Multi-Confluence Signal System📊 OPTIMIZED MULTI-CONFLUENCE SIGNAL SYSTEM
A professional-grade trading indicator that combines multiple technical analysis methods to generate high-probability buy and sell signals. Designed for daily timeframe Bitcoin/crypto trading with optimized parameters based on real market backtesting.
🎯 KEY FEATURES:
- Multi-Confluence Scoring (8 components) - Each signal shows strength rating
- Smart Top & Bottom Detection - Catches reversals using price action patterns
- Ichimoku Cloud Integration - Dynamic support/resistance visualization
- Dual EMA System (20/50) - Clear trend identification
- RSI + MACD + Volume Confirmation - Multi-indicator validation
- Signal Alternation - Only shows directional changes (no repeated signals)
- Minimal Bar Spacing - Prevents signal clustering and overtrading
✅ OPTIMIZED FOR:
- Catching parabolic tops with rejection wicks
- Identifying capitulation bottoms in downtrends
- Avoiding false signals during consolidation
- 4-8 quality signals per 4-month period on daily charts
- Works in both trending and volatile markets
🔧 TECHNICAL COMPONENTS:
- EMA 20/50 trend system
- RSI (14) with adjusted overbought/oversold levels (68/32)
- MACD for momentum confirmation
- Ichimoku Cloud for trend context
- Volume analysis (1.3x threshold)
- Candlestick pattern recognition (engulfing, hammers, shooting stars)
- Capitulation detection for extreme moves
- Price extension filters (±5-10% from EMAs)
⚠️ BEST PRACTICES:
- Optimized for Daily timeframe
- Combine with your own risk management
- Higher scores = higher probability trades
- Wait for signal confirmation on candle close
- Use in conjunction with key support/resistance levels
💡 SIGNAL LOGIC:
BUY signals trigger on: Capitulation candles, extreme oversold + reversal patterns, MACD turnarounds in downtrends, or high confluence scores with bullish patterns
SELL signals trigger on: Rejection wicks at tops, bearish engulfings with overbought RSI, parabolic extensions, MACD reversals, or high confluence scores with bearish patterns
📈 Created through iterative backtesting and optimization on Bitcoin price action from 2024-2025.
⭐ Free to use • Leave feedback • Happy trading!
ค้นหาในสคริปต์สำหรับ "backtest"
No-Trade Zones UTC+7This indicator helps you visualize and backtest your preferred trading hours. For example, if you have a 9-to-5 job, you obviously can’t trade during that time — and when backtesting, you should avoid those hours too. It also marks weekends if you prefer not to trade on those days.
By highlighting no-trade periods directly on the chart, you can easily see when you shouldn’t be taking trades, without constantly checking the time or date by hovering over the chart. It makes backtesting smoother and more realistic for your personal schedule.
Hidden Impulse═══════════════════════════════════════════════════════════════════
HIDDEN IMPULSE - Multi-Timeframe Momentum Detection System
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OVERVIEW
Hidden Impulse is an advanced momentum oscillator that combines the Schaff Trend Cycle (STC) and Force Index into a comprehensive multi-timeframe trading system. Unlike standard implementations of these indicators, this script introduces three distinct trading setups with specific entry conditions, multi-timeframe confirmation, and trend filtering.
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ORIGINALITY & KEY FEATURES
This indicator is original in the following ways:
1. DUAL-TIMEFRAME STC ANALYSIS
Standard STC implementations work on a single timeframe. This script
simultaneously analyzes STC on both your trading timeframe and a higher
timeframe, providing trend context and filtering out low-probability signals.
2. FORCE INDEX INTEGRATION
The script combines STC with Force Index (volume-weighted price momentum)
to confirm the strength behind price moves. This combination helps identify
when momentum shifts are backed by genuine buying/selling pressure.
3. THREE DISTINCT TRADING SETUPS
Rather than generic overbought/oversold signals, the indicator provides
three specific, rule-based setups:
- Setup A: Classic trend-following entries with multi-timeframe confirmation
- Setup B: Divergence-based reversal entries (highest probability)
- Setup C: Mean-reversion bounce trades at extreme levels
4. INTELLIGENT FILTERING
All signals are filtered through:
- 50 EMA trend direction (prevents counter-trend trades)
- Higher timeframe STC alignment (ensures macro trend agreement)
- Force Index confirmation (validates volume support)
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HOW IT WORKS - TECHNICAL EXPLANATION
SCHAFF TREND CYCLE (STC) CALCULATION:
The STC is a cyclical oscillator that combines MACD concepts with stochastic
smoothing to create earlier and smoother trend signals.
Step 1: Calculate MACD
- Fast MA = EMA(close, Length1) — default 23
- Slow MA = EMA(close, Length2) — default 50
- MACD Line = Fast MA - Slow MA
Step 2: First Stochastic Smoothing
- Apply stochastic calculation to MACD
- Stoch1 = 100 × (MACD - Lowest(MACD, Smoothing)) / (Highest(MACD, Smoothing) - Lowest(MACD, Smoothing))
- Smooth result with EMA(Stoch1, Smoothing) — default 10
Step 3: Second Stochastic Smoothing
- Apply stochastic calculation again to the smoothed stochastic
- This creates the final STC value between 0-100
The dual stochastic smoothing makes STC more responsive than MACD while
being smoother than traditional stochastics.
FORCE INDEX CALCULATION:
Force Index measures the power behind price movements by incorporating volume:
Force Raw = (Close - Close ) × Volume
Force Index = EMA(Force Raw, Period) — default 13
Interpretation:
- Positive Force Index = Buying pressure (bulls in control)
- Negative Force Index = Selling pressure (bears in control)
- Force Index crossing zero = Momentum shift
- Divergences with price = Weakening momentum (reversal signal)
TREND FILTER:
A 50-period EMA serves as the trend filter:
- Price above EMA50 = Uptrend → Only LONG signals allowed
- Price below EMA50 = Downtrend → Only SHORT signals allowed
This prevents counter-trend trading which accounts for most losing trades.
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THE THREE TRADING SETUPS - DETAILED
SETUP A: CLASSIC MOMENTUM ENTRY
Concept: Enter when STC exits oversold/overbought zones with trend confirmation
LONG CONDITIONS:
1. Higher timeframe STC > 25 (macro trend is up)
2. Primary timeframe STC crosses above 25 (momentum turning up)
3. Force Index crosses above 0 OR already positive (volume confirms)
4. Price above 50 EMA (local trend is up)
SHORT CONDITIONS:
1. Higher timeframe STC < 75 (macro trend is down)
2. Primary timeframe STC crosses below 75 (momentum turning down)
3. Force Index crosses below 0 OR already negative (volume confirms)
4. Price below 50 EMA (local trend is down)
Best for: Trending markets, continuation trades
Win rate: Moderate (60-65%)
Risk/Reward: 1:2 to 1:3
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SETUP B: DIVERGENCE REVERSAL (HIGHEST PROBABILITY)
Concept: Identify exhaustion points where price makes new extremes but
momentum (Force Index) fails to confirm
BULLISH DIVERGENCE:
1. Price makes a lower low (LL) over 10 bars
2. Force Index makes a higher low (HL) — refuses to follow price down
3. STC is below 25 (oversold condition)
Trigger: STC starts rising AND Force Index crosses above zero
BEARISH DIVERGENCE:
1. Price makes a higher high (HH) over 10 bars
2. Force Index makes a lower high (LH) — refuses to follow price up
3. STC is above 75 (overbought condition)
Trigger: STC starts falling AND Force Index crosses below zero
Why this works: Divergences signal that the current trend is losing steam.
When volume (Force Index) doesn't confirm new price extremes, a reversal
is likely.
Best for: Reversal trading, range-bound markets
Win rate: High (70-75%)
Risk/Reward: 1:3 to 1:5
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SETUP C: QUICK BOUNCE AT EXTREMES
Concept: Catch rapid mean-reversion moves when price touches EMA50 in
extreme STC zones
LONG CONDITIONS:
1. Price touches 50 EMA from above (pullback in uptrend)
2. STC < 15 (extreme oversold)
3. Force Index > 0 (buyers stepping in)
SHORT CONDITIONS:
1. Price touches 50 EMA from below (pullback in downtrend)
2. STC > 85 (extreme overbought)
3. Force Index < 0 (sellers stepping in)
Best for: Scalping, quick mean-reversion trades
Win rate: Moderate (55-60%)
Risk/Reward: 1:1 to 1:2
Note: Use tighter stops and quick profit-taking
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HOW TO USE THE INDICATOR
STEP 1: CONFIGURE TIMEFRAMES
Primary Timeframe (STC - Primary Timeframe):
- Leave empty to use your current chart timeframe
- This is where you'll take trades
Higher Timeframe (STC - Higher Timeframe):
- Default: 30 minutes
- Recommended ratios:
* 5min chart → 30min higher TF
* 15min chart → 1H higher TF
* 1H chart → 4H higher TF
* Daily chart → Weekly higher TF
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STEP 2: ADJUST STC PARAMETERS FOR YOUR MARKET
Default (23/50/10) works well for stocks and forex, but adjust for:
CRYPTO (volatile):
- Length 1: 15
- Length 2: 35
- Smoothing: 8
(Faster response for rapid price movements)
STOCKS (standard):
- Length 1: 23
- Length 2: 50
- Smoothing: 10
(Balanced settings)
FOREX MAJORS (slower):
- Length 1: 30
- Length 2: 60
- Smoothing: 12
(Filters out noise in 24/7 markets)
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STEP 3: ENABLE YOUR PREFERRED SETUPS
Toggle setups based on your trading style:
Conservative Trader:
✓ Setup B (Divergence) — highest win rate
✗ Setup A (Classic) — only in strong trends
✗ Setup C (Bounce) — too aggressive
Trend Trader:
✓ Setup A (Classic) — primary signals
✓ Setup B (Divergence) — for entries on pullbacks
✗ Setup C (Bounce) — not suitable for trending
Scalper:
✓ Setup C (Bounce) — quick in-and-out
✓ Setup B (Divergence) — high probability scalps
✗ Setup A (Classic) — too slow
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STEP 4: READ THE SIGNALS
ON THE CHART:
Labels appear when conditions are met:
Green labels:
- "LONG A" — Setup A long entry
- "LONG B DIV" — Setup B divergence long (best signal)
- "LONG C" — Setup C bounce long
Red labels:
- "SHORT A" — Setup A short entry
- "SHORT B DIV" — Setup B divergence short (best signal)
- "SHORT C" — Setup C bounce short
IN THE INDICATOR PANEL (bottom):
- Blue line = Primary timeframe STC
- Orange dots = Higher timeframe STC (optional)
- Green/Red bars = Force Index histogram
- Dashed lines at 25/75 = Entry/Exit zones
- Background shading = Oversold (green) / Overbought (red)
INFO TABLE (top-right corner):
Shows real-time status:
- STC values for both timeframes
- Force Index direction
- Price position vs EMA
- Current trend direction
- Active signal type
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TRADING STRATEGY & RISK MANAGEMENT
ENTRY RULES:
Priority ranking (best to worst):
1st: Setup B (Divergence) — wait for these
2nd: Setup A (Classic) — in confirmed trends only
3rd: Setup C (Bounce) — scalping only
Confirmation checklist before entry:
☑ Signal label appears on chart
☑ TREND in info table matches signal direction
☑ Higher timeframe STC aligned (check orange dots or table)
☑ Force Index confirming (check histogram color)
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STOP LOSS PLACEMENT:
Setup A (Classic):
- LONG: Below recent swing low
- SHORT: Above recent swing high
- Typical: 1-2 ATR distance
Setup B (Divergence):
- LONG: Below the divergence low
- SHORT: Above the divergence high
- Typical: 0.5-1.5 ATR distance
Setup C (Bounce):
- LONG: 5-10 pips below EMA50
- SHORT: 5-10 pips above EMA50
- Typical: 0.3-0.8 ATR distance
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TAKE PROFIT TARGETS:
Conservative approach:
- Exit when STC reaches opposite level
- LONG: Exit when STC > 75
- SHORT: Exit when STC < 25
Aggressive approach:
- Hold until opposite signal appears
- Trail stop as STC moves in your favor
Partial profits:
- Take 50% at 1:2 risk/reward
- Let remaining 50% run to target
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WHAT TO AVOID:
❌ Trading Setup A in sideways/choppy markets
→ Wait for clear trend or use Setup B only
❌ Ignoring higher timeframe STC
→ Always check orange dots align with your direction
❌ Taking signals against the major trend
→ If weekly trend is down, be cautious with longs
❌ Overtrading Setup C
→ Maximum 2-3 bounce trades per session
❌ Trading during low volume periods
→ Force Index becomes unreliable
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ALERTS CONFIGURATION
The indicator includes 8 alert types:
Individual setup alerts:
- "Setup A - LONG" / "Setup A - SHORT"
- "Setup B - DIV LONG" / "Setup B - DIV SHORT" ⭐ recommended
- "Setup C - BOUNCE LONG" / "Setup C - BOUNCE SHORT"
Combined alerts:
- "ANY LONG" — fires on any long signal
- "ANY SHORT" — fires on any short signal
Recommended alert setup:
- Create "Setup B - DIV LONG" and "Setup B - DIV SHORT" alerts
- These are the highest probability signals
- Set "Once Per Bar Close" to avoid false alerts
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VISUALIZATION SETTINGS
Show Labels on Chart:
Toggle on/off the signal labels (green/red)
Disable for cleaner chart once you're familiar with the indicator
Show Higher TF STC:
Toggle the orange dots showing higher timeframe STC
Useful for visual confirmation of multi-timeframe alignment
Info Panel:
Cannot be disabled — always shows current status
Positioned top-right to avoid chart interference
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EXAMPLE TRADE WALKTHROUGH
SETUP B DIVERGENCE LONG EXAMPLE:
1. Market Context:
- Price in downtrend, below 50 EMA
- Multiple lower lows forming
- STC below 25 (oversold)
2. Divergence Formation:
- Price makes new low at $45.20
- Force Index refuses to make new low (higher low forms)
- This indicates selling pressure weakening
3. Signal Trigger:
- STC starts turning up
- Force Index crosses above zero
- Label appears: "LONG B DIV"
4. Trade Execution:
- Entry: $45.50 (current price at signal)
- Stop Loss: $44.80 (below divergence low)
- Target 1: $47.90 (STC reaches 75) — risk/reward 1:3.4
- Target 2: Opposite signal or trail stop
5. Trade Management:
- Price rallies to $47.20
- STC reaches 68 (approaching target zone)
- Take 50% profit, move stop to breakeven
- Exit remaining at $48.10 when STC crosses 75
Result: 3.7R gain
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ADVANCED TIPS
1. MULTI-TIMEFRAME CONFLUENCE
For highest probability trades, wait for:
- Primary TF signal
- Higher TF STC aligned (>25 for longs, <75 for shorts)
- Even higher TF trend in same direction (manual check)
2. VOLUME CONFIRMATION
Watch the Force Index histogram:
- Increasing bar size = Strengthening momentum
- Decreasing bar size = Weakening momentum
- Use this to gauge signal strength
3. AVOID THESE MARKET CONDITIONS
- Major news events (Force Index becomes erratic)
- Market open first 30 minutes (volatility spikes)
- Low liquidity instruments (Force Index unreliable)
- Extreme trending days (wait for pullbacks)
4. COMBINE WITH SUPPORT/RESISTANCE
Best signals occur near:
- Key horizontal levels
- Fibonacci retracements
- Previous day's high/low
- Psychological round numbers
5. SESSION AWARENESS
- Asia session: Use lower timeframes, Setup C works well
- London session: Setup A and B both effective
- New York session: All setups work, highest volume
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INDICATOR WINDOWS LAYOUT
MAIN CHART:
- Price action
- 50 EMA (green/red)
- Signal labels
- Info panel
INDICATOR WINDOW:
- STC oscillator (blue line, 0-100 scale)
- Higher TF STC (orange dots, optional)
- Force Index histogram (green/red bars)
- Reference levels (25, 50, 75)
- Background zones (green oversold, red overbought)
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PERFORMANCE OPTIMIZATION
For best results:
Backtesting:
- Test on your specific instrument and timeframe
- Adjust STC parameters if win rate < 55%
- Record which setup works best for your market
Position Sizing:
- Risk 1-2% per trade
- Setup B can use 2% risk (higher win rate)
- Setup C should use 1% risk (lower win rate)
Trade Frequency:
- Setup B: 2-5 signals per week (be patient)
- Setup A: 5-10 signals per week
- Setup C: 10+ signals per week (scalping)
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CREDITS & REFERENCES
This indicator builds upon established technical analysis concepts:
Schaff Trend Cycle:
- Developed by Doug Schaff (1996)
- Original concept published in Technical Analysis of Stocks & Commodities
- Implementation based on standard STC formula
Force Index:
- Developed by Dr. Alexander Elder
- Described in "Trading for a Living" (1993)
- Classic volume-momentum indicator
The multi-timeframe integration, three-setup system, and specific
entry conditions are original contributions of this indicator.
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DISCLAIMER
This indicator is a technical analysis tool and does not guarantee profits.
Past performance is not indicative of future results. Always:
- Use proper risk management
- Test on demo account first
- Combine with fundamental analysis
- Never risk more than you can afford to lose
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SUPPORT & QUESTIONS
If you find this indicator helpful, please:
- Leave a like and comment
- Share your feedback and results
- Report any bugs or issues
For questions about usage or optimization for specific markets,
feel free to comment below.
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ALISH WEEK LABELS THE ALISH WEEK LABELS
Overview
This indicator programmatically delineates each trading week and encapsulates its realized price range in a live-updating, filled rectangle. A week is defined in America/Toronto time from Monday 00:00 to Friday 16:00. Weekly market open to market close, For every week, the script draws:
a vertical start line at the first bar of Monday 00:00,
a vertical end line at the first bar at/after Friday 16:00, and
a white, semi-transparent box whose top tracks the highest price and whose bottom tracks the lowest price observed between those two temporal boundaries.
The drawing is timeframe-agnostic (M1 → 1D): the box expands in real time while the week is open and freezes at the close boundary.
Time Reference and Session Boundaries
All scheduling decisions are computed with time functions called using the fixed timezone string "America/Toronto", ensuring correct behavior across DST transitions without relying on chart timezone. The start condition is met at the first bar where (dayofweek == Monday && hour == 0 && minute == 0); on higher timeframes where an exact 00:00 bar may not exist, a fallback checks for the first Monday bar using ta.change(dayofweek). The close condition is met on the first bar at or after Friday 16:00 (Toronto), which guarantees deterministic closure on intraday and higher timeframes.
State Model
The indicator maintains minimal persistent state using var globals:
week_open (bool): whether the current weekly session is active.
wk_hi / wk_lo (float): rolling extrema for the active week.
wk_box (box): the graphical rectangle spanning × .
wk_start_line and a transient wk_end_line (line): vertical delimiters at the week’s start and end.
Two dynamic arrays (boxes, vlines) store object handles to support bounded history and deterministic garbage collection.
Update Cycle (Per Bar)
On each bar the script executes the following pipeline:
Start Check: If no week is open and the start condition is satisfied, instantiate wk_box anchored at the current bar_index, prime wk_hi/wk_lo with the bar’s high/low, create the start line, and push both handles to their arrays.
Accrual (while week_open): Update wk_hi/wk_lo using math.max/min with current bar extremes. Propagate those values to the active wk_box via box.set_top/bottom and slide box.set_right to the current bar_index to keep the box flush with live price.
Close Check: If at/after Friday 16:00, finalize the week by freezing the right edge (box.set_right), drawing the end line, pushing its handle, and flipping week_open false.
Retention Pruning: Enforce a hard cap on historical elements by deleting the oldest objects when counts exceed configured limits.
Drawing Semantics
The range container is a filled white rectangle (bgcolor = color.new(color.white, 100 − opacity)), with a solid white border for clear contrast on dark or light themes. Start/end boundaries are full-height vertical white lines (y1=+1e10, y2=−1e10) to guarantee visibility across auto-scaled y-axes. This approach avoids reliance on price-dependent anchors for the lines and is robust to large volatility spikes.
Multi-Timeframe Behavior
Because session logic is driven by wall-clock time in the Toronto zone, the indicator remains consistent across chart resolutions. On coarse timeframes where an exact boundary bar might not exist, the script legally approximates by triggering on the first available bar within or immediately after the boundary (e.g., Friday 16:00 occurs between two 4-hour bars). The box therefore represents the true realized high/low of the bars present in that timeframe, which is the correct visual for that resolution.
Inputs and Defaults
Weeks to keep (show_weeks_back): integer, default 40. Controls retention of historical boxes/lines to avoid UI clutter and resource overhead.
Fill opacity (fill_opacity): integer 0–100, default 88. Controls how solid the white fill appears; border color is fixed pure white for crisp edges.
Time zone is intentionally fixed to "America/Toronto" to match the strategy definition and maintain consistent historical backtesting.
Performance and Limits
Objects are reused only within a week; upon closure, handles are stored and later purged when history limits are exceeded. The script sets generous but safe caps (max_boxes_count/max_lines_count) to accommodate 40 weeks while preserving Editor constraints. Per-bar work is O(1), and pruning loops are bounded by the configured history length, keeping runtime predictable on long histories.
Edge Cases and Guarantees
DST Transitions: Using a fixed IANA time zone ensures Friday 16:00 and Monday 00:00 boundaries shift correctly when DST changes in Toronto.
Weekend Gaps/Holidays: If the market lacks bars exactly at boundaries, the nearest subsequent bar triggers the start/close logic; range statistics still reflect observed prices.
Live vs Historical: During live sessions the box edge advances every bar; when replaying history or backtesting, the same rules apply deterministically.
Scope (Intentional Simplicity)
This tool is strictly a visual framing indicator. It does not compute labels, statistics, alerts, or extended S/R projections. Its single responsibility is to clearly present the week’s realized range in the Toronto session window so you can layer your own execution or analytics on top.
Strategy with Reference Lines📊 Strategy with Reference Lines
Description:
This strategy uses a contrarian approach based on the analysis of the previous candle to identify entry and exit points. The strategy draws horizontal reference lines at important levels of the previous candle and generates buy/sell signals based on the candle's direction.
Key Features:
🔹 Multi-Timeframe Analysis: Configurable for 1H, 2H, 3H, 4H, 6H, 12H, and 1D
🔹 Reference Lines: High, low, close, and midpoint (50%) of the previous candle
🔹 Visual Signals: Labels with prices and actions (BUY/SELL/TP)
🔹 Optional Trading: Enable/disable automatic order execution
🔹 Complete System: Automatic entry, Take Profit, and Stop Loss
🔹 Alerts: Notifications when a new candle is detected
Strategy Logic:
When the previous candle is POSITIVE:
Signal: 🔴 SELL at the previous candle's close
Take Profit: 🎯 Midpoint (50%) of the previous candle
Stop Loss: 🔴 High of the previous candle
When the previous candle is NEGATIVE:
Signal: 🟢 BUY at the previous candle's close
Take Profit: 🎯 Midpoint (50%) of the previous candle
Stop Loss: 🟢 Low of the previous candle
Visual Elements:
Green Line: High of the previous candle (when positive)
Red Line: Low of the previous candle (when negative)
Yellow Line: Close of the previous candle (always present)
Blue Line: Midpoint (50%) of the previous candle (always present)
Labels: Prices and actions with emojis for easy identification
Settings:
Timeframe: Default 4H (configurable)
Auto Trading: Disabled by default (safety)
Alerts: Include entry prices, TP, and SL
Recommended Usage:
✅ Visual Analysis: Use with trading disabled for analysis
✅ Backtesting: Enable trading to test historically
✅ Swing Trading: Ideal for 4H or higher timeframes
✅ Risk Management: Automatic SL and TP for protection
Risk Disclaimer:
This strategy is for educational and analysis purposes only. Always test in a simulation environment before using with real capital. Trading involves significant risks and may result in losses.
Keltner Channel Enhanced [DCAUT]█ Keltner Channel Enhanced
📊 ORIGINALITY & INNOVATION
The Keltner Channel Enhanced represents an important advancement over standard Keltner Channel implementations by introducing dual flexibility in moving average selection for both the middle band and ATR calculation. While traditional Keltner Channels typically use EMA for the middle band and RMA (Wilder's smoothing) for ATR, this enhanced version provides access to 25+ moving average algorithms for both components, enabling traders to fine-tune the indicator's behavior to match specific market characteristics and trading approaches.
Key Advancements:
Dual MA Algorithm Flexibility: Independent selection of moving average types for middle band (25+ options) and ATR smoothing (25+ options), allowing optimization of both trend identification and volatility measurement separately
Enhanced Trend Sensitivity: Ability to use faster algorithms (HMA, T3) for middle band while maintaining stable volatility measurement with traditional ATR smoothing, or vice versa for different trading strategies
Adaptive Volatility Measurement: Choice of ATR smoothing algorithm affects channel responsiveness to volatility changes, from highly reactive (SMA, EMA) to smoothly adaptive (RMA, TEMA)
Comprehensive Alert System: Five distinct alert conditions covering breakouts, trend changes, and volatility expansion, enabling automated monitoring without constant chart observation
Multi-Timeframe Compatibility: Works effectively across all timeframes from intraday scalping to long-term position trading, with independent optimization of trend and volatility components
This implementation addresses key limitations of standard Keltner Channels: fixed EMA/RMA combination may not suit all market conditions or trading styles. By decoupling the trend component from volatility measurement and allowing independent algorithm selection, traders can create highly customized configurations for specific instruments and market phases.
📐 MATHEMATICAL FOUNDATION
Keltner Channel Enhanced uses a three-component calculation system that combines a flexible moving average middle band with ATR-based (Average True Range) upper and lower channels, creating volatility-adjusted trend-following bands.
Core Calculation Process:
1. Middle Band (Basis) Calculation:
The basis line is calculated using the selected moving average algorithm applied to the price source over the specified period:
basis = ma(source, length, maType)
Supported algorithms include EMA (standard choice, trend-biased), SMA (balanced and symmetric), HMA (reduced lag), WMA, VWMA, TEMA, T3, KAMA, and 17+ others.
2. Average True Range (ATR) Calculation:
ATR measures market volatility by calculating the average of true ranges over the specified period:
trueRange = max(high - low, abs(high - close ), abs(low - close ))
atrValue = ma(trueRange, atrLength, atrMaType)
ATR smoothing algorithm significantly affects channel behavior, with options including RMA (standard, very smooth), SMA (moderate smoothness), EMA (fast adaptation), TEMA (smooth yet responsive), and others.
3. Channel Calculation:
Upper and lower channels are positioned at specified multiples of ATR from the basis:
upperChannel = basis + (multiplier × atrValue)
lowerChannel = basis - (multiplier × atrValue)
Standard multiplier is 2.0, providing channels that dynamically adjust width based on market volatility.
Keltner Channel vs. Bollinger Bands - Key Differences:
While both indicators create volatility-based channels, they use fundamentally different volatility measures:
Keltner Channel (ATR-based):
Uses Average True Range to measure actual price movement volatility
Incorporates gaps and limit moves through true range calculation
More stable in trending markets, less prone to extreme compression
Better reflects intraday volatility and trading range
Typically fewer band touches, making touches more significant
More suitable for trend-following strategies
Bollinger Bands (Standard Deviation-based):
Uses statistical standard deviation to measure price dispersion
Based on closing prices only, doesn't account for intraday range
Can compress significantly during consolidation (squeeze patterns)
More touches in ranging markets
Better suited for mean-reversion strategies
Provides statistical probability framework (95% within 2 standard deviations)
Algorithm Combination Effects:
The interaction between middle band MA type and ATR MA type creates different indicator characteristics:
Trend-Focused Configuration (Fast MA + Slow ATR): Middle band uses HMA/EMA/T3, ATR uses RMA/TEMA, quick trend changes with stable channel width, suitable for trend-following
Volatility-Focused Configuration (Slow MA + Fast ATR): Middle band uses SMA/WMA, ATR uses EMA/SMA, stable trend with dynamic channel width, suitable for volatility trading
Balanced Configuration (Standard EMA/RMA): Classic Keltner Channel behavior, time-tested combination, suitable for general-purpose trend following
Adaptive Configuration (KAMA + KAMA): Self-adjusting indicator responding to efficiency ratio, suitable for markets with varying trend strength and volatility regimes
📊 COMPREHENSIVE SIGNAL ANALYSIS
Keltner Channel Enhanced provides multiple signal categories optimized for trend-following and breakout strategies.
Channel Position Signals:
Upper Channel Interaction:
Price Touching Upper Channel: Strong bullish momentum, price moving more than typical volatility range suggests, potential continuation signal in established uptrends
Price Breaking Above Upper Channel: Exceptional strength, price exceeding normal volatility expectations, consider adding to long positions or tightening trailing stops
Price Riding Upper Channel: Sustained strong uptrend, characteristic of powerful bull moves, stay with trend and avoid premature profit-taking
Price Rejection at Upper Channel: Momentum exhaustion signal, consider profit-taking on longs or waiting for pullback to middle band for reentry
Lower Channel Interaction:
Price Touching Lower Channel: Strong bearish momentum, price moving more than typical volatility range suggests, potential continuation signal in established downtrends
Price Breaking Below Lower Channel: Exceptional weakness, price exceeding normal volatility expectations, consider adding to short positions or protecting against further downside
Price Riding Lower Channel: Sustained strong downtrend, characteristic of powerful bear moves, stay with trend and avoid premature covering
Price Rejection at Lower Channel: Momentum exhaustion signal, consider covering shorts or waiting for bounce to middle band for reentry
Middle Band (Basis) Signals:
Trend Direction Confirmation:
Price Above Basis: Bullish trend bias, middle band acts as dynamic support in uptrends, consider long positions or holding existing longs
Price Below Basis: Bearish trend bias, middle band acts as dynamic resistance in downtrends, consider short positions or avoiding longs
Price Crossing Above Basis: Potential trend change from bearish to bullish, early signal to establish long positions
Price Crossing Below Basis: Potential trend change from bullish to bearish, early signal to establish short positions or exit longs
Pullback Trading Strategy:
Uptrend Pullback: Price pulls back from upper channel to middle band, finds support, and resumes upward, ideal long entry point
Downtrend Bounce: Price bounces from lower channel to middle band, meets resistance, and resumes downward, ideal short entry point
Basis Test: Strong trends often show price respecting the middle band as support/resistance on pullbacks
Failed Test: Price breaking through middle band against trend direction signals potential reversal
Volatility-Based Signals:
Narrow Channels (Low Volatility):
Consolidation Phase: Channels contract during periods of reduced volatility and directionless price action
Breakout Preparation: Narrow channels often precede significant directional moves as volatility cycles
Trading Approach: Reduce position sizes, wait for breakout confirmation, avoid range-bound strategies within channels
Breakout Direction: Monitor for price breaking decisively outside channel range with expanding width
Wide Channels (High Volatility):
Trending Phase: Channels expand during strong directional moves and increased volatility
Momentum Confirmation: Wide channels confirm genuine trend with substantial volatility backing
Trading Approach: Trend-following strategies excel, wider stops necessary, mean-reversion strategies risky
Exhaustion Signs: Extreme channel width (historical highs) may signal approaching consolidation or reversal
Advanced Pattern Recognition:
Channel Walking Pattern:
Upper Channel Walk: Price consistently touches or exceeds upper channel while staying above basis, very strong uptrend signal, hold longs aggressively
Lower Channel Walk: Price consistently touches or exceeds lower channel while staying below basis, very strong downtrend signal, hold shorts aggressively
Basis Support/Resistance: During channel walks, price typically uses middle band as support/resistance on minor pullbacks
Pattern Break: Price crossing basis during channel walk signals potential trend exhaustion
Squeeze and Release Pattern:
Squeeze Phase: Channels narrow significantly, price consolidates near middle band, volatility contracts
Direction Clues: Watch for price positioning relative to basis during squeeze (above = bullish bias, below = bearish bias)
Release Trigger: Price breaking outside narrow channel range with expanding width confirms breakout
Follow-Through: Measure squeeze height and project from breakout point for initial profit targets
Channel Expansion Pattern:
Breakout Confirmation: Rapid channel widening confirms volatility increase and genuine trend establishment
Entry Timing: Enter positions early in expansion phase before trend becomes overextended
Risk Management: Use channel width to size stops appropriately, wider channels require wider stops
Basis Bounce Pattern:
Clean Bounce: Price touches middle band and immediately reverses, confirms trend strength and entry opportunity
Multiple Bounces: Repeated basis bounces indicate strong, sustainable trend
Bounce Failure: Price penetrating basis signals weakening trend and potential reversal
Divergence Analysis:
Price/Channel Divergence: Price makes new high/low while staying within channel (not reaching outer band), suggests momentum weakening
Width/Price Divergence: Price breaks to new extremes but channel width contracts, suggests move lacks conviction
Reversal Signal: Divergences often precede trend reversals or significant consolidation periods
Multi-Timeframe Analysis:
Keltner Channels work particularly well in multi-timeframe trend-following approaches:
Three-Timeframe Alignment:
Higher Timeframe (Weekly/Daily): Identify major trend direction, note price position relative to basis and channels
Intermediate Timeframe (Daily/4H): Identify pullback opportunities within higher timeframe trend
Lower Timeframe (4H/1H): Time precise entries when price touches middle band or lower channel (in uptrends) with rejection
Optimal Entry Conditions:
Best Long Entries: Higher timeframe in uptrend (price above basis), intermediate timeframe pulls back to basis, lower timeframe shows rejection at middle band or lower channel
Best Short Entries: Higher timeframe in downtrend (price below basis), intermediate timeframe bounces to basis, lower timeframe shows rejection at middle band or upper channel
Risk Management: Use higher timeframe channel width to set position sizing, stops below/above higher timeframe channels
🎯 STRATEGIC APPLICATIONS
Keltner Channel Enhanced excels in trend-following and breakout strategies across different market conditions.
Trend Following Strategy:
Setup Requirements:
Identify established trend with price consistently on one side of basis line
Wait for pullback to middle band (basis) or brief penetration through it
Confirm trend resumption with price rejection at basis and move back toward outer channel
Enter in trend direction with stop beyond basis line
Entry Rules:
Uptrend Entry:
Price pulls back from upper channel to middle band, shows support at basis (bullish candlestick, momentum divergence)
Enter long on rejection/bounce from basis with stop 1-2 ATR below basis
Aggressive: Enter on first touch; Conservative: Wait for confirmation candle
Downtrend Entry:
Price bounces from lower channel to middle band, shows resistance at basis (bearish candlestick, momentum divergence)
Enter short on rejection/reversal from basis with stop 1-2 ATR above basis
Aggressive: Enter on first touch; Conservative: Wait for confirmation candle
Trend Management:
Trailing Stop: Use basis line as dynamic trailing stop, exit if price closes beyond basis against position
Profit Taking: Take partial profits at opposite channel, move stops to basis
Position Additions: Add to winners on subsequent basis bounces if trend intact
Breakout Strategy:
Setup Requirements:
Identify consolidation period with contracting channel width
Monitor price action near middle band with reduced volatility
Wait for decisive breakout beyond channel range with expanding width
Enter in breakout direction after confirmation
Breakout Confirmation:
Price breaks clearly outside channel (upper for longs, lower for shorts), channel width begins expanding from contracted state
Volume increases significantly on breakout (if using volume analysis)
Price sustains outside channel for multiple bars without immediate reversal
Entry Approaches:
Aggressive: Enter on initial break with stop at opposite channel or basis, use smaller position size
Conservative: Wait for pullback to broken channel level, enter on rejection and resumption, tighter stop
Volatility-Based Position Sizing:
Adjust position sizing based on channel width (ATR-based volatility):
Wide Channels (High ATR): Reduce position size as stops must be wider, calculate position size using ATR-based risk calculation: Risk / (Stop Distance in ATR × ATR Value)
Narrow Channels (Low ATR): Increase position size as stops can be tighter, be cautious of impending volatility expansion
ATR-Based Risk Management: Use ATR-based risk calculations, position size = 0.01 × Capital / (2 × ATR), use multiples of ATR (1-2 ATR) for adaptive stops
Algorithm Selection Guidelines:
Different market conditions benefit from different algorithm combinations:
Strong Trending Markets: Middle band use EMA or HMA, ATR use RMA, capture trends quickly while maintaining stable channel width
Choppy/Ranging Markets: Middle band use SMA or WMA, ATR use SMA or WMA, avoid false trend signals while identifying genuine reversals
Volatile Markets: Middle band and ATR both use KAMA or FRAMA, self-adjusting to changing market conditions reduces manual optimization
Breakout Trading: Middle band use SMA, ATR use EMA or SMA, stable trend with dynamic channels highlights volatility expansion early
Scalping/Day Trading: Middle band use HMA or T3, ATR use EMA or TEMA, both components respond quickly
Position Trading: Middle band use EMA/TEMA/T3, ATR use RMA or TEMA, filter out noise for long-term trend-following
📋 DETAILED PARAMETER CONFIGURATION
Understanding and optimizing parameters is essential for adapting Keltner Channel Enhanced to specific trading approaches.
Source Parameter:
Close (Most Common): Uses closing price, reflects daily settlement, best for end-of-day analysis and position trading, standard choice
HL2 (Median Price): Smooths out closing bias, better represents full daily range in volatile markets, good for swing trading
HLC3 (Typical Price): Gives more weight to close while including full range, popular for intraday applications, slightly more responsive than HL2
OHLC4 (Average Price): Most comprehensive price representation, smoothest option, good for gap-prone markets or highly volatile instruments
Length Parameter:
Controls the lookback period for middle band (basis) calculation:
Short Periods (10-15): Very responsive to price changes, suitable for day trading and scalping, higher false signal rate
Standard Period (20 - Default): Represents approximately one month of trading, good balance between responsiveness and stability, suitable for swing and position trading
Medium Periods (30-50): Smoother trend identification, fewer false signals, better for position trading and longer holding periods
Long Periods (50+): Very smooth, identifies major trends only, minimal false signals but significant lag, suitable for long-term investment
Optimization by Timeframe: 1-15 minute charts use 10-20 period, 30-60 minute charts use 20-30 period, 4-hour to daily charts use 20-40 period, weekly charts use 20-30 weeks.
ATR Length Parameter:
Controls the lookback period for Average True Range calculation, affecting channel width:
Short ATR Periods (5-10): Very responsive to recent volatility changes, standard is 10 (Keltner's original specification), may be too reactive in whipsaw conditions
Standard ATR Period (10 - Default): Chester Keltner's original specification, good balance between responsiveness and stability, most widely used
Medium ATR Periods (14-20): Smoother channel width, ATR 14 aligns with Wilder's original ATR specification, good for position trading
Long ATR Periods (20+): Very smooth channel width, suitable for long-term trend-following
Length vs. ATR Length Relationship: Equal values (20/20) provide balanced responsiveness, longer ATR (20/14) gives more stable channel width, shorter ATR (20/10) is standard configuration, much shorter ATR (20/5) creates very dynamic channels.
Multiplier Parameter:
Controls channel width by setting ATR multiples:
Lower Values (1.0-1.5): Tighter channels with frequent price touches, more trading signals, higher false signal rate, better for range-bound and mean-reversion strategies
Standard Value (2.0 - Default): Chester Keltner's recommended setting, good balance between signal frequency and reliability, suitable for both trending and ranging strategies
Higher Values (2.5-3.0): Wider channels with less frequent touches, fewer but potentially higher-quality signals, better for strong trending markets
Market-Specific Optimization: High volatility markets (crypto, small-caps) use 2.5-3.0 multiplier, medium volatility markets (major forex, large-caps) use 2.0 multiplier, low volatility markets (bonds, utilities) use 1.5-2.0 multiplier.
MA Type Parameter (Middle Band):
Critical selection that determines trend identification characteristics:
EMA (Exponential Moving Average - Default): Standard Keltner Channel choice, Chester Keltner's original specification, emphasizes recent prices, faster response to trend changes, suitable for all timeframes
SMA (Simple Moving Average): Equal weighting of all data points, no directional bias, slower than EMA, better for ranging markets and mean-reversion
HMA (Hull Moving Average): Minimal lag with smooth output, excellent for fast trend identification, best for day trading and scalping
TEMA (Triple Exponential Moving Average): Advanced smoothing with reduced lag, responsive to trends while filtering noise, suitable for volatile markets
T3 (Tillson T3): Very smooth with minimal lag, excellent for established trend identification, suitable for position trading
KAMA (Kaufman Adaptive Moving Average): Automatically adjusts speed based on market efficiency, slow in ranging markets, fast in trends, suitable for markets with varying conditions
ATR MA Type Parameter:
Determines how Average True Range is smoothed, affecting channel width stability:
RMA (Wilder's Smoothing - Default): J. Welles Wilder's original ATR smoothing method, very smooth, slow to adapt to volatility changes, provides stable channel width
SMA (Simple Moving Average): Equal weighting, moderate smoothness, faster response to volatility changes than RMA, more dynamic channel width
EMA (Exponential Moving Average): Emphasizes recent volatility, quick adaptation to new volatility regimes, very responsive channel width changes
TEMA (Triple Exponential Moving Average): Smooth yet responsive, good balance for varying volatility, suitable for most trading styles
Parameter Combination Strategies:
Conservative Trend-Following: Length 30/ATR Length 20/Multiplier 2.5, MA Type EMA or TEMA/ATR MA Type RMA, smooth trend with stable wide channels, suitable for position trading
Standard Balanced Approach: Length 20/ATR Length 10/Multiplier 2.0, MA Type EMA/ATR MA Type RMA, classic Keltner Channel configuration, suitable for general purpose swing trading
Aggressive Day Trading: Length 10-15/ATR Length 5-7/Multiplier 1.5-2.0, MA Type HMA or EMA/ATR MA Type EMA or SMA, fast trend with dynamic channels, suitable for scalping and day trading
Breakout Specialist: Length 20-30/ATR Length 5-10/Multiplier 2.0, MA Type SMA or WMA/ATR MA Type EMA or SMA, stable trend with responsive channel width
Adaptive All-Conditions: Length 20/ATR Length 10/Multiplier 2.0, MA Type KAMA or FRAMA/ATR MA Type KAMA or TEMA, self-adjusting to market conditions
Offset Parameter:
Controls horizontal positioning of channels on chart. Positive values shift channels to the right (future) for visual projection, negative values shift left (past) for historical analysis, zero (default) aligns with current price bars for real-time signal analysis. Offset affects only visual display, not alert conditions or actual calculations.
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Keltner Channel Enhanced provides improvements over standard implementations while maintaining proven effectiveness.
Response Characteristics:
Standard EMA/RMA Configuration: Moderate trend lag (approximately 0.4 × length periods), smooth and stable channel width from RMA smoothing, good balance for most market conditions
Fast HMA/EMA Configuration: Approximately 60% reduction in trend lag compared to EMA, responsive channel width from EMA ATR smoothing, suitable for quick trend changes and breakouts
Adaptive KAMA/KAMA Configuration: Variable lag based on market efficiency, automatic adjustment to trending vs. ranging conditions, self-optimizing behavior reduces manual intervention
Comparison with Traditional Keltner Channels:
Enhanced Version Advantages:
Dual Algorithm Flexibility: Independent MA selection for trend and volatility vs. fixed EMA/RMA, separate tuning of trend responsiveness and channel stability
Market Adaptation: Choose configurations optimized for specific instruments and conditions, customize for scalping, swing, or position trading preferences
Comprehensive Alerts: Enhanced alert system including channel expansion detection
Traditional Version Advantages:
Simplicity: Fewer parameters, easier to understand and implement
Standardization: Fixed EMA/RMA combination ensures consistency across users
Research Base: Decades of backtesting and research on standard configuration
When to Use Enhanced Version: Trading multiple instruments with different characteristics, switching between trending and ranging markets, employing different strategies, algorithm-based trading systems requiring customization, seeking optimization for specific trading style and timeframe.
When to Use Standard Version: Beginning traders learning Keltner Channel concepts, following published research or trading systems, preferring simplicity and standardization, wanting to avoid optimization and curve-fitting risks.
Performance Across Market Conditions:
Strong Trending Markets: EMA or HMA basis with RMA or TEMA ATR smoothing provides quicker trend identification, pullbacks to basis offer excellent entry opportunities
Choppy/Ranging Markets: SMA or WMA basis with RMA ATR smoothing and lower multipliers, channel bounce strategies work well, avoid false breakouts
Volatile Markets: KAMA or FRAMA with EMA or TEMA, adaptive algorithms excel by automatic adjustment, wider multipliers (2.5-3.0) accommodate large price swings
Low Volatility/Consolidation: Channels narrow significantly indicating consolidation, algorithm choice less impactful, focus on detecting channel width contraction for breakout preparation
Keltner Channel vs. Bollinger Bands - Usage Comparison:
Favor Keltner Channels When: Trend-following is primary strategy, trading volatile instruments with gaps, want ATR-based volatility measurement, prefer fewer higher-quality channel touches, seeking stable channel width during trends.
Favor Bollinger Bands When: Mean-reversion is primary strategy, trading instruments with limited gaps, want statistical framework based on standard deviation, need squeeze patterns for breakout identification, prefer more frequent trading opportunities.
Use Both Together: Bollinger Band squeeze + Keltner Channel breakout is powerful combination, price outside Bollinger Bands but inside Keltner Channels indicates moderate signal, price outside both indicates very strong signal, Bollinger Bands for entries and Keltner Channels for trend confirmation.
Limitations and Considerations:
General Limitations:
Lagging Indicator: All moving averages lag price, even with reduced-lag algorithms
Trend-Dependent: Works best in trending markets, less effective in choppy conditions
No Direction Prediction: Indicates volatility and deviation, not future direction, requires confirmation
Enhanced Version Specific Considerations:
Optimization Risk: More parameters increase risk of curve-fitting historical data
Complexity: Additional choices may overwhelm beginning traders
Backtesting Challenges: Different algorithms produce different historical results
Mitigation Strategies:
Use Confirmation: Combine with momentum indicators (RSI, MACD), volume, or price action
Test Parameter Robustness: Ensure parameters work across range of values, not just optimized ones
Multi-Timeframe Analysis: Confirm signals across different timeframes
Proper Risk Management: Use appropriate position sizing and stops
Start Simple: Begin with standard EMA/RMA before exploring alternatives
Optimal Usage Recommendations:
For Maximum Effectiveness:
Start with standard EMA/RMA configuration to understand classic behavior
Experiment with alternatives on demo account or paper trading
Match algorithm combination to market condition and trading style
Use channel width analysis to identify market phases
Combine with complementary indicators for confirmation
Implement strict risk management using ATR-based position sizing
Focus on high-quality setups rather than trading every signal
Respect the trend: trade with basis direction for higher probability
Complementary Indicators:
RSI or Stochastic: Confirm momentum at channel extremes
MACD: Confirm trend direction and momentum shifts
Volume: Validate breakouts and trend strength
ADX: Measure trend strength, avoid Keltner signals in weak trends
Support/Resistance: Combine with traditional levels for high-probability setups
Bollinger Bands: Use together for enhanced breakout and volatility analysis
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. Keltner Channel Enhanced has limitations and should not be used as the sole basis for trading decisions. While the flexible moving average selection for both trend and volatility components provides valuable adaptability across different market conditions, algorithm performance varies with market conditions, and past characteristics do not guarantee future results.
Key considerations:
Always use multiple forms of analysis and confirmation before entering trades
Backtest any parameter combination thoroughly before live trading
Be aware that optimization can lead to curve-fitting if not done carefully
Start with standard EMA/RMA settings and adjust only when specific conditions warrant
Understand that no moving average algorithm can eliminate lag entirely
Consider market regime (trending, ranging, volatile) when selecting parameters
Use ATR-based position sizing and risk management on every trade
Keltner Channels work best in trending markets, less effective in choppy conditions
Respect the trend direction indicated by price position relative to basis line
The enhanced flexibility of dual algorithm selection provides powerful tools for adaptation but requires responsible use, thorough understanding of how different algorithms behave under various market conditions, and disciplined risk management.
Crypto Mean Reversion System (Pullback & Bounce)Mean Reversion Theory
The indicator operates on the principle that extreme price movements in crypto markets tend to revert toward their mean over time.
Consider this a valuable aid for your dollar-cost averaging strategy, effectively identifying periods ripe for accumulating or divesting from the market.
Research shows that:
Short-term momentum often persists briefly after surges, but extreme moves trigger mean reversion
Sharp drops exhibit strong bounce patterns, especially after capitulation events
Longer timeframes (7-day) show stronger mean reversion tendencies than shorter ones (1-day)
Timeframe Analysis
1-Day Timeframe
Pullback probabilities: 45-85% depending on surge magnitude
Bounce probabilities: 55-95% depending on drop severity
Captures immediate overextension and panic selling
More volatile but faster signal generation
7-Day Timeframe
Pullback probabilities: 50-90% (higher confidence)
Bounce probabilities: 50-90% (slightly moderated)
Filters out noise and identifies sustained trends
Stronger mean reversion signals due to extended moves
Probability Tiers
Pullback Risk (After Surges)
Moderate (45-60%): 5-10% surge → Expected -3% to -12% pullback
High (55-70%): 10-15% surge → Expected -5% to -18% pullback
Very High (65-80%): 15-25% surge → Expected -10% to -25% pullback
Extreme (75-90%): 25%+ surge → Expected -15% to -40% pullback
Bounce Probability (After Drops)
Moderate (55-65%): -5% to -10% drop → Expected +3% to +10% bounce
High (65-75%): -10% to -15% drop → Expected +6% to +18% bounce
Very High (75-85%): -15% to -25% drop → Expected +10% to +30% bounce
Extreme (85-95%): -25%+ drop → Expected +18% to +45% bounce
The probability ranges are derived from:
Crypto volatility patterns: Higher volatility than traditional assets creates stronger mean reversion
Behavioral finance: Extreme moves trigger emotional trading (FOMO/panic) that reverses
Historical backtesting: Probability estimates based on typical reversion patterns in crypto markets
Timeframe correlation: Longer timeframes show increased reversion probability due to reduced noise
Key Features
Dual-direction signals: Identifies both overbought (pullback) and oversold (bounce) conditions
Multi-timeframe confirmation: 1D and 7D analysis for different trading styles
Customizable thresholds: Adjust sensitivity based on asset volatility
Visual alerts: Color-coded labels and table for quick assessment
Risk categorization: Clear severity levels for position sizing
LA - MACD EMA BandsOverview of the "LA - MACD EMA Bands" Indicator
For Better view, use this indicator along with "LA - EMA Bands with MTF Dashboard"
The "LA - MACD EMA Bands" is a custom technical indicator written in Pine Script v6 for TradingView. It builds on the traditional Moving Average Convergence Divergence (MACD) oscillator by incorporating additional smoothing via Exponential Moving Averages (EMAs) and Bollinger Bands (BB) applied directly to the MACD line. This creates a multi-layered momentum and volatility tool displayed in a separate pane below the price chart (not overlaid on the price itself).
The indicator allows for customization, such as selecting a different timeframe (for multi-timeframe analysis) and adjusting period lengths. It fetches data from the specified timeframe using request.security with lookahead enabled to avoid repainting issues. The core idea is to provide insights into momentum trends, crossovers, and volatility expansions/contractions in the MACD's behavior, making it suitable for identifying potential trend reversals, continuations, or ranging markets.
Unlike a standard MACD, which focuses primarily on momentum via a single line, signal line, and histogram, this version emphasizes longer-term smoothing and volatility boundaries. It uses visual fills between lines to highlight bullish/bearish conditions, aiding quick interpretation. Below, I'll break down each component, its calculation, visual representation, and practical uses.
Detailed Breakdown of Each Component and Its Uses
MACD Line (Blue Line, Labeled 'MACD Line')
Calculation: This is the core MACD value, computed as the difference between a fast EMA (default length 12) and a slow EMA (default length 144) of the input source (default: close price). The EMAs are calculated on data from the selected timeframe.
Visuals: Plotted as a solid blue line.
Uses:
Measures momentum: When above zero, it indicates bullish momentum (prices rising faster in the short term); below zero, bearish momentum.
Trend identification: Rising MACD suggests strengthening uptrends; falling suggests downtrends.
Divergence spotting: Compare with price action—e.g., if price makes higher highs but MACD makes lower highs, it signals potential bearish reversal (and vice versa for bullish divergence).
In trading: Often used for entry/exit signals when crossing the zero line or other lines in the indicator.
MACD EMA (Red Line, Labeled 'MACD EMA')
Calculation: A 12-period EMA applied to the MACD Line itself.
Visuals: Plotted as a solid red line.
Uses:
Acts as a signal line for the MACD, smoothing out short-term noise.
Crossover signals: When the MACD Line crosses above the MACD EMA, it can signal a bullish buy opportunity; crossing below suggests a bearish sell.
Trend confirmation: Helps filter false signals in choppy markets by requiring confirmation from this slower-moving average.
In trading: Useful for momentum-based strategies, like entering trades on crossovers in alignment with the overall trend.
Fill Between MACD Line and MACD EMA (Green/Red Shaded Area, Titled 'MACD Fill')
Calculation: The area between the MACD Line and MACD EMA is filled with color based on their relative positions.
Color Logic: Green (with 57% transparency) if MACD Line > MACD EMA (bullish); red if MACD Line < MACD EMA (bearish).
Visuals: Semi-transparent fill for easy visibility without overwhelming the lines.
Uses:
Quick visual cue for momentum shifts: Green areas highlight bullish phases; red for bearish.
Enhances readability: Makes crossovers more apparent at a glance, especially in fast-moving markets.
In trading: Can be used to time entries/exits or as a filter (e.g., only take long trades in green zones).
Bollinger Bands on MACD (BB Upper: Black Dotted, BB Basis: Maroon Dotted, BB Lower: Black Dotted)
Calculation: Bollinger Bands applied to the MACD Line.
BB Basis: 144-period EMA of the MACD Line.
BB Standard Deviation: 144-period stdev of the MACD Line.
BB Upper: BB Basis + (2.0 * BB Stdev)
BB Lower: BB Basis - (2.0 * BB Stdev)
Visuals: Upper and lower bands as black dotted lines; basis as maroon dotted
Uses:
Volatility measurement: Bands expand during high momentum volatility (strong trends) and contract during low volatility (ranging or consolidation).
Mean reversion: When MACD Line touches or exceeds the upper band, it may signal overbought conditions (potential sell); lower band for oversold (potential buy).
Squeeze detection: Narrow bands (squeeze) often precede big moves—watch for breakouts.
In trading: Combines momentum with volatility; e.g., a MACD Line breakout above the upper band could confirm a strong uptrend.
BB Basis EMA (Green Line, Labeled 'BB Basis EMA')
Calculation: A 72-period EMA applied to the BB Basis (which is already a 144-period EMA of the MACD Line).
Visuals: Solid green line.
Uses:
Further smoothing: Provides a longer-term view of the MACD's average behavior, reducing noise from the BB Basis.
Trend direction: Acts as a baseline for the BB system—above it suggests bullish bias in momentum volatility; below, bearish.
Crossover with BB Basis: Can signal shifts in volatility trends (e.g., BB Basis crossing above BB Basis EMA indicates increasing bullish volatility).
In trading: Useful for confirming longer-term trends or as a filter for BB-based signals.
Fill Between BB Basis and BB Basis EMA (Gray Shaded Area, Titled 'BB Basis Fill')
Calculation: The area between BB Basis and BB Basis EMA is filled.
Color Logic: Currently set to a constant semi-transparent gray regardless of position.
Visuals: Semi-transparent gray fill.
Uses:
Highlights divergence: Shows when the shorter-term BB Basis deviates from its longer-term EMA, indicating potential volatility shifts.
Visual aid for crossovers: Makes it easier to spot when BB Basis crosses its EMA.
In trading: Could be used to identify overextensions in volatility (e.g., wide gray areas might signal impending mean reversion).
Zero Line (Black Horizontal Line)
Calculation: A simple horizontal line at y=0.
Visuals: Solid black line.
Uses:
Reference point: Divides bullish (above) from bearish (below) territory for all MACD-related lines.
In trading: Crossovers of the zero line by the MACD Line or BB Basis can signal major trend changes.
How It Differs from a Normal MACD
A standard MACD (e.g., the built-in TradingView MACD with defaults 12/26/9) consists of:
MACD Line: EMA(12) - EMA(26).
Signal Line: EMA(MACD Line, 9).
Histogram: MACD Line - Signal Line (bars showing convergence/divergence).
Key differences in "LA - MACD EMA Bands":
Periods: Uses a much longer slow EMA (144 vs. 26), making it more sensitive to long-term trends but less reactive to short-term price action. The MACD EMA is 12 periods (vs. 9), further emphasizing smoothing.
No Histogram: Replaces the histogram with fills and bands for visual emphasis on crossovers and volatility.
Added Bollinger Bands: Applies BB directly to the MACD Line (with a long 144-period basis), introducing volatility analysis absent in standard MACD. This helps detect "squeezes" or expansions in momentum.
Additional EMA Layer: The BB Basis EMA (72-period) adds a secondary smoothing level to the BB system, providing a hierarchical view of momentum (short-term MACD → mid-term BB → long-term EMA).
Multi-Timeframe Support: Built-in option for higher timeframes, unlike basic MACD.
Focus: Standard MACD is purely momentum-focused; this version integrates volatility (via BB) and multi-layer smoothing, making it better for trend-following in volatile markets but potentially overwhelming for beginners.
Overall, this indicator transforms the MACD from a simple oscillator into a comprehensive momentum-volatility hybrid, reducing false signals in trending markets but introducing lag.
Overall Pros and Cons
Pros:
Enhanced Visualization: Fills and bands make trends, crossovers, and volatility easier to spot without needing multiple indicators.
Reduced Noise: Longer periods (144, 72) smooth out whipsaws, ideal for swing or position trading in trending assets like stocks or forex.
Volatility Integration: BB adds a dimension not in standard MACD, helping identify breakouts or consolidations.
Customizable: Inputs for timeframes and lengths allow adaptation to different assets/timeframes.
Multi-Layered Insights: Combines short-term signals (MACD crossovers) with long-term confirmation (BB EMA), improving signal reliability.
Cons:
Lagging Nature: Long periods (e.g., 144) delay signals, missing early entries in fast markets or leading to late exits.
Complexity: Multiple lines and fills can clutter the pane, requiring experience to interpret; beginners might misread it.
Potential Overfitting: Custom periods (12/144/12/144/72) may work well on historical data but underperform in live trading without backtesting.
No Built-in Alerts/Signals: Relies on visual interpretation; users must manually set alerts for crossovers.
Resource Intensive: On lower timeframes or with lookahead, it might slow chart loading on Trading View.
This indicator shines in strategies combining momentum and volatility, like trend-following with BB squeezes, but test it on your assets (e.g., via backtesting) to ensure it fits your style.
For Better view, use this indicator along with "LA - EMA Bands with MTF Dashboard"
Twisted Forex's Doji + Area StrategyTitle
Twisted Forex’s Doji + Area Strategy
Description
What this strategy does
This strategy looks for doji candles forming inside or near supply/demand areas . Areas are built from swing pivots and sized with ATR, then tracked for retests (“confirmations”). When a doji prints close to an area and quality checks pass, the strategy places a trade with the stop beyond the doji and a configurable R:R target.
How areas (zones) are built
• Swings are detected with a user-set pivot length.
• Each swing spawns a horizontal area centered at the pivot price with half-height = zoneHalfATR × ATR .
• Duplicates are de-duplicated by center distance (ATR-scaled).
• Areas fade when broken beyond a buffer or after an optional age (expiry).
• Retests are recorded when price touches and then bounces away from the area; repeated reactions increase the zone’s “strength”.
Signal logic (summary)
Doji detection: strict or loose body criteria with optional minimum wick fractions and ATR-scaled minimum range.
Proximity: price must be inside/near a supply or demand area (proxATR × ATR).
Side resolution: overlap is resolved by (a) which side price penetrates more, (b) fast/slow EMA trend, or (c) nearest distance. Optional “previous candle flip” can bias long after a bearish candle and short after a bullish one.
Optional 1-bar confirmation: the bar after the doji must close away from the area by confirmATR × ATR .
Quality filter (Off/Soft/Strict): four checks—(i) wick rejection past the edge, (ii) doji closes in an edge “band” of the area, (iii) fresh touch (cooldown), (iv) approach impulse over a short lookback. In Strict , thresholds auto-tighten.
Orders & exits
• Long: stop below doji low minus buffer; Short: above doji high plus buffer.
• Target = rrMultiple × risk distance .
• Pyramiding is off by default.
Position sizing
You can size from the script or from Strategy Properties:
• Script-driven (default): set Position sizing = “Risk % of equity” and choose riskPercent (e.g., 1.0%). The script applies safe floors/rounding (FX micro-lots by default) so quantity never rounds to zero.
• Properties-driven : toggle Use TV Properties → Order size ON, then pick “Percent of equity” in Properties (e.g., 1%). The header includes safe defaults so trades still place.
Key inputs to explore
• Zone building : pivotLen, zoneHalfATR, minDepartureATR, expiryBars, breakATR, leftBars, dedupeATR.
• Doji & proximity : strictDoji, dojiBodyFrac, minWickFrac, minRangeATR, proxATR, minBarsBetween.
• Overlap resolution : usePenetration, useTrend (EMA 21/55), “previous candle flip”, needNextBarConf & confirmATR.
• Quality : qualityMode (Off/Soft/Strict), minQualPass/kStrict, wickPenATR, edgeBandFrac, approachLookback, approachMinATR, freshTouchBars.
• Zone strength gating : minStrengthSoft / minStrengthStrict.
• HTF confluence (optional) : useHTFTrend (HTF EMA 34/89) and/or useHTFZoneProx (HTF swing bands).
Tips to make it cleaner / higher quality
• Turn needNextBarConf ON and use confirmATR = 0.10–0.15 .
• Increase approachMinATR (e.g., 0.35–0.45) to require a stronger pre-touch impulse.
• Raise minStrengthSoft/Strict (e.g., 4–6) so only well-reacted zones can signal.
• Use signalsOnlyConfirmed ON if you prefer trades only from zones with retests (the script falls back gracefully when none exist yet).
• Nudge proxATR to 0.5–0.6 to demand tighter proximity to the level.
• Optional: enable useHTFTrend to filter counter-trend setups.
Default settings used in this publication
• Initial capital: 100,000 (illustrative).
• Slippage: 1 tick; Commission: 0% (you can raise commission if you prefer—spread is partly modeled by slippage).
• Sizing: Risk % of equity via inputs; riskPercent = 1.0% ; FX uses micro-lot floors by default.
• Quality: Off by default (Soft/Strict available).
• HTF trend gate: Off by default.
Backtesting notes
For a meaningful sample size, test on liquid symbols/timeframes that yield 100+ trades (e.g., majors on 5–15m over 1–2 years). Backtests are modelled and broker costs/spread vary—validate on your feed and forward-test.
How to read the chart
Shaded bands are supply (above) and demand (below). Brighter bands are the nearest K per side (visual aid). BUY/SELL labels mark entries; colored dots show entry/SL/TP levels. You can hide zones or unconfirmed zones for a cleaner view.
Disclaimer
This is educational material, not financial advice. Trading involves risk. Always test and size responsibly.
Alt buy signal 1H Entry + 4H Confirm (MACD + Stoch RSI + HMA)This indicator is a multi-timeframe (MTF) analysis tool designed for the ALT trading , capturing entry signals on the 1-hour (1H) timeframe and confirming trends on the 4-hour (4H) timeframe. It combines MACD, Stoch RSI, and Hull Moving Average (HMA) to identify precise buy opportunities, particularly at reversal points after a downtrend or during trend shifts. It visually marks both past and current BUY signals for easy reference.
Key Features:
1H Entry Signal (Early Ping): Triggers on a MACD golden cross (below 0) combined with a Stoch RSI oversold cross (below 20), offering an initial buy opportunity.
4H Trend Confirmation (Entry Ready): Validates the trend with a 4H MACD histogram rising (in negative territory) or a golden cross, plus a Stoch RSI turn-up (above 30).
Past BUY Display: Labels past data points where these conditions were met as "1H BUY" or "FULL BUY," facilitating backtesting.
HMA Filter: Optional HMA(16) to confirm price breakouts, enhancing trend validation.
Purpose: Ideal for short-term scalping and swing trading. Supports a two-step strategy: initial partial entry on 1H signals, followed by additional entry on 4H confirmation.
Usage Instructions
Installation: Add the indicator to an IMX/USDT 1H chart on TradingView.
Signal Interpretation:
lime "1H BUY": 1H conditions met, consider initial entry (stop-loss: 3-5% below recent low).
green "FULL BUY": 1H+4H conditions met, confirm trend for additional entry (take-profit: 10% below recent swing high).
Customization: Adjust TF (1H/4H), MACD/Stoch RSI parameters, and HMA usage via the input settings.
Alert Setup: Enable alerts for "ENTRY READY" (1H+4H) or "EARLY PING" (1H only) conditions.
Advantages
Accuracy: Reduces false signals by combining MACD golden cross below 0 with Stoch RSI oversold conditions.
Dual Confirmation: 1H for quick timing and 4H for trend validation, improving risk management.
Visualization: Past BUY points enable easy backtesting and pattern recognition.
Flexibility: 4H confirmation mode adjustable (histogram rise or golden cross).
Limitations
Timeframe Dependency: Optimized for 1H charts; may not work on other timeframes.
Market Conditions: Potential whipsaws in sideways markets; additional filters (e.g., RSI > 50) recommended.
Manual Management: Stop-loss and take-profit require user discretion.
Basic Odds Enhancer: Supply Zone for ShortsHow to Use/Adjust:
On your chart, it marks bars where a 20-bar high coincides with high volume and bearish divergence—flag these as supply zones.
Tweak supply_threshold to 2.0 for stricter volume (fewer but stronger signals).
For zones, manually draw rectangles around the flagged area (use Drawing Tools > Rectangle).
Backtest: Apply to historical data (e.g., EUR/USD 4H) and check win rate with shorts on retests.
This setup typically yields 2-5 signals per week on major pairs, depending on volatility. Test on a demo account, and combine with market context (e.g., avoid shorts in strong uptrends).
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
References
Ang, A. (2014) *Asset Management: A Systematic Approach to Factor Investing*. Oxford: Oxford University Press.
Ang, A., Piazzesi, M. and Wei, M. (2006) 'What does the yield curve tell us about GDP growth?', *Journal of Econometrics*, 131(1-2), pp. 359-403.
Asness, C.S. (2003) 'Fight the Fed Model', *The Journal of Portfolio Management*, 30(1), pp. 11-24.
Asness, C.S., Moskowitz, T.J. and Pedersen, L.H. (2013) 'Value and Momentum Everywhere', *The Journal of Finance*, 68(3), pp. 929-985.
Baker, M. and Wurgler, J. (2006) 'Investor Sentiment and the Cross-Section of Stock Returns', *The Journal of Finance*, 61(4), pp. 1645-1680.
Baker, M. and Wurgler, J. (2007) 'Investor Sentiment in the Stock Market', *Journal of Economic Perspectives*, 21(2), pp. 129-152.
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Sharpe, W.F. (1994) 'The Sharpe Ratio', *The Journal of Portfolio Management*, 21(1), pp. 49-58.
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Order Block Volumatic FVG StrategyInspired by: Volumatic Fair Value Gaps —
License: CC BY-NC-SA 4.0 (Creative Commons Attribution–NonCommercial–ShareAlike).
This script is a non-commercial derivative work that credits the original author and keeps the same license.
What this strategy does
This turns BigBeluga’s visual FVG concept into an entry/exit strategy. It scans bullish and bearish FVG boxes, measures how deep price has mitigated into a box (as a percentage), and opens a long/short when your mitigation threshold and filters are satisfied. Risk is managed with a fixed Stop Loss % and a Trailing Stop that activates only after a user-defined profit trigger.
Additions vs. the original indicator
✅ Strategy entries based on % mitigation into FVGs (long/short).
✅ Lower-TF volume split using upticks/downticks; fallback if LTF data is missing (distributes prior bar volume by close’s position in its H–L range) to avoid NaN/0.
✅ Per-FVG total volume filter (min/max) so you can skip weak boxes.
✅ Age filter (min bars since the FVG was created) to avoid fresh/immature boxes.
✅ Bull% / Bear% share filter (the 46%/53% numbers you see inside each FVG).
✅ Optional candle confirmation and cooldown between trades.
✅ Risk management: fixed SL % + Trailing Stop with a profit trigger (doesn’t trail until your trigger is reached).
✅ Pine v6 safety: no unsupported args, no indexof/clamp/when, reverse-index deletes, guards against zero/NaN.
How a trade is decided (logic overview)
Detect FVGs (same rules as the original visual logic).
For each FVG currently intersected by the bar, compute:
Mitigation % (how deep price has entered the box).
Bull%/Bear% split (internal volume share).
Total volume (printed on the box) from LTF aggregation or fallback.
Age (bars) since the box was created.
Apply your filters:
Mitigation ≥ Long/Short threshold.
Volume between your min and max (if enabled).
Age ≥ min bars (if enabled).
Bull% / Bear% within your limits (if enabled).
(Optional) the current candle must be in trade direction (confirm).
If multiple FVGs qualify on the same bar, the strategy uses the most recent one.
Enter long/short (no pyramiding).
Exit with:
Fixed Stop Loss %, and
Trailing Stop that only starts after price reaches your profit trigger %.
Input settings (quick guide)
Mitigation source: close or high/low. Use high/low for intrabar touches; close is stricter.
Mitigation % thresholds: minimal mitigation for Long and Short.
TOTAL Volume filter: skip FVGs with too little/too much total volume (per box).
Bull/Bear share filter: require, e.g., Long only if Bull% ≥ 50; avoid Short when Bull% is high (Short Bull% max).
Age filter (bars): e.g., ≥ 20–30 bars to avoid fresh boxes.
Confirm candle: require candle direction to match the trade.
Cooldown (bars): minimum bars between entries.
Risk:
Stop Loss % (fixed from entry price).
Activate trailing at +% profit (the trigger).
Trailing distance % (the trailing gap once active).
Lower-TF aggregation:
Auto: TF/Divisor → picks 1/3/5m automatically.
Fixed: choose 1/3/5/15m explicitly.
If LTF can’t be fetched, fallback allocates prior bar’s volume by its close position in the bar’s H–L.
Suggested starting presets (you should optimize per market)
Mitigation: 60–80% for both Long/Short.
Bull/Bear share:
Long: Bull% ≥ 50–70, Bear% ≤ 100.
Short: Bull% ≤ 60 (avoid shorting into strong support), Bear% ≥ 0–70 as you prefer.
Age: ≥ 20–30 bars.
Volume: pick a min that filters noise for your symbol/timeframe.
Risk: SL 4–6%, trailing trigger 1–2%, distance 1–2% (crypto example).
Set slippage/fees in Strategy Properties.
Notes, limitations & best practices
Data differences: The LTF split uses request.security_lower_tf. If the exchange/data feed has sparse LTF data, the fallback kicks in (it’s deliberate to avoid NaNs but is a heuristic).
Real-time vs backtest: The current bar can update until close; results on historical bars use closed data. Use “Bar Replay” to understand intrabar effects.
No pyramiding: Only one position at a time. Modify pyramiding in the header if you need scaling.
Assets: For spot/crypto, TradingView “volume” is exchange volume; in some markets it may be tick volume—interpret filters accordingly.
Risk disclosure: Past performance ≠ future results. Use appropriate position sizing and risk controls; this is not financial advice.
Credits
Visual FVG concept and original implementation: BigBeluga.
This derivative strategy adds entry/exit logic, volume/age/share filters, robust LTF handling, and risk management while preserving the original spirit.
License remains CC BY-NC-SA 4.0 (non-commercial, attribution required, share-alike).
Seasonality con números RAMÓN SEGOVIAMonthly Bands – Colored Monthly Stripes for Statistical Analysis
Short Description
This indicator paints vertical background stripes by calendar month on your chart, making it easy to run statistical/seasonality analysis, compare monthly performance, and visually identify recurring patterns across assets and timeframes.
How It Works
Detects each new month and applies a background band spanning from the first to the last candle of that month.
Alternates colors automatically so consecutive months are easy to distinguish, or use a single uniform color for a clean look.
Optional: add dotted lines at the start/end of each month for precise separation.
Inputs / Settings
Color mode: alternating (odd/even months) or single.
Colors & opacity of the bands.
Border style: none / solid / dotted.
Highlight specific months: e.g., “Jan, Apr, Oct” with a different color.
Labels option: show month & year abbreviations at the top/bottom of the chart.
Drawing zone: full background vs. price-only area (to avoid covering lower indicators).
Typical Use Cases
Seasonality studies: identify historically bullish/bearish months.
Visual backtesting: segment the chart by months to evaluate strategy performance.
Context tracking: quickly locate reports, monthly closes, or economic cycles.
Compatibility
Works on all timeframes, including intraday (each band covers the full calendar month).
Lightweight and visual-only; doesn’t interfere with price or indicators.
Pro Tips
Combine with monthly returns (%) or candle counters to quantify each stripe.
Use labels when preparing clean presentations or trade journal screenshots.
Notes
This is a visual tool only, not a buy/sell signal generator.
Default settings are optimized for clarity and minimal clutter.
EMA Crossover Cloud w/Range-Bound FilterA focused 1-minute EMA crossover trading strategy designed to identify high-probability momentum trades while filtering out low-volatility consolidation periods that typically result in whipsaw losses. Features intelligent range-bound detection and progressive market attention alerts to help traders manage focus and avoid overtrading during unfavorable conditions.
Key Features:
EMA Crossover Signals: 10/20 EMA crossovers with volume surge confirmation (1.3x 20-bar average)
Range-Bound Filter: Automatically detects when price is consolidating in tight ranges (0.5% threshold) and blocks trading signals during these periods
Progressive Consolidation Stages: Visual alerts progress through Range Bound (red) → Coiling (yellow) → Loading (orange) → Trending (green) to indicate market compression and potential breakout timing
Market Attention Gauge: Helps manage focus between active trading and other activities with states: Active (watch close), Building (check frequently), Quiet (check occasionally), Dead (handle other business)
Smart RSI Exits: Cloud-based and RSI extreme level exits with conservative stop losses
Dual Mode Operation: Separate settings allow full backtesting performance while providing visual stay-out warnings for manual trading
How to Use:
Entry Signals: Trade aqua up-triangles (long) and orange down-triangles (short) when they appear with volume confirmation
Stay-Out Warnings: Ignore gray "RANGE" triangles - these indicate crossovers during range-bound periods that should be avoided
Monitor Top-Right Display:
Range: Current 60-bar dollar range
Attention: Market activity level for focus management
Status: Consolidation stage (trade green/yellow, avoid red, prepare for orange)
Position Sizing: Default 167 shares per signal, optimized for the crossover frequency
Alerts: Enable consolidation stage alerts and market attention alerts for automated notifications
Recommended Settings:
Timeframe: 1-minute charts
Symbol: Optimized for volatile stocks like TSLA
"Apply Filter to Backtest": Keep OFF for realistic backtesting, ON to see filtered results
Risk Management:
The strategy includes built-in overtrading protection by identifying and blocking trades during low-volatility periods. The progressive consolidation alerts help identify when markets are "loading" for significant moves, allowing traders to position appropriately for higher-probability setups.
Extremum Range MA Crossover Strategy1. Principle of Work & Strategy Logic ⚙️📈
Main idea: The strategy tries to catch the moment of a breakout from a price consolidation range (flat) and the start of a new trend. It combines two key elements:
Moving Average (MA) 📉: Acts as a dynamic support/resistance level and trend filter.
Range Extremes (Range High/Low) 🔺🔻: Define the borders of the recent price channel or consolidation.
The strategy does not attempt to catch absolute tops and bottoms. Instead, it enters an already formed move after the breakout, expecting continuation.
Type: Trend-following, momentum-based.
Timeframes: Works on different TFs (H1, H4, D), but best suited for H4 and higher, where breakouts are more meaningful.
2. Justification of Indicators & Settings ⚙️
A. Moving Average (MA) 📊
Why used: Core of the strategy. It smooths price fluctuations and helps define the trend. The price (via extremes) must cross the MA → signals a potential trend shift or strengthening.
Parameters:
maLength = 20: Default length (≈ one trading month, 20-21 days). Good balance between sensitivity & smoothing.
Lower TF → reduce (10–14).
Higher TF → increase (50).
maSource: Defines price source (default = Close). Alternatives (HL2, HLC3) → smoother, less noisy MA.
maType: Default = EMA (Exponential MA).
Why EMA? Faster reaction to recent price changes vs SMA → useful for breakout strategies.
Other options:
SMA 🟦 – classic, slowest.
WMA 🟨 – weights recent data stronger.
HMA 🟩 – near-zero lag, but “nervous,” more false signals.
DEMA/TEMA 🟧 – even faster & more sensitive than EMA.
VWMA 🔊 – volume-weighted.
ZLEMA ⏱ – reduced lag.
👉 Choice = tradeoff between speed of reaction & false signals.
B. Range Extremes (Previous High/Low) 📏
Why used: Define borders of recent trading range.
prevHigh = local resistance.
prevLow = local support.
Break of these levels on close = trigger.
Parameters:
lookbackPeriod = 5: Searches for highest high / lowest low of last 5 candles. Very recent range.
Higher value (10–20) → wider, stronger ranges but rarer signals.
3. Entry & Exit Rules 🎯
Long signals (BUY) 🟢📈
Condition (longCondition): Previous Low crosses MA from below upwards.
→ Price bounced from the bottom & strong enough to push range border above MA.
Execution: Auto-close short (if any) → open long.
Short signals (SELL) 🔴📉
Condition (shortCondition): Previous High crosses MA from above downwards.
→ Price rejected from the top, upper border failed above MA.
Execution: Auto-close long (if any) → open short.
Exit conditions 🚪
Exit Long (exitLongCondition): Close below prevLow.
→ Uptrend likely ended, range shifts down.
Exit Short (exitShortCondition): Close above prevHigh.
→ Downtrend likely ended, range shifts up.
⚠️ Important: Exit = only on candle close beyond extremes (not just wick).
4. Trading Settings ⚒️
overlay = true → indicators shown on chart.
initial_capital = 10000 💵.
default_qty_type = strategy.cash, default_qty_value = 100 → trades fixed $100 per order (not lots). Can switch to % of equity.
commission_type = strategy.commission.percent, commission_value = 0.1 → default broker fee = 0.1%. Adjust for your broker!
slippage = 3 → slippage = 3 ticks. Adjust to asset liquidity.
currency = USD.
margin_long = 100, margin_short = 100 → no leverage (100% margin).
5. Visualization on Chart 📊
The strategy draws 3 lines:
🔵 MA line (thickness 2).
🔴 Previous High (last N candles).
🟢 Previous Low (last N candles).
Also: entry/exit arrows & equity curve shown in backtest.
Disclaimer ⚠️📌
Risk Warning: This description & code are for educational purposes only. Not financial advice. Trading (Forex, Stocks, Crypto) carries high risk and may lead to full capital loss. You trade at your own risk.
Testing: Always backtest & demo test first. Past results ≠ future profits.
Responsibility: Author of this strategy & description is not responsible for your trading decisions or losses.
Hilly's Reversal Scalping Strategy - 5 Min CandlesHow to Use
Copy the Code: Copy the script above.
Paste in TradingView: Open TradingView, go to the Pine Editor (bottom of the chart), paste the code, and click “Add to Chart.”
Set Timeframe: Ensure the chart is set to 5-minute candles (TradingView: right-click chart > Timeframe > 5 Minutes).
Check for Errors: Verify no errors appear in the Pine Editor console.
Apply to Chart: Use a liquid crypto pair (e.g., BTC/USDT, ETH/USDT on Binance or Coinbase).
Verify Signals:
Green “BUY” labels and triangle-up arrows for bullish reversals (e.g., bullish engulfing, hammer, doji, morning star, three white soldiers, double bottom in a downtrend).
Red “SELL” labels and triangle-down arrows for bearish reversals (e.g., bearish engulfing, shooting star, doji, evening star, three black crows, double top in an uptrend).
Green/red background highlights for signal candles.
Backtest: Use TradingView’s Strategy Tester to evaluate performance over 1–3 months, checking Net Profit, Win Rate, and Drawdown.
Demo Test: Run on a demo account to confirm signal visibility and performance before trading with real funds.
Troubleshooting
If Errors Occur: If any errors appear in TradingView’s Pine Editor console (e.g., “Syntax error” or “Invalid argument”), please share the exact error messages to diagnose environment-specific issues.
Signal Overload: If too many signals appear, increase patternLookback to 15 or set volFilter = volume > volMa * 2.0.
Missed Signals: If signals are too rare, set useVolumeFilter=false or reduce patternLookback to 5.
Additional Features: If you need alerts, other indicators (e.g., EMA, RSI), or dynamic arrow sizing, please specify. Note that dynamic sizing caused errors previously, so I’ve kept size=size.normal.
Hilly 3.0 Advanced Crypto Scalping Strategy - 1 & 5 Min ChartsHow to Use
Copy the Code: Copy the script above.
Paste in TradingView: Open TradingView, go to the Pine Editor (bottom of the chart), paste the code, and click “Add to Chart.”
Check for Errors: Verify no errors appear in the Pine Editor console. The script uses Pine Script v5 (@version=5).
Select Timeframe:
1-Minute Chart: Use defaults (emaFastLen=7, emaSlowLen=14, rsiLen=10, rsiOverbought=80, rsiOversold=20, slPerc=0.5, tpPerc=1.0, useCandlePatterns=false, patternLookback=10).
5-Minute Chart: Adjust to emaFastLen=9, emaSlowLen=21, rsiLen=14, rsiOverbought=75, rsiOversold=25, slPerc=0.8, tpPerc=1.5, useCandlePatterns=true, patternLookback=10.
Apply to Chart: Use a liquid crypto pair (e.g., BTC/USDT, ETH/USDT on Binance or Coinbase).
Verify Signals:
Green “BUY” or “EMA BUY” labels and triangle-up arrows below candles for bullish signals (EMA crossovers, bullish engulfing, hammer, doji, morning star, three white soldiers, double bottom).
Red “SELL” or “EMA SELL” labels and triangle-down arrows above candles for bearish signals (EMA crossovers, bearish engulfing, shooting star, doji, evening star, three black crows, double top).
Green/red background highlights for signal candles.
Backtest: Use TradingView’s Strategy Tester to evaluate performance over 1–3 months, checking Net Profit, Win Rate, and Drawdown.
Demo Test: Run on a demo account to confirm signal visibility and performance before trading with real funds.
Hilly 2.0 Advanced Crypto Scalping Strategy - 1 & 5 Min ChartsHow to Use
Copy the Code: Copy the script above.
Paste in TradingView: Open TradingView, go to the Pine Editor (bottom of the chart), paste the code, and click “Add to Chart.”
Check for Errors: Verify no errors appear in the Pine Editor console. The script uses Pine Script v5 (@version=5).
Select Timeframe:
1-Minute Chart: Use defaults (emaFastLen=7, emaSlowLen=14, rsiLen=10, rsiOverbought=80, rsiOversold=20, slPerc=0.5, tpPerc=1.0, useCandlePatterns=false).
5-Minute Chart: Adjust to emaFastLen=9, emaSlowLen=21, rsiLen=14, rsiOverbought=75, rsiOversold=25, slPerc=0.8, tpPerc=1.5, useCandlePatterns=true.
Apply to Chart: Use a liquid crypto pair (e.g., BTC/USDT, ETH/USDT on Binance or Coinbase).
Verify Signals:
Green “BUY” or “EMA BUY” labels and triangle-up arrows below candles.
Red “SELL” or “EMA SELL” labels and triangle-down arrows above candles.
Green/red background highlights for signal candles.
Arrows use size.normal for consistent visibility.
Backtest: Use TradingView’s Strategy Tester to evaluate performance over 1–3 months, checking Net Profit, Win Rate, and Drawdown.
Demo Test: Run on a demo account to confirm signal visibility and performance before trading with real funds.
Laguerre-Kalman Adaptive Filter | AlphaNattLaguerre-Kalman Adaptive Filter |AlphaNatt
A sophisticated trend-following indicator that combines Laguerre polynomial filtering with Kalman optimal estimation to create an ultra-smooth, low-lag trend line with exceptional noise reduction capabilities.
"The perfect trend line adapts to market conditions while filtering out noise - this indicator achieves both through advanced mathematical techniques rarely seen in retail trading."
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🎯 KEY FEATURES
Dual-Filter Architecture: Combines two powerful filtering methods for superior performance
Adaptive Volatility Adjustment: Automatically adapts to market conditions
Minimal Lag: Laguerre polynomials provide faster response than traditional moving averages
Optimal Noise Reduction: Kalman filtering removes market noise while preserving trend
Clean Visual Design: Color-coded trend visualization (cyan/pink)
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📊 THE MATHEMATICS
1. Laguerre Filter Component
The Laguerre filter uses a cascade of four all-pass filters with a single gamma parameter:
4th order IIR (Infinite Impulse Response) filter
Single parameter (gamma) controls all filter characteristics
Provides smoother output than EMA with similar lag
Based on Laguerre polynomials from quantum mechanics
2. Kalman Filter Component
Implements a simplified Kalman filter for optimal estimation:
Prediction-correction algorithm from aerospace engineering
Dynamically adjusts based on estimation error
Provides mathematically optimal estimate of true price trend
Reduces noise while maintaining responsiveness
3. Adaptive Mechanism
Monitors market volatility in real-time
Adjusts filter parameters based on current conditions
More responsive in trending markets
More stable in ranging markets
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⚙️ INDICATOR SETTINGS
Laguerre Gamma (0.1-0.99): Controls filter smoothness. Higher = smoother but more lag
Adaptive Period (5-100): Lookback for volatility calculation
Kalman Noise Reduction (0.1-2.0): Higher = more noise filtering
Trend Threshold (0.0001-0.01): Minimum change to register trend shift
Recommended Settings:
Scalping: Gamma: 0.6, Period: 10, Noise: 0.3
Day Trading: Gamma: 0.8, Period: 20, Noise: 0.5 (default)
Swing Trading: Gamma: 0.9, Period: 30, Noise: 0.8
Position Trading: Gamma: 0.95, Period: 50, Noise: 1.2
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📈 TRADING SIGNALS
Primary Signals:
Cyan Line: Bullish trend - price above filter and filter ascending
Pink Line: Bearish trend - price below filter or filter descending
Color Change: Potential trend reversal point
Entry Strategies:
Trend Continuation: Enter on pullback to filter line in trending market
Trend Reversal: Enter on color change with volume confirmation
Breakout: Enter when price crosses filter with momentum
Exit Strategies:
Exit long when line turns from cyan to pink
Exit short when line turns from pink to cyan
Use filter as trailing stop in strong trends
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✨ ADVANTAGES OVER TRADITIONAL INDICATORS
Vs. Moving Averages:
Significantly less lag while maintaining smoothness
Adaptive to market conditions
Better noise filtering
Vs. Standard Filters:
Dual-filter approach provides optimal estimation
Mathematical foundation from signal processing
Self-adjusting parameters
Vs. Other Trend Indicators:
Cleaner signals with fewer whipsaws
Works across all timeframes
No repainting or lookahead bias
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🎓 MATHEMATICAL BACKGROUND
The Laguerre filter was developed by John Ehlers, applying Laguerre polynomials (used in quantum mechanics) to financial markets. These polynomials provide an elegant solution to the lag-smoothness tradeoff that plagues traditional moving averages.
The Kalman filter, developed by Rudolf Kalman in 1960, is used in everything from GPS systems to spacecraft navigation. It provides the mathematically optimal estimate of a system's state given noisy measurements.
By combining these two approaches, this indicator achieves what neither can alone: a smooth, responsive trend line that adapts to market conditions while filtering out noise.
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💡 TIPS FOR BEST RESULTS
Confirm with Volume: Strong trends should have increasing volume
Multiple Timeframes: Use higher timeframe for trend, lower for entry
Combine with Momentum: RSI or MACD can confirm filter signals
Market Conditions: Adjust noise parameter based on market volatility
Backtesting: Always test settings on your specific instrument
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⚠️ IMPORTANT NOTES
No indicator is perfect - always use proper risk management
Best suited for trending markets
May produce false signals in choppy/ranging conditions
Not financial advice - for educational purposes only
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🚀 CONCLUSION
The Laguerre-Kalman Adaptive Filter represents a significant advancement in technical analysis, bringing institutional-grade mathematical techniques to retail traders. Its unique combination of polynomial filtering and optimal estimation provides a clean, reliable trend-following tool that adapts to changing market conditions.
Whether you're scalping on the 1-minute chart or position trading on the daily, this indicator provides clear, actionable signals with minimal false positives.
"In the world of technical analysis, the edge comes from using better mathematics. This indicator delivers that edge."
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Developed by AlphaNatt | Professional Quantitative Trading Tools
Version: 1.0
Last Updated: 2025
Pine Script: v6
License: Open Source
Not financial advice. Always DYOR
[blackcat] L1 Value Trend IndicatorOVERVIEW
The L1 Value Trend Indicator is a sophisticated technical analysis tool designed for TradingView users seeking advanced market trend identification and trading signals. This comprehensive indicator combines multiple analytical techniques to provide traders with a holistic view of market dynamics, helping identify potential entry and exit points through various signal mechanisms. 📈 It features a main Value Trend line along with a lagged version, golden cross and dead cross signals, and multiple technical indicators including RSI, Williams %R, Stochastic %K/D, and Relative Strength calculations. The indicator also includes reference levels for support and resistance analysis, making it a versatile tool for both short-term and long-term trading strategies. ✅
FEATURES
📈 Primary Value Trend Line: Calculates a smoothed value trend using a combination of SMA and custom smoothing techniques
🔍 Value Trend Lag: Implements a lagged version of the main trend line for cross-over analysis
🚀 Golden Cross & Dead Cross Signals: Identifies buy/sell opportunities when the main trend line crosses its lagged version
💸 Multi-Indicator Integration: Combines multiple technical analysis tools for comprehensive market view
📊 RSI Calculations: Includes 6-period, 7-period, and 13-period RSI calculations for momentum analysis
📈 Williams %R: Provides overbought/oversold conditions using the Williams %R formula
📉 Stochastic Oscillator: Implements both Stochastic %K and %D calculations for momentum confirmation
📋 Relative Strength: Calculates relative strength based on highest highs and current price
✅ Visual Labels: Displays BUY and SELL labels on chart when crossover conditions are met
📣 Alert Conditions: Provides automated alert conditions for golden cross and dead cross events
📌 Reference Levels: Plots entry (25) and exit (75) reference lines for support/resistance analysis
HOW TO USE
Copy the Script: Copy the complete Pine Script code from the original file
Open TradingView: Navigate to TradingView website or application
Access Pine Editor: Go to the Pine Script editor (usually found in the chart toolbar)
Paste Code: Paste the copied script into the editor
Save Script: Save the script with a descriptive name like " L1 Value Trend Indicator"
Select Chart: Choose the chart where you want to apply the indicator
Add Indicator: Apply the indicator to your chart
Configure Parameters: Adjust input parameters to customize behavior
Monitor Signals: Watch for golden cross (BUY) and dead cross (SELL) signals
Use Reference Levels: Monitor entry (25) and exit (75) lines for support/resistance levels
LIMITATIONS
⚠️ Potential Repainting: The script may repaint due to lookahead bias in some calculations
📉 Lookahead Bias: Some calculations may reference future values, potentially causing repainting issues
🔄 Parameter Sensitivity: Results may vary significantly with different parameter settings
📉 Computational Complexity: May impact chart performance with heavy calculations on large datasets
📊 Resource Usage: Requires significant processing power for multiple indicator calculations
🔄 Data Sensitivity: Results may be affected by data quality and market conditions
NOTES
📈 Signal Timing: Cross-over signals may lag behind actual price movements
📉 Parameter Optimization: Optimal parameters may vary by market conditions and asset type
📋 Market Conditions: Performance may vary significantly across different market environments
📈 Multi-Indicator: Combine signals with other technical indicators for confirmation
📉 Timeframe Analysis: Use multiple timeframes for enhanced signal accuracy
📋 Volume Analysis: Incorporate volume data for additional confirmation
📈 Strategy Integration: Consider using this indicator as part of a broader trading strategy
📉 Risk Management: Use signals as part of a comprehensive risk management approach
📋 Backtesting: Test parameter combinations with historical data before live trading
THANKS
🙏 Original Creator: blackcat1402 creates the L1 Value Trend Indicator
📚 Community Contributions: Recognition to TradingView community for continuous improvements and contributions
📈 Collaborative Development: Appreciation for collaborative efforts in enhancing technical analysis tools
📉 TradingView Community: Special thanks to TradingView community members for their ongoing support and feedback
📋 Educational Resources: Recognition of educational resources that helped in understanding technical analysis principles
Squeeze Momentum Regression Clouds [SciQua]╭──────────────────────────────────────────────╮
☁️ Squeeze Momentum Regression Clouds
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🔍 Overview
The Squeeze Momentum Regression Clouds (SMRC) indicator is a powerful visual tool for identifying price compression , trend strength , and slope momentum using multiple layers of linear regression Clouds. Designed to extend the classic squeeze framework, this indicator captures the behavior of price through dynamic slope detection, percentile-based spread analytics, and an optional UI for trend inspection — across up to four customizable regression Clouds .
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⚙️ Core Features
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Up to 4 Regression Clouds – Each Cloud is created from a top and bottom linear regression line over a configurable lookback window.
Slope Detection Engine – Identifies whether each band is rising, falling, or flat based on slope-to-ATR thresholds.
Spread Compression Heatmap – Highlights compressed zones using yellow intensity, derived from historical spread analysis.
Composite Trend Scoring – Aggregates directional signals from each Cloud using your chosen weighting model.
Color-Coded Candles – Optional candle coloring reflects the real-time composite score.
UI Table – A toggleable info table shows slopes, compression levels, percentile ranks, and direction scores for each Cloud.
Gradient Cloud Styling – Apply gradient coloring from Cloud 1 to Cloud 4 for visual slope intensity.
Weight Aggregation Options – Use equal weighting, inverse-length weighting, or max pooling across Clouds to determine composite trend strength.
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🧪 How to Use the Indicator
1. Understand Trend Bias with Cloud Colors
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Each Cloud changes color based on its current slope:
Green indicates a rising trend.
Red indicates a falling trend.
Gray indicates a flat slope — often seen during chop or transitions.
Cloud 1 typically reflects short-term structure, while Cloud 4 represents long-term directional bias. Watch for multi-Cloud alignment — when all Clouds are green or red, the trend is strong. Divergence among Clouds often signals a potential shift.
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2. Use Compression Heat to Anticipate Breakouts
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The space between each Cloud’s top and bottom regression lines is measured, normalized, and analyzed over time. When this spread tightens relative to its history, the script highlights the band with a yellow compression glow .
This visual cue helps identify squeeze zones before volatility expands. If you see compression paired with a changing slope color (e.g., gray to green), this may indicate an impending breakout.
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3. Leverage the Optional Table UI
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The indicator includes a dynamic, floating table that displays real-time metrics per Cloud. These include:
Slope direction and value , with historical Min/Max reference.
Top and Bottom percentile ranks , showing how price sits within the Cloud range.
Current spread width , compared to its historical norms.
Composite score , which blends trend, slope, and compression for that Cloud.
You can customize the table’s position, theme, transparency, and whether to show a combined summary score in the header.
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4. Analyze Candle Color for Composite Signals
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When enabled, the indicator colors candles based on a weighted composite score. This score factors in:
The signed slope of each Cloud (up, down, or flat)
The percentile pressure from the top and bottom bands
The degree of spread compression
Expect green candles in bullish trend phases, red candles during bearish regimes, and gray candles in mixed or low-conviction zones.
Candle coloring provides a visual shorthand for market conditions , useful for intraday scanning or historical backtesting.
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🧰 Configuration Guidance
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To tailor the indicator to your strategy:
Use Cloud lengths like 21, 34, 55, and 89 for a balanced multi-timeframe view.
Adjust the slope threshold (default 0.05) to control how sensitive the trend coloring is.
Set the spread floor (e.g., 0.15) to tune when compression is detected and visualized.
Choose your weighting style : Inverse Length (favor faster bands), Equal, or Max Pooling (most aggressive).
Set composite weights to emphasize trend slope, percentile bias, or compression—depending on your market edge.
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✅ Best Practices
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Use aligned Cloud colors across all bands to confirm trend conviction.
Combine slope direction with compression glow for early breakout entry setups.
In choppy markets, watch for Clouds 1 and 2 turning flat while Clouds 3 and 4 remain directional — a sign of potential trend exhaustion or consolidation.
Keep the table enabled during backtesting to manually evaluate how each Cloud behaved during price turns and consolidations.
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📌 License & Usage Terms
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This script is provided under the Creative Commons Attribution-NonCommercial 4.0 International License .
✅ You are allowed to:
Use this script for personal or educational purposes
Study, learn, and adapt it for your own non-commercial strategies
❌ You are not allowed to:
Resell or redistribute the script without permission
Use it inside any paid product or service
Republish without giving clear attribution to the original author
For commercial licensing , private customization, or collaborations, please contact Joshua Danford directly.
BUY in HASH RibbonsHash Ribbons Indicator (BUY Signal)
A TradingView Pine Script v6 implementation for identifying Bitcoin miner capitulation (“Springs”) and recovery phases based on hash rate data. It marks potential low-risk buying opportunities by tracking short- and long-term moving averages of the network hash rate.
⸻
Key Features
• Hash Rate SMAs
• Short-term SMA (default: 30 days)
• Long-term SMA (default: 60 days)
• Phase Markers
• Gray circle: Short SMA crosses below long SMA (start of capitulation)
• White circles: Ongoing capitulation, with brighter white when the short SMA turns upward
• Yellow circle: Short SMA crosses back above long SMA (end of capitulation)
• Orange circle: Buy signal once hash rate recovery aligns with bullish price momentum (10-day price SMA crosses above 20-day price SMA)
• Display Modes
• Ribbons: Plots the two SMAs as colored bands—red for capitulation, green for recovery
• Oscillator: Shows the percentage difference between SMAs as a histogram (red for negative, blue for positive)
• Optional Overlays
• Bitcoin halving dates (2012, 2016, 2020, 2024) with dashed lines and labels
• Raw hash rate data in EH/s
• Alerts
• Configurable alerts for capitulation start, recovery, and buy signals
⸻
How It Works
1. Data Source: Fetches daily hash rate values from a selected provider (e.g., IntoTheBlock, Quandl).
2. Capitulation Detection: When the 30-day SMA falls below the 60-day SMA, miners are likely capitulating.
3. Recovery Identification: A rising 30-day SMA during capitulation signals miner recovery.
4. Buy Signal: Confirmed when the hash rate recovery coincides with a bullish shift in price momentum (10-day price SMA > 20-day price SMA).
⸻
Inputs
Hash Rate Short SMA: 30 days
Hash Rate Long SMA: 60 days
Plot Signals: On
Plot Halvings: Off
Plot Raw Hash Rate: Off
⸻
Considerations
• Timeframe: Best applied on daily charts to capture meaningful miner behavior.
• Data Reliability: Ensure the chosen hash rate source provides consistent, gap-free data.
• Risk Management: Use alongside other technical indicators (e.g., RSI, MACD) and fundamental analysis.
• Backtesting: Evaluate performance over different market cycles before live deployment.
