange withdraws are open: 1) Negative divergences indicate XMR outflows from the exchange under consideration, 2) Postive divergences indicate XMR inflows from Kraken to the exchange.
This appears to be moderately correlated with price movements in Monero (but not always). There is also the theory that positive accumulation is a leading indication of a growing probability of postive price action in the general crypto market, and negative accumulation is a leading indicator of an upcoming peak. In other words, exchanges like to accumulate Monero quietly during calm downtimes, and they like to manage its price from gaining too much attention (pump) during broad market positivity.
BACKGROUND
It's well known among XMR traders that most exchanges are operating on a heavy fractional reserve basis as regards Monero. The past 2 years have seen regular and repeated withdraw freezes, sometimes for weeks/months at a time. Occasionally, liquidity stress tests have been performed, with predictable results - none of these exchanges are able to continue supporting withdraws.
Kraken is the only exchange of meaningful volume that has never frozen withdraws for more than an hour or so. Thus, we theorize that Kraken is operating with all, or most of the XMR they claim to have.
Furthermore, we have seen in the past, large price negative price divergences of these fractional reserve exchanges relative to Kraken. As the social outcry grew stronger for this malfeasance, these exchanges have gone to greater lengths to hide their price divergences.
On minute-by-minute ; hour-by-hour basis, typically, a look with the naked eye would show oscillation around the zero point. But when you average it out, especially on lower timeframes (like the 1 and 5 min candles), you can very clearly see that when withdraws are shut down, these exchanges simultaneously diverge their prices downwards as well.
DETAILED DESCRIPTION
The ideal view of price divergence would compare second-by-second prices, and then run something like a rolling 4-hr or 1-day SMA to average out the overall divergences. However, due to limitations of TradingView, this is impractical/impossible for actual usage/viewing. As a result, a balance must be struck, when selecting the combination of the candle period, and the SMA lookback length.
I find that 5min candles, with a 48-period lookback (that equates to a rolling 4-hour SMA), offers the best view of recent and historical price divergence activity. This of course means that we're only sampling price divergences once every 5 minutes, but it still provides a decent look at what's happening. If this script gets popular, I wouldn't be surprised if these exchanges start timing their candle closes to mask their misdeeds, but that's of course speculative on my part.
The other important factor here, *IS TO MULTIPLY BY VOLUME*. Some of these no-volume exchanges have large price divergences. But if they're not doing any real volume, then it doesn't really have any real market impact. Thus, I recommend keeping the "Make volume adjustment" option on.
If that ends up happening, we'll have to infer that by comparing the difference in close prices, vs the difference in the highest or lowest intra-candle prices (wicks). Typically a divergence should have all 3 showing similar results.
Notes regarding "Sum_of_All": This only makes sense when multiplying by volume. So only check this if you also made the volume adjustment. Generally I believe that *Binance* sets the tone. However, we have seen numerous occasions where Binance diverges down, and the others diverge up. I believe this is a social influence tactic, since most people look at Binance price. Meanwhile, they're trying to accumulate some small amount on the other exchanges to minimize their overall loss. This of course assumes collusion by these exchanges, which is a high likely hood, seeing as how in May 2021, they all diverged together simultaneously (among other evidence).
USAGE
I recommend using your browser zoom, to see data beyond 1 month in the past.
Lookback - The number of candles over which to conduct a moving average. On 5-min candles for example, here's how the math works out:
12 - Equates to a 1 hr MA
24 - 2 hrs
48 - 4 hrs (default)
288 - 1 day
2880 - 10 days
Make Volume Adjustment - Recommend that you usually keep this on.
Line Widths - Set to preference
Show_Close_Price? - You can compute the difference at candle close. Or you can check the other boxes to compare the highest/lowest prices for intra candle prices (wicks).
Show Sum_of_All? - You can sum all of the differences, which only makes sense if you're making the volume adjustement. Default is off. Below, you can also choose which exchanges to include in the sum.
This works best on lower timeframes, like the 1m, 5, and 15m charts. I personally use 5m, with 48 or 96 length lookback. You get a better view of the real time price divergences that way.
TrueOpens [AY]¹ See how price reacts to key multi-day and monthly open levels—perfect for S/R-focused traders.
Experimental indicator for tracking multi-day openings and ICT True Month Open levels, ideal for S/R traders.
TrueOpens ¹ – Multi-Day & True Month Open Levels
This indicator is experimental and designed to help traders visually track opening price levels across multiple days, along with the ICT True Month Open (TMO).
Key Features:
Supports up to 12 configurable multi-day opening sessions, each with independent color, style, width, and label options.
Automatically detects the True Month Open using the ICT method (2nd Monday of each month) and plots it on the chart.
Lines can extend dynamically and are limited to a user-defined number of historical bars for clarity.
Fully customizable timezones, label sizes, and display options.
This indicator is ideal for observing how price interacts with key levels, especially for traders who favor support and resistance-based strategies.
Disclaimer: This is an analytical tool for observation purposes. It does not provide buy or sell signals. Users should combine it with their own analysis and risk management.
EMA + MACD + RSI Strategy"This strategy combines EMA crossovers (20/50), MACD momentum (12/26/9), and RSI thresholds (14-period) to generate signals. It triggers a BUY when:
EMA20 crosses above EMA50,
MACD line > Signal line,
RSI > 50.
SELL signals activate on the opposite conditions. Visual alerts plot directly on the chart
12H Range (Body + Wicks)12H Range (Body + Wicks)
This indicator highlights the 12-hour candle ranges across any chart timeframe. Each 12H candle is split into its body and wicks, with customizable fill and border colors.
Features:
Draws the full 12H range (Open → Close for the body, High/Low for wicks).
Live updates as the current 12H candle forms.
Works on all chart timeframes.
Custom colors for bullish/bearish bodies and wicks.
Option to show only the current 12H block or keep a history of past ranges.
Lightweight performance with adjustable max bars to keep on chart.
Optional high/low guide lines for quick reference.
Use Cases:
Identify where price is trading relative to the current or past 12H session ranges.
Visualize intraday bias with clear body/wick separation.
Combine with your strategy to trade range reclaims, breaks, or reversals.
Tip: Change the reference timeframe input (default = 12H/720min) to quickly adapt for 4H, 6H, daily, etc.
Becak I-series: Indicator Floating Panels v.80Becak I-series: Floating Panels v.80th (Indonesia Independence Days)
What it does:
This indicator creates three floating overlay panels that display MACD, RSI, and Stochastic oscillators directly on your price chart. Unlike traditional separate panes, these panels hover over your chart with customizable positioning and transparency, providing a clean, space-efficient way to monitor multiple technical indicators simultaneously.
When to use:
When you need to monitor momentum, trend strength, and overbought/oversold conditions without cluttering your workspace
Perfect for traders who want quick visual access to multiple oscillators while maintaining focus on price action
Ideal for any timeframe and asset class (stocks, crypto, forex, commodities)
How it works:
The script calculates standard MACD (12,26,9), RSI (14), and Stochastic (14,3,3) values, then renders them as floating panels with:
MACD Panel: Shows MACD line (blue), Signal line (orange), and histogram (green/red bars)
RSI Panel: Displays RSI line (purple) with overbought (70) and oversold (30) reference levels
Stochastic Panel: Shows %K (blue) and %D (orange) lines with optional buy/sell signals and highlighted overbought/oversold zones
Customization options:
Position: Choose Top, Bottom, or Auto-Center placement
Size: Adjust panel height (15-35% of chart) and spacing between panels
Positioning: Fine-tune vertical center offset and horizontal positioning
Appearance: Toggle panel backgrounds and adjust transparency (50-95%)
Parameters: Modify all indicator lengths and overbought/oversold levels
Signals: Enable/disable Stochastic crossover signals
Display: Control lookback period (30-100 bars) and right margin spacing
Universal compatibility: Works seamlessly across all asset types with automatic range detection and scaling.
DIRGAHAYU HARI KEMERDEKAAN KE 80 - INDONESIA ... MERDEKA!!!!!
CleanBreak Lines (Break + First Retest)CleanBreak lines draws one robust support line (green) from swing lows and one robust resistance line (red) from swing highs, then optionally signals a confirmed break and the first clean retest back to that line. Lines are scored with a transparent W-Score (0–100) so traders can judge quality at a glance. The script is non-repainting and uses only confirmed bar data.
What it does
Auto-builds two trendlines that aim to represent meaningful support and resistance.
Uses a median-based slope so outliers and single spikes do not distort the line.
Computes a W-Score per line from three things: touches, span (how long it held), and respect (staying on the correct side).
Optionally triggers a single, tightly-gated signal on Break + First Retest.
How it works (plain English)
Detect recent swing highs and swing lows.
Fit one line through highs and one through lows using a robust, median-style slope estimate.
Score each line: more clean touches and longer span raise the W-Score; frequent violations lower it.
A break requires a candle close beyond the line by a small ATR margin.
A first retest requires price to come back to the line within a limited number of bars and hold on close.
A single arrow may print on that confirmed retest, with optional alerts.
What it is not
Not a prediction model and not a promises-of-profit tool.
Not a multi-signal spammer: by design it aims to allow one retest entry per break.
Not a regression channel or machine-learning system.
How to use
At a glance: treat the green line as candidate support and the red line as candidate resistance.
Conservative approach: wait for a break on close and then the first retest to hold; use the arrow as a prompt, not a command.
Context-only mode: hide arrows in Style if you want the lines and W-Score only.
Inputs (brief)
Core: Swing Length, Max Pivots, Min Touches, Min Span Bars.
Scoring: Touches Max (cap), Weights for touches vs span, Min W-Score to arm.
Break and Retest: Break Margin x ATR, Retest Tolerance x ATR, Retest Window (bars).
Visuals: Show Labels, Show Table, Line Width, Fade When Refit.
Recommended presets
Cleaner, fewer signals: Min Touches 4–5, Min Span Bars 100–150, Min W-Score 70–80, Break Margin 0.40–0.60 ATR, Retest Tolerance 0.10–0.15 ATR, Retest Window 8–12 bars.
Lines-only: keep defaults and uncheck the two plotshapes in Style.
Alerts
CB Long Retest: break above the red line and first retest holds.
CB Short Retest: break below the green line and first retest holds.
Use “Once per bar close” for consistency.
On-chart table (if enabled)
RES / SUP: W-Score and distance from price in ATR terms.
Status: “Waiting Long RT”, “Waiting Short RT”, or “Idle”.
Thresholds: MinScore and Retest bars for quick context.
Timeframes
Works well on 1h to 1D. On very low timeframes, raise Break Margin x ATR to reduce whipsaw effects. On higher timeframes, increase Min Touches and Min Span Bars.
Non-repainting policy
All logic uses confirmed pivots and confirmed bar closes.
Breaks and retests are validated on close; alerts reference only confirmed conditions.
No lookahead in any request.security call.
Original implementation focused on a median-based robust slope for auto trendlines, plus a transparent W-Score and a single retest gate.
Disclosure
This script is for education and charting. It does not guarantee outcomes, and past behavior does not imply future results. Always validate on historical data and practice risk management.
Advanced VWAP CalendarThe Advanced VWAP Calendar is a designed to plot Volume Weighted Average Price (VWAP) lines anchored to user-defined and preset time periods, including weekly, monthly, quarterly, and custom anchors. As of August 15, 2025, this indicator provides traders with a robust tool for analyzing price trends relative to volume-weighted averages, with clear labeling and extensive customization options. Below is a summary of its key features and functionality, with technical details and code references updated to focus on user-facing behavior and presentation, while preserving all other aspects of the original summary.
Key Features
Multiple Time Period VWAPs:
Weekly VWAPs: Supports up to five VWAPs for a user-selected month and year, starting at midnight each Monday (e.g., W1 Aug 2025, W2 Aug 2025). Enabled via a single toggle, with anchors automatically set to the first Monday of the chosen month.
Monthly VWAPs: Plots VWAPs for all 12 months of a selected year (e.g., Jan 2025, Feb 2025) or a single user-specified month/year. Labels use month abbreviations (e.g., "Aug 2025").
Quarterly VWAPs: Covers four quarters of a selected year (e.g., Q1 2025, Q2 2025), with options to enable all quarters or individual ones (Q1–Q4).
Legacy VWAPs: Provides monthly and quarterly VWAPs for a user-selected legacy year (e.g., 2024), labeled with a "Legacy" prefix (e.g., "Legacy Jan 2024," "Legacy Q1 2024"), with similar enablement options.
Custom VWAPs: Includes 10 fully customizable VWAPs, each with user-defined anchor times, labels (e.g., "Q1 2025"), colors, line widths (1–5), text colors, bubble styles, text sizes (8–40), and background options.
Clear and Dynamic Labeling:
Labels appear to the right of the chart, showing the VWAP value (e.g., "Q1 2025 123.45").
Weekly labels follow a "W# Month Year" format (e.g., "W1 Aug 2025").
Monthly labels use abbreviated months (e.g., "Aug 2025"), while quarterly labels use "Q# Year" (e.g., "Q3 2025").
Legacy labels include a "Legacy" prefix (e.g., "Legacy Q1 2024").
Labels support customizable text sizes (tiny to huge) and can be displayed with or without a background, with optional bubble styles.
Flexible Customization:
Each VWAP can be enabled or disabled independently, with user inputs for anchor times, labels, and visual properties.
Colors are predefined for weekly (red, orange, blue, green, purple), monthly (varied), quarterly (red, blue, green, yellow), and legacy VWAPs, but custom VWAPs allow any color selection.
Line widths and text sizes are adjustable, ensuring visual clarity and chart readability.
This indicator was a dual effort, code was heavily contributed in effort by AzDxB, major credit and THANKS goes to him www.tradingview.com
Ray Dalio's All Weather Strategy - Portfolio CalculatorTHE ALL WEATHER STRATEGY INDICATOR: A GUIDE TO RAY DALIO'S LEGENDARY PORTFOLIO APPROACH
Introduction: The Genesis of Financial Resilience
In the sprawling corridors of Bridgewater Associates, the world's largest hedge fund managing over 150 billion dollars in assets, Ray Dalio conceived what would become one of the most influential investment strategies of the modern era. The All Weather Strategy, born from decades of market observation and rigorous backtesting, represents a paradigm shift from traditional portfolio construction methods that have dominated Wall Street since Harry Markowitz's seminal work on Modern Portfolio Theory in 1952.
Unlike conventional approaches that chase returns through market timing or stock picking, the All Weather Strategy embraces a fundamental truth that has humbled countless investors throughout history: nobody can consistently predict the future direction of markets. Instead of fighting this uncertainty, Dalio's approach harnesses it, creating a portfolio designed to perform reasonably well across all economic environments, hence the evocative name "All Weather."
The strategy emerged from Bridgewater's extensive research into economic cycles and asset class behavior, culminating in what Dalio describes as "the Holy Grail of investing" in his bestselling book "Principles" (Dalio, 2017). This Holy Grail isn't about achieving spectacular returns, but rather about achieving consistent, risk-adjusted returns that compound steadily over time, much like the tortoise defeating the hare in Aesop's timeless fable.
HISTORICAL DEVELOPMENT AND EVOLUTION
The All Weather Strategy's origins trace back to the tumultuous economic periods of the 1970s and 1980s, when traditional portfolio construction methods proved inadequate for navigating simultaneous inflation and recession. Raymond Thomas Dalio, born in 1949 in Queens, New York, founded Bridgewater Associates from his Manhattan apartment in 1975, initially focusing on currency and fixed-income consulting for corporate clients.
Dalio's early experiences during the 1970s stagflation period profoundly shaped his investment philosophy. Unlike many of his contemporaries who viewed inflation and deflation as opposing forces, Dalio recognized that both conditions could coexist with either economic growth or contraction, creating four distinct economic environments rather than the traditional two-factor models that dominated academic finance.
The conceptual breakthrough came in the late 1980s when Dalio began systematically analyzing asset class performance across different economic regimes. Working with a small team of researchers, Bridgewater developed sophisticated models that decomposed economic conditions into growth and inflation components, then mapped historical asset class returns against these regimes. This research revealed that traditional portfolio construction, heavily weighted toward stocks and bonds, left investors vulnerable to specific economic scenarios.
The formal All Weather Strategy emerged in 1996 when Bridgewater was approached by a wealthy family seeking a portfolio that could protect their wealth across various economic conditions without requiring active management or market timing. Unlike Bridgewater's flagship Pure Alpha fund, which relied on active trading and leverage, the All Weather approach needed to be completely passive and unleveraged while still providing adequate diversification.
Dalio and his team spent months developing and testing various allocation schemes, ultimately settling on the 30/40/15/7.5/7.5 framework that balances risk contributions rather than dollar amounts. This approach was revolutionary because it focused on risk budgeting—ensuring that no single asset class dominated the portfolio's risk profile—rather than the traditional approach of equal dollar allocations or market-cap weighting.
The strategy's first institutional implementation began in 1996 with a family office client, followed by gradual expansion to other wealthy families and eventually institutional investors. By 2005, Bridgewater was managing over $15 billion in All Weather assets, making it one of the largest systematic strategy implementations in institutional investing.
The 2008 financial crisis provided the ultimate test of the All Weather methodology. While the S&P 500 declined by 37% and many hedge funds suffered double-digit losses, the All Weather strategy generated positive returns, validating Dalio's risk-balancing approach. This performance during extreme market stress attracted significant institutional attention, leading to rapid asset growth in subsequent years.
The strategy's theoretical foundations evolved throughout the 2000s as Bridgewater's research team, led by co-chief investment officers Greg Jensen and Bob Prince, refined the economic framework and incorporated insights from behavioral economics and complexity theory. Their research, published in numerous institutional white papers, demonstrated that traditional portfolio optimization methods consistently underperformed simpler risk-balanced approaches across various time periods and market conditions.
Academic validation came through partnerships with leading business schools and collaboration with prominent economists. The strategy's risk parity principles influenced an entire generation of institutional investors, leading to the creation of numerous risk parity funds managing hundreds of billions in aggregate assets.
In recent years, the democratization of sophisticated financial tools has made All Weather-style investing accessible to individual investors through ETFs and systematic platforms. The availability of high-quality, low-cost ETFs covering each required asset class has eliminated many of the barriers that previously limited sophisticated portfolio construction to institutional investors.
The development of advanced portfolio management software and platforms like TradingView has further democratized access to institutional-quality analytics and implementation tools. The All Weather Strategy Indicator represents the culmination of this trend, providing individual investors with capabilities that previously required teams of portfolio managers and risk analysts.
Understanding the Four Economic Seasons
The All Weather Strategy's theoretical foundation rests on Dalio's observation that all economic environments can be characterized by two primary variables: economic growth and inflation. These variables create four distinct "economic seasons," each favoring different asset classes. Rising growth benefits stocks and commodities, while falling growth favors bonds. Rising inflation helps commodities and inflation-protected securities, while falling inflation benefits nominal bonds and stocks.
This framework, detailed extensively in Bridgewater's research papers from the 1990s, suggests that by holding assets that perform well in each economic season, an investor can create a portfolio that remains resilient regardless of which season unfolds. The elegance lies not in predicting which season will occur, but in being prepared for all of them simultaneously.
Academic research supports this multi-environment approach. Ang and Bekaert (2002) demonstrated that regime changes in economic conditions significantly impact asset returns, while Fama and French (2004) showed that different asset classes exhibit varying sensitivities to economic factors. The All Weather Strategy essentially operationalizes these academic insights into a practical investment framework.
The Original All Weather Allocation: Simplicity Masquerading as Sophistication
The core All Weather portfolio, as implemented by Bridgewater for institutional clients and later adapted for retail investors, maintains a deceptively simple static allocation: 30% stocks, 40% long-term bonds, 15% intermediate-term bonds, 7.5% commodities, and 7.5% Treasury Inflation-Protected Securities (TIPS). This allocation may appear arbitrary to the uninitiated, but each percentage reflects careful consideration of historical volatilities, correlations, and economic sensitivities.
The 30% stock allocation provides growth exposure while limiting the portfolio's overall volatility. Stocks historically deliver superior long-term returns but with significant volatility, as evidenced by the Standard & Poor's 500 Index's average annual return of approximately 10% since 1926, accompanied by standard deviation exceeding 15% (Ibbotson Associates, 2023). By limiting stock exposure to 30%, the portfolio captures much of the equity risk premium while avoiding excessive volatility.
The combined 55% allocation to bonds (40% long-term plus 15% intermediate-term) serves as the portfolio's stabilizing force. Long-term bonds provide substantial interest rate sensitivity, performing well during economic slowdowns when central banks reduce rates. Intermediate-term bonds offer a balance between interest rate sensitivity and reduced duration risk. This bond-heavy allocation reflects Dalio's insight that bonds typically exhibit lower volatility than stocks while providing essential diversification benefits.
The 7.5% commodities allocation addresses inflation protection, as commodity prices typically rise during inflationary periods. Historical analysis by Bodie and Rosansky (1980) demonstrated that commodities provide meaningful diversification benefits and inflation hedging capabilities, though with considerable volatility. The relatively small allocation reflects commodities' high volatility and mixed long-term returns.
Finally, the 7.5% TIPS allocation provides explicit inflation protection through government-backed securities whose principal and interest payments adjust with inflation. Introduced by the U.S. Treasury in 1997, TIPS have proven effective inflation hedges, though they underperform nominal bonds during deflationary periods (Campbell & Viceira, 2001).
Historical Performance: The Evidence Speaks
Analyzing the All Weather Strategy's historical performance reveals both its strengths and limitations. Using monthly return data from 1970 to 2023, spanning over five decades of varying economic conditions, the strategy has delivered compelling risk-adjusted returns while experiencing lower volatility than traditional stock-heavy portfolios.
During this period, the All Weather allocation generated an average annual return of approximately 8.2%, compared to 10.5% for the S&P 500 Index. However, the strategy's annual volatility measured just 9.1%, substantially lower than the S&P 500's 15.8% volatility. This translated to a Sharpe ratio of 0.67 for the All Weather Strategy versus 0.54 for the S&P 500, indicating superior risk-adjusted performance.
More impressively, the strategy's maximum drawdown over this period was 12.3%, occurring during the 2008 financial crisis, compared to the S&P 500's maximum drawdown of 50.9% during the same period. This drawdown mitigation proves crucial for long-term wealth building, as Stein and DeMuth (2003) demonstrated that avoiding large losses significantly impacts compound returns over time.
The strategy performed particularly well during periods of economic stress. During the 1970s stagflation, when stocks and bonds both struggled, the All Weather portfolio's commodity and TIPS allocations provided essential protection. Similarly, during the 2000-2002 dot-com crash and the 2008 financial crisis, the portfolio's bond-heavy allocation cushioned losses while maintaining positive returns in several years when stocks declined significantly.
However, the strategy underperformed during sustained bull markets, particularly the 1990s technology boom and the 2010s post-financial crisis recovery. This underperformance reflects the strategy's conservative nature and diversified approach, which sacrifices potential upside for downside protection. As Dalio frequently emphasizes, the All Weather Strategy prioritizes "not losing money" over "making a lot of money."
Implementing the All Weather Strategy: A Practical Guide
The All Weather Strategy Indicator transforms Dalio's institutional-grade approach into an accessible tool for individual investors. The indicator provides real-time portfolio tracking, rebalancing signals, and performance analytics, eliminating much of the complexity traditionally associated with implementing sophisticated allocation strategies.
To begin implementation, investors must first determine their investable capital. As detailed analysis reveals, the All Weather Strategy requires meaningful capital to implement effectively due to transaction costs, minimum investment requirements, and the need for precise allocations across five different asset classes.
For portfolios below $50,000, the strategy becomes challenging to implement efficiently. Transaction costs consume a disproportionate share of returns, while the inability to purchase fractional shares creates allocation drift. Consider an investor with $25,000 attempting to allocate 7.5% to commodities through the iPath Bloomberg Commodity Index ETF (DJP), currently trading around $25 per share. This allocation targets $1,875, enough for only 75 shares, creating immediate tracking error.
At $50,000, implementation becomes feasible but not optimal. The 30% stock allocation ($15,000) purchases approximately 37 shares of the SPDR S&P 500 ETF (SPY) at current prices around $400 per share. The 40% long-term bond allocation ($20,000) buys 200 shares of the iShares 20+ Year Treasury Bond ETF (TLT) at approximately $100 per share. While workable, these allocations leave significant cash drag and rebalancing challenges.
The optimal minimum for individual implementation appears to be $100,000. At this level, each allocation becomes substantial enough for precise implementation while keeping transaction costs below 0.4% annually. The $30,000 stock allocation, $40,000 long-term bond allocation, $15,000 intermediate-term bond allocation, $7,500 commodity allocation, and $7,500 TIPS allocation each provide sufficient size for effective management.
For investors with $250,000 or more, the strategy implementation approaches institutional quality. Allocation precision improves, transaction costs decline as a percentage of assets, and rebalancing becomes highly efficient. These larger portfolios can also consider adding complexity through international diversification or alternative implementations.
The indicator recommends quarterly rebalancing to balance transaction costs with allocation discipline. Monthly rebalancing increases costs without substantial benefits for most investors, while annual rebalancing allows excessive drift that can meaningfully impact performance. Quarterly rebalancing, typically on the first trading day of each quarter, provides an optimal balance.
Understanding the Indicator's Functionality
The All Weather Strategy Indicator operates as a comprehensive portfolio management system, providing multiple analytical layers that professional money managers typically reserve for institutional clients. This sophisticated tool transforms Ray Dalio's institutional-grade strategy into an accessible platform for individual investors, offering features that rival professional portfolio management software.
The indicator's core architecture consists of several interconnected modules that work seamlessly together to provide complete portfolio oversight. At its foundation lies a real-time portfolio simulation engine that tracks the exact value of each ETF position based on current market prices, eliminating the need for manual calculations or external spreadsheets.
DETAILED INDICATOR COMPONENTS AND FUNCTIONS
Portfolio Configuration Module
The portfolio setup begins with the Portfolio Configuration section, which establishes the fundamental parameters for strategy implementation. The Portfolio Capital input accepts values from $1,000 to $10,000,000, accommodating everyone from beginning investors to institutional clients. This input directly drives all subsequent calculations, determining exact share quantities and portfolio values throughout the implementation period.
The Portfolio Start Date function allows users to specify when they began implementing the All Weather Strategy, creating a clear demarcation point for performance tracking. This feature proves essential for investors who want to track their actual implementation against theoretical performance, providing realistic assessment of strategy effectiveness including timing differences and implementation costs.
Rebalancing Frequency settings offer two options: Monthly and Quarterly. While monthly rebalancing provides more precise allocation control, quarterly rebalancing typically proves more cost-effective for most investors due to reduced transaction costs. The indicator automatically detects the first trading day of each period, ensuring rebalancing occurs at optimal times regardless of weekends, holidays, or market closures.
The Rebalancing Threshold parameter, adjustable from 0.5% to 10%, determines when allocation drift triggers rebalancing recommendations. Conservative settings like 1-2% maintain tight allocation control but increase trading frequency, while wider thresholds like 3-5% reduce trading costs but allow greater allocation drift. This flexibility accommodates different risk tolerances and cost structures.
Visual Display System
The Show All Weather Calculator toggle controls the main dashboard visibility, allowing users to focus on chart visualization when detailed metrics aren't needed. When enabled, this comprehensive dashboard displays current portfolio value, individual ETF allocations, target versus actual weights, rebalancing status, and performance metrics in a professionally formatted table.
Economic Environment Display provides context about current market conditions based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated regime detection, this feature helps users understand which economic "season" currently prevails and which asset classes should theoretically benefit.
Rebalancing Signals illuminate when portfolio drift exceeds user-defined thresholds, highlighting specific ETFs that require adjustment. These signals use color coding to indicate urgency: green for balanced allocations, yellow for moderate drift, and red for significant deviations requiring immediate attention.
Advanced Label System
The rebalancing label system represents one of the indicator's most innovative features, providing three distinct detail levels to accommodate different user needs and experience levels. The "None" setting displays simple symbols marking portfolio start and rebalancing events without cluttering the chart with text. This minimal approach suits experienced investors who understand the implications of each symbol.
"Basic" label mode shows essential information including portfolio values at each rebalancing point, enabling quick assessment of strategy performance over time. These labels display "START $X" for portfolio initiation and "RBL $Y" for rebalancing events, providing clear performance tracking without overwhelming detail.
"Detailed" labels provide comprehensive trading instructions including exact buy and sell quantities for each ETF. These labels might display "RBL $125,000 BUY 15 SPY SELL 25 TLT BUY 8 IEF NO TRADES DJP SELL 12 SCHP" providing complete implementation guidance. This feature essentially transforms the indicator into a personal portfolio manager, eliminating guesswork about exact trades required.
Professional Color Themes
Eight professionally designed color themes adapt the indicator's appearance to different aesthetic preferences and market analysis styles. The "Gold" theme reflects traditional wealth management aesthetics, while "EdgeTools" provides modern professional appearance. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making, while "Quant" employs high-contrast combinations favored by quantitative analysts.
"Ocean," "Fire," "Matrix," and "Arctic" themes provide distinctive visual identities for traders who prefer unique chart aesthetics. Each theme automatically adjusts for dark or light mode optimization, ensuring optimal readability across different TradingView configurations.
Real-Time Portfolio Tracking
The portfolio simulation engine continuously tracks five separate ETF positions: SPY for stocks, TLT for long-term bonds, IEF for intermediate-term bonds, DJP for commodities, and SCHP for TIPS. Each position's value updates in real-time based on current market prices, providing instant feedback about portfolio performance and allocation drift.
Current share calculations determine exact holdings based on the most recent rebalancing, while target shares reflect optimal allocation based on current portfolio value. Trade calculations show precisely how many shares to buy or sell during rebalancing, eliminating manual calculations and potential errors.
Performance Analytics Suite
The indicator's performance measurement capabilities rival professional portfolio analysis software. Sharpe ratio calculations incorporate current risk-free rates obtained from Treasury yield data, providing accurate risk-adjusted performance assessment. Volatility measurements use rolling periods to capture changing market conditions while maintaining statistical significance.
Portfolio return calculations track both absolute and relative performance, comparing the All Weather implementation against individual asset classes and benchmark indices. These metrics update continuously, providing real-time assessment of strategy effectiveness and implementation quality.
Data Quality Monitoring
Sophisticated data quality checks ensure reliable indicator operation across different market conditions and potential data interruptions. The system monitors all five ETF price feeds plus economic data sources, providing quality scores that alert users to potential data issues that might affect calculations.
When data quality degrades, the indicator automatically switches to fallback values or alternative data sources, maintaining functionality during temporary market data interruptions. This robust design ensures consistent operation even during volatile market conditions when data feeds occasionally experience disruptions.
Risk Management and Behavioral Considerations
Despite its sophisticated design, the All Weather Strategy faces behavioral challenges that have derailed countless well-intentioned investment plans. The strategy's conservative nature means it will underperform growth stocks during bull markets, potentially by substantial margins. Maintaining discipline during these periods requires understanding that the strategy optimizes for risk-adjusted returns over absolute returns.
Behavioral finance research by Kahneman and Tversky (1979) demonstrates that investors feel losses approximately twice as intensely as equivalent gains. This loss aversion creates powerful psychological pressure to abandon defensive strategies during bull markets when aggressive portfolios appear more attractive. The All Weather Strategy's bond-heavy allocation will seem overly conservative when technology stocks double in value, as occurred repeatedly during the 2010s.
Conversely, the strategy's defensive characteristics provide psychological comfort during market stress. When stocks crash 30-50%, as they periodically do, the All Weather portfolio's modest losses feel manageable rather than catastrophic. This emotional stability enables investors to maintain their investment discipline when others capitulate, often at the worst possible times.
Rebalancing discipline presents another behavioral challenge. Selling winners to buy losers contradicts natural human tendencies but remains essential for the strategy's success. When stocks have outperformed bonds for several quarters, rebalancing requires selling high-performing stock positions to purchase seemingly stagnant bond positions. This action feels counterintuitive but captures the strategy's systematic approach to risk management.
Tax considerations add complexity for taxable accounts. Frequent rebalancing generates taxable events that can erode after-tax returns, particularly for high-income investors facing elevated capital gains rates. Tax-advantaged accounts like 401(k)s and IRAs provide ideal vehicles for All Weather implementation, eliminating tax friction from rebalancing activities.
Capital Requirements and Cost Analysis
Comprehensive cost analysis reveals the capital requirements for effective All Weather implementation. Annual expenses include management fees for each ETF, transaction costs from rebalancing, and bid-ask spreads from trading less liquid securities.
ETF expense ratios vary significantly across asset classes. The SPDR S&P 500 ETF charges 0.09% annually, while the iShares 20+ Year Treasury Bond ETF charges 0.20%. The iShares 7-10 Year Treasury Bond ETF charges 0.15%, the Schwab US TIPS ETF charges 0.05%, and the iPath Bloomberg Commodity Index ETF charges 0.75%. Weighted by the All Weather allocations, total expense ratios average approximately 0.19% annually.
Transaction costs depend heavily on broker selection and account size. Premium brokers like Interactive Brokers charge $1-2 per trade, resulting in $20-40 annually for quarterly rebalancing. Discount brokers may charge higher per-trade fees but offer commission-free ETF trading for selected funds. Zero-commission brokers eliminate explicit trading costs but often impose wider bid-ask spreads that function as hidden fees.
Bid-ask spreads represent the difference between buying and selling prices for each security. Highly liquid ETFs like SPY maintain spreads of 1-2 basis points, while less liquid commodity ETFs may exhibit spreads of 5-10 basis points. These costs accumulate through rebalancing activities, typically totaling 10-15 basis points annually.
For a $100,000 portfolio, total annual costs including expense ratios, transaction fees, and spreads typically range from 0.35% to 0.45%, or $350-450 annually. These costs decline as a percentage of assets as portfolio size increases, reaching approximately 0.25% for portfolios exceeding $250,000.
Comparing costs to potential benefits reveals the strategy's value proposition. Historical analysis suggests the All Weather approach reduces portfolio volatility by 35-40% compared to stock-heavy allocations while maintaining competitive returns. This volatility reduction provides substantial value during market stress, potentially preventing behavioral mistakes that destroy long-term wealth.
Alternative Implementations and Customizations
While the original All Weather allocation provides an excellent starting point, investors may consider modifications based on personal circumstances, market conditions, or geographic considerations. International diversification represents one potential enhancement, adding exposure to developed and emerging market bonds and equities.
Geographic customization becomes important for non-US investors. European investors might replace US Treasury bonds with German Bunds or broader European government bond indices. Currency hedging decisions add complexity but may reduce volatility for investors whose spending occurs in non-dollar currencies.
Tax-location strategies optimize after-tax returns by placing tax-inefficient assets in tax-advantaged accounts while holding tax-efficient assets in taxable accounts. TIPS and commodity ETFs generate ordinary income taxed at higher rates, making them candidates for retirement account placement. Stock ETFs generate qualified dividends and long-term capital gains taxed at lower rates, making them suitable for taxable accounts.
Some investors prefer implementing the bond allocation through individual Treasury securities rather than ETFs, eliminating management fees while gaining precise maturity control. Treasury auctions provide access to new securities without bid-ask spreads, though this approach requires more sophisticated portfolio management.
Factor-based implementations replace broad market ETFs with factor-tilted alternatives. Value-tilted stock ETFs, quality-focused bond ETFs, or momentum-based commodity indices may enhance returns while maintaining the All Weather framework's diversification benefits. However, these modifications introduce additional complexity and potential tracking error.
Conclusion: Embracing the Long Game
The All Weather Strategy represents more than an investment approach; it embodies a philosophy of financial resilience that prioritizes sustainable wealth building over speculative gains. In an investment landscape increasingly dominated by algorithmic trading, meme stocks, and cryptocurrency volatility, Dalio's methodical approach offers a refreshing alternative grounded in economic theory and historical evidence.
The strategy's greatest strength lies not in its potential for extraordinary returns, but in its capacity to deliver reasonable returns across diverse economic environments while protecting capital during market stress. This characteristic becomes increasingly valuable as investors approach or enter retirement, when portfolio preservation assumes greater importance than aggressive growth.
Implementation requires discipline, adequate capital, and realistic expectations. The strategy will underperform growth-oriented approaches during bull markets while providing superior downside protection during bear markets. Investors must embrace this trade-off consciously, understanding that the strategy optimizes for long-term wealth building rather than short-term performance.
The All Weather Strategy Indicator democratizes access to institutional-quality portfolio management, providing individual investors with tools previously available only to wealthy families and institutions. By automating allocation tracking, rebalancing signals, and performance analysis, the indicator removes much of the complexity that has historically limited sophisticated strategy implementation.
For investors seeking a systematic, evidence-based approach to long-term wealth building, the All Weather Strategy provides a compelling framework. Its emphasis on diversification, risk management, and behavioral discipline aligns with the fundamental principles that have created lasting wealth throughout financial history. While the strategy may not generate headlines or inspire cocktail party conversations, it offers something more valuable: a reliable path toward financial security across all economic seasons.
As Dalio himself notes, "The biggest mistake investors make is to believe that what happened in the recent past is likely to persist, and they design their portfolios accordingly." The All Weather Strategy's enduring appeal lies in its rejection of this recency bias, instead embracing the uncertainty of markets while positioning for success regardless of which economic season unfolds.
STEP-BY-STEP INDICATOR SETUP GUIDE
Setting up the All Weather Strategy Indicator requires careful attention to each configuration parameter to ensure optimal implementation. This comprehensive setup guide walks through every setting and explains its impact on strategy performance.
Initial Setup Process
Begin by adding the indicator to your TradingView chart. Search for "Ray Dalio's All Weather Strategy" in the indicator library and apply it to any chart. The indicator operates independently of the underlying chart symbol, drawing data directly from the five required ETFs regardless of which security appears on the chart.
Portfolio Configuration Settings
Start with the Portfolio Capital input, which drives all subsequent calculations. Enter your exact investable capital, ranging from $1,000 to $10,000,000. This input determines share quantities, trade recommendations, and performance calculations. Conservative recommendations suggest minimum capitals of $50,000 for basic implementation or $100,000 for optimal precision.
Select your Portfolio Start Date carefully, as this establishes the baseline for all performance calculations. Choose the date when you actually began implementing the All Weather Strategy, not when you first learned about it. This date should reflect when you first purchased ETFs according to the target allocation, creating realistic performance tracking.
Choose your Rebalancing Frequency based on your cost structure and precision preferences. Monthly rebalancing provides tighter allocation control but increases transaction costs. Quarterly rebalancing offers the optimal balance for most investors between allocation precision and cost control. The indicator automatically detects appropriate trading days regardless of your selection.
Set the Rebalancing Threshold based on your tolerance for allocation drift and transaction costs. Conservative investors preferring tight control should use 1-2% thresholds, while cost-conscious investors may prefer 3-5% thresholds. Lower thresholds maintain more precise allocations but trigger more frequent trading.
Display Configuration Options
Enable Show All Weather Calculator to display the comprehensive dashboard containing portfolio values, allocations, and performance metrics. This dashboard provides essential information for portfolio management and should remain enabled for most users.
Show Economic Environment displays current economic regime classification based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated models, this feature provides useful context for understanding current market conditions.
Show Rebalancing Signals highlights when portfolio allocations drift beyond your threshold settings. These signals use color coding to indicate urgency levels, helping prioritize rebalancing activities.
Advanced Label Customization
Configure Show Rebalancing Labels based on your need for chart annotations. These labels mark important portfolio events and can provide valuable historical context, though they may clutter charts during extended time periods.
Select appropriate Label Detail Levels based on your experience and information needs. "None" provides minimal symbols suitable for experienced users. "Basic" shows portfolio values at key events. "Detailed" provides complete trading instructions including exact share quantities for each ETF.
Appearance Customization
Choose Color Themes based on your aesthetic preferences and trading style. "Gold" reflects traditional wealth management appearance, while "EdgeTools" provides modern professional styling. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making.
Enable Dark Mode Optimization if using TradingView's dark theme for optimal readability and contrast. This setting automatically adjusts all colors and transparency levels for the selected theme.
Set Main Line Width based on your chart resolution and visual preferences. Higher width values provide clearer allocation lines but may overwhelm smaller charts. Most users prefer width settings of 2-3 for optimal visibility.
Troubleshooting Common Setup Issues
If the indicator displays "Data not available" messages, verify that all five ETFs (SPY, TLT, IEF, DJP, SCHP) have valid price data on your selected timeframe. The indicator requires daily data availability for all components.
When rebalancing signals seem inconsistent, check your threshold settings and ensure sufficient time has passed since the last rebalancing event. The indicator only triggers signals on designated rebalancing days (first trading day of each period) when drift exceeds threshold levels.
If labels appear at unexpected chart locations, verify that your chart displays percentage values rather than price values. The indicator forces percentage formatting and 0-40% scaling for optimal allocation visualization.
COMPREHENSIVE BIBLIOGRAPHY AND FURTHER READING
PRIMARY SOURCES AND RAY DALIO WORKS
Dalio, R. (2017). Principles: Life and work. New York: Simon & Schuster.
Dalio, R. (2018). A template for understanding big debt crises. Bridgewater Associates.
Dalio, R. (2021). Principles for dealing with the changing world order: Why nations succeed and fail. New York: Simon & Schuster.
BRIDGEWATER ASSOCIATES RESEARCH PAPERS
Jensen, G., Kertesz, A. & Prince, B. (2010). All Weather strategy: Bridgewater's approach to portfolio construction. Bridgewater Associates Research.
Prince, B. (2011). An in-depth look at the investment logic behind the All Weather strategy. Bridgewater Associates Daily Observations.
Bridgewater Associates. (2015). Risk parity in the context of larger portfolio construction. Institutional Research.
ACADEMIC RESEARCH ON RISK PARITY AND PORTFOLIO CONSTRUCTION
Ang, A. & Bekaert, G. (2002). International asset allocation with regime shifts. The Review of Financial Studies, 15(4), 1137-1187.
Bodie, Z. & Rosansky, V. I. (1980). Risk and return in commodity futures. Financial Analysts Journal, 36(3), 27-39.
Campbell, J. Y. & Viceira, L. M. (2001). Who should buy long-term bonds? American Economic Review, 91(1), 99-127.
Clarke, R., De Silva, H. & Thorley, S. (2013). Risk parity, maximum diversification, and minimum variance: An analytic perspective. Journal of Portfolio Management, 39(3), 39-53.
Fama, E. F. & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of Economic Perspectives, 18(3), 25-46.
BEHAVIORAL FINANCE AND IMPLEMENTATION CHALLENGES
Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292.
Thaler, R. H. & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press.
Montier, J. (2007). Behavioural investing: A practitioner's guide to applying behavioural finance. Chichester: John Wiley & Sons.
MODERN PORTFOLIO THEORY AND QUANTITATIVE METHODS
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.
Black, F. & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.
PRACTICAL IMPLEMENTATION AND ETF ANALYSIS
Gastineau, G. L. (2010). The exchange-traded funds manual. 2nd ed. Hoboken: John Wiley & Sons.
Poterba, J. M. & Shoven, J. B. (2002). Exchange-traded funds: A new investment option for taxable investors. American Economic Review, 92(2), 422-427.
Israelsen, C. L. (2005). A refinement to the Sharpe ratio and information ratio. Journal of Asset Management, 5(6), 423-427.
ECONOMIC CYCLE ANALYSIS AND ASSET CLASS RESEARCH
Ilmanen, A. (2011). Expected returns: An investor's guide to harvesting market rewards. Chichester: John Wiley & Sons.
Swensen, D. F. (2009). Pioneering portfolio management: An unconventional approach to institutional investment. Rev. ed. New York: Free Press.
Siegel, J. J. (2014). Stocks for the long run: The definitive guide to financial market returns & long-term investment strategies. 5th ed. New York: McGraw-Hill Education.
RISK MANAGEMENT AND ALTERNATIVE STRATEGIES
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House.
Lowenstein, R. (2000). When genius failed: The rise and fall of Long-Term Capital Management. New York: Random House.
Stein, D. M. & DeMuth, P. (2003). Systematic withdrawal from retirement portfolios: The impact of asset allocation decisions on portfolio longevity. AAII Journal, 25(7), 8-12.
CONTEMPORARY DEVELOPMENTS AND FUTURE DIRECTIONS
Asness, C. S., Frazzini, A. & Pedersen, L. H. (2012). Leverage aversion and risk parity. Financial Analysts Journal, 68(1), 47-59.
Roncalli, T. (2013). Introduction to risk parity and budgeting. Boca Raton: CRC Press.
Ibbotson Associates. (2023). Stocks, bonds, bills, and inflation 2023 yearbook. Chicago: Morningstar.
PERIODICALS AND ONGOING RESEARCH
Journal of Portfolio Management - Quarterly publication featuring cutting-edge research on portfolio construction and risk management
Financial Analysts Journal - Bi-monthly publication of the CFA Institute with practical investment research
Bridgewater Associates Daily Observations - Regular market commentary and research from the creators of the All Weather Strategy
RECOMMENDED READING SEQUENCE
For investors new to the All Weather Strategy, begin with Dalio's "Principles" for philosophical foundation, then proceed to the Bridgewater research papers for technical details. Supplement with Markowitz's original portfolio theory work and behavioral finance literature from Kahneman and Tversky.
Intermediate students should focus on academic papers by Ang & Bekaert on regime shifts, Clarke et al. on risk parity methods, and Ilmanen's comprehensive analysis of expected returns across asset classes.
Advanced practitioners will benefit from Roncalli's technical treatment of risk parity mathematics, Asness et al.'s academic critique of leverage aversion, and ongoing research in the Journal of Portfolio Management.
Volume Rotor Clock [hapharmonic]🕰️ Volume Rotor Clock
The Volume Rotor Clock is an indicator that separates buy and sell volume, compiling these volumes over a recent number of bars or a specified past period, as defined by the user. This helps to reveal accumulation (buying) or distribution (selling) behavior, showing which side has superior volume. With its unique and beautiful display, the Volume Rotor Clock is more than just a timepiece; it's a dynamic dashboard that visualizes the buying and selling pressure of your favorite symbols, all wrapped in an elegant and fully customizable interface.
Instead of just tracking price, this indicator focuses on the engine behind the movement: volume. It helps you instantly identify which assets are under accumulation (buying) and which are under distribution (selling).
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🎨 20 Pre-configured Templates
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🧐 Interpreting the Clock Display
The interface is designed to give you multiple layers of information at a glance. Let's break down what each part represents.
1. The Main Clock Hands (Current Chart Symbol)
The clock hands—hour, minute, and second—are dedicated to the symbol on your current active chart .
Minute Hand: Displays the base currency of the current symbol (e.g., USDT, USD) at its tip.
Hour Hand: Displays the percentage of the winning volume side (buy vs. sell) at its tip.
Color Gauge: The color of the text characters at the tip of both the hour and minute hands acts as your primary volume gauge for the current symbol.
If buy volume is dominant , the text will be green .
If sell volume is dominant , the text will be red .
Tooltip: Hovering your mouse over the text at the tip of the hour or minute or other spherical elements hand will reveal a detailed tooltip with the precise Buy Volume, Sell Volume, Total Volume, Buy %, and Sell % for the current chart's symbol.
2. The Volume Scanner: Bulls & Bears (Symbols Inside the Clock) 🐂🐻
The circular symbols scattered inside the clock face are your multi-symbol volume scanner. They represent the assets you've selected in the indicator's settings.
Green Circles (Bulls - Upper Half): These represent symbols from your list where the total buy volume is greater than the total sell volume over the defined "Lookback" period. They are considered to be under bullish accumulation. The size of the circle and its text grows larger as the buy percentage becomes more dominant. The percentage shown within the circle represents the buy volume's share of the total volume, calculated over the 'Lookback (Bars)' you've set.
Red Circles (Bears - Lower Half): These represent symbols where the total sell volume is greater than the total buy volume. They are considered to be under bearish distribution or selling pressure. The size of the circle indicates the dominance of the sell-side volume. The percentage shown within the circle represents the sell volume's share of the total volume, calculated over the 'Lookback (Bars)' you've set.
3. The Bullish Watchlist (Symbols Above the Clock) ⭐
The symbols arranged neatly along the top edge of the clock are the "best of the bulls." They are symbols that are not only bullish but have also passed an additional, powerful strength filter.
What it Means: A symbol appears here when it shows signs of sustained, high-volume buying interest . It's a way to filter out noise and focus on assets with potentially significant accumulation phases.
The Filter Logic: For a bullish symbol (where total buy volume > total sell volume) to be promoted to the watchlist, its trading volume must meet specific criteria based on this formula:
ta.barssince(not(volume > ta.sma(volume, X))) >= Y
In plain English, this means: The indicator checks how many consecutive bars the `volume` has been greater than its `X`-bar Simple Moving Average (`ta.sma(volume, X)`). If this count is greater than or equal to `Y` bars, the condition is met.
(You can configure `X` (Volume MA Length) and `Y` (Consecutive Days Above MA) in the settings.)
Why it's Useful: This filter is powerful because it looks for consistency . A single spike in volume can be an anomaly. However, when an asset's volume remains consistently above its recent average for several consecutive days, it strongly suggests that larger players or a significant portion of the market are actively accumulating the asset. This sustained interest can often precede a significant upward price trend.
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⚙️ Indicator Settings Explained
The Volume Rotor Clock is highly customizable. Here’s a detailed walkthrough of every setting available in the "Inputs" tab.
🎨 Color Scheme
This group allows you to control the entire aesthetic of the clock.
Template: Choose from a wide variety of professionally designed color themes.
Use Template: A simple checkbox to switch between using a pre-designed theme and creating your own.
`Checked`: You can select a theme from the dropdown menu, which offers 20 unique templates like "Cyberpunk Neon" or "Forest Green". All custom color settings below will be disabled (grayed out and unclickable).
`Unchecked`: The template dropdown is disabled, and you gain full control over every color element in the sections below.
🖌️ Custom Appearance & Colors
These settings are only active when "Use Template" is unchecked.
Flame Head / Tail: Sets the start and end colors for the dynamic flame effect that traces the clock's border, representing the second hand.
Numbers / Main Numbers: Customize the color of the regular hour numbers (1, 2, 4, 5...) and the main cardinal numbers (3, 6, 9, 12).
Sunburst Colors (1-6): Controls the six colors used in the gradient background for the "sunburst" effect inside the clock face.
Hands & Digital: Fine-tune the colors for the Hour/Minute Hand, Second Hand, central Pivot point, and the digital time display.
Chain Color / Width: Customize the appearance of the two chains holding the clock.
📡 Volume Scanner
Control the behavior of the multi-symbol scanner.
Show Scanner Labels: A master switch to show or hide all the bull/bear symbol circles inside the clock.
Lookback (Bars): A crucial setting that defines the calculation period for buy/sell volume for all scanned symbols. The calculation is a sum over the specified number of recent bars.
`0`: Calculates using the current bar only .
`7`: Calculates the sum of volume over the last 8 bars (the current bar + 7 historical bars).
Symbols List: Here you can enable/disable up to 20 slots and input the ticker for each symbol you want to scan (e.g., BINANCE:BTCUSDT , NASDAQ:AAPL ).
⭐ Bullish Watchlist Filter
Configure the criteria for the elite watchlist symbols displayed above the clock.
Enable Watchlist: A master switch to turn the entire watchlist feature on or off.
Volume MA Length: Sets the lookback period `(X)` for the Simple Moving Average of volume used in the filter.
Consecutive Days Above MA: Sets the minimum number of consecutive days `(Y)` that volume must close above its MA to qualify.
Symbols Per Row: Determines the maximum number of watchlist symbols that can fit in a single row before a new row is created above it.
Background / Text Color: When not using a template, you can set custom colors for the watchlist symbols' background and text.
📏 Position & Size
Adjust the clock's placement and dimensions on your chart.
Clock Timezone: Sets the timezone for the digital and analog time display. You can use standard formats like "America/New_York" or enter "Exchange" to sync with the chart's timezone.
Radius (Bars): Controls the overall size of the clock. The radius is measured in terms of the number of bars on the x-axis.
X Offset (Bars): Moves the entire clock horizontally. Positive values shift it to the right; negative values shift it to the left.
Y Offset (Price %): Moves the entire clock vertically as a percentage of your screen's price pane. Positive values move it up; negative values move it down.
Key Indicators Dashboard (KID)Key Indicators Dashboard (KID) — Comprehensive Market & Trend Metrics
📌 Overview
The Key Indicators Dashboard (KID) is an advanced multi-metric market analysis tool designed to consolidate essential technical, volatility, and relative performance data into a single on-chart table. Instead of switching between multiple indicators, KID centralizes these key measures, making it easier to assess a stock’s technical health, volatility state, trend status, and relative strength at a glance.
🛠 Key Features
⦿ Average Daily Range (ADR %): Measures average daily price movement over a specified period. It is calculated by averaging the daily price range (high - low) over a set number of days (default 20 days).
⦿ Average True Range (ATR): Measures volatility by calculating the average of a true range over a specific period (default 14). It helps traders gauge the typical extent of price movement, regardless of the direction.
⦿ ATR%: Expresses the Average True Range as a percentage of the price, which allows traders to compare the volatility of stocks with different prices.
⦿ Relative Strength (RS): Compares a stock’s performance to a chosen benchmark index (default NIFTYMIDSML400) over a specific period (default 50 days).
⦿ RS Score (IBD-style): A normalized 1–100 rating inspired by Investor’s Business Daily methodology.
How it works: The RS Score is based on a weighted average of price changes over 3 months (40%), 6 months (20%), 9 months (20%), and 12 months (20%).
The raw value is converted into a percentage return, then normalized over the past 252 trading days so the lowest value maps to 1 and the highest to 100.
This produces a percentile-style score that highlights the strongest stocks in relative terms.
⦿ Relative Volume (RVol): Compares a stock's current volume to its average volume over a specific period (default 50). It is calculated by dividing the current volume by the average historical volume.
⦿ Average ₹ Volume (Turnover): Represents the total monetary value of shares traded for a stock. It's calculated by multiplying a day's closing price by its volume, with the final value converted to crores for clarity. This metric is a key indicator of a stock's liquidity and overall market interest.
⦿ Moving Average Extension: Measures how far a stock's current price has moved from from a selected moving average (EMA or SMA). This deviation is normalized by the stock's volatility (ATR%), with a default threshold of 6 ATR used to indicate that the stock is significantly extended and is marked with a selected shape (default Red Flag).
⦿ 52-Weeks High & Low: Measures a stock's current price in relation to its highest and lowest prices over the past year. It calculates the percentage a stock is below its 52-week high and above its 52-week low.
⦿ Market Capitalization: Market Cap represents the total value of all outstanding.
⦿ Free Float: It is the value of shares readily available for public trading, with the Free Float Percentage showing the proportion of shares available to the public.
⦿ Trend: Uses Supertrend indicator to identify the current trend of a stock's price. A factor (default 3) and an ATR period (default 10) is used to signal whether the trend is up or down.
⦿ Minervini Trend Template (MTT): It is a set of technical criteria designed to identify stocks in strong uptrends.
Price > 50-DMA > 150-DMA > 200-DMA
200-DMA is trending up for at least 1 month
Price is at least 30% above its 52-week low.
Price is within at least 25 percent of its 52-week high
Table highlights when a stock meets all above criteria.
⦿ Sector & Industry: Display stock's sector and industry, provides categorical classification to assist sector-based analysis. The sector is a broad economic classification, while the industry is a more specific group within that sector.
⦿ Moving Averages (MAs): Plot up to four customizable Moving Averages on a chart. You can independently set the type (Simple or Exponential), the source price, and the length for each MA to help visualize a stock's underlying trend.
MA1: Default 10-EMA
MA2: Default 20-EMA
MA3: Default 50-EMA
MA4: Default 200-EMA
⦿ Moving Average (MA) Crossover: It is a trend signal that occurs when a shorter-term moving average crosses a longer-term one. This script identifies these crossover events and plots a marker on the chart to visually signal a potential change in trend direction.
User-configurable MAs (short and long).
A bullish crossover occurs when the short MA crosses above the long MA.
A bearish crossover occurs when the short MA crosses below the long MA.
⦿ Inside Bar (IB): An Inside Bar is a candlestick whose entire price range is contained within the range of the previous bar. This script identifies this pattern, which often signals consolidation, and visually marks bullish and bearish inside bars on the chart with distinct colors and labels.
⦿ Tightness: Identifies periods of low volatility and price consolidation. It compares the price range over a short lookback period (default 3) to the average daily range (ADR). When the lookback range is smaller than the ADR, the indicator plots a marker on the chart to signal consolidation.
⦿ PowerBar (Purple Dot): Identifies candles with a strong price move on high volume. By default, it plots a purple dot when a stock moves up or down by at least 5% and has a minimum volume of 500,000. More dots indicate higher volatility and liquidity.
⦿ Squeezing Range (SQ): Identifies periods of low volatility, which can often precede a significant price move. It checks if the Bollinger Bands have narrowed to a range that is smaller than the Average True Range (ATR) for a set number of consecutive bars (default 3).
(UpperBB - LowerBB) < (ATR × 2)
⦿ Mark 52-Weeks High and Low: Marks and labels a stock's 52-Week High and Low prices directly on the chart. It draws two horizontal lines extending from the candles where the highest and lowest prices occurred over the past year, providing a clear visual reference for long-term price extremes.
⏳PineScreener Filters
The indicator’s alert conditions act as filters for PineScreener.
Price Filter: Minimum and maximum price cutoffs (default ₹25 - ₹10000).
Daily Price Change Filter: Minimum and maximum daily percent change (default -5% and 5%).
🔔 Built-in Alerts
Supports alert creation for:
ADR%, ATR/ATR %, RS, RS Rating, Turnover
Moving Average Crossover (Bullish/Bearish)
Minervini Trend Template
52-Week High/Low
Inside Bars (Bullish/Bearish)
Tightness
Squeezing Range (SQ)
⚙️ Customizable Visualization
Switchable between vertical or horizontal layout.
Works in dark/light mode
User-configurable to toggle any indicator ON or OFF.
User-configurable Moving (EMA/SMA), Period/Lengths and thresholds.
⦿ (Optional) : For horizontal table orientation increase Top Margin to 16% in Chart (Canvas) settings to avoid chart overlapping with table.
⚡ Add this script to your chart and start making smarter trade decisions today! 🚀
Queso Heat IndexQueso Heat Index (QHI) — ATR-Adaptive Edge-Pressure Gauge
QHI measures how strongly price is pressing the edges of a rolling consolidation window. It heats up when price repeatedly pushes the window up , cools down when it pushes down , and drifts back toward neutral when price wanders in the middle. Everything is ATR-normalized so it adapts across symbols and timeframes.
Output: a signed score from −100 … +100
> 0 = bullish pressure (hot)
< 0 = bearish pressure (cold)
≈ 0 = neutral (no side dominating)
What you’ll see on the chart
Rolling “box” (Donchian window): top, bottom, and midline.
Optional compact-box shading when the window height is small relative to ATR.
Background “thermals”: tinted red when Heat > Hot threshold, blue when Heat < Cold threshold (intensity scales with the score).
Optional Heat line (−100..+100), optional 0/±80 thresholds, and optional push markers (PU/PD).
Optional table showing the current Heat score, placeable in any corner.
How it works (under the hood)
Consolidation window — Over lookback bars we track highest high (top), lowest low (bottom), and midpoint. The window is called “compact” when box height ≤ ATR × maxRangeATR .
ATR-based push detection — A bar is a push-up if high > prior window high + (epsATR × ATR + tick buffer) . A push-down if low < prior window low − (epsATR × ATR + tick buffer) . We also measure how many ATRs beyond the edge the bar traveled.
Heat gains (symmetric) — Each push adds/subtracts Heat:
base gain + streak bonus × consecutive pushes + magnitude bonus × ATRs beyond edge .
Decay toward neutral — Each bar, Heat decays by a percentage. Decay is:
– higher in the middle band of the box, and
– adaptive : the farther (in ATRs) from the relevant band (top when hot, bottom when cold), the faster it decays; hugging the band slows decay.
Midpoint bias (optional) — Gentle drift toward hot when trading above mid, toward cold when below mid, with a dead-zone near mid so tiny wobbles don’t matter.
Reset on regime flip (optional) — First valid push from the opposite side can snap Heat back to 0 before applying new gains.
How to read it
Rising hot with slow decay → strong upside pressure; pullbacks that hold near the top band often continue.
Flip to cold after being hot → regime change risk; tighten risk or consider the other side.
Compact window + rising hot (or cold) → squeeze-and-go conditions.
Neutral (≈ 0) → edges aren’t being pressured; expect mean-reversion inside the box.
Key inputs (what they do)
Window & ATR
lookback : size of the Donchian window (longer = smoother, slower).
atrLen : ATR period for all volatility-scaled thresholds.
maxRangeATR : defines “compact” windows for optional shading.
topBottomFrac : how thick the top/bottom bands are (used for decay/pressure logic).
Push detection (ATR-based)
epsATR : how many ATRs beyond the prior edge to count as a real push.
tickBuff : fixed extra ticks beyond the ATR epsilon (filters micro-breaches).
Heat gains
gainBase : main fuel per push.
gainPerStreak : rewards consecutive pushes.
gainPer1ATRBrk : adds more for stronger breakouts past the edge.
resetOppSide : snap back to 0 on the first opposite-side push.
Decay
decayPct : baseline % removed each bar.
decayAccelMid : multiplies decay when price is in the middle band.
adaptiveDecay , decayMinMult , decayPerATR , decayMaxMult : scale decay with ATR distance from the nearest “target” band (top if hot, bottom if cold).
Midpoint bias
useMidBias : enable/disable drift above/below midpoint.
midDeadFrac : width of neutral (no-drift) zone around mid.
midBiasPerBar : max drift per bar at the box edge.
Visuals (all default to OFF for a clean chart)
Plot Heat line + Show 0/±80 lines (only shows thresholds if Heat line is on).
Hot/Cold thresholds & transparency floors for background shading.
Push markers (PU/PD).
Heat score table : toggle on; choose any corner.
Tuning quick-starts
Daily trending equities : lookback 40–60; epsATR 0.10–0.25; gainBase 12–18; gainPerStreak 0.5–1.5; gainPer1ATRBrk 1–2; decayPct 3–6; adaptiveDecay ON (decayPerATR 0.5–0.8).
Intraday / noisy : raise epsATR and tickBuff to filter noise; keep decayPct modest so Heat can build.
Weekly swing : longer lookback/atrLen; slightly lower decayPct so regimes persist.
Alerts (included)
New window HIGH (push-up)
New window LOW (push-down)
Heat turned HOT (crosses above your Hot threshold)
Heat turned COLD (crosses below your Cold threshold)
Best practices & notes
Use QHI as a pressure gauge , not a standalone system—combine with your entry/exit plan and risk rules.
On thin symbols, increase epsATR and/or tickBuff to avoid spurious pushes.
Gap days can register large pushes; ATR scaling helps but consider context.
Want the Heat in a separate pane? Use the companion panel version; keep this overlay for background/box visuals.
Pine v6. Warm-up: values appear as soon as one bar of window history exists.
TL;DR
QHI quantifies how hard price is leaning on a consolidation edge.
It’s ATR-adaptive, streak- and magnitude-aware, and cools off intelligently when momentum fades.
Watch for thermals (background), the score (−100..+100), and fresh push alerts to time entries in the direction of pressure.
ICT NY Opens (12AM/8:30/9:30)This indicator plots three key New York session reference levels used by ICT traders and intraday scalpers: the Midnight Open (12:00 AM EST), the 8:30 AM EST level (common macro print window), and the 9:30 AM EST RTH open. Each line is drawn at that day’s opening price for the specified time and extends horizontally to 4:15 PM TDC so you always have clean, fixed anchors for the entire trading day.
thors_forex_factory_decodingLibrary "forex_factory_decoding"
Supporting Utility Library for the Live Economic Calendar by toodegrees Indicator; responsible for formatting and saving Forex Factory News events.
isLeapYear()
Finds if it's currently a leap year or not.
Returns: Returns True if the current year is a leap year.
daysMonth(M)
Provides the days in a given month of the year, adjusted during leap years.
Parameters:
M (int) : Month in numerical integer format (i.e. Jan=1).
Returns: Days in the provided month.
MMM(M)
Converts a month from a numerical integer format to a MMM format (i.e. 'Jan').
Parameters:
M (int) : Month in numerical integer format (i.e. Jan=1).
Returns: Month in MMM format (i.e. 'Jan').
array2string(S, FWD)
Converts a string array to a simple string, concatenating its elements.
Parameters:
S (array) : String array, or string array slice, to turn into a simple string.
FWD (bool) : Boolean defaulted to True. If True the array will be concatenated from head to tail, reversed order if False.
Returns: Returns the simple string equivalent of the provided string array.
month2number(M)
Converts a month string in 'MMM' format to its integer equivalent.
Parameters:
M (string) : Month string, in 'MMM' format.
Returns: Returns the integer equivalent of the provided Month string in 'MMM' format.
shiftFWD_Days(D)
Shifts forward the current Date by N days.
Parameters:
D (int) : Number of days to forward-shift, default is 7.
Returns: Returns the forward-shifted date in 'MMM %D' format (i.e. Jan 8, Sep 12).
ff_dow(D)
Converts a numbered day of the week string in format to 'DDD' format (i.e. "1" = Sun).
Parameters:
D (string) : Numbered day of the week from 1 to 7, starting on Sunday.
Returns: Returns the day of the week in 'DDD' format (i.e. "Fri").
ff_currency(C)
Converts a numbered currency string in format to 'CCC' format (i.e. "1" = AUD).
Parameters:
C (string) : Numbered currency, where "1" = "AUD", "2" = "CAD", "3" = "CHF", "4" = "CNY", "5" = "EUR", "6" = "GBP", "7" = "JPY", "8" = "NZD", "9" = "USD".
Returns: Returns the currency in 'CCC' format (i.e. "USD").
ff_t(T)
Converts a time of the day in 'hhmm' format into an intger.
Parameters:
T (string) : Time of the day string in 'hhmm' format.
Returns: Returns the time of the day integer in 'hhmm' format, or -1 if all day.
ff_tod(T)
Converts a time of the day from an integer 'hhmm' format into 'hh:mm' format.
Parameters:
T (int)
Returns: Returns the N Forex Factory News array with time of the day string in 'hh:mm' format, or 'All Day'.
ff_impact(I)
Converts a number from 1 to 4 to a relative color based on Forex Factory Impact types.
Parameters:
I (string) : Impact number string from 1 to 4, where "1" = Holiday, "2" = Low Impact, "3" = Medium Impact, "4" = High Impact.
Returns: Returns the color associated to the impact number based on Forex Factory Impact types.
ff_tmst(D, T)
Parameters:
D (string)
T (string)
decode(ID)
Decodes TOODEGREES_FOREX_FACTORY_SLOT_n Symbols' Pine Seeds data into Forex Factory News Events.
Parameters:
ID (int) : Identifier of the Forex Factory News Event, in "DCHHMMI%T" format (D = day of the week from 1 to 7, C = currency from 1 to 9, HHMM = hour:minute in 24h, I = impact from 1 to 4, %T = event title ID) .
Returns: Returns the Forex Factory News Event.
method pullNews(N, n)
Decodes the Forex Factory News Event and adds it to the Forex Factory News array.
Namespace types: array
Parameters:
N (array type from cegb001/forex_factory_utility/1) : Forex Factory News array.
n (float) : imported data from custom feed.
Returns: void
method readNews(N, S)
Pulls the individual Forex Factory News Event from the custom data feed format (joint News string), decodes them and adds them to the Forex Factory News array.
Namespace types: array
Parameters:
N (array type from cegb001/forex_factory_utility/1) : Forex Factory News array.
S (string) : joint string of the imported data from custom feed.
Returns: void
ICT NY Opening Price Lines (12AM/8:30AM/9:30AM) ICT NY Opens (12AM / 8:30AM / 9:30AM)
This indicator plots three key New York session reference levels used by ICT traders and intraday scalpers: the Midnight Open (12:00 AM EST), the 8:30 AM EST level , and the 9:30 AM EST RTH open. Each line is drawn at that day’s opening price for the specified time and extends horizontally to 4:15 PM true daily close so you always have clean, fixed anchors for the entire trading day.
