Terminal : USD Based Stock Markets Change (%)Hello.
This script is a simple USD Based Stock Markets Change (%) Data Terminal.
You can also set the period to look back manually in the menu.
In this way, an idea can be obtained about Countries' Stock Markets.
And you can observe the stock exchanges of relatively positive and negative countries from others.
Features
Value changes on a percentage basis (%)
Stock exchange values are calculated in dollar terms.
Due to the advantage of movement, future data were chosen instead of spot values on the required instruments.
Stock Markets
Usa : S&P 500 Futures
Japan: Nikkei 225 Futures
England: United Kingdom ( FTSE ) 100
Australia: Australia 200
Canada: S&P / TSX Composite
Switzerland: Swiss Market Index
New Zealand: NZX 50 Index
China: SSE Composite (000001)
Denmark: OMX Copenhagen 25 Index
Hong-Kong: Hang Seng Index Futures
India: Nifty 50
Norway: Oslo Bors All Share Index
Russia: MOEX Russia Index
Sweden: OMX Stockholm Index
Singapore: Singapore 30
Turkey: BIST 100
South Africa: South Africa Top 40 Index
Spain: IBEX 35
France: CAC 40
Italy: FTSE MIB Index
Netherlands: Netherlands 25
Germany : DAX
Regards.
חפש סקריפטים עבור "Futures"
CBG Key Numbers v6Here is my opening range, key numbers indicator. It takes the Opening Range (5 minutes by default) and then plots the opening range and up to 7 extensions of that range above and below.
It's amazing how the OR is stamped up on the rest of the day's price movements.
2 strategies (at least) are to play the OR range breakout and to fade when price hits an extreme range.
You have total control over how you set up the various lines and colors.
If you start overlaying the trading day with the OR and it's extensions, you will see amazing patterns become clear. For example, the pump and reverse. This is where price pumps right out of the opening and then reverses later in the morning.
I have the opening price set to big circles as this is one of the most important reference points during the day.
Important: For some reason, the 9:30 am time Opening acts differently for equities and futures . For equities, you can set the time values to 0930. But for futures , to capture the Open at 9:30, you have to set the time values to start at 0830. I haven't been able to find a better solution but setting the times manually works. Make sure to set all the time values on the Options screen.
There is one more setting of interest. It is called IB Target Amount. This is a number above and below the opening range that I have observed price to hit whenever there's a breakout. This will allow you to predict a price target on breakouts. For SPY , I have found that price usually breaks out to at least 50 cents. On ES futures , it's 6 dollars. This can help you lock in 10% and 20% when trading options and is a great tool. That's why I have it so prominent in red. You will also see price return to this level during the day and act as support or resistance.
Please disregard the red and green shaded rectangles. They are my own support and resistance zones and TV wouldn't let me hide them from the picture. :-)
I mostly use this on a 5 minute chart but any timeframe will work.
Open Interest Market Facilitation IndexOriginal script from ChartChampions :
Let's start.
This script was created by using Open Interest instead of Volume in the Market Facilitation Index.
Thus, it can make a difference in the Future and CFD Markets.
If your financial instrument is not from these markets, that is, if Open Interest is not used, you can choose Volume.
You can set "FUTURES" and "OTHERS" from the menu.
If you use the Open Interest (FUTURES) option in the menu on 1W bars and defined Future markets, it will not repaint.
This is the best use for Open Interests, as data is extracted from Quandl and CFTC COT reports are published once a week.
Color Change Rules :
In my version :
Green Bars = Green
Fade Bars = Orange
Fake Bars = Blue
Squat Bars = Red
To show the difference in the presentation, both the Futures option using Open Interest and the Others option using Volume were published to compare.
You can observe the difference.
Best regards.
plot CFTC COT disaggregated dataCFTC COT data is exported by quandl.com to tradingview
COT@quandl:
www.quandl.com
COT@tradingview
www.tradingview.com
How to use this script:
Select and load CFTC COT data for the commodity ticker in the chart
Will by default take current ticker, or allow to avvverride it with another
Traders' categories are those for commodities, not financial futs
Select And Configure :
-categories to be plotted
-Futures/Futures+Options
-by num_contracts/percent
-plot"Tot Spreads %" selection when also "Show as % of OI" selected
This script supercedes my other "MY_ CFTC GC/SI/CL (Disaggregated)" script
Delay Estimatorthis script can be used to adjust one of the free realtime tickers (such as SPX500, NAS100) to "look" like one of the delayed futures tickers (such as ES1!, NQ1!). This basically allows us to get an estimate of the realtime futures ticker price.
it uses bollinger bands to adjust the volatility and offset of the realtime ticker
it also provides a decimator to reduce the price into ticks, since futures often use 4 ticks per point
(CRISIS) BTC money flow oscilator V0.1Modified version of Ricado Santos' money flow oscilator.
now featuring 3 oscilators. Makes it easier to obseerve when dumps/pumps are targeting OKC futures contracts or just looking for divergences.
1x Aggregate of OKcoin:BTCCNY, Houbi:BTCCNY & Bitfinex:BTCUSD (Orange)
1X OKcoin 1 Week futures (pink)
1x OKcoin 3 Month futures (purple)
Herrick Payoff IndexThe Herrick Payoff Index is designed to show the amount of money flowing into or out of a futures contract. The Index uses open interest during its calculations, therefore, the security being analyzed must contain open interest.
The Herrick Payoff Index was developed by John Herrick.When the Herrick Payoff Index is above zero, it shows that money is flowing into the futures contract (which is bullish). When the Index is below zero, it shows that money is flowing out of the futures contract (which is bearish).
The interpretation of the Herrick Payoff Index involves looking for divergences between the Index and prices.
Bitcoin vs. S&P 500 Performance Comparison**Full Description:**
**Overview**
This indicator provides an intuitive visual comparison of Bitcoin's performance versus the S&P 500 by shading the chart background based on relative strength over a rolling lookback period.
**How It Works**
- Calculates percentage returns for both Bitcoin and the S&P 500 (ES1! futures) over a specified lookback period (default: 75 bars)
- Compares the returns and shades the background accordingly:
- **Green/Teal Background**: Bitcoin is outperforming the S&P 500
- **Red/Maroon Background**: S&P 500 is outperforming Bitcoin
- Displays a real-time performance difference label showing the exact percentage spread
**Key Features**
✓ Rolling performance comparison using customizable lookback period (default 75 bars)
✓ Clean visual representation with adjustable transparency
✓ Works on any timeframe (optimized for daily charts)
✓ Real-time performance differential display
✓ Uses ES1! (E-mini S&P 500 continuous futures) for accurate comparison
✓ Fine-tuning adjustment factor for precise calibration
**Use Cases**
- Identify market regimes where Bitcoin outperforms or underperforms traditional equities
- Spot trend changes in relative performance
- Assess risk-on vs risk-off periods
- Compare Bitcoin's momentum against broader market conditions
- Time entries/exits based on relative strength shifts
**Settings**
- **S&P 500 Symbol**: Default ES1! (can be changed to SPX or other indices)
- **Lookback Period**: Number of bars for performance calculation (default: 75)
- **Adjustment Factor**: Fine-tune calibration to match specific data feeds
- **Transparency Controls**: Customize background shading intensity
- **Show/Hide Label**: Toggle performance difference display
**Best Practices**
- Use on daily timeframe for swing trading and position analysis
- Combine with other momentum indicators for confirmation
- Watch for color transitions as potential regime change signals
- Consider using multiple timeframes for comprehensive analysis
**Technical Details**
The indicator calculates rolling percentage returns using the formula: ((Current Price / Price ) - 1) × 100, then compares Bitcoin's return to the S&P 500's return over the same period. The background color dynamically updates based on which asset is showing stronger performance.
WaveTrend Oscillator [WT] — LazyBear Modified by PickMyTradeThis strategy is built upon the well-known “WaveTrend Oscillator ” indicator originally published by LazyBear. The WaveTrend is widely used across markets for spotting momentum reversals, especially when the oscillator crosses the signal line within overbought and oversold zones.
The PickMyTrade team has converted this classic indicator into a fully automated, risk-managed trading strategy designed to maintain consistent performance with controlled drawdown. It includes additional trend and momentum filters, along with a complete capital protection framework aimed at meeting the strict requirements of prop firm evaluations.
This version focuses heavily on profit factor stability, drawdown reduction, and risk management, making it suitable for traders who must operate within strict rules.
What Makes This Version Different?
LazyBear’s original WaveTrend indicator provides excellent reversal detection, but this strategy expands it significantly by adding:
1. Risk-Management System Designed for Prop Firm Rules
Maximum equity drawdown limit
Daily loss limit
Automatic “stop trading” mode when limits are reached
Optional position size reduction as drawdown increases
These protections help keep the account within typical challenge requirements such as 5–10% max drawdown.
2. ATR-Based Stop Loss and Risk-to-Reward Targeting
Adaptive volatility-based stop losses
Configurable R:R take-profit targets
Position sizing based on percent of equity or fixed contracts
Ensures consistent risk exposure across all market types.
3. Improved WaveTrend Logic
While the original WT uses simple crossovers, this strategy adds several filters:
Histogram momentum direction (WT1 – WT2)
RSI momentum confirmation
ADX trend-strength filter
Optional overbought/oversold only mode (classic WT behavior)
These help reduce false reversals and improve the signal quality in choppy environments.
4. Multi-Market & Multi-Timeframe Compatibility
The strategy performs reliably on:
Stocks
Crypto
Forex
Indices
Futures
It works on both short-term charts (15m) and higher timeframes (1H, 4H).
Higher timeframes tend to show lower drawdown and higher profit factors.
5. No Repainting
All logic is based strictly on closed candles.
The strategy does not repaint past signals.
How It Works
WaveTrend Core Logic
WT1 = EMA-smoothed channel deviation
WT2 = smoothed signal of WT1
Main signals come from crossovers of WT1 and WT2
Optimal conditions occur below oversold or above overbought zones
Entry Conditions
Long Entry
WT1 crosses above WT2
Histogram momentum positive
RSI supports upward momentum
ADX confirms trend strength (optional)
Optional: WT1 below oversold threshold
Short Entry
WT1 crosses below WT2
Histogram momentum negative
RSI supports downward momentum
ADX confirms trend strength (optional)
Optional: WT1 above overbought threshold
Exit Conditions
Opposite WaveTrend signal
ATR stop loss hit
Take profit target hit
Optional: trend strength on ADX weakens
Optional: momentum reversal detected
Risk-Management System
Developed specifically with prop-firm challenge rules in mind.
Equity Protection
Max equity drawdown limit (e.g., 10–15%)
Daily loss limit (e.g., 3–5%)
Full suspension of trading once limits are hit
Sizing Controls
Position size shrinks automatically as drawdown increases
Minimum size multiplier to maintain controlled exposure
Prevents overleveraging during account recovery cycles
Stop Loss & Take Profit
ATR-based dynamic stop loss
Configurable percentage stop loss
Configurable take profit with desired R:R ratio
Credits
Original Indicator: LazyBear
Original Script: “WaveTrend Oscillator ”
All indicator logic belongs to the original creator.
Strategy Conversion & Risk-Management Enhancements: PickMyTrade Team
About PickMyTrade
PickMyTrade specializes in converting TradingView strategies into fully automated trading systems with live execution across multiple brokers.
Supported connections include:
Rithmic
TradeStation
Interactive Brokers
TradeLocker
ProjectX
Tradovate (futures)
For automation and execution services:
pickmytrade.io
pickmytrade.trade
Disclaimer
This strategy is for educational and informational purposes only.
It does not guarantee profits and is not financial advice.
Trading involves significant risk.
Always test in simulation before trading live.
Past results do not guarantee future performance.
Slippage, commissions, and execution delay will impact results.
Dimensional Resonance ProtocolDimensional Resonance Protocol
🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION
The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states.
The Complexity Hypothesis:
Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics.
Takens' Embedding Theorem (1981):
A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation.
Mathematical Foundation:
From single observable x(t), create embedding vectors:
X(t) =
Where:
• d = Embedding dimension (default 5)
• τ = Time delay (default 3 bars)
• x(t) = Price or return at time t
Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series.
Why This Matters:
Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect:
• Stable regions : Predictable behavior (trade opportunities)
• Chaotic regions : Unpredictable behavior (avoid trading)
• Critical transitions : Phase changes between regimes
Phase Space Magnitude Calculation:
phase_magnitude = sqrt(Σ ² for i = 0 to d-1)
This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation.
📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA)
Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states?
Recurrence Plot Foundation:
A recurrence occurs when two phase space points are closer than threshold ε:
R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0
This creates a binary matrix showing when the system revisits similar states.
Key RQA Metrics:
1. Recurrence Rate (RR):
RR = (Number of recurrent points) / (Total possible pairs)
• RR near 0: System never repeats (highly stochastic)
• RR = 0.1-0.3: Moderate recurrence (tradeable patterns)
• RR > 0.5: System stuck in attractor (ranging market)
• RR near 1: System frozen (no dynamics)
Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck.
2. Determinism (DET):
Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths).
DET = (Recurrence points on diagonals) / (Total recurrence points)
• DET < 0.3: Random dynamics
• DET = 0.3-0.7: Moderate determinism (patterns with noise)
• DET > 0.7: Strong determinism (technical patterns reliable)
Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck).
Threshold Selection (ε):
Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns.
🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT
Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns.
Algorithm (Bandt & Pompe, 2002):
1. Take overlapping windows of length n (default n=4)
2. For each window, record the rank order pattern
Example: → pattern (ranks from lowest to highest)
3. Count frequency of each possible pattern
4. Calculate Shannon entropy of pattern distribution
Mathematical Formula:
H_perm = -Σ p(π) · ln(p(π))
Where π ranges over all n! possible permutations, p(π) is the probability of pattern π.
Normalized to :
H_norm = H_perm / ln(n!)
Interpretation:
• H < 0.3 : Very ordered, crystalline structure (strong trending)
• H = 0.3-0.5 : Ordered regime (tradeable with patterns)
• H = 0.5-0.7 : Moderate complexity (mixed conditions)
• H = 0.7-0.85 : Complex dynamics (challenging to trade)
• H > 0.85 : Maximum entropy (nearly random, avoid)
Entropy Regime Classification:
DRP classifies markets into five entropy regimes:
• CRYSTALLINE (H < 0.3): Maximum order, persistent trends
• ORDERED (H < 0.5): Clear patterns, momentum strategies work
• MODERATE (H < 0.7): Mixed dynamics, adaptive required
• COMPLEX (H < 0.85): High entropy, mean reversion better
• CHAOTIC (H ≥ 0.85): Near-random, minimize trading
Why Permutation Entropy?
Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy:
• Works directly on time series
• Robust to monotonic transformations
• Computationally efficient
• Captures temporal structure, not just distribution
• Immune to outliers (uses ranks, not values)
⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY
The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos.
Physical Meaning:
Two trajectories starting infinitely close will diverge at exponential rate e^(λt):
Distance(t) ≈ Distance(0) × e^(λt)
Interpretation:
• λ > 0 : Positive Lyapunov exponent = CHAOS
- Small errors grow exponentially
- Long-term prediction impossible
- System is sensitive, unpredictable
- AVOID TRADING
• λ ≈ 0 : Near-zero = CRITICAL STATE
- Edge of chaos
- Transition zone between order and disorder
- Moderate predictability
- PROCEED WITH CAUTION
• λ < 0 : Negative Lyapunov exponent = STABLE
- Small errors decay
- Trajectories converge
- System is predictable
- OPTIMAL FOR TRADING
Estimation Method:
DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars):
For each bar i in window:
δ₀ = |x - x | (initial separation)
δ₁ = |x - x | (previous separation)
if δ₁ > 0:
ratio = δ₀ / δ₁
log_ratios += ln(ratio)
λ ≈ average(log_ratios)
Stability Classification:
• STABLE : λ < 0 (negative growth rate)
• CRITICAL : |λ| < 0.1 (near neutral)
• CHAOTIC : λ > 0.2 (strong positive growth)
Signal Filtering:
By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down.
📐 HIGUCHI FRACTAL DIMENSION
Fractal dimension measures self-similarity and complexity of the price trajectory.
Theoretical Background:
A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve):
• D ≈ 1.0 : Smooth, persistent trending
• D ≈ 1.5 : Random walk (Brownian motion)
• D ≈ 2.0 : Highly irregular, space-filling
Higuchi Method (1988):
For a time series of length N, construct k different curves by taking every k-th point:
L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k)
For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k):
D = slope of log(L) vs log(1/k)
Market Interpretation:
• D < 1.35 : Strong trending, persistent (Hurst > 0.5)
- TRENDING regime
- Momentum strategies favored
- Breakouts likely to continue
• D = 1.35-1.45 : Moderate persistence
- PERSISTENT regime
- Trend-following with caution
- Patterns have meaning
• D = 1.45-1.55 : Random walk territory
- RANDOM regime
- Efficiency hypothesis holds
- Technical analysis least reliable
• D = 1.55-1.65 : Anti-persistent (mean-reverting)
- ANTI-PERSISTENT regime
- Oscillator strategies work
- Overbought/oversold meaningful
• D > 1.65 : Highly complex, choppy
- COMPLEX regime
- Avoid directional bets
- Wait for regime change
Signal Filtering:
Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning.
🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW
Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer.
Schreiber's Definition (2000):
Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future:
TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past)
Where H is Shannon entropy.
Key Properties:
1. Directional : TE(X→Y) ≠ TE(Y→X) in general
2. Non-linear : Detects complex causal relationships
3. Model-free : No assumptions about functional form
4. Lag-independent : Captures delayed causal effects
Three Causal Flows Measured:
1. Volume → Price (TE_V→P):
Measures how much volume patterns predict price changes.
• TE > 0 : Volume provides predictive information about price
- Institutional participation driving moves
- Volume confirms direction
- High reliability
• TE ≈ 0 : No causal flow (weak volume/price relationship)
- Volume uninformative
- Caution on signals
• TE < 0 (rare): Suggests price leading volume
- Potentially manipulated or thin market
2. Volatility → Momentum (TE_σ→M):
Does volatility expansion predict momentum changes?
• Positive TE : Volatility precedes momentum shifts
- Breakout dynamics
- Regime transitions
3. Structure → Price (TE_S→P):
Do support/resistance patterns causally influence price?
• Positive TE : Structural levels have causal impact
- Technical levels matter
- Market respects structure
Net Causal Flow:
Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P
• Net > +0.1 : Bullish causal structure
• Net < -0.1 : Bearish causal structure
• |Net| < 0.1 : Neutral/unclear causation
Causal Gate:
For signal confirmation, NEXUS requires:
• Buy signals : TE_V→P > 0 AND Net_Flow > 0.05
• Sell signals : TE_V→P > 0 AND Net_Flow < -0.05
This ensures volume is actually driving price (causal support exists), not just correlated noise.
Implementation Note:
Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner.
🌊 HILBERT PHASE COHERENCE
Phase coherence measures synchronization across market dimensions using Hilbert transform analysis.
Hilbert Transform Theory:
For a signal x(t), the Hilbert transform H (t) creates an analytic signal:
z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t))
Where:
• A(t) = Instantaneous amplitude
• φ(t) = Instantaneous phase
Instantaneous Phase:
φ(t) = arctan(H (t) / x(t))
The phase represents where the signal is in its natural cycle—analogous to position on a unit circle.
Four Dimensions Analyzed:
1. Momentum Phase : Phase of price rate-of-change
2. Volume Phase : Phase of volume intensity
3. Volatility Phase : Phase of ATR cycles
4. Structure Phase : Phase of position within range
Phase Locking Value (PLV):
For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization:
PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩|
Where ⟨·⟩ is time average over window.
Interpretation:
• PLV = 0 : Completely random phase relationship (no synchronization)
• PLV = 0.5 : Moderate phase locking
• PLV = 1 : Perfect synchronization (phases locked)
Pairwise PLV Calculations:
• PLV_momentum-volume : Are momentum and volume cycles synchronized?
• PLV_momentum-structure : Are momentum cycles aligned with structure?
• PLV_volume-structure : Are volume and structural patterns in phase?
Overall Phase Coherence:
Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3
Signal Confirmation:
Emergence signals require coherence ≥ threshold (default 0.70):
• Below 0.70: Dimensions not synchronized, no coherent market state
• Above 0.70: Dimensions in phase, coherent behavior emerging
Coherence Direction:
The summed phase angles indicate whether synchronized dimensions point bullish or bearish:
Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure)
• Direction > 0 : Phases pointing upward (bullish synchronization)
• Direction < 0 : Phases pointing downward (bearish synchronization)
🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT
The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence.
Eight Components with Weights:
1. Phase Coherence (20%):
Direct contribution: coherence × 0.20
Measures dimensional synchronization.
2. Entropy Regime (15%):
Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0
Rewards low entropy (ordered, predictable states).
3. Lyapunov Stability (12%):
• λ < 0 (stable): +0.12
• |λ| < 0.1 (critical): +0.08
• λ > 0.2 (chaotic): +0.0
Requires stable, predictable dynamics.
4. Fractal Dimension Trending (12%):
Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0
Rewards trending fractal structure (D < 1.45).
5. Dimensional Resonance (12%):
Contribution: |dimensional_resonance| × 0.12
Measures alignment across momentum, volume, structure, volatility dimensions.
6. Causal Flow Strength (9%):
Contribution: |net_causal_flow| × 0.09
Rewards strong causal relationships.
7. Phase Space Embedding (10%):
Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0
Rewards strong trajectory in reconstructed phase space.
8. Recurrence Quality (10%):
Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8
Rewards deterministic patterns with moderate recurrence.
Total Emergence Score:
E = Σ(components) ∈
Capped at 1.0 maximum.
Emergence Direction:
Separate calculation determining bullish vs bearish:
• Dimensional resonance sign
• Net causal flow sign
• Phase magnitude correlation with momentum
Signal Threshold:
Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals.
Why Emergence Matters:
Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order.
These are the highest-probability trade opportunities because the entire system is resonating in the same direction.
🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE
DRP generates two tiers of signals with different requirements:
TIER 1: EMERGENCE SIGNALS (Primary)
Requirements:
1. Emergence score ≥ threshold (default 0.75)
2. Phase coherence ≥ threshold (default 0.70)
3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish)
4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction
5. Stability zone (if enabled): λ < 0 or |λ| < 0.1
6. Price confirmation: Close > open (bulls) or close < open (bears)
7. Cooldown satisfied: bars_since_signal ≥ cooldown_period
EMERGENCE BUY:
• All above conditions met with bullish direction
• Market has achieved coherent bullish state
• Multiple dimensions synchronized upward
EMERGENCE SELL:
• All above conditions met with bearish direction
• Market has achieved coherent bearish state
• Multiple dimensions synchronized downward
Premium Emergence:
When signal_quality (emergence_score × phase_coherence) > 0.7:
• Displayed as ★ star symbol
• Highest conviction trades
• Maximum dimensional alignment
Standard Emergence:
When signal_quality 0.5-0.7:
• Displayed as ◆ diamond symbol
• Strong signals but not perfect alignment
TIER 2: RESONANCE SIGNALS (Secondary)
Requirements:
1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish)
2. Fractal dimension < 1.5 (trending/persistent regime)
3. Price confirmation matches direction
4. NOT in chaotic regime (λ < 0.2)
5. Cooldown satisfied
6. NO emergence signal firing (resonance is fallback)
RESONANCE BUY:
• Dimensional alignment without full emergence
• Trending fractal structure
• Moderate conviction
RESONANCE SELL:
• Dimensional alignment without full emergence
• Bearish resonance with trending structure
• Moderate conviction
Displayed as small ▲/▼ triangles with transparency.
Signal Hierarchy:
IF emergence conditions met:
Fire EMERGENCE signal (★ or ◆)
ELSE IF resonance conditions met:
Fire RESONANCE signal (▲ or ▼)
ELSE:
No signal
Cooldown System:
After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions.
Cooldown tracks using bar_index:
bars_since_signal = current_bar_index - last_signal_bar_index
cooldown_ok = bars_since_signal >= cooldown_period
🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY
DRP provides rich visual feedback across four distinct layers:
LAYER 1: COHERENCE FIELD (Background)
Colored background intensity based on phase coherence:
• No background : Coherence < 0.5 (incoherent state)
• Faint glow : Coherence 0.5-0.7 (building coherence)
• Stronger glow : Coherence > 0.7 (coherent state)
Color:
• Cyan/teal: Bullish coherence (direction > 0)
• Red/magenta: Bearish coherence (direction < 0)
• Blue: Neutral coherence (direction ≈ 0)
Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible.
LAYER 2: STABILITY/CHAOS ZONES
Background color indicating Lyapunov regime:
• Green tint (95% transparent): λ < 0, STABLE zone
- Safe to trade
- Patterns meaningful
• Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone
- Edge of chaos
- Moderate risk
• Red tint (85% transparent): λ > 0.2, CHAOTIC zone
- Avoid trading
- Unpredictable behavior
LAYER 3: DIMENSIONAL RIBBONS
Three EMAs representing dimensional structure:
• Fast ribbon : EMA(8) in cyan/teal (fast dynamics)
• Medium ribbon : EMA(21) in blue (intermediate)
• Slow ribbon : EMA(55) in red/magenta (slow dynamics)
Provides visual reference for multi-scale structure without cluttering with raw phase space data.
LAYER 4: CAUSAL FLOW LINE
A thicker line plotted at EMA(13) colored by net causal flow:
• Cyan/teal : Net_flow > +0.1 (bullish causation)
• Red/magenta : Net_flow < -0.1 (bearish causation)
• Gray : |Net_flow| < 0.1 (neutral causation)
Shows real-time direction of information flow.
EMERGENCE FLASH:
Strong background flash when emergence signals fire:
• Cyan flash for emergence buy
• Red flash for emergence sell
• 80% transparency for visibility without obscuring price
📊 COMPREHENSIVE DASHBOARD
Real-time monitoring of all complexity metrics:
HEADER:
• 🌀 DRP branding with gold accent
CORE METRICS:
EMERGENCE:
• Progress bar (█ filled, ░ empty) showing 0-100%
• Percentage value
• Direction arrow (↗ bull, ↘ bear, → neutral)
• Color-coded: Green/gold if active, gray if low
COHERENCE:
• Progress bar showing phase locking value
• Percentage value
• Checkmark ✓ if ≥ threshold, circle ○ if below
• Color-coded: Cyan if coherent, gray if not
COMPLEXITY SECTION:
ENTROPY:
• Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC)
• Numerical value (0.00-1.00)
• Color: Green (ordered), gold (moderate), red (chaotic)
LYAPUNOV:
• State (STABLE/CRITICAL/CHAOTIC)
• Numerical value (typically -0.5 to +0.5)
• Status indicator: ● stable, ◐ critical, ○ chaotic
• Color-coded by state
FRACTAL:
• Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX)
• Dimension value (1.0-2.0)
• Color: Cyan (trending), gold (random), red (complex)
PHASE-SPACE:
• State (STRONG/ACTIVE/QUIET)
• Normalized magnitude value
• Parameters display: d=5 τ=3
CAUSAL SECTION:
CAUSAL:
• Direction (BULL/BEAR/NEUTRAL)
• Net flow value
• Flow indicator: →P (to price), P← (from price), ○ (neutral)
V→P:
• Volume-to-price transfer entropy
• Small display showing specific TE value
DIMENSIONAL SECTION:
RESONANCE:
• Progress bar of absolute resonance
• Signed value (-1 to +1)
• Color-coded by direction
RECURRENCE:
• Recurrence rate percentage
• Determinism percentage display
• Color-coded: Green if high quality
STATE SECTION:
STATE:
• Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING
• Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning)
• Color-coded by state
SIGNALS:
• E: count of emergence signals
• R: count of resonance signals
⚙️ KEY PARAMETERS EXPLAINED
Phase Space Configuration:
• Embedding Dimension (3-10, default 5): Reconstruction dimension
- Low (3-4): Simple dynamics, faster computation
- Medium (5-6): Balanced (recommended)
- High (7-10): Complex dynamics, more data needed
- Rule: d ≥ 2D+1 where D is true dimension
• Time Delay (τ) (1-10, default 3): Embedding lag
- Fast markets: 1-2
- Normal: 3-4
- Slow markets: 5-10
- Optimal: First minimum of mutual information (often 2-4)
• Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity
- Tight (0.01-0.05): Very similar states only
- Medium (0.08-0.15): Balanced
- Loose (0.20-0.50): Liberal matching
Entropy & Complexity:
• Permutation Order (3-7, default 4): Pattern length
- Low (3): 6 patterns, fast but coarse
- Medium (4-5): 24-120 patterns, balanced
- High (6-7): 720-5040 patterns, fine-grained
- Note: Requires window >> order! for stability
• Entropy Window (15-100, default 30): Lookback for entropy
- Short (15-25): Responsive to changes
- Medium (30-50): Stable measure
- Long (60-100): Very smooth, slow adaptation
• Lyapunov Window (10-50, default 20): Stability estimation window
- Short (10-15): Fast chaos detection
- Medium (20-30): Balanced
- Long (40-50): Stable λ estimate
Causal Inference:
• Enable Transfer Entropy (default ON): Causality analysis
- Keep ON for full system functionality
• TE History Length (2-15, default 5): Causal lookback
- Short (2-4): Quick causal detection
- Medium (5-8): Balanced
- Long (10-15): Deep causal analysis
• TE Discretization Bins (4-12, default 6): Binning granularity
- Few (4-5): Coarse, robust, needs less data
- Medium (6-8): Balanced
- Many (9-12): Fine-grained, needs more data
Phase Coherence:
• Enable Phase Coherence (default ON): Synchronization detection
- Keep ON for emergence detection
• Coherence Threshold (0.3-0.95, default 0.70): PLV requirement
- Loose (0.3-0.5): More signals, lower quality
- Balanced (0.6-0.75): Recommended
- Strict (0.8-0.95): Rare, highest quality
• Hilbert Smoothing (3-20, default 8): Phase smoothing
- Low (3-5): Responsive, noisier
- Medium (6-10): Balanced
- High (12-20): Smooth, more lag
Fractal Analysis:
• Enable Fractal Dimension (default ON): Complexity measurement
- Keep ON for full analysis
• Fractal K-max (4-20, default 8): Scaling range
- Low (4-6): Faster, less accurate
- Medium (7-10): Balanced
- High (12-20): Accurate, slower
• Fractal Window (30-200, default 50): FD lookback
- Short (30-50): Responsive FD
- Medium (60-100): Stable FD
- Long (120-200): Very smooth FD
Emergence Detection:
• Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence
- Sensitive (0.5-0.65): More signals
- Balanced (0.7-0.8): Recommended
- Strict (0.85-0.95): Rare signals
• Require Causal Gate (default ON): TE confirmation
- ON: Only signal when causality confirms
- OFF: Allow signals without causal support
• Require Stability Zone (default ON): Lyapunov filter
- ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical)
- OFF: Allow signals in chaotic regimes (risky)
• Signal Cooldown (1-50, default 5): Minimum bars between signals
- Fast (1-3): Rapid signal generation
- Normal (4-8): Balanced
- Slow (10-20): Very selective
- Ultra (25-50): Only major regime changes
Signal Configuration:
• Momentum Period (5-50, default 14): ROC calculation
• Structure Lookback (10-100, default 20): Support/resistance range
• Volatility Period (5-50, default 14): ATR calculation
• Volume MA Period (10-50, default 20): Volume normalization
Visual Settings:
• Customizable color scheme for all elements
• Toggle visibility for each layer independently
• Dashboard position (4 corners) and size (tiny/small/normal)
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: System Familiarization (Week 1)
Goal: Understand complexity metrics and dashboard interpretation
Setup:
• Enable all features with default parameters
• Watch dashboard metrics for 500+ bars
• Do NOT trade yet
Actions:
• Observe emergence score patterns relative to price moves
• Note coherence threshold crossings and subsequent price action
• Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC)
• Correlate Lyapunov state with signal reliability
• Track which signals appear (emergence vs resonance frequency)
Key Learning:
• When does emergence peak? (usually before major moves)
• What entropy regime produces best signals? (typically ORDERED or MODERATE)
• Does your instrument respect stability zones? (stable λ = better signals)
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to instrument characteristics
Requirements:
• Understand basic dashboard metrics from Phase 1
• Have 1000+ bars of history loaded
Embedding Dimension & Time Delay:
• If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2)
• If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5)
• Sweet spot: 4-8 emergence signals per 100 bars
Coherence Threshold:
• Check dashboard: What's typical coherence range?
• If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65
• If coherence often >0.80: Can raise threshold to 0.75-0.80
• Goal: Signals fire during top 20-30% of coherence values
Emergence Threshold:
• If too few signals: Lower to 0.65-0.70
• If too many signals: Raise to 0.80-0.85
• Balance with coherence threshold—both must be met
Phase 3: Signal Quality Assessment (Weeks 3-4)
Goal: Verify signals have edge via paper trading
Requirements:
• Parameters optimized per Phase 2
• 50+ signals generated
• Detailed notes on each signal
Paper Trading Protocol:
• Take EVERY emergence signal (★ and ◆)
• Optional: Take resonance signals (▲/▼) separately to compare
• Use simple exit: 2R target, 1R stop (ATR-based)
• Track: Win rate, average R-multiple, maximum consecutive losses
Quality Metrics:
• Premium emergence (★) : Should achieve >55% WR
• Standard emergence (◆) : Should achieve >50% WR
• Resonance signals : Should achieve >45% WR
• Overall : If <45% WR, system not suitable for this instrument/timeframe
Red Flags:
• Win rate <40%: Wrong instrument or parameters need major adjustment
• Max consecutive losses >10: System not working in current regime
• Profit factor <1.0: No edge despite complexity analysis
Phase 4: Regime Awareness (Week 5)
Goal: Understand which market conditions produce best signals
Analysis:
• Review Phase 3 trades, segment by:
- Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC)
- Lyapunov state (STABLE vs CRITICAL vs CHAOTIC)
- Fractal regime (TRENDING vs RANDOM vs COMPLEX)
Findings (typical patterns):
• Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal
• Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal
• Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these)
Optimization:
• If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70
• If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic
• If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate with minimal capital at risk
Requirements:
• Paper trading shows: WR >48%, PF >1.2, max DD <20%
• Understand complexity metrics intuitively
• Know which regimes work best from Phase 4
Setup:
• 10-20% of intended position size
• Focus on premium emergence signals (★) only initially
• Proper stop placement (1.5-2.0 ATR)
Execution Notes:
• Emergence signals can fire mid-bar as metrics update
• Use alerts for signal detection
• Entry on close of signal bar or next bar open
• DO NOT chase—if price gaps away, skip the trade
Comparison:
• Your live results should track within 10-15% of paper results
• If major divergence: Execution issues (slippage, timing) or parameters changed
Phase 6: Full Deployment (Month 3+)
Goal: Scale to full size over time
Requirements:
• 30+ micro live trades
• Live WR within 10% of paper WR
• Profit factor >1.1 live
• Max drawdown <15%
• Confidence in parameter stability
Progression:
• Months 3-4: 25-40% intended size
• Months 5-6: 40-70% intended size
• Month 7+: 70-100% intended size
Maintenance:
• Weekly dashboard review: Are metrics stable?
• Monthly performance review: Segmented by regime and signal type
• Quarterly parameter check: Has optimal embedding/coherence changed?
Advanced:
• Consider different parameters per session (high vs low volatility)
• Track phase space magnitude patterns before major moves
• Combine with other indicators for confluence
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Phase Space Revelation:
Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D.
The Permutation Entropy Discovery:
Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering.
The Lyapunov Filter Breakthrough:
Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself.
The Transfer Entropy Challenge:
Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters.
The Phase Coherence Insight:
Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason.
The Eight-Component Emergence Formula:
Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it.
The Cooldown Necessity:
Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude.
• NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty.
• NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data.
• NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections.
• NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off.
Core Assumptions:
1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly).
2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong.
3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise).
4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets).
5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive).
6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT).
Performs Best On:
• ES, NQ, RTY (major US index futures - high liquidity, clean volume data)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis)
• Liquid commodities: CL (crude oil), GC (gold), NG (natural gas)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure)
• Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis
• Exotic forex pairs - erratic spreads, thin volume
• Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis
• Pre-market/after-hours - gappy, thin, different dynamics
• Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions
• Highly manipulated instruments - spoofing and layering create false coherence
Known Weaknesses:
• Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed.
• Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument.
• Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable.
• Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive.
• False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through.
• Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this.
• Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless.
Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals.
Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size.
The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it.
Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe.
The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic.
Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations.
Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
📁 DOCUMENTATION
The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research:
Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory.
Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics.
Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data.
Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions.
Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales.
Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory.
The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?).
This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor.
Multiple oscillator-type calculations contribute to dimensional analysis:
Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection.
Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized.
Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed.
Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional.
The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states.
While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space.
Volatility analysis permeates the system:
ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score.
Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility).
Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory.
Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable.
Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions.
The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator.
CLOSING STATEMENT
DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence .
This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity.
When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance .
These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state.
Most indicators see shadows on the wall. DRP reconstructs the cave.
"In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Get_rich_aggressively_v5# 🚀 GET RICH AGGRESSIVELY v5 - TIER SYSTEM
### Precision Futures Scalping | NQ • ES • YM • GC • BTC
### *Leave Every Trade With Money*
---
## 📋 QUICK CHEATSHEET
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ GRA v5 SIGNAL REQUIREMENTS │
├─────────────────────────────────────────────────────────────────────────────┤
│ ✓ TIER MET Points ≥ 10 (B), ≥ 50 (A), ≥ 100 (S) │
│ ✓ VOLUME ≥ 1.3x average │
│ ✓ DELTA ≥ 55% dominance (buyers OR sellers) │
│ ✓ DIRECTION Candle color = Delta direction │
│ ✓ SESSION In London (3-5AM) or NY (9:30-11:30AM) if filter ON │
├─────────────────────────────────────────────────────────────────────────────┤
│ TIER ACTIONS │
├─────────────────────────────────────────────────────────────────────────────┤
│ 🥇 S-TIER (100+ pts) │ HOLD LONGER │ Big institutional move │
│ 🥈 A-TIER (50-99 pts) │ HOLD A BIT │ Medium move, trail to BE │
│ 🥉 B-TIER (10-49 pts) │ CLOSE QUICK │ Scalp 5-10 pts, exit fast │
│ ❌ NO TIER (< 10 pts) │ NO TRADE │ Not enough conviction │
├─────────────────────────────────────────────────────────────────────────────┤
│ SESSION PRIORITY │
├─────────────────────────────────────────────────────────────────────────────┤
│ 🔵 LONDON OPEN 03:00-05:00 ET │ IB forms 03:00-04:00 │
│ 🟢 NY OPEN 09:30-11:30 ET │ IB forms 09:30-10:30 │
│ 📊 IB BREAKOUT Close beyond IB + Impulse + 1.3x Vol = HIGH CONVICTION│
├─────────────────────────────────────────────────────────────────────────────┤
│ VOLUME PROFILE ZONES │
├─────────────────────────────────────────────────────────────────────────────┤
│ 🔵 HVN (Blue BG) High volume = Support/Resistance, expect consolidation │
│ 🟡 LVN (Yellow BG) Low volume = Breakout acceleration, fast moves │
│ 🟣 POC Point of Control = Institutional fair value │
│ 🟣 VAH/VAL Value Area edges = S/R zones │
├─────────────────────────────────────────────────────────────────────────────┤
│ MARKET STATE DECODER │
├─────────────────────────────────────────────────────────────────────────────┤
│ TREND UP │ Price > EMA20 + CVD rising │ Trade WITH the trend │
│ TREND DN │ Price < EMA20 + CVD falling │ Trade WITH the trend │
│ RETRACE │ Price/CVD diverging │ Pullback, prepare for entry │
│ RANGE │ No clear direction │ Reduce size or skip │
├─────────────────────────────────────────────────────────────────────────────┤
│ 💎 HIGH CONVICTION UPGRADE │
├─────────────────────────────────────────────────────────────────────────────┤
│ Purple diamond (◆) appears when: │
│ • Strong delta (≥65%) + Strong volume (≥2x) + Market in imbalance │
│ → Consider upgrading tier (B→A, A→S) for position sizing │
└─────────────────────────────────────────────────────────────────────────────┘
```
---
## 🎯 THE TIER SYSTEM
The tier system classifies candles by **point movement** to determine trade management:
| Tier | Points | Action | Expected R:R |
|:----:|:------:|:------:|:------------:|
| 🥇 **S-TIER** | 100+ | HOLD LONGER | 2:1+ |
| 🥈 **A-TIER** | 50-99 | HOLD A BIT | 1.5:1 |
| 🥉 **B-TIER** | 10-49 | CLOSE QUICK | 1:1 |
| ❌ **NO TIER** | < 10 | NO TRADE | — |
---
## ✅ SIGNAL REQUIREMENTS
**ALL conditions must be TRUE for a signal:**
```
SIGNAL = TIER + VOLUME + DELTA + DIRECTION + SESSION
☐ Points ≥ 10 (minimum B-tier)
☐ Volume ≥ 1.3x average
☐ Delta dominance ≥ 55%
☐ Candle direction = Delta direction
☐ In session (if filter ON)
ANY FALSE = NO SIGNAL = NO TRADE
```
---
## 📊 VOLUME DOMINANCE ANALYSIS
This is the **core edge** of GRA v5. We use intrabar analysis to determine who is in control:
```
VOLUME ANALYSIS BREAKDOWN
Total Volume = Buy Volume + Sell Volume
Buy Volume: Who pushed price UP within the bar
Sell Volume: Who pushed price DOWN within the bar
Delta = Buy Volume - Sell Volume
Buy Dominance = Buy Volume / Total Volume
Sell Dominance = Sell Volume / Total Volume
≥ 55% = ONE SIDE IN CONTROL
≥ 65% = STRONG DOMINANCE (high conviction)
```
**Direction Confirmation Matrix:**
| Candle | Delta | Signal |
|:-------|:------|:-------|
| 🟢 Bullish | 55%+ Buyers | ✅ LONG |
| 🟢 Bullish | 55%+ Sellers | ❌ Trap |
| 🔴 Bearish | 55%+ Sellers | ✅ SHORT |
| 🔴 Bearish | 55%+ Buyers | ❌ Trap |
---
## 🕐 SESSION CONTEXT
### Initial Balance (IB) Framework
The **first hour** of each session establishes the IB range. Institutions use this for the day's framework.
```
SESSION WINDOWS (Eastern Time):
LONDON:
├── IB Period: 03:00 - 04:00 ← Range established
├── Trade Window: 03:00 - 05:00 ← Best signals
└── Extension Targets: 1.5x, 2.0x
NY:
├── IB Period: 09:30 - 10:30 ← Range established
├── Trade Window: 09:30 - 11:30 ← Best signals
└── Extension Targets: 1.5x, 2.0x
```
### IB Breakout Signals
```
L▲ / L▼ = London IB Breakout (Blue)
N▲ / N▼ = NY IB Breakout (Orange)
Confirmation Required:
☐ Close beyond IB level (not just wick)
☐ Impulse candle (body > 60% of range)
☐ Volume > 1.3x average
```
**IB Statistics:**
- 97% of days break either IB high or low
- 1.5x extension = first profit target
- 2.0x extension = full range target
- ~66% of London sessions sweep Asian high/low first
---
## 📈 VIRTUAL VOLUME PROFILE ZONES
GRA v5 calculates volume profile zones **without drawing the profile**, giving you the key levels:
### Zone Types
| Zone | Background | Meaning | Action |
|:-----|:-----------|:--------|:-------|
| **HVN** | 🔵 Blue | High Volume Node | S/R zone, expect consolidation |
| **LVN** | 🟡 Yellow | Low Volume Node | Breakout zone, fast acceleration |
| **POC** | 🟣 Purple dots | Point of Control | Institutional fair value |
| **VAH/VAL** | 🟣 Purple lines | Value Area edges | S/R boundaries |
### How to Use
```
ENTERING A TRADE:
At HVN:
├── Expect price to consolidate
├── Look for rejection/absorption
└── Better for reversals
At LVN:
├── Expect fast price movement
├── Don't fight the direction
└── Better for breakouts
Near POC:
├── Institutional fair value
├── Strong magnet effect
└── Watch for volume at POC
```
---
## 🔄 MARKET STATE DETECTION
GRA v5 classifies the market into four states using **CVD + Price Action**:
```
CVD Direction
↑ Rising ↓ Falling
┌─────────────┬─────────────┐
Price > EMA20 │ TREND UP │ RETRACE │
│ (Go Long) │ (Pullback) │
├─────────────┼─────────────┤
Price < EMA20 │ RETRACE │ TREND DN │
│ (Pullback) │ (Go Short) │
└─────────────┴─────────────┘
```
| State | Meaning | Action |
|:------|:--------|:-------|
| **TREND UP** | Buyers in control | Trade long, follow signals |
| **TREND DN** | Sellers in control | Trade short, follow signals |
| **RETRACE** | Pullback against trend | Prepare for continuation entry |
| **RANGE** | No clear direction | Reduce size or wait |
---
## 💎 HIGH CONVICTION UPGRADES
When extra conditions align, GRA v5 marks the signal with a **purple diamond**:
```
HIGH CONVICTION = Base Signal + Strong Delta (65%+) + Strong Volume (2x+) + Imbalance State
```
**Action:** Consider upgrading tier for position sizing:
- B-Tier → A-Tier management
- A-Tier → S-Tier management
---
## 📋 TRADING BY TIER
### 🥇 S-TIER (100+ points)
| | |
|:--|:--|
| **Entry** | Candle close |
| **Target** | IB extension / Next S/R |
| **Management** | HOLD LONGER |
**Rules:**
- Watch next candle - continues? HOLD
- Same tier same direction? ADD
- Opposite tier signal? EXIT on close
- Never close early unless reversal signal
### 🥈 A-TIER (50-99 points)
| | |
|:--|:--|
| **Entry** | Candle close |
| **Target** | 1.5x initial risk minimum |
| **Management** | HOLD A BIT |
**Rules:**
- Target 1.5:1 R:R minimum
- Trail to breakeven after 1:1
- If stalls, take profit
- Upgrade to S-tier management if high conviction
### 🥉 B-TIER (10-49 points)
| | |
|:--|:--|
| **Entry** | Candle close |
| **Target** | 5-10 points MAX |
| **Management** | CLOSE QUICK |
**Rules:**
- Exit in 1-3 candles
- DO NOT hold for more
- Any doubt = EXIT
- Quick scalp mentality
---
## ⚙️ SETTINGS BY INSTRUMENT
| Setting | NQ/ES | YM | GC | BTC |
|:--------|:-----:|:--:|:--:|:---:|
| **Timeframe** | 1-5 min | 1-5 min | 5-15 min | 1-15 min |
| **S-Tier** | 100 pts | 100 pts | 15 pts | 500 pts |
| **A-Tier** | 50 pts | 50 pts | 8 pts | 250 pts |
| **B-Tier** | 10 pts | 15 pts | 3 pts | 50 pts |
| **Min Volume** | 1.3x | 1.3x | 1.5x | 1.3x |
| **Delta %** | 55% | 55% | 58% | 55% |
| **Best Time** | 9:30-11:30 ET | 9:30-11:30 ET | 3-5AM & 8:30-10:30 ET | 24/7 |
---
## 📊 TABLE LEGEND
The info panel displays real-time market data:
| Row | Shows | Colors |
|:----|:------|:-------|
| **Pts** | Candle points | Gold/Green/Yellow by tier |
| **Tier** | S/A/B/X | Gold/Green/Yellow/White |
| **Vol** | Volume ratio | Yellow (2x+) / Green (1.3x+) / Red |
| **Delta** | Buy/Sell % | Green (buy) / Red (sell) / White |
| **CVD** | Direction | Green ▲ / Red ▼ |
| **State** | Market state | Green/Red/Orange/Gray |
| **Sess** | Session | Yellow if active |
| **Zone** | VP zone | Blue/Yellow/Purple |
| **Sig** | Signal | Green/Red if active |
---
## 🔔 ALERTS
| Alert | When | Action |
|:------|:-----|:-------|
| **S-TIER LONG/SHORT** | S-tier signal | Hold longer |
| **A-TIER LONG/SHORT** | A-tier signal | Hold a bit |
| **B-TIER LONG/SHORT** | B-tier signal | Close quick |
| **LON IB BREAK UP/DN** | London IB breakout | Major session move |
| **NY IB BREAK UP/DN** | NY IB breakout | Major session move |
| **HIGH CONVICTION** | Upgraded signal | Consider larger size |
| **LONDON/NY OPEN** | Session start | Get ready |
---
## 💰 THE GOLDEN RULE
> ### **LEAVE EVERY TRADE WITH MONEY**
>
> | Situation | Rule |
> |:----------|:-----|
> | B-Tier | Small win > Small loss |
> | A-Tier | Trail to BE, lock profit |
> | S-Tier | Let it run to target |
> | No Signal | NO TRADE |
> | Wrong Side | EXIT immediately |
>
> **Capital preserved = Trade tomorrow**
---
## ⚠️ DISCLAIMER
> Risk management is **YOUR** responsibility.
> Never risk more than 1-2% per trade.
> Paper trade until you understand the signals.
> Past performance ≠ future results.
---
### Get Rich. Stay Rich. Trade Aggressively. 🚀
**Get Rich Aggressively v5**
*Precision Futures Scalping*
Strat Reversal MTF TableStrat Reversal MTF Table — Your Complete Multi-Timeframe Strat Command Center
Take your Strat trading to the next level with an indicator that shows every reversal, on every timeframe, in one powerful visual dashboard.
Designed for traders who demand speed, clarity, and full Strat alignment, the Strat Reversal MTF Table instantly identifies all major bullish and bearish reversal patterns:
Bullish Patterns
2-1-2
3-1-2
1-3-2
3-2-2
Bearish Patterns
2-1-2
3-1-2
1-3-2
3-2-2
Each signal is displayed with:
Clear pattern name (e.g., “2-1-2 Bull”)
Automatic trigger price
Timeframe label
Color-coded background (Bullish / Bearish / Neutral)
Whether you trade options, equities, futures, or crypto, this indicator makes it effortless to see what’s flipping — and where the strongest setups are emerging.
🔥 Key Features
📊 Multi-Timeframe Scanning (1 min → Daily)
Monitor 7 customizable timeframes at once.
From scalping to swing trading, you always know which timeframe is turning.
⚡ Real-Time OR Close-Confirmed Logic
Choose your style:
Realtime (Wick Mode) → Fast entries
Close-Confirmed → Stronger validation
Ideal for traders who want precision on any timeframe.
🎨 Clean & Customizable Dashboard
Move the table anywhere on the chart
Adjust text size
Choose your own colors
Lightweight and non-intrusive
A perfect blend of simplicity and power.
📩 Instant Alerts, Built In
Get notified instantly when:
Any timeframe reverses
A specific timeframe flips
Multiple reversals fire across the stack
The indicator works great with TradingView’s push notifications, email, and webhooks.
🎯 What This Helps You Do
✔ Catch Strat reversals as they happen
✔ Quickly spot full-timeframe alignment
✔ Improve your entries for options plays
✔ Avoid chop by reading higher-timeframe intent
✔ Trade more confidently with automated trigger levels
This indicator is built for Strat traders who want to trade smarter, faster, and cleaner.
✨ Perfect For
Strat Traders
Options Traders
Futures Scalpers
Intraday & Swing Traders
Quant/Algo-inspired traders
Anyone following Rob Smith’s methodology
Mancini Levels (with alerts, majors only option)This indicator displays Support and Resistance levels on ES or MES (E-mini and Micro E-mini S&P 500 Index Futures) charts by parsing text copied and pasted by the user.
(The levels displayed on the chart above are not valid, they are for illustration only)
Features
Option to display only the major levels
The chart on the left displays both major and minor levels, distinguished by color and line style. The chart on the right shows only the major levels; minor levels are disabled:
Alert function for when the price approaches a major level or zone (within a customizable distance).
The script provides a trigger for alerts. When creating an alert, you can then choose your desired frequency (Only once/Once per bar/Once per bar close/Once per minute) from the TradingView alert pop-up.
The alert message contains the current price and the approached major level price.
Customizable Lookback Period
Set how many days into the past the lines should appear (Subject to a maximum of 5000 bars).
To display lines for the current day only, set this value to 1.
Functions only on ES or MES (E-mini and Micro E-mini S&P 500 Index Futures) charts, as the text format is intended for these instruments.
How to Use
Copy and paste the support and resistance levels into the indicator's "Supports" and "Resistances" input fields.
Format Example:
For the "Supports" input: 6772-6770 (major), 6764 (major), 6757, 6751-54
For the "Resistances" input: 6799 (major), 6814, 6828-30, 6839-40 (major)
The indicator supports the display of zone levels in multiple formats
(e.g., 6235-45 and 6235-6245 and 6245-6235 are all valid).
For hundred- or thousand-point rollovers, please use only the full number format: 5995-6005.
The indicator includes an error-checking system to help you troubleshoot common setup issues.
An on-chart error label will be displayed on the chart if:
The chart instrument is not ES or MES.
The "Supports" and "Resistances" fields are both empty.
A data formatting error is detected (e.g., non-numeric characters, incomplete zones, etc.).
How It Works
For optimal resource efficiency and performance, the script executes all computationally intensive tasks only once, on the very first bar when the chart loads (if barstate.isfirst).
One-time Parsing: The parsing, splitting, and conversion of the text (string) formatted levels, which are provided in the settings, occurs only once.
Persistent Objects: The lines (line.new), fills (linefill.new), and price labels (label.new) that mark the levels are all persistent graphical objects. The script creates these on the first bar and stores their references in arrays declared with the var keyword.
No Redrawing: On subsequent bars, the indicator does not delete and redraw these objects. It merely updates the x-axis position of the existing lines and labels (line.set_x1, line.set_x2, label.set_x) on the last bar (if barstate.islast), ensuring they always remain on the right edge of the chart, following the formation of new bars.
By default, TradingView charts have a limit of 50 lines and 50 labels. Given that the number of levels often exceeds this, the script's drawing logic is as follows:
The number of displayable lines and labels has been increased (to 500) in the indicator's declaration line.
The script applies a prioritized order when drawing levels and labels. Major levels have priority over minor levels during drawing.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice.
Trading involves substantial risk of loss and is not suitable for every investor. Past performance shown in examples is not indicative of future results.
The indicator provides signals and calculations, but trading decisions are solely your responsibility. Always:
Test strategies on paper before using real money
Never risk more than you can afford to lose
Understand that all trading involves risk
Consider seeking advice from a licensed financial advisor
The publisher makes no guarantees regarding accuracy, profitability, or performance. Use at your own risk.
MTF Scalper - alemicihanMulti-Timeframe Scalper Strategy: Aligning the Big Picture for Quick Gains
This article presents a robust futures trading strategy designed for high-frequency scalping in the crypto market. It’s built on the principle of minimizing risk by ensuring that short-term entries are always aligned with the dominant, higher-timeframe trend.
The Core Concept: Alignment is Key
A Balanced Trend Follower approach, now refined for rapid scalping, uses a Multi-Timeframe (MTF) confirmation system to filter out market noise and increase the probability of a successful trade.
The strategy operates on a Low Timeframe (LTF) chart (e.g., 3m, 5m, or 15m) but only executes trades if the direction is validated by three Higher Timeframes (HTF).
ComponentPurposeFunctionHTF (D, 4h, 1h) EMA => Trend Confirmation =>Checks if the current price is above/below all three Exponential Moving Averages (EMA 20). This provides a strong directional bias.
LTF (5m) Stochastic RSI => Momentum Entry => Generates the actual buy/sell signal by spotting a swift crossover, indicating fresh momentum in the direction of the confirmed HTF trend.
How The Signal Is Generated
Trend Alignment: The system first confirms the trend. If the price is trading above the Daily, 4-Hour, and 1-Hour EMAs, the market is deemed to be in a Strong LONG Trend. Only LONG signals are permitted.
Momentum Trigger: Once the trend is confirmed, a Long Signal is generated only when the Stochastic K-Line crosses above the D-Line, indicating a momentum shift (a pullback ending) towards the main trend direction.
Short Signal: The inverse logic applies to the Short Trend confirmation and entry signal.
Mandatory Risk Management: ATR-Based Exit
Given the high leverage nature of futures and scalping, static Stop-Loss (SL) and Take-Profit (TP) levels are inefficient. This strategy uses the Average True Range (ATR) indicator to dynamically set profit and loss targets based on current market volatility.
Stop Loss (SL): Set dynamically at 1.5 x ATR below (for long) or above (for short) the entry price. This gives the trade enough room to breathe without risking excessive capital.
Take Profit (TP): Set dynamically at 3.0 x ATR, establishing a robust Risk-to-Reward Ratio of 1:2.
Final Thoughts on Testing
This sophisticated approach combines the reliability of MTF analysis with the speed of momentum indicators. However, data analysis is key. Backtesting these parameters (EMA, ATR Multipliers, RSI/Stochastic lengths) on your chosen asset (like BTC/USDT or ETH/USDT) and timeframe is crucial to achieving optimal performance.
Coinbase Premium Index (Custom Tickers)📊 Coinbase Premium Index (Auto Symbol Support)
1. Overview
The Coinbase Premium Index is a widely used indicator to gauge the sentiment difference between US institutional investors (Coinbase Pro) and global retail/futures traders (Binance).
This script calculates the percentage difference between the Coinbase (USD pair) price and the Binance (USDT pair) price.
2. Key Features
🔄 Auto Symbol Matching (New): You no longer need to manually change tickers when switching charts.
If you are looking at a SOL/USDT chart, the indicator automatically detects "SOL" and compares COINBASE:SOLUSD vs BINANCE:SOLUSDT.
🛠 Manual Mode: Includes a manual override option if you wish to compare specific fixed tickers (e.g., strictly BTC).
🎨 Dynamic Visuals:
Histogram: Color-coded bars (Green/Red) indicate positive or negative premiums.
Smart Label: Displays the real-time premium value on the chart. The label color adapts to the trend, and hovering over it shows a Tooltip confirming exactly which tickers are being compared.
3. How to Interpret
The premium indicates the flow of funds and buying pressure:
🟢 Positive Premium (Green Bar):
Coinbase Price > Binance Price
Interpretation: Strong buying pressure from US institutions or spot whales. Often considered a Bullish signal.
🔴 Negative Premium (Red Bar):
Coinbase Price < Binance Price
Interpretation: Strong selling from US investors, or overheated buying in the offshore futures market (Binance). Often considered a Bearish or mean-reversion signal.
4. Settings Guide
Ticker Mode:
Auto (Current Chart): Automatically sets the comparison based on your current chart's base currency (Recommended).
Manual (Custom): Uses the specific tickers defined in the manual input fields below.
Manual Inputs: Enter tickers here if using Manual Mode (Default: COINBASE:BTCUSD vs BINANCE:BTCUSDT).
Bar & Label Settings: Customize colors, transparency, and the vertical position (Y-Offset) of the data label to fit your chart layout.
Mars Signals - Ultimate Institutional Suite v3.0(Joker)Comprehensive Trading Manual
Mars Signals – Ultimate Institutional Suite v3.0 (Joker)
## Chapter 1 – Philosophy & System Architecture
This script is not a simple “buy/sell” indicator.
Mars Signals – UIS v3.0 (Joker) is designed as an institutional-style analytical assistant that layers several methodologies into a single, coherent framework.
The system is built on four core pillars:
1. Smart Money Concepts (SMC)
- Detection of Order Blocks (professional demand/supply zones).
- Detection of Fair Value Gaps (FVGs) (price imbalances).
2. Smart DCA Strategy
- Combination of RSI and Bollinger Bands
- Identifies statistically discounted zones for scaling into spot positions or exiting shorts.
3. Volume Profile (Visible Range Simulation)
- Distribution of volume by price, not by time.
- Identification of POC (Point of Control) and high-/low-volume areas.
4. Wyckoff Helper – Spring
- Detection of bear traps, liquidity grabs, and sharp bullish reversals.
All four pillars feed into a Confluence Engine (Scoring System).
The final output is presented in the Dashboard, with a clear, human-readable signal:
- STRONG LONG 🚀
- WEAK LONG ↗
- NEUTRAL / WAIT
- WEAK SHORT ↘
- STRONG SHORT 🩸
This allows the trader to see *how many* and *which* layers of the system support a bullish or bearish bias at any given time.
## Chapter 2 – Settings Overview
### 2.1 General & Dashboard Group
- Show Dashboard Panel (`show_dash`)
Turns the dashboard table in the corner of the chart ON/OFF.
- Show Signal Recommendation (`show_rec`)
- If enabled, the textual signal (STRONG LONG, WEAK SHORT, etc.) is displayed.
- If disabled, you only see feature status (ON/OFF) and the current price.
- Dashboard Position (`dash_pos`)
Determines where the dashboard appears on the chart:
- `Top Right`
- `Bottom Right`
- `Top Left`
### 2.2 Smart Money (SMC) Group
- Enable SMC Strategy (`show_smc`)
Globally enables or disables the Order Block and FVG logic.
- Order Block Pivot Lookback (`ob_period`)
Main parameter for detecting key pivot highs/lows (swing points).
- Default value: 5
- Concept:
A bar is considered a pivot low if its low is lower than the lows of the previous 5 and the next 5 bars.
Similarly, a pivot high has a high higher than the previous 5 and the next 5 bars.
These pivots are used as anchors for Order Blocks.
- Increasing `ob_period`:
- Fewer levels.
- But levels tend to be more significant and reliable.
- In highly volatile markets (major news, war events, FOMC, etc.),
using values 7–10 is recommended to filter out weak levels.
- Show Fair Value Gaps (`show_fvg`)
Enables/disables the drawing of FVG zones (imbalances).
- Bullish OB Color (`c_ob_bull`)
- Color of Bullish Order Blocks (Demand Zones).
- Default: semi-transparent green (transparency ≈ 80).
- Bearish OB Color (`c_ob_bear`)
- Color of Bearish Order Blocks (Supply Zones).
- Default: semi-transparent red.
- Bullish FVG Color (`c_fvg_bull`)
- Color of Bullish FVG (upward imbalance), typically yellow.
- Bearish FVG Color (`c_fvg_bear`)
- Color of Bearish FVG (downward imbalance), typically purple.
### 2.3 Smart DCA Strategy Group
- Enable DCA Zones (`show_dca`)
Enables the Smart DCA logic and visual labels.
- RSI Length (`rsi_len`)
Lookback period for RSI (default: 14).
- Shorter → more sensitive, more noise.
- Longer → fewer signals, higher reliability.
- Bollinger Bands Length (`bb_len`)
Moving average period for Bollinger Bands (default: 20).
- BB Multiplier (`bb_mult`)
Standard deviation multiplier for Bollinger Bands (default: 2.0).
- For extremely volatile markets, values like 2.5–3.0 can be used so that only extreme deviations trigger a DCA signal.
### 2.4 Volume Profile (Visible Range Sim) Group
- Show Volume Profile (`show_vp`)
Enables the simulated Volume Profile bars on the right side of the chart.
- Volume Lookback Bars (`vp_lookback`)
Number of bars used to compute the Volume Profile (default: 150).
- Higher values → broader historical context, heavier computation.
- Row Count (`vp_rows`)
Number of vertical price segments (rows) to divide the total price range into (default: 30).
- Width (%) (`vp_width`)
Relative width of each volume bar as a percentage.
In the code, bar widths are scaled relative to the row with the maximum volume.
> Technical note: Volume Profile calculations are executed only on the last bar (`barstate.islast`) to keep the script performant even on higher timeframes.
### 2.5 Wyckoff Helper Group
- Show Wyckoff Events (`show_wyc`)
Enables detection and plotting of Wyckoff Spring events.
- Volume MA Length (`vol_ma_len`)
Length of the moving average on volume.
A bar is considered to have Ultra Volume if its volume is more than 2× the volume MA.
## Chapter 3 – Smart Money Strategy (Order Blocks & FVG)
### 3.1 What Is an Order Block?
An Order Block (OB) represents the footprint of large institutional orders:
- Bullish Order Block (Demand Zone)
The last selling region (bearish candle/cluster) before a strong upward move.
- Bearish Order Block (Supply Zone)
The last buying region (bullish candle/cluster) before a strong downward move.
Institutions and large players place heavy orders in these regions. Typical price behavior:
- Price moves away from the zone.
- Later returns to the same zone to fill unfilled orders.
- Then continues the larger trend.
In the script:
- If `pl` (pivot low) forms → a Bullish OB is created.
- If `ph` (pivot high) forms → a Bearish OB is created.
The box is drawn:
- From `bar_index ` to `bar_index`.
- Between `low ` and `high `.
- `extend=extend.right` extends the OB into the future, so it acts as a dynamic support/resistance zone.
- Only the last 4 OB boxes are kept to avoid clutter.
### 3.2 Order Block Color Guide
- Semi-transparent Green (`c_ob_bull`)
- Represents a Bullish Order Block (Demand Zone).
- Interpretation: a price region with a high probability of bullish reaction.
- Semi-transparent Red (`c_ob_bear`)
- Represents a Bearish Order Block (Supply Zone).
- Interpretation: a price region with a high probability of bearish reaction.
Overlap (Multiple OBs in the Same Area)
When two or more Order Blocks overlap:
- The shared area appears visually denser/stronger.
- This suggests higher order density.
- Such zones can be treated as high-priority levels for entries, exits, and stop-loss placement.
### 3.3 Demand/Supply Logic in the Scoring Engine
is_in_demand = low <= ta.lowest(low, 20)
is_in_supply = high >= ta.highest(high, 20)
- If current price is near the lowest lows of the last 20 bars, it is considered in a Demand Zone → positive impact on score.
- If current price is near the highest highs of the last 20 bars, it is considered in a Supply Zone → negative impact on score.
This logic complements Order Blocks and helps the Dashboard distinguish whether:
- Market is currently in a statistically cheap (long-friendly) area, or
- In a statistically expensive (short-friendly) area.
### 3.4 Fair Value Gaps (FVG)
#### Concept
When the market moves aggressively:
- Some price levels are skipped and never traded.
- A gap between wicks/shadows of consecutive candles appears.
- These regions are called Fair Value Gaps (FVGs) or Imbalances.
The market generally “dislikes” imbalance and often:
- Returns to these zones in the future.
- Fills the gap (rebalance).
- Then resumes its dominant direction.
#### Implementation in the Code
Bullish FVG (Yellow)
fvg_bull_cond = show_smc and show_fvg and low > high and close > high
if fvg_bull_cond
box.new(bar_index , high , bar_index, low, ...)
Core condition:
`low > high ` → the current low is above the high of two bars ago; the space between them is an untraded gap.
Bearish FVG (Purple)
fvg_bear_cond = show_smc and show_fvg and high < low and close < low
if fvg_bear_cond
box.new(bar_index , low , bar_index, high, ...)
Core condition:
`high < low ` → the current high is below the low of two bars ago; again a price gap exists.
#### FVG Color Guide
- Transparent Yellow (`c_fvg_bull`) – Bullish FVG
Often acts like a magnet for price:
- Price tends to retrace into this zone,
- Fill the imbalance,
- And then continue higher.
- Transparent Purple (`c_fvg_bear`) – Bearish FVG
Price tends to:
- Retrace upward into the purple area,
- Fill the imbalance,
- And then resume downward movement.
#### Trading with FVGs
- FVGs are *not* standalone entry signals.
They are best used as:
- Targets (take-profit zones), or
- Reaction areas where you expect a pause or reversal.
Examples:
- If you are long, a bearish FVG above is often an excellent take-profit zone.
- If you are short, a bullish FVG below is often a good cover/exit zone.
### 3.5 Core SMC Trading Templates
#### Reversal Long
1. Price trades down into a green Order Block (Demand Zone).
2. A bullish confirmation candle (Close > Open) forms inside or just above the OB.
3. If this zone is close to or aligned with a bullish FVG (yellow), the signal is reinforced.
4. Entry:
- At the close of the confirmation candle, or
- Using a limit order near the upper boundary of the OB.
5. Stop-loss:
- Slightly below the OB.
- If the OB is broken decisively and price consolidates below it, the zone loses validity.
6. Targets:
- The next FVG,
- Or the next red Order Block (Supply Zone) above.
#### Reversal Short
The mirror scenario:
- Price rallies into a red Order Block (Supply).
- A bearish confirmation candle forms (Close < Open).
- FVG/premium structure above can act as a confluence.
- Stop-loss goes above the OB.
- Targets: lower FVGs or subsequent green OBs below.
## Chapter 4 – Smart DCA Strategy (RSI + Bollinger Bands)
### 4.1 Smart DCA Concept
- Classic DCA = buying at fixed time intervals regardless of price.
- Smart DCA = scaling in only when:
- Price is statistically cheaper than usual, and
- The market is in a clear oversold condition.
Code logic:
rsi_val = ta.rsi(close, rsi_len)
= ta.bb(close, bb_len, bb_mult)
dca_buy = show_dca and rsi_val < 30 and close < bb_lower
dca_sell = show_dca and rsi_val > 70 and close > bb_upper
Conditions:
- DCA Buy – Smart Scale-In Zone
- RSI < 30 → oversold.
- Close < lower Bollinger Band → price has broken below its typical volatility envelope.
- DCA Sell – Overbought/Distribution Zone
- RSI > 70 → overbought.
- Close > upper Bollinger Band → price is extended far above the mean.
### 4.2 Visual Representation on the Chart
- Green “DCA” Label Below Candle
- Shape: `labelup`.
- Color: lime background, white text.
- Meaning: statistically attractive level for laddered spot entries or short exits.
- Red “SELL” Label Above Candle
- Warning that the market is in an extended, overbought condition.
- Suitable for profit-taking on longs or considering short entries (with proper confluence and risk management).
- Light Green Background (`bgcolor`)
- When `dca_buy` is true, the candle background turns very light green (high transparency).
- This helps visually identify DCA Zones across the chart at a glance.
### 4.3 Practical Use in Trading
#### Spot Trading
Used to build a better average entry price:
- Every time a DCA label appears, allocate a fixed portion of capital (e.g., 2–5%).
- Combining DCA signals with:
- Green OBs (Demand Zones), and/or
- The Volume Profile POC
makes the zone structurally more important.
#### Futures Trading
- Longs
- Use DCA Buy signals as low-risk zones for opening or adding to longs when:
- Price is inside a green OB, or
- The Dashboard already leans LONG.
- Shorts
- Use DCA Sell signals as:
- Exit zones for longs, or
- Areas to initiate shorts with stops above structural highs.
## Chapter 5 – Volume Profile (Visible Range Simulation)
### 5.1 Concept
Traditional volume (histogram under the chart) shows volume over time.
Volume Profile shows volume by price level:
- At which prices has the highest trading activity occurred?
- Where did buyers and sellers agree the most (High Volume Nodes – HVNs)?
- Where did price move quickly due to low participation (Low Volume Nodes – LVNs)?
### 5.2 Implementation in the Script
Executed only when `show_vp` is enabled and on the last bar:
1. The last `vp_lookback` bars (default 150) are processed.
2. The minimum low and maximum high over this window define the price range.
3. This price range is divided into `vp_rows` segments (e.g., 30 rows).
4. For each row:
- All bars are scanned.
- If the mid-price `(high + low ) / 2` falls inside a row, that bar’s volume is added to the row total.
5. The row with the greatest volume is stored as `max_vol_idx` (the POC row).
6. For each row, a volume box is drawn on the right side of the chart.
### 5.3 Color Scheme
- Semi-transparent Orange
- The row with the maximum volume – the Point of Control (POC).
- Represents the strongest support/resistance level from a volume perspective.
- Semi-transparent Blue
- Other volume rows.
- The taller the bar → the higher the volume → the stronger the interest at that price band.
### 5.4 Trading Applications
- If price is above POC and retraces back into it:
→ POC often acts as support, suitable for long setups.
- If price is below POC and rallies into it:
→ POC often acts as resistance, suitable for short setups or profit-taking.
HVNs (Tall Blue Bars)
- Represent areas of equilibrium where the market has spent time and traded heavily.
- Price tends to consolidate here before choosing a direction.
LVNs (Short or Nearly Empty Bars)
- Represent low participation zones.
- Price often moves quickly through these areas – useful for targeting fast moves.
## Chapter 6 – Wyckoff Helper – Spring
### 6.1 Spring Concept
In the Wyckoff framework:
- A Spring is a false break of support.
- The market briefly trades below a well-defined support level, triggers stop losses,
then sharply reverses upward as institutional buyers absorb liquidity.
This movement:
- Clears out weak hands (retail sellers).
- Provides large players with liquidity to enter long positions.
- Often initiates a new uptrend.
### 6.2 Code Logic
Conditions for a Spring:
1. The current low is lower than the lowest low of the previous 50 bars
→ apparent break of a long-standing support.
2. The bar closes bullish (Close > Open)
→ the breakdown was rejected.
3. Volume is significantly elevated:
→ `volume > 2 × volume_MA` (Ultra Volume).
When all conditions are met and `show_wyc` is enabled:
- A pink diamond is plotted below the bar,
- With the label “Spring” – one of the strongest long signals in this system.
### 6.3 Trading Use
- After a valid Spring, markets frequently enter a meaningful bullish phase.
- The highest quality setups occur when:
- The Spring forms inside a green Order Block, and
- Near or on the Volume Profile POC.
Entries:
- At the close of the Spring bar, or
- On the first pullback into the mid-range of the Spring candle.
Stop-loss:
- Slightly below the Spring’s lowest point (wick low plus a small buffer).
## Chapter 7 – Confluence Engine & Dashboard
### 7.1 Scoring Logic
For each bar, the script:
1. Resets `score` to 0.
2. Adjusts the score based on different signals.
SMC Contribution
if show_smc
if is_in_demand
score += 1
if is_in_supply
score -= 1
- Being in Demand → `+1`
- Being in Supply → `-1`
DCA Contribution
if show_dca
if dca_buy
score += 2
if dca_sell
score -= 2
- DCA Buy → `+2` (strong, statistically driven long signal)
- DCA Sell → `-2`
Wyckoff Spring Contribution
if show_wyc
if wyc_spring
score += 2
- Spring → `+2` (entry of strong money)
### 7.2 Mapping Score to Dashboard Signal
- score ≥ 2 → STRONG LONG 🚀
Multiple bullish conditions aligned.
- score = 1 → WEAK LONG ↗
Some bullish bias, but only one layer clearly positive.
- score = 0 → NEUTRAL / WAIT
Rough balance between buying and selling forces; staying flat is usually preferable.
- score = -1 → WEAK SHORT ↘
Mild bearish bias, suited for cautious or short-term plays.
- score ≤ -2 → STRONG SHORT 🩸
Convergence of several bearish signals.
### 7.3 Dashboard Structure
The dashboard is a two-column table:
- Row 0
- Column 0: `"Mars Signals"` – black background, white text.
- Column 1: `"UIS v3.0"` – black background, yellow text.
- Row 1
- Column 0: `"Price:"` (light grey background).
- Column 1: current closing price (`close`) with a semi-transparent blue background.
- Row 2
- Column 0: `"SMC:"`
- Column 1:
- `"ON"` (green) if `show_smc = true`
- `"OFF"` (grey) otherwise.
- Row 3
- Column 0: `"DCA:"`
- Column 1:
- `"ON"` (green) if `show_dca = true`
- `"OFF"` (grey) otherwise.
- Row 4
- Column 0: `"Signal:"`
- Column 1: signal text (`status_txt`) with background color `status_col`
(green, red, teal, maroon, etc.)
- If `show_rec = false`, these cells are cleared.
## Chapter 8 – Visual Legend (Colors, Shapes & Actions)
For quick reading inside TradingView, the visual elements are described line by line instead of a table.
Chart Element: Green Box
Color / Shape: Transparent green rectangle
Core Meaning: Bullish Order Block (Demand Zone)
Suggested Trader Response: Look for longs, Smart DCA adds, closing or reducing shorts.
Chart Element: Red Box
Color / Shape: Transparent red rectangle
Core Meaning: Bearish Order Block (Supply Zone)
Suggested Trader Response: Look for shorts, or take profit on existing longs.
Chart Element: Yellow Area
Color / Shape: Transparent yellow zone
Core Meaning: Bullish FVG / upside imbalance
Suggested Trader Response: Short take-profit zone or expected rebalance area.
Chart Element: Purple Area
Color / Shape: Transparent purple zone
Core Meaning: Bearish FVG / downside imbalance
Suggested Trader Response: Long take-profit zone or temporary supply region.
Chart Element: Green "DCA" Label
Color / Shape: Green label with white text, plotted below the candle
Core Meaning: Smart ladder-in buy zone, DCA buy opportunity
Suggested Trader Response: Spot DCA entry, partial short exit.
Chart Element: Red "SELL" Label
Color / Shape: Red label with white text, plotted above the candle
Core Meaning: Overbought / distribution zone
Suggested Trader Response: Take profit on longs, consider initiating shorts.
Chart Element: Light Green Background (bgcolor)
Color / Shape: Very transparent light-green background behind bars
Core Meaning: Active DCA Buy zone
Suggested Trader Response: Treat as a discount zone on the chart.
Chart Element: Orange Bar on Right
Color / Shape: Transparent orange horizontal bar in the volume profile
Core Meaning: POC – price with highest traded volume
Suggested Trader Response: Strong support or resistance; key reference level.
Chart Element: Blue Bars on Right
Color / Shape: Transparent blue horizontal bars in the volume profile
Core Meaning: Other volume levels, showing high-volume and low-volume nodes
Suggested Trader Response: Use to identify balance zones (HVN) and fast-move corridors (LVN).
Chart Element: Pink "Spring" Diamond
Color / Shape: Pink diamond with white text below the candle
Core Meaning: Wyckoff Spring – liquidity grab and potential major bullish reversal
Suggested Trader Response: One of the strongest long signals in the suite; look for high-quality long setups with tight risk.
Chart Element: STRONG LONG in Dashboard
Color / Shape: Green background, white text in the Signal row
Core Meaning: Multiple bullish layers in confluence
Suggested Trader Response: Consider initiating or increasing longs with strict risk management.
Chart Element: STRONG SHORT in Dashboard
Color / Shape: Red background, white text in the Signal row
Core Meaning: Multiple bearish layers in confluence
Suggested Trader Response: Consider initiating or increasing shorts with a logical, well-placed stop.
## Chapter 9 – Timeframe-Based Trading Playbook
### 9.1 Timeframe Selection
- Scalping
- Timeframes: 1M, 5M, 15M
- Objective: fast intraday moves (minutes to a few hours).
- Recommendation: focus on SMC + Wyckoff.
Smart DCA on very low timeframes may introduce excessive noise.
- Day Trading
- Timeframes: 15M, 1H, 4H
- Provides a good balance between signal quality and frequency.
- Recommendation: use the full stack – SMC + DCA + Volume Profile + Wyckoff + Dashboard.
- Swing Trading & Position Investing
- Timeframes: Daily, Weekly
- Emphasis on Smart DCA + Volume Profile.
- SMC and Wyckoff are used mainly to fine-tune swing entries within larger trends.
### 9.2 Scenario A – Scalping Long
Example: 5-Minute Chart
1. Price is declining into a green OB (Bullish Demand).
2. A candle with a long lower wick and bullish close (Pin Bar / Rejection) forms inside the OB.
3. A Spring diamond appears below the same candle → very strong confluence.
4. The Dashboard shows at least WEAK LONG ↗, ideally STRONG LONG 🚀.
5. Entry:
- On the close of the confirmation candle, or
- On the first pullback into the mid-range of that candle.
6. Stop-loss:
- Slightly below the OB.
7. Targets:
- Nearby bearish FVG above, and/or
- The next red OB.
### 9.3 Scenario B – Day-Trading Short
Recommended Timeframes: 1H or 4H
1. The market completes a strong impulsive move upward.
2. Price enters a red Order Block (Supply).
3. In the same zone, a purple FVG appears or remains unfilled.
4. On a lower timeframe (e.g., 15M), RSI enters overbought territory and a DCA Sell signal appears.
5. The main timeframe Dashboard (1H) shows WEAK SHORT ↘ or STRONG SHORT 🩸.
Trade Plan
- Open a short near the upper boundary of the red OB.
- Place the stop above the OB or above the last swing high.
- Targets:
- A yellow FVG lower on the chart, and/or
- The next green OB (Demand) below.
### 9.4 Scenario C – Swing / Investment with Smart DCA
Timeframes: Daily / Weekly
1. On the daily or weekly chart, each time a green “DCA” label appears:
- Allocate a fixed fraction of your capital (e.g., 3–5%) to that asset.
2. Check whether this DCA zone aligns with the orange POC of the Volume Profile:
- If yes → the quality of the entry zone is significantly higher.
3. If the DCA signal sits inside a daily green OB, the probability of a medium-term bottom increases.
4. Always build the position laddered, never all-in at a single price.
Exits for investors:
- Near weekly red OBs or large purple FVG zones.
- Ideally via partial profit-taking rather than closing 100% at once.
### 9.5 Case Study 1 – BTCUSDT (15-Minute)
- Context: Price has sold off down towards 65,000 USD.
- A green OB had previously formed at that level.
- Near the lower boundary of this OB, a partially filled yellow FVG is present.
- As price returns to this region, a Spring appears.
- The Dashboard shifts from NEUTRAL / WAIT to WEAK LONG ↗.
Plan
- Enter a long near the OB low.
- Place stop below the Spring low.
- First target: a purple FVG around 66,200.
- Second (optional) target: the first red OB above that level.
### 9.6 Case Study 2 – Meme Coin (PEPE – 4H)
- After a strong pump, price enters a corrective phase.
- On the 4H chart, RSI drops below 30; price breaks below the lower Bollinger Band → a DCA label prints.
- The Volume Profile shows the POC at approximately the same level.
- The Dashboard displays STRONG LONG 🚀.
Plan
- Execute laddered buys in the combined DCA + POC zone.
- Place a protective stop below the last significant swing low.
- Target: an expected 20–30% upside move towards the next red OB or purple FVG.
## Chapter 10 – Risk Management, Psychology & Advanced Tuning
### 10.1 Risk Management
No signal, regardless of its strength, replaces risk control.
Recommendations:
- In futures, do not expose more than 1–3% of account equity to risk per trade.
- Adjust leverage to the volatility of the instrument (lower leverage for highly volatile altcoins).
- Place stop-losses in zones where the idea is clearly invalidated:
- Below/above the relevant Order Block or Spring, not randomly in the middle of the structure.
### 10.2 Market-Specific Parameter Tuning
- Calmer Markets (e.g., major FX pairs)
- `ob_period`: 3–5.
- `bb_mult`: 2.0 is usually sufficient.
- Highly Volatile Markets (Crypto, news-driven assets)
- `ob_period`: 7–10 to highlight only the most robust OBs.
- `bb_mult`: 2.5–3.0 so that only extreme deviations trigger DCA.
- `vol_ma_len`: increase (e.g., to ~30) so that Spring triggers only on truly exceptional
volume spikes.
### 10.3 Trading Psychology
- STRONG LONG 🚀 does not mean “risk-free”.
It means the probability of a successful long, given the model’s logic, is higher than average.
- Treat Mars Signals as a confirmation and context system, not a full replacement for your own decision-making.
- Example of disciplined thinking:
- The Dashboard prints STRONG LONG,
- But price is simultaneously testing a multi-month macro resistance or a major negative news event is imminent,
- In such cases, trade smaller, widen stops appropriately, or skip the trade.
## Chapter 11 – Technical Notes & FAQ
### 11.1 Does the Script Repaint?
- Order Blocks and Springs are based on completed pivot structures and confirmed candles.
- Until a pivot is confirmed, an OB does not exist; after confirmation, behavior is stable under classic SMC assumptions.
- The script is designed to be structurally consistent rather than repainting signals arbitrarily.
### 11.2 Computational Load of Volume Profile
- On the last bar, the script processes up to `vp_lookback` bars × `vp_rows` rows.
- On very low timeframes with heavy zooming, this can become demanding.
- If you experience performance issues:
- Reduce `vp_lookback` or `vp_rows`, or
- Temporarily disable Volume Profile (`show_vp = false`).
### 11.3 Multi-Timeframe Behavior
- This version of the script is not internally multi-timeframe.
All logic (OB, DCA, Spring, Volume Profile) is computed on the active timeframe only.
- Practical workflow:
- Analyze overall structure and key zones on higher timeframes (4H / Daily).
- Use lower timeframes (15M / 1H) with the same tool for timing entries and exits.
## Conclusion
Mars Signals – Ultimate Institutional Suite v3.0 (Joker) is a multi-layer trading framework that unifies:
- Price structure (Order Blocks & FVG),
- Statistical behavior (Smart DCA via RSI + Bollinger),
- Volume distribution by price (Volume Profile with POC, HVN, LVN),
- Liquidity events (Wyckoff Spring),
into a single, coherent system driven by a transparent Confluence Scoring Engine.
The final output is presented in clear, actionable language:
> STRONG LONG / WEAK LONG / NEUTRAL / WEAK SHORT / STRONG SHORT
The system is designed to support professional decision-making, not to replace it.
Used together with strict risk management and disciplined execution,
Mars Signals – UIS v3.0 (Joker) can serve as a central reference manual and operational guide
for your trading workflow, from scalping to swing and investment positioning.
Liquidity Sweep + BOS Retest System — Prop Firm Edition🟦 Liquidity Sweep + BOS Retest System — Prop Firm Edition
A High-Probability Smart Money Strategy Built for NQ, ES, and Funding Accounts
🚀 Overview
The Liquidity Sweep + BOS Retest System (Prop Firm Edition) is a precision-engineered SMC strategy built specifically for prop firm traders. It mirrors institutional liquidity behavior and combines it with strict account-safe entry rules to help traders pass and maintain funding accounts with consistency.
Unlike typical indicators, this system waits for three confirmations — liquidity sweep, displacement, and a clean retest — before executing any trade. Every component is optimized for low drawdown, high R:R, and prop-firm-approved risk management.
Whether you’re trading Apex, TakeProfitTrader, FFF, or OneUp Trader, this system gives you a powerful mechanical framework that keeps you within rules while identifying the market’s highest-probability reversal zones.
🔥 Key Features
1. Liquidity Sweep Detection (Stop Hunt Logic)
Automatically identifies when price clears a previous swing high/low with a sweep confirmation candle.
✔ Filters noise
✔ Eliminates early entries
✔ Locks onto true liquidity grabs
2. Automatic Break of Structure (BOS) Confirmation
Price must show true displacement by breaking structure opposite the sweep direction.
✔ Confirms momentum shift
✔ Removes fake reversals
✔ Ensures institutional intent
3. Precision Retest Entry Model
The strategy enters only when price retests the BOS level at premium/discount pricing.
✔ Zero chasing
✔ Extremely tight stop loss placement
✔ Prop-firm-friendly controlled risk
4. Built-In Risk & Trade Management
SL set at swept liquidity
TP set by user-defined R:R multiplier
Optional session filter (NY Open by default)
One trade at a time (no pyramiding)
Automatically resets logic after each trade
This prevents overtrading — the #1 cause of evaluation and account breaches.
5. Designed for Prop Firm Futures Trading
This script is optimized for:
Trailing/static drawdown accounts
Micro contract precision
Funding evaluations
Low-risk, high-probability setups
Structured, rule-based execution
It reduces randomness and emotional trading by automating the highest-quality SMC sequence.
🎯 The Trading Model Behind the System
Step 1 — Liquidity Sweep
Price must take out a recent high/low and close back inside structure.
This confirms stop-hunting behavior and marks the beginning of a potential reversal.
Step 2 — BOS (Break of Structure)
Price must break the opposite side swing with a displacement candle. This validates a directional shift.
Step 3 — Retest Entry
The system waits for price to retrace into the BOS level and signal continuation.
This creates optimal R:R entry with minimal drawdown.
📈 Best Markets
NQ (NASDAQ Futures) – Highly recommended
ES, YM, RTY
Gold (XAUUSD)
FX majors
Crypto (with high volatility)
Works best on 1m, 2m, 5m, or 15m depending on your trading style.
🧠 Why Traders Love This System
✔ No signals until all confirmations align
✔ Reduces overtrading and emotional decisions
✔ Follows market structure instead of random indicators
✔ Perfect for maintaining long-term funded accounts
✔ Built around institutional-grade concepts
✔ Makes your trading consistent, calm, and rules-based
⚙️ Recommended Settings
Session: 06:30–08:00 MST (NY Open)
R:R: 1.5R – 3R
Contracts: Start with 1–2 micros
Markets: NQ for best structure & volume
📦 What’s Included
Complete strategy logic
All plots, labels, sweep markers & BOS alerts
BOS retest entry automation
Session filtering
Stop loss & take profit system
Full SMC logic pipeline
🏁 Summary
The Liquidity Sweep + BOS Retest System is a complete, prop-firm-ready, structure-based strategy that automates one of the cleanest and most reliable SMC entry models. It is designed to keep you safe, consistent, and rule-compliant while capturing premium institutional setups.
If you want to trade with confidence, discipline, and prop-firm precision — this system is for you.
Good Luck -BG
Moving Average Band StrategyOverview
The Moving Average Band Strategy is a fully customizable breakout and trend-continuation system designed for traders who need both simplicity and control.
The strategy creates adaptive bands around a user-selected moving average and executes trades when price breaks out of these bands, with advanced risk-management settings including optional Risk:Reward targets.
This script is suitable for intraday, swing, and positional traders across all markets — equities, futures, crypto, and forex.
Key Features
✔ Six Moving Average Types
Choose the MA that best matches your trading style:
SMA
EMA
WMA
HMA
VWMA
RMA
✔ Dynamic Bands
Upper Band built from MA of highs
Lower Band built from MA of lows
Adjustable band offset (%)
Color-coded band fill indicating price position
✔ Configurable Strategy Preferences
Toggle Long and/or Short trades
Toggle Risk:Reward Take-Profit
Adjustable Risk:Reward Ratio
Default position sizing: % of equity (configurable via strategy settings)
Entry Conditions
Long Entry
A long trade triggers when:
Price crosses above the Upper Band
Long trades are enabled
No existing long position is active
Short Entry
A short trade triggers when:
Price crosses below the Lower Band
Short trades are enabled
No existing short position is active
Clear entry markers and price labels appear on the chart.
Risk Management
This strategy includes a complete set of risk-controls:
Stop-Loss (Fixed at Entry)
Long SL: Lower Band
Short SL: Upper Band
These levels remain constant for the entire trade.
Optional Risk:Reward Take-Profit
Enabled/disabled using a toggle switch.
When enabled:
Long TP = Entry + (Risk × Risk:Reward Ratio)
Short TP = Entry – (Risk × Risk:Reward Ratio)
When disabled:
Exits are handled by reverse crossover signals.
Exit Conditions
Long Exit
Stop-Loss Hit (touch-based)
Take-Profit Hit (if enabled)
Reverse Band Crossover (if TP disabled)
Short Exit
Stop-Loss Hit (touch-based)
Take-Profit Hit (if enabled)
Reverse Band Crossover (if TP disabled)
Exit markers and price labels are plotted automatically.
Visual Tools
To improve clarity:
Upper & Lower Band (blue, adjustable width)
Middle Line
Dynamic band fill (green/red/yellow)
SL & TP line plotting when in position
Entry/Exit markers
Price labels for all executed trades
These are built to help users visually follow the strategy logic.
Alerts Included
Every trading event is covered:
Long Entry
Short Entry
Long SL / TP / Cross Exit
Short SL / TP / Cross Exit
Combined Alert for webhook/automation (JSON-formatted)
Perfect for algo trading, Discord bots, or automation platforms.
Best For
This strategy performs best in:
Trending markets
Breakout environments
High-momentum instruments
Clean intraday swings
Works seamlessly on:
Stocks
Index futures
Commodities
Crypto
Forex
⚠️ Important Disclaimer
This script is for educational purposes only.
Trading involves risk. Backtest results are not indicative of future performance.
Always validate settings and use proper position sizing.
BTC CME Gaps Detector [SwissAlgo]BTC CME Gaps Detector
Track Unfilled Gaps & Identify Price Magnets
------------------------------------------------------
Overview
The BTC CME Gap Detector identifies and tracks unfilled price gaps on any timeframe (1-minute recommended for scalping) to gauge potential trading bias.
Verify Gap Behavior Yourself : Use TradingView's Replay Mode on the 1-Minute chart to observe how the price interacts with gaps. Load the BTC1! ticker (Bitcoin CME Futures), enable Replay Mode, and play forward through time (for example: go back 15 days). You may observe patterns such as price frequently returning to fill gaps, nearest gaps acting as near-term targets, and gaps serving as potential support/resistance zones. Some gaps may fill quickly, while others may remain open for longer periods. This hands-on analysis lets you independently assess how gaps may influence price movement in real market conditions and whether you may use this indicator as a complement to your trading analysis.
------------------------------------------------------
Purpose
Price gaps occur when there is a discontinuity between consecutive candles - when the current candle's low is above the previous candle's high (gap up), or when the current candle's high is below the previous candle's low (gap down).
This indicator identifies and tracks these gaps on any timeframe to help traders:
Identify gap zones that may attract price (potential "price magnets")
Monitor gap fill progression
Assess potential directional bias based on nearest unfilled gaps (long, short)
Analyze market structure and liquidity imbalances
------------------------------------------------------
Why Use This Indicator?
Universal Gap Detection : Identifies all gaps on any timeframe (1-minute, hourly, daily, etc.)
Multi-Candle Mitigation Tracking : Detects gap fills that occur across multiple candles
Distance Analysis : Shows percentage distance to nearest bullish and bearish gaps
Visual Representation : Color-coded boxes indicate gap status (active vs. mitigated)
Age Filtering : Option to display only gaps within specified time periods (3/6/12/24 months), as older gaps may lose relevance
ATR-Based Sizing : Minimum gap size adjusts to instrument volatility to filter noise (i.e. small gaps)
------------------------------------------------------
Trading Concept
Gaps represent price zones where no trading occurred. Historical market behavior suggests that unfilled gaps may attract price action as markets tend to revisit areas of incomplete price discovery. This phenomenon creates potential trading opportunities:
Bullish gaps (above current price) may act as upside targets where the price could move to fill the gap
Bearish gaps (below current price) may act as downside targets where price could move to fill the gap
The nearest gap often provides directional bias, as closer gaps may have a higher probability of being filled in the near term
This indicator helps quantify gap proximity and provides a visual reference for these potential target zones.
EXAMPLE
Step 1: Bearish Gaps Appear Below Price
Step 2: Price Getting Close to Fill Gap
Step 3: Gap Mitigated Gap
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Recommended Setup
Timeframe: 1-minute chart recommended for maximum gap detection frequency. Works on all timeframes (higher timeframes will show fewer, larger gaps).
Symbol: Any tradable instrument. Originally designed for BTC1! (CME Bitcoin Futures) but compatible with all symbols.
Settings:
ATR Length: 14 (default)
Min Gap Size: 0.5x ATR (adjust based on timeframe and noise level)
Gap Age Limit: 3 months (configurable)
Max Historical Gaps: 300 (adjustable 1-500)
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How It Works
Gap Detection : Identifies price discontinuities on every candle where:
Gap up: current candle low > previous candle high
Gap down: current candle high < previous candle low
Minimum gap size filter (ATR-based) eliminates insignificant gaps
Mitigation Tracking : Monitors when price touches both gap boundaries. A gap is marked as filled when the price has touched both the top and bottom of the gap zone, even if this occurs across multiple candles.
Visual Elements :
Green boxes: Unfilled gaps above current price (potential bullish targets)
Red boxes: Unfilled gaps below current price (potential bearish targets)
Gray boxes: Filled gaps (historical reference)
Labels: Display gap type, price level, and distance percentage
Analysis Table: Shows :
Distance % to nearest bullish gap (above price)
Distance % to nearest bearish gap (below price)
Trade bias (LONG if nearest gap is above, SHORT if nearest gap is below)
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Key Features
Detects gaps on any timeframe (1m, 5m, 1h, 1D, etc.)
Boxes extend 500 bars forward for active gaps, stop at the fill bar for mitigated gaps
Real-time distance calculations update on every candle
Configurable age filter removes outdated gaps
ATR multiplier ensures gap detection adapts to market volatility and timeframe
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Disclaimer
This indicator is provided for informational and educational purposes only.
It does not constitute financial advice, investment recommendations, or trading signals. The concept that gaps attract price is based on historical observation and does not guarantee future results.
Gap fills are not certain - gaps may remain unfilled indefinitely, or the price may reverse before reaching a gap. This indicator should not be used as the sole basis for trading decisions.
All trading involves substantial risk, including the potential loss of principal. Users should conduct their own research, apply proper risk management, test strategies thoroughly, and consult with qualified financial professionals before making trading decisions.
The authors and publishers are not responsible for any losses incurred through the use of this indicator.
ES VIX on MNQ🧭 ES + VIX Overlay on MNQ
This indicator overlays the ES (S&P 500 futures) and VIX (Volatility Index) directly on the MNQ (Micro Nasdaq Futures) chart, allowing traders to visualize in real time the correlation, divergence, and volatility influence between the three instruments.
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⚙️ How It Works
• The VIX is dynamically rescaled to the MNQ’s daily open, so its moves appear on the same price scale.
• The ES line is projected based on its percentage move relative to the session open (18:00 NY).
• Both are plotted in sync with MNQ to expose relative strength and divergence zones that often precede strong directional moves.
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🧩 Inputs
• VIX Symbol: choose between VIX, CBOE:VIX, TVC:VIX
• Invert VIX Correlation: flips the VIX line for inverse-correlation setups
• VIX Step: controls how sensitively the VIX moves on the MNQ scale
• ES Symbol: defines the ES contract (e.g. ES1!)
• Show Signals: toggles on/off buy & sell markers
• Step (points): minimum distance between MNQ and VIX for a valid signal
• Block Signals: disables signals between 16:15 – 03:15 (illiquid hours)
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💡 Signal Logic
The system tracks crossings between MNQ and the projected VIX line:
• Buy signal → when MNQ crosses above the VIX and expands upward by ≥ X points.
• Sell signal → when MNQ crosses below the VIX and expands downward by ≥ X points.
A time filter avoids noise during low-volume sessions.
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📊 Visual Guide
• Cyan line = VIX on MNQ scale
• Orange line = ES on MNQ scale
• Labels on the right = current VIX / ES values
• BUY/SELL markers = potential volatility-based reversals
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🚀 Practical Use
Perfect for traders who monitor:
• VIX–price divergence
• ES vs MNQ momentum confirmation
• Early volatility expansions before trend moves
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💬 Core Idea:
“Volatility leads — price confirms.”
Intraday Perpetual Premium & Z-ScoreThis indicator measures the real-time premium of a perpetual futures contract relative to its spot market and interprets it through a statistical lens.
It helps traders detect when funding pressure is building, when leverage is being unwound, and when crowding in the futures market may precede volatility.
How it works
• Premium (%) = (Perp – Spot) ÷ Spot × 100
The script fetches both spot and perpetual prices and calculates their percentage difference each minute.
• Rolling Mean & Z-Score
Over a 4-hour look-back, it computes the average premium and standard deviation to derive a Z-Score, showing how stretched current sentiment is.
• Dynamic ±2σ Bands highlight statistically extreme premiums or discounts.
• Rate of Change (ROC) over one hour gauges the short-term directional acceleration of funding flows.
Colour & Label Interpretation
Visual cue Meaning Trading Implication
🟢 Green bars + “BULL Pressure” Premium rising faster than mean Leverage inflows → momentum strengthening
🔴 Red bars + “BEAR Pressure” Premium shrinking Leverage unwind → pull-back or consolidation
⚠️ Orange “EXTREME Premium/Discount” Crowded trade → heightened reversal risk
⚪ Grey bars Neutral Balanced conditions
Alerts
• Bull Pressure Alert → funding & premium rising (momentum building)
• Bear Pressure Alert → premium falling (deleveraging)
• Extreme Premium Alert → crowded longs; potential top
• Extreme Discount Alert → capitulation; possible bottom
Use case
Combine this indicator with your Heikin-Ashi, RSI, and MACD confluence rules:
• Enter only when your oscillators are low → curling up and Bull Pressure triggers.
• Trim or exit when Bear Pressure or Extreme Premium appears.
• Watch for Extreme Discount during flushes as an early bottoming clue.
HEK Dinamik Fiyat Kanalı Stratejisi v1HEK Dynamic Price Channel Strategy
Concept
The HEK Dynamic Price Channel provides a channel structure that expands and contracts according to price momentum and time-based equilibrium.
Unlike fixed-band systems, it evaluates the interaction between price and its balance line through an adaptive channel width that dynamically adjusts to changing market conditions.
How It Works
When the price reacts to the midline, the channel bands automatically reposition themselves.
Touching the upper band indicates a strengthening trend, while touching the lower band signals weakening momentum.
This adaptive mechanism helps filter out false signals during sudden directional changes, enhancing overall signal quality.
Advantages
✅ Maintains trend continuity while avoiding overtrading.
✅ Automatically adapts to changing volatility conditions.
✅ Detects early signals of short- and mid-term trend reversals.
Applications
Directional confirmation in spot and futures markets.
A supporting tool in channel breakout strategies.
Identifying price consolidation and equilibrium zones.
Note
This strategy is intended for educational and research purposes only.
It should not be considered financial advice. Always consult a professional financial advisor before making investment decisions.
© HEK — Adaptive Channel Approach on Dynamic Market Structures
6 gün önce
Sürüm Notları
HEK Dynamic Price Channel Strategy
Concept
The HEK Dynamic Price Channel provides a channel structure that expands and contracts according to price momentum and time-based equilibrium.
Unlike fixed-band systems, it evaluates the interaction between price and its balance line through an adaptive channel width that dynamically adjusts to changing market conditions.
How It Works
When the price reacts to the midline, the channel bands automatically reposition themselves.
Touching the upper band indicates a strengthening trend, while touching the lower band signals weakening momentum.
This adaptive mechanism helps filter out false signals during sudden directional changes, enhancing overall signal quality.
Advantages
✅ Maintains trend continuity while avoiding overtrading.
✅ Automatically adapts to changing volatility conditions.
✅ Detects early signals of short- and mid-term trend reversals.
Applications
Directional confirmation in spot and futures markets.
A supporting tool in channel breakout strategies.
Identifying price consolidation and equilibrium zones.
Note
This strategy is intended for educational and research purposes only.
It should not be considered financial advice. Always consult a professional financial advisor before making investment decisions.
© HEK — Adaptive Channel Approach on Dynamic Market Structures
EMA100 Breakout by shubhThis indicator is a clean, price-action-based breakout system designed for disciplined trend trading on any timeframe — especially for Nifty and Bank Nifty spot, futures, and options charts.
It uses a single 100-period EMA to define trend direction and waits for decisive candle closes across the EMA to trigger potential entries.
The logic ensures only one active trade at a time, enforcing patience and clarity in decision-making.
⚙️ Core Logic
Buy Setup
A bullish candle closes above the 100 EMA while its open was below the EMA.
Entry occurs at candle close.
Stop-Loss (SL): Low of the signal candle.
Target (TP): 4 × the SL distance (Risk : Reward = 1 : 4).
Sell Setup
A bearish candle closes below the 100 EMA while its open was above the EMA.
Entry occurs at candle close.
Stop-Loss (SL): High of the signal candle.
Target (TP): 4 × the SL distance.
Trade Management
Only one trade may run at a time (either long or short).
New signals are ignored until the current position hits SL or TP.
Transparent labels show Entry, SL, and TP levels on chart.
Dotted lines visualize active Stop-Loss (red) and Target (green).
Exit markers:
✅ Target Hit
❌ Stop Loss Hit
🧠 Key Advantages
Simple and transparent trend-following logic.
Enforces disciplined “one-trade-at-a-time” behavior.
High risk-to-reward (1 : 4).
Works across timeframes — 5 min to Daily.
Ideal for intraday and positional setups.
📊 Suggested Use
Apply on Nifty / Bank Nifty spot or futures charts.
Works on any instrument with clear momentum swings.
Best confirmation when EMA 100 acts as dynamic support/resistance.
⚠️ Disclaimer
This script is for educational and research purposes only.
It is not financial advice or an invitation to trade.
Always backtest thoroughly and manage risk responsibly before applying in live markets.
