AlphaTrend - Medium Term Trend Probability Indicator on TOTALESWHAT IS ALPHATREND?
AlphaTrend is a consensus-based trend identification system that combines 7 independent trend detection methodologies into a single probability score. Designed for medium-term trading (days to weeks), it aggregates diverse analytical approaches—from volatility-adjusted moving averages to statistical oscillators—to determine directional bias with quantifiable confidence.
Unlike single-indicator systems prone to false signals during consolidation, AlphaTrend requires majority agreement across multiple uncorrelated methods before generating directional signals, significantly reducing whipsaws in choppy markets.
METHODOLOGY - THE 7-INDICATOR VOTING SYSTEM
Each indicator analyzes trend from a mathematically distinct perspective and casts a vote: +1 (bullish), -1 (bearish), or 0 (neutral). The average of all 7 votes creates the final probability score ranging from -1 (strong bearish) to +1 (strong bullish).
1. FLXWRT RMA (VOLATILITY-ADJUSTED BASELINE)
Method: RMA (Running Moving Average) with ATR-based dynamic bands
Calculation:
RMA = Running MA of price over 12 periods
ATR = Average True Range over 20 periods
Long Signal: Price > RMA + ATR
Short Signal: Price < RMA - ATR
Logic: Trend confirmed only when price breaks beyond volatility-adjusted boundaries, not just the moving average itself. This filters noise by requiring momentum sufficient to overcome recent volatility.
Why it works: Standard MA crossovers generate excessive false signals in ranging markets. Adding ATR bands ensures price has genuine directional momentum, not just minor fluctuations.
Settings:
RMA Length (12): Base trend smoothing
ATR Length (20): Volatility measurement period
2. BOOSTED MOVING AVERAGE (MOMENTUM-ENHANCED TREND)
Method: Double EMA with acceleration boost factor
Calculation:
EMA1 = EMA(close, length)
EMA2 = EMA(close, length/2) // Faster EMA
Boosted Value = EMA2 + sensitivity × (EMA2 - EMA1)
Final = EMA smoothing of Boosted Value
Logic: Amplifies the difference between fast and slow EMAs to emphasize trend momentum. The boost factor (1.3) accelerates response to directional moves while subsequent smoothing prevents over-reaction.
Why it works: Traditional MAs lag price action. The boost mechanism projects trend direction forward by amplifying the momentum differential between two EMAs, providing earlier signals without sacrificing reliability.
Settings:
Length (36): Base EMA period
Boost Factor (1.3): Momentum amplification multiplier
Originality: This is a proprietary enhancement to standard double EMA systems. Most indicators simply cross fast/slow EMAs; this one mathematically projects momentum trajectory.
3. HEIKIN ASHI TREND (T3-SMOOTHED CANDLES)
Method: Heikin Ashi candles with T3 exponential smoothing
Calculation:
Heikin Ashi candles = Smoothed OHLC transformation
T3 Smoothing = Triple-exponential smoothing (Tillson T3)
Signal: T3(HA_Open) crosses T3(HA_Close)
Logic: Heikin Ashi candles filter intrabar noise by averaging consecutive bars. T3 smoothing adds additional filtering using Tillson's generalized DEMA algorithm with custom volume factor.
Why it works: Regular candlesticks contain high-frequency noise. Heikin Ashi transformation creates smoother trends, and T3 smoothing eliminates remaining whipsaws while maintaining responsiveness. The T3 algorithm specifically addresses the lag-vs-smoothness tradeoff.
Settings:
T3 Length (13): Smoothing period
T3 Factor (0.3): Volume factor for T3 algorithm
Percent Squeeze (0.2): Sensitivity adjustment
Technical Note: T3 is superior to simple EMA smoothing because it applies the generalized DEMA formula recursively, reducing lag while maintaining smooth output.
4. VIISTOP (ATR-BASED TREND FILTER)
Method: Simple trend detection using price position vs smoothed baseline with ATR confirmation
Calculation:
Baseline = SMA(close, 16)
ATR = ATR(16)
Uptrend: Close > Baseline
Downtrend: Close < Baseline
Logic: The simplest component—pure price position relative to medium-term average. While basic, it provides a "sanity check" against over-optimized indicators.
Why it works: Sometimes the simplest approach is most robust. In strong trends, price consistently stays above/below its moving average. This indicator prevents the system from over-complicating obvious directional moves.
Settings:
Length (16): Baseline period
Multiplier (2.8): ATR scaling (not actively used in vote logic)
Purpose in Ensemble: Provides grounding in basic trend logic. Complex indicators can sometimes generate counterintuitive signals; ViiStop ensures the system stays aligned with fundamental price positioning.
5. NORMALIZED KAMA OSCILLATOR (ADAPTIVE EFFICIENCY-BASED TREND)
Method: Kaufman Adaptive Moving Average normalized to oscillator format
Calculation:
Efficiency Ratio = |Close - Close | / Sum(|Close - Close |, 8)
Smoothing Constant = ER × (Fast SC - Slow SC) + Slow SC
KAMA = Adaptive moving average using dynamic smoothing
Normalized = (KAMA - Lowest) / (Highest - Lowest) - 0.5
Logic: KAMA adjusts its smoothing speed based on market efficiency. In trending markets (high efficiency), it speeds up. In ranging markets (low efficiency), it slows down. Normalization converts absolute values to -0.5/+0.5 oscillator for consistent voting.
Why it works: Fixed-period moving averages perform poorly across varying market conditions. KAMA's adaptive nature makes it effective in both trending and choppy environments by automatically adjusting its responsiveness.
Settings:
Fast Period (9): Maximum responsiveness
Slow Period (21): Minimum responsiveness
ER Period (8): Efficiency calculation window
Normalization Lookback (35): Oscillator scaling period
Mathematical Significance: Kaufman's algorithm is one of the most sophisticated adaptive smoothing methods in technical analysis. The Efficiency Ratio mathematically quantifies trend strength vs noise.
6. LÉVY FLIGHT RSI (HEAVY-TAILED MOMENTUM)
Method: Modified RSI using Lévy distribution weighting for gains/losses
Calculation:
Weighted Gain = (Max(Price Change, 0))^Alpha
Weighted Loss = (-Min(Price Change, 0))^Alpha
RSI = 100 - (100 / (1 + RMA(Gain) / RMA(Loss)))
Centered RSI = RSI - 50
Logic: Standard RSI treats all price changes linearly. Lévy Flight RSI applies power-law weighting (Alpha = 1.5) to emphasize larger moves, modeling heavy-tailed distributions observed in real market data.
Why it works: Market returns exhibit "fat tails"—large moves occur more frequently than normal distribution predicts. Lévy distributions (Alpha between 1-2) better model this behavior. By weighting larger price changes more heavily, this RSI variant becomes more sensitive to genuine momentum shifts while filtering small noise.
Settings:
RSI Length (14): Standard period
Alpha (1.5): Lévy exponent (1=linear, 2=quadratic)
MA Length (12): Final smoothing
Originality: Standard RSI uses unweighted gains/losses. This implementation applies stochastic process theory (Lévy flights) from quantitative finance to create a momentum indicator more aligned with actual market behavior.
Mathematical Background: Lévy flights describe random walks with heavy-tailed step distributions, observed in financial markets, animal foraging patterns, and human mobility. Alpha=1.5 balances between normal distribution (Alpha=2) and Cauchy distribution (Alpha=1).
7. REGULARIZED-MA OSCILLATOR (Z-SCORED TREND DEVIATION)
Method: Moving average converted to z-score oscillator
Calculation:
MA = EMA(close, 19)
Mean = SMA(MA, 30)
Std Dev = Standard Deviation(MA, 30)
Z-Score = (MA - Mean) / Std Dev
Logic: Converts absolute MA values to statistical standard deviations from mean. Positive z-score = MA above its typical range (bullish), negative = below range (bearish).
Why it works: Raw moving averages don't indicate strength—a 50-day MA at $50k vs $60k has no contextual meaning. Z-scoring normalizes this to "how unusual is current MA level?" This makes signals comparable across different price levels and time periods.
Settings:
Length (19): Base MA period
Regularization Length (30): Statistical normalization window
Statistical Significance: Z-scores are standard in quantitative analysis. This indicator asks: "Is the current trend statistically significant or just random noise?"
AGGREGATION METHODOLOGY
Voting System:
Each indicator returns: +1 (bullish), -1 (bearish), or 0 (neutral)
Total Score = Sum of all 7 votes (-7 to +7)
Average Score = Total / 7 (-1.00 to +1.00)
Signal Generation:
Long Signal: Average > 0 (majority bullish)
Short Signal: Average < 0 (majority bearish)
Neutral: Average = 0 (perfect split or all neutral)
Why Equal Weighting:
Each indicator represents a fundamentally different analytical approach:
Volatility-adjusted (RMA, ViiStop)
Momentum-based (Boosted MA, Lévy RSI)
Adaptive smoothing (KAMA)
Statistical (MA Oscillator)
Noise-filtered (Heikin Ashi T3)
Equal weighting ensures no single methodology dominates. This diversification reduces bias and improves robustness across market conditions.
ORIGINALITY - WHY THIS COMBINATION WORKS
Traditional Multi-Indicator Approaches:
Combine similar indicators (multiple MAs, multiple oscillators)
Use arbitrary thresholds for each indicator
Don't normalize signals (hard to compare RSI to MACD)
Often just "if RSI > 70 AND MACD > 0 = buy"
AlphaTrend MTPI Innovations:
Methodological Diversity: Includes volatility-adaptive (RMA), momentum-enhanced (Boosted MA), efficiency-based (KAMA), heavy-tailed statistics (Lévy RSI), and smoothed candles (HA). No redundant indicators.
Binary Voting: Each indicator reduces to simple +1/-1/0 vote, making aggregation transparent and preventing any indicator from overwhelming the consensus.
Medium-Term Optimization: Parameter choices (12-36 period averages) specifically target multi-day to multi-week trends, not scalping or long-term positioning.
Advanced Mathematics: Incorporates Tillson T3, Kaufman Efficiency Ratio, Lévy distributions, and statistical z-scoring—not just basic MAs and RSIs.
No Overfit Risk: With 7 diverse components voting equally, the system can't overfit to any specific market regime. If trending markets favor KAMA, but choppy markets favor Boosted MA, the ensemble stays robust.
Why 7 Indicators, Not 3 or 10:
Fewer than 5: Insufficient diversification, single indicator failures impact results heavily
More than 9: Diminishing returns, redundancy increases, computational load grows
7 provides: Odd number (no ties), sufficient diversity, manageable complexity
VISUAL COMPONENTS
1. Bar Coloring:
Cyan bars: Bullish consensus (average score > 0)
Magenta bars: Bearish consensus (average score < 0)
No color: Neutral (score = 0 or date filter disabled)
2. MTPI Summary Table (Bottom Center):
MTPI Signal: Current directional bias (LONG/SHORT/NEUTRAL)
Average Score: Precise consensus reading (-1.00 to +1.00)
3. Indicator Status Table (Bottom Right):
Shows all 7 individual indicator scores
Score column: +1 (bullish), -1 (bearish), 0 (neutral)
Signal column: Text interpretation of each vote
Color-coded cells: Cyan (long), Magenta (short), Gray (neutral)
HOW TO USE
For Swing Trading (Recommended - Days to Weeks):
Entry Signals:
Strong Long: 5+ indicators bullish (score ≥ 0.71)
Standard Long: 4+ indicators bullish (score ≥ 0.57)
Weak Long: Simple majority (score > 0) — use with caution
Exit Signals:
Hard Stop: Score flips negative (consensus reverses)
Partial Take Profit: Score drops to +0.30 or below (weakening)
Trailing Stop: Use ATR-based stop below entry
Position Sizing:
Strong signals (|score| > 0.7): Full position
Moderate signals (0.4-0.7): 50-75% position
Weak signals (< 0.4): 25-50% or skip
For Trend Confirmation:
Use alongside your primary strategy for confluence
Only take trades when AlphaTrend agrees with your analysis
Avoid counter-trend trades when score is extreme (|score| > 0.7)
Best Timeframes:
4H: Primary timeframe for swing trading
1D: Position trading and major trend identification
1H: Active trading (shorter hold periods)
< 1H: Not recommended (designed for medium-term)
Market Conditions:
Trending markets: System excels (consensus emerges quickly)
Ranging markets: Expect mixed signals (score oscillates near zero)
High volatility: RMA and ViiStop provide stabilization
Low volatility: KAMA and Boosted MA maintain responsiveness
SETTINGS EXPLAINED
General Settings:
Use Date Filter: Enable/disable historical backtesting range
Start Date: When to begin signal generation (default: Jan 1, 2018)
Flxwrt RMA Settings:
RMA Length (12): Base trend smoothing
ATR Length (20): Volatility measurement period
Source: Price input (default: close)
Boosted MA Settings:
Length (36): Base EMA period
Boost Factor (1.3): Momentum amplification
Source: Price input
Heikin Ashi Settings:
Percent Squeeze (0.2): Sensitivity adjustment
T3 Factor (0.3): Tillson volume factor
T3 Length (13): Smoothing period
ViiStop Settings:
Length (16): Baseline period
Multiplier (2.8): ATR scaling
Source: Price input
KAMA Settings:
Fast Period (9): Maximum responsiveness
Slow Period (21): Minimum responsiveness
ER Period (8): Efficiency calculation
Normalization Lookback (35): Oscillator scaling
Levy RSI Settings:
RSI Length (14): Standard period
Alpha (1.5): Lévy exponent (power-law weighting)
MA Length (12): Final smoothing
Source: Price input
MA Oscillator Settings:
Length (19): Base MA period
Regularize Length (30): Z-score normalization window
PERFORMANCE CHARACTERISTICS
Strengths:
✅ Reduced whipsaws vs single indicators
✅ Works across varying market conditions (adaptive components)
✅ Transparent methodology (see every vote)
✅ Customizable to trading style via timeframe selection
✅ No curve-fitting (equal weighting, no optimization)
Limitations:
⚠️ Medium-term focus (not for scalping or very long-term)
⚠️ Lagging by design (consensus requires confirmation)
⚠️ Less effective in violent reversals (momentum carries votes)
⚠️ Requires clean price data (gaps/thin volume can distort)
ALERTS & AUTOMATION
No built-in alerts in current version (visual-only indicator). Users can create custom alerts based on:
Bar color changes (cyan to magenta or vice versa)
Average score crossing above/below thresholds
Specific indicator status changes in the table
BEST PRACTICES
Risk Management:
Never risk more than 1-2% per trade regardless of score
Use stop losses (ATR-based recommended)
Scale positions based on signal strength
Don't average down on losing positions
Combining with Other Analysis:
✅ Support/Resistance levels for entries
✅ Volume confirmation (accumulation/distribution)
✅ Market structure (higher highs/lower lows)
✅ Volatility regimes (adjust position size)
❌ Don't combine with redundant trend indicators (adds no value)
❌ Don't override strong consensus with gut feeling
❌ Don't use on news-driven spikes (wait for stabilization)
Backtesting Notes:
Use "Date Filter" to test specific periods
Forward-test before live deployment
Remember: consensus systems perform best in trending markets, expect reduced edge in ranges
IMPORTANT NOTES
Not a standalone strategy - Use with proper risk management
Requires clean data - Works best on liquid markets with tight spreads
Medium-term by design - Don't expect scalping signals
No magic - No indicator predicts the future; this shows current trend probability
Diversification within - The 7-component ensemble IS the diversification strategy
Not financial advice. This indicator identifies medium-term trend probability based on multi-component consensus. Past performance does not guarantee future results. Always use proper risk management and position sizing.
在脚本中搜索"stop loss"
RSI Bollinger Bands [DCAUT]█ RSI Bollinger Bands
📊 ORIGINALITY & INNOVATION
The RSI Bollinger Bands indicator represents a meaningful advancement in momentum analysis by combining two proven technical tools: the Relative Strength Index (RSI) and Bollinger Bands. This combination addresses a significant limitation in traditional RSI analysis - the use of fixed overbought/oversold thresholds (typically 70/30) that fail to adapt to changing market volatility conditions.
Core Innovation:
Rather than relying on static threshold levels, this indicator applies Bollinger Bands statistical analysis directly to RSI values, creating dynamic zones that automatically adjust based on recent momentum volatility. This approach helps reduce false signals during low volatility periods while remaining sensitive to genuine extremes during high volatility conditions.
Key Enhancements Over Traditional RSI:
Dynamic Thresholds: Overbought/oversold zones adapt to market conditions automatically, eliminating the need for manual threshold adjustments across different instruments and timeframes
Volatility Context: Band width provides immediate visual feedback about momentum volatility, helping traders distinguish between stable trends and erratic movements
Reduced False Signals: During ranging markets, narrower bands filter out minor RSI fluctuations that would trigger traditional fixed-threshold signals
Breakout Preparation: Band squeeze patterns (similar to price-based BB) signal potential momentum regime changes before they occur
Self-Referencing Analysis: By measuring RSI against its own statistical behavior rather than arbitrary levels, the indicator provides more relevant context
📐 MATHEMATICAL FOUNDATION
Two-Stage Calculation Process:
Stage 1: RSI Calculation
RSI = 100 - (100 / (1 + RS))
where RS = Average Gain / Average Loss over specified period
The RSI normalizes price momentum into a bounded 0-100 scale, making it ideal for statistical band analysis.
Stage 2: Bollinger Bands on RSI
Basis = MA(RSI, BB Length)
Upper Band = Basis + (StdDev(RSI, BB Length) × Multiplier)
Lower Band = Basis - (StdDev(RSI, BB Length) × Multiplier)
Band Width = Upper Band - Lower Band
The Bollinger Bands measure RSI's standard deviation from its own moving average, creating statistically-derived dynamic zones.
Statistical Interpretation:
Under normal distribution assumptions with default 2.0 multiplier, approximately 95% of RSI values should fall within the bands
Band touches represent statistically significant momentum extremes relative to recent behavior
Band width expansion indicates increasing momentum volatility (strengthening trend or increasing uncertainty)
Band width contraction signals momentum consolidation and potential regime change preparation
📊 COMPREHENSIVE SIGNAL ANALYSIS
Visual Color Signals:
This indicator features dynamic color fills that highlight extreme momentum conditions:
Green Fill (Above Upper Band):
Appears when RSI breaks above the upper band, indicating exceptionally strong bullish momentum
Represents dynamic overbought zone - not necessarily a reversal signal but a warning of extreme conditions
In strong uptrends, green fills can persist as RSI "rides the band" - this indicates sustained momentum strength
Exit of green zone (RSI falling back below upper band) often signals initial momentum weakening
Red Fill (Below Lower Band):
Appears when RSI breaks below the lower band, indicating exceptionally weak bearish momentum
Represents dynamic oversold zone - potential reversal or continuation signal depending on trend context
In strong downtrends, red fills can persist as RSI "rides the band" - this indicates sustained selling pressure
Exit of red zone (RSI rising back above lower band) often signals initial momentum recovery
Position-Based Signals:
Upper Band Interactions:
RSI Touching Upper Band: Dynamic overbought condition - momentum is extremely strong relative to recent volatility, potential exhaustion or continuation depending on trend context
RSI Riding Upper Band: Sustained strong momentum, often seen in powerful trends, not necessarily an immediate reversal signal but warrants monitoring for exhaustion
RSI Crossing Below Upper Band: Initial momentum weakening signal, particularly significant if accompanied by price divergence
Lower Band Interactions:
RSI Touching Lower Band: Dynamic oversold condition - momentum is extremely weak relative to recent volatility, potential reversal or continuation of downtrend
RSI Riding Lower Band: Sustained weak momentum, common in strong downtrends, monitor for potential exhaustion
RSI Crossing Above Lower Band: Initial momentum strengthening signal, early indication of potential reversal or consolidation
Basis Line Signals:
RSI Above Basis: Bullish momentum regime - upward pressure dominant
RSI Below Basis: Bearish momentum regime - downward pressure dominant
Basis Crossovers: Momentum regime shifts, more significant when accompanied by band width changes
RSI Oscillating Around Basis: Balanced momentum, often indicates ranging market conditions
Volatility-Based Signals:
Band Width Patterns:
Narrow Bands (Squeeze): Momentum volatility compression, often precedes significant directional moves, similar to price coiling patterns
Expanding Bands: Increasing momentum volatility, indicates trend acceleration or growing uncertainty
Narrowest Band in 100 Bars: Extreme compression alert, high probability of upcoming volatility expansion
Advanced Pattern Recognition:
Divergence Analysis:
Bullish Divergence: Price makes lower lows while RSI touches or stays above previous lower band touch, suggests downward momentum weakening
Bearish Divergence: Price makes higher highs while RSI touches or stays below previous upper band touch, suggests upward momentum weakening
Hidden Bullish: Price makes higher lows while RSI makes lower lows at the lower band, indicates strong underlying bullish momentum
Hidden Bearish: Price makes lower highs while RSI makes higher highs at the upper band, indicates strong underlying bearish momentum
Band Walk Patterns:
Upper Band Walk: RSI consistently touching or staying near upper band indicates exceptionally strong trend, wait for clear break below basis before considering reversal
Lower Band Walk: RSI consistently at lower band signals very weak momentum, requires break above basis for reversal confirmation
🎯 STRATEGIC APPLICATIONS
Strategy 1: Mean Reversion Trading
Setup Conditions:
Market Type: Ranging or choppy markets with no clear directional trend
Timeframe: Works best on lower timeframes (5m-1H) or during consolidation phases
Band Characteristic: Normal to narrow band width
Entry Rules:
Long Entry: RSI touches or crosses below lower band, wait for RSI to start rising back toward basis before entry
Short Entry: RSI touches or crosses above upper band, wait for RSI to start falling back toward basis before entry
Confirmation: Use price action confirmation (candlestick reversal patterns) at band touches
Exit Rules:
Target: RSI returns to basis line or opposite band
Stop Loss: Fixed percentage or below recent swing low/high
Time Stop: Exit if position not profitable within expected timeframe
Strategy 2: Trend Continuation Trading
Setup Conditions:
Market Type: Clear trending market with higher highs/lower lows
Timeframe: Medium to higher timeframes (1H-Daily)
Band Characteristic: Expanding or wide bands indicating strong momentum
Entry Rules:
Long Entry in Uptrend: Wait for RSI to pull back to basis line or slightly below, enter when RSI starts rising again
Short Entry in Downtrend: Wait for RSI to rally to basis line or slightly above, enter when RSI starts falling again
Avoid Counter-Trend: Do not fade RSI at bands during strong trends (band walk patterns)
Exit Rules:
Trailing Stop: Move stop to break-even when RSI reaches opposite band
Trend Break: Exit when RSI crosses basis against trend direction with conviction
Band Squeeze: Reduce position size when bands start narrowing significantly
Strategy 3: Breakout Preparation
Setup Conditions:
Market Type: Consolidating market after significant move or at key technical levels
Timeframe: Any timeframe, but longer timeframes provide more reliable breakouts
Band Characteristic: Narrowest band width in recent 100 bars (squeeze alert)
Preparation Phase:
Identify band squeeze condition (bands at multi-period narrowest point)
Monitor price action for consolidation patterns (triangles, rectangles, flags)
Prepare bracket orders for both directions
Wait for band expansion to begin
Entry Execution:
Breakout Confirmation: Enter in direction of RSI band breakout (RSI breaks above upper band or below lower band)
Price Confirmation: Ensure price also breaks corresponding technical level
Volume Confirmation: Look for volume expansion supporting the breakout
Risk Management:
Stop Loss: Place beyond consolidation pattern opposite extreme
Position Sizing: Use smaller size due to false breakout risk
Quick Exit: Exit immediately if RSI returns inside bands within 1-3 bars
Strategy 4: Multi-Timeframe Analysis
Timeframe Selection:
Higher Timeframe: Daily or 4H for trend context
Trading Timeframe: 1H or 15m for entry signals
Confirmation Timeframe: 5m or 1m for precise entry timing
Analysis Process:
Trend Identification: Check higher timeframe RSI position relative to bands, trade only in direction of higher timeframe momentum
Setup Formation: Wait for trading timeframe RSI to show pullback to basis in trending direction
Entry Timing: Use confirmation timeframe RSI band touch or crossover for precise entry
Alignment Confirmation: All timeframes should show RSI moving in same direction for highest probability setups
📋 DETAILED PARAMETER CONFIGURATION
RSI Source:
Close (Default): Standard price point, balances responsiveness and reliability
HL2: Reduces noise from intrabar volatility, provides smoother RSI values
HLC3 or OHLC4: Further smoothing for very choppy markets, slower to respond but more stable
Volume-Weighted: Consider using VWAP or volume-weighted prices for additional liquidity context
RSI Length Parameter:
Shorter Periods (5-10): More responsive but generates more signals, suitable for scalping or very active trading, higher noise level
Standard (14): Default and most widely used setting, proven balance between responsiveness and reliability, recommended starting point
Longer Periods (21-30): Smoother momentum measurement, fewer but potentially more reliable signals, better for swing trading or position trading
Optimization Note: Test across different market regimes, optimal length often varies by instrument volatility characteristics
RSI MA Type Parameter:
RMA (Default): Wilder's original smoothing method, provides traditional RSI behavior with balanced lag, most widely recognized and tested, recommended for standard technical analysis
EMA: Exponential smoothing gives more weight to recent values, faster response to momentum changes, suitable for active trading and trending markets, reduces lag compared to RMA
SMA: Simple average treats all periods equally, smoothest output with highest lag, best for filtering noise in choppy markets, useful for long-term position analysis
WMA: Weighted average emphasizes recent data less aggressively than EMA, middle ground between SMA and EMA characteristics, balanced responsiveness for swing trading
Advanced Options: Full access to 25+ moving average types including HMA (reduced lag), DEMA/TEMA (enhanced responsiveness), KAMA/FRAMA (adaptive behavior), T3 (smoothness), Kalman Filter (optimal estimation)
Selection Guide: RMA for traditional analysis and backtesting consistency, EMA for faster signals in trending markets, SMA for stability in ranging markets, adaptive types (KAMA/FRAMA) for varying volatility regimes
BB Length Parameter:
Short Length (10-15): Tighter bands that react quickly to RSI changes, more frequent band touches, suitable for active trading styles
Standard (20): Balanced approach providing meaningful statistical context without excessive lag
Long Length (30-50): Smoother bands that filter minor RSI fluctuations, captures only significant momentum extremes, fewer but higher quality signals
Relationship to RSI Length: Consider BB Length greater than RSI Length for cleaner signals
BB MA Type Parameter:
SMA (Default): Standard Bollinger Bands calculation using simple moving average for basis line, treats all periods equally, widely recognized and tested approach
EMA: Exponential smoothing for basis line gives more weight to recent RSI values, creates more responsive bands that adapt faster to momentum changes, suitable for trending markets
RMA: Wilder's smoothing provides consistent behavior aligned with traditional RSI when using RMA for both RSI and BB calculations
WMA: Weighted average for basis line balances recent emphasis with historical context, middle ground between SMA and EMA responsiveness
Advanced Options: Full access to 25+ moving average types for basis calculation, including HMA (reduced lag), DEMA/TEMA (enhanced responsiveness), KAMA/FRAMA (adaptive to volatility changes)
Selection Guide: SMA for standard Bollinger Bands behavior and backtesting consistency, EMA for faster band adaptation in dynamic markets, matching RSI MA type creates unified smoothing behavior
BB Multiplier Parameter:
Conservative (1.5-1.8): Tighter bands resulting in more frequent touches, useful in low volatility environments, higher signal frequency but potentially more false signals
Standard (2.0): Default setting representing approximately 95% confidence interval under normal distribution, widely accepted statistical threshold
Aggressive (2.5-3.0): Wider bands capturing only extreme momentum conditions, fewer but potentially more significant signals, reduces false signals in high volatility
Adaptive Approach: Consider adjusting multiplier based on instrument characteristics, lower multiplier for stable instruments, higher for volatile instruments
Parameter Optimization Workflow:
Start with default parameters (RSI:14, BB:20, Mult:2.0)
Test across representative sample period including different market regimes
Adjust RSI length based on desired responsiveness vs stability tradeoff
Tune BB length to match your typical holding period
Modify multiplier to achieve desired signal frequency
Validate on out-of-sample data to avoid overfitting
Document optimal parameters for different instruments and timeframes
Reference Levels Display:
Enabled (Default): Shows traditional 30/50/70 levels for comparison with dynamic bands, helps visualize the adaptive advantage
Disabled: Cleaner chart focusing purely on dynamic zones, reduces visual clutter for experienced users
Educational Value: Keeping reference levels visible helps understand how dynamic bands differ from fixed thresholds across varying market conditions
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Comparison with Traditional RSI:
Fixed Threshold RSI Limitations:
In ranging low-volatility markets: RSI rarely reaches 70/30, missing tradable extremes
In trending high-volatility markets: RSI frequently breaks through 70/30, generating excessive false reversal signals
Across different instruments: Same thresholds applied to volatile crypto and stable forex pairs produce inconsistent results
Threshold Adjustment Problem: Manually changing thresholds for different conditions is subjective and lagging
RSI Bollinger Bands Advantages:
Automatic Adaptation: Bands adjust to current volatility regime without manual intervention
Consistent Logic: Same statistical approach works across different instruments and timeframes
Reduced False Signals: Band width filtering helps distinguish meaningful extremes from noise
Additional Information: Band width provides volatility context missing in standard RSI
Objective Extremes: Statistical basis (standard deviations) provides objective extreme definition
Comparison with Price-Based Bollinger Bands:
Price BB Characteristics:
Measures absolute price volatility
Affected by large price gaps and outliers
Band position relative to price not normalized
Difficult to compare across different price scales
RSI BB Advantages:
Normalized Scale: RSI's 0-100 bounds make band interpretation consistent across all instruments
Momentum Focus: Directly measures momentum extremes rather than price extremes
Reduced Gap Impact: RSI calculation smooths price gaps impact on band calculations
Comparable Analysis: Same RSI BB appearance across stocks, forex, crypto enables consistent strategy application
Performance Characteristics:
Signal Quality:
Higher Signal-to-Noise Ratio: Dynamic bands help filter RSI oscillations that don't represent meaningful extremes
Context-Aware Alerts: Band width provides volatility context helping traders adjust position sizing and stop placement
Reduced Whipsaws: During consolidations, narrower bands prevent premature signals from minor RSI movements
Responsiveness:
Adaptive Lag: Band calculation introduces some lag, but this lag is adaptive to current conditions rather than fixed
Faster Than Manual Adjustment: Automatic band adjustment is faster than trader's ability to manually modify thresholds
Balanced Approach: Combines RSI's inherent momentum lag with BB's statistical smoothing for stable yet responsive signals
Versatility:
Multi-Strategy Application: Supports both mean reversion (ranging markets) and trend continuation (trending markets) approaches
Universal Instrument Coverage: Works effectively across equities, forex, commodities, cryptocurrencies without parameter changes
Timeframe Agnostic: Same interpretation applies from 1-minute charts to monthly charts
Limitations and Considerations:
Known Limitations:
Dual Lag Effect: Combines RSI's momentum lag with BB's statistical lag, making it less suitable for very short-term scalping
Requires Volatility History: Needs sufficient bars for BB calculation, less effective immediately after major regime changes
Statistical Assumptions: Assumes RSI values are somewhat normally distributed, extreme trending conditions may violate this
Not a Standalone System: Like all indicators, should be combined with price action analysis and risk management
Optimal Use Cases:
Best for swing trading and position trading timeframes
Most effective in markets with alternating volatility regimes
Ideal for traders who use multiple instruments and timeframes
Suitable for systematic trading approaches requiring consistent logic
Suboptimal Conditions:
Very low timeframes (< 5 minutes) where lag becomes problematic
Instruments with extreme volatility spikes (gap-prone markets)
Markets in strong persistent trends where mean reversion rarely occurs
Periods immediately following major structural changes (new trading regime)
USAGE NOTES
This indicator is designed for technical analysis and educational purposes to help traders understand the interaction between momentum measurement and statistical volatility bands. The RSI Bollinger Bands has limitations and should not be used as the sole basis for trading decisions.
Important Considerations:
No Predictive Guarantee: Past band touches and patterns do not guarantee future price behavior
Market Regime Dependency: Indicator performance varies significantly between trending and ranging market conditions
Complementary Analysis Required: Should be used alongside price action, support/resistance levels, and fundamental analysis
Risk Management Essential: Always use proper position sizing, stop losses, and risk controls regardless of signal quality
Parameter Sensitivity: Different instruments and timeframes may require parameter optimization for optimal results
Continuous Monitoring: Band characteristics change with market conditions, requiring ongoing assessment
Recommended Supporting Analysis:
Price structure analysis (support/resistance, trend lines)
Volume confirmation for breakout signals
Multiple timeframe alignment
Market context awareness (news events, session times)
Correlation analysis with related instruments
The indicator aims to provide adaptive momentum analysis that adjusts to changing market volatility, but traders must apply sound judgment, proper risk management, and comprehensive market analysis in their decision-making process.
Signalgo VSignalgo V: Technical Overview and Unique Aspects
Signalgo V is a technical indicator for TradingView that integrates multiple layers of analysis: moving averages, MACD, Bollinger Bands and RSI to deliver buy and sell signals. Below is an informational breakdown of how the indicator functions, its input parameters, signal logic, exit methodology, and how it stands apart from traditional moving average (MA) tools, without disclosing specifics that allow for code duplication.
How Signalgo V Works
1. Multi-Layered Technical Synthesis
Signalgo V processes several technical studies simultaneously:
Fast/Slow Moving Averages: Uses either EMA or SMA (user-selected) with adjustable periods. These are central to initial trend detection through crossovers.
MACD Filter: MACD line vs. signal line cross-check ensures trend direction is supported by both momentum and MA structure.
RSI Confirmation: The RSI is monitored to verify that signals are not excessively overbought or oversold, tuning the system to changing momentum regimes.
Bollinger Bands Context: Entry signals are only considered when price action is beyond the Bollinger Bands envelope, which further filters for unusually strong movements.
These strict, multi-indicator entry criteria are designed to ensure only the most robust signals are surfaced, each is contingent on the presence of aligned trend, momentum and volatility.
2. Exit Methodology
Take-Profit Levels: After entering a trade, the strategy automatically sets three predefined profit targets (TP1, TP2, TP3). If the price reaches any of these targets, the system marks it, helping you lock in profits at different stages.
Stop-Loss System: Simultaneously, a stop-loss (SL) value is set, protecting you from significant losses if the market moves against your position.
Dynamic Adjustment: When the first profit target (TP1) is hit, the system can automatically move the stop-loss to your entry price. This means your worst-case outcome is break-even from that point, reducing downside risk.
Trailing Stop-Loss: After TP1 is reached, a dynamic trailing stop can activate. This allows the stop-loss to follow the price as it moves in your favor, aiming to capture more profit if the trend continues, while still protecting your gains if the price reverses.
Visual Markers: The system plots all important exit levels (profit targets, stop-loss, trailing stop) directly on the chart. Optional labels also appear whenever a target or stop-loss is hit, making it easy to see progress.
Visual cues (labels) are plotted directly on the bar where a buy or sell signal triggers, clarifying entry points and aiding manual exit/risk management decisions.
Input Parameters
rsiLen: Lookback period for RSI calculation.
rsiOB and rsiOS: Overbought/oversold thresholds, adaptive to the indicator’s multi-layered logic.
maFastLen and maSlowLen: Periods for fast and slow MAs.
maType: EMA or SMA selectable for both MAs.
bbLen: Length for Bollinger Bands mean calculation.
bbMult: Standard deviation multiplier for BB width.
macdFast, macdSlow, macdSig: Standard MACD parameterization for nuanced momentum oversight.
What Separates Signalgo V from Traditional Moving Average Indicators
Composite Signal Architecture: Where traditional MA systems generate signals solely on MA crossovers, Signalgo V requires layered, cross-confirmational logic across trend (MAs), momentum (MACD), volatility (Bollinger Bands), and market strength (RSI).
Adaptive Volatility Context: MA signals only “count” when price is meaningfully breaking out of its volatility envelope, filtering out most unremarkable crosses that plague basic MA strategies.
Integrated Multi-Factor Filters: Strict compliance with all layers of signal logic is enforced. A marked improvement over MA strategies that lack secondary or tertiary confirmation.
Non-Redundant Event Limiting: Each entry is labeled as a unique event. The indicator does not repeat signals on subsequent bars unless all entry conditions are freshly met.
Trading Strategy Application
Trend Identification: By requiring concurrence among MA, MACD, RSI, and BB, this tool identifies only those trends with robust, multifactor support.
Breakout and Momentum Entry: Signals are bias-toward trades that initiate at likely breakout points (outside BB range), combined with fresh momentum and trend alignment.
Manual Discretion for Exits: The design is to empower traders with high-confidence entries and leave risk management or partial profit-taking adaptive to trader style, using visual cues from all component indicators.
Alert Generation: Each buy/sell event optionally triggers an alert, supporting systematic monitoring without constant chart watching.
ST+ TP1-TP5 + CALL/PUT 1. The Indicator's General Concept
The indicator works by:
Using the Supertrend indicator to determine when a new trend (bullish or bearish) begins.
Once a new trend is detected:
It determines the entry price.
It calculates the stop-loss (SL).
It calculates five profit levels, TP1 to TP5.
It draws horizontal lines on the chart representing the entry, SL, TP1-TP5, with labels on the right side (as shown in the image).
It can also display a CALL or PUT symbol above the signal candle.
It tracks price movement to determine if a target has been reached or if the stop-loss has been hit.
2. The Inputs That Control the Indicator
You can modify these values according to your strategy:
ATR Length → The number of candles used to calculate volatility.
Supertrend Factor → Controls the sensitivity of the supertrend. (The higher the value, the fewer the signals.)
TP1 to TP5 → ATR multipliers to set targets.
SL → ATR multiplier to set stop loss.
Extend Bars → The distance the lines extend to the right before the bar.
Show CALL/PUT → Shows or hides the trend signal.
Show TP Flags → Enables or disables small TP flags above the candles.
3. Determining the Trend
The indicator uses Supertrend to determine:
Is the market in an uptrend or a downtrend?
If the trend changes from bearish to bullish, it registers a CALL signal.
If the trend changes from bullish to bearish, it registers a PUT signal.
The first candle at which this change occurs is called a reversal candle.
4. Calculating Levels
When a reversal candle occurs:
Entry price = closing price of the candle.
Stop Loss (SL):
For an uptrend = Price - ATR × Multiplier.
For a downtrend = Price + ATR × Multiplier.
Profit Levels (TP1, TP5):
If up → Price + ATR × (multipliers).
If down → Price - ATR × (multipliers).
5. Drawing Lines and Labels
Draws horizontal lines representing:
Entry (green)
SL (red)
TP1-TP5 (blue)
Places labels on the right side of the chart, as shown in the image:
Each label shows the price level.
The label reads: "TP1: 123.45" or "Entry: 120.00", etc.
The positions of the lines and labels are updated automatically with each new candle.
6. Showing CALL and PUT Signals
If the new trend is up, a green CALL label will appear above the reversal candle.
If the new trend is down, a red PUT label will appear above the reversal candle.
7. Target Tracking and Stop Loss
The indicator tracks each candle after the signal:
If the price touches one of the targets (TP1 to TP5):
It marks this target.
It stops tracking this target so that it does not repeat the signal.
If the price touches the Stop Loss (SL):
It closes the trade and stops tracking completely.
8. Blue Flags Option
There is an additional option:
If you enable it, a small blue flag will appear above or below the candle when any target is reached.
If you disable it, you won't see these flags; you'll just see the sidebars and labels.
9. Live and Dynamic Update
The indicator uses an automatic update every minute.
Ensures that all lines and labels remain fixed at the last candlestick of the analysis.
10. Trade Lifecycle
Wait for a reversal in a supertrend.
At the first reversal → set Entry/SL/TP1..TP5.
Draw lines and labels on the chart.
Monitor price action:
If any TP is met → mark it as met.
If the SL is reached → cancel the trade.
Wait for a new signal to begin a new cycle.
Conclusion
The indicator provides you with a complete visual trading system.
Defines entry points, stop-losses, and profit targets.
Everything is displayed on the chart with clear colored lines and labels.
Keeps targets organized and prevents duplicate signals.
Can be used on any timeframe or market.
AltCoin & MemeCoin Index Correlation [Eddie_Bitcoin]🧠 Philosophy of the Strategy
The AltCoin & MemeCoin Index Correlation Strategy by Eddie_Bitcoin is a carefully engineered trend-following system built specifically for the highly volatile and sentiment-driven world of altcoins and memecoins.
This strategy recognizes that crypto markets—especially niche sectors like memecoins—are not only influenced by individual price action but also by the relative strength or weakness of their broader sector. Hence, it attempts to improve the reliability of trading signals by requiring alignment between a specific coin’s trend and its sector-wide index trend.
Rather than treating each crypto asset in isolation, this strategy dynamically incorporates real-time dominance metrics from custom indices (OTHERS.D and MEME.D) and combines them with local price action through dual exponential moving average (EMA) crossovers. Only when both the asset and its sector are moving in the same direction does it allow for trade entries—making it a confluence-based system rather than a single-signal strategy.
It supports risk-aware capital allocation, partial exits, configurable stop loss and take profit levels, and a scalable equity-compounding model.
✅ Why did I choose OTHERS.D and MEME.D as reference indices?
I selected OTHERS.D and MEME.D because they offer a sector-focused view of crypto market dynamics, especially relevant when trading altcoins and memecoins.
🔹 OTHERS.D tracks the market dominance of all cryptocurrencies outside the top 10 by market cap.
This excludes not only BTC and ETH, but also major stablecoins like USDT and USDC, making it a cleaner indicator of risk appetite across true altcoins.
🔹 This is particularly useful for detecting "Altcoin Season"—periods where capital rotates away from Bitcoin and flows into smaller-cap coins.
A rising OTHERS.D often signals the start of broader altcoin rallies.
🔹 MEME.D, on the other hand, captures the speculative behavior of memecoin segments, which are often driven by retail hype and social media activity.
It's perfect for timing momentum shifts in high-risk, high-reward tokens.
By using these indices, the strategy aligns entries with broader sector trends, filtering out noise and increasing the probability of catching true directional moves, especially in phases of capital rotation and altcoin risk-on behavior.
📐 How It Works — Core Logic and Execution Model
At its heart, this strategy employs dual EMA crossover detection—one pair for the asset being traded and one pair for the selected market index.
A trade is only executed when both EMA crossovers agree on the direction. For example:
Long Entry: Coin's fast EMA > slow EMA and Index's fast EMA > slow EMA
Short Entry: Coin's fast EMA < slow EMA and Index's fast EMA < slow EMA
You can disable the index filter and trade solely based on the asset’s trend just to make a comparison and see if improves a classic EMA crossover strategy.
Additionally, the strategy includes:
- Adaptive position sizing, based on fixed capital or current equity (compound mode)
- Take Profit and Stop Loss in percentage terms
- Smart partial exits when trend momentum fades
- Date filtering for precise backtesting over specific timeframes
- Real-time performance stats, equity tracking, and visual cues on chart
⚙️ Parameters & Customization
🔁 EMA Settings
Each EMA pair is customizable:
Coin Fast EMA: Default = 47
Coin Slow EMA: Default = 50
Index Fast EMA: Default = 47
Index Slow EMA: Default = 50
These control the sensitivity of the trend detection. A wider spread gives smoother, slower entries; a narrower spread makes it more responsive.
🧭 Index Reference
The correlation mechanism uses CryptoCap sector dominance indexes:
OTHERS.D: Dominance of all coins EXCLUDING Top 10 ones
MEME.D: Dominance of all Meme coins
These are dynamically calculated using:
OTHERS_D = OTHERS_cap / TOTAL_cap * 100
MEME_D = MEME_cap / TOTAL_cap * 100
You can select:
Reference Index: OTHERS.D or MEME.D
Or disable the index reference completely (Don't Use Index Reference)
💰 Position Sizing & Risk Management
Two capital allocation models are supported:
- Fixed % of initial capital (default)
- Compound profits, which scales positions as equity grows
Settings:
- Compound profits?: true/false
- % of equity: Between 1% and 200% (default = 10%)
This is critical for users who want to balance growth with risk.
🎯 Take Profit / Stop Loss
Customizable thresholds determine automatic exits:
- TakeProfit: Default = 99999 (disabled)
- StopLoss: Default = 5 (%)
These exits are percentage-based and operate off the entry price vs. current close.
📉 Trend Weakening Exit (Scale Out)
If the position is in profit but the trend weakens (e.g., EMA color signals trend loss), the strategy can partially close a configurable portion of the position:
- Scale Position on Weak Trend?: true/false
- Scaled Percentage: % to close (default = 65%)
This feature is useful for preserving profits without exiting completely.
📆 Date Filter
Useful for segmenting performance over specific timeframes (e.g., bull vs bear markets):
- Filter Date Range of Backtest: ON/OFF
- Start Date and End Date: Custom time range
OTHER PARAMETERS EXPLANATION (Strategy "Properties" Tab):
- Initial Capital is set to 100 USD
- Commission is set to 0.055% (The ones I have on Bybit)
- Slippage is set to 3 ticks
- Margin (short and long) are set to 0.001% to avoid "overspending" your initial capital allocation
📊 Visual Feedback and Debug Tools
📈 EMA Trend Visualization
The slow EMA line is dynamically color-coded to visually display the alignment between the asset trend and the index trend:
Lime: Coin and index both bullish
Teal: Only coin bullish
Maroon: Only index bullish
Red: Both bearish
This allows for immediate visual confirmation of current trend strength.
💬 Real-Time PnL Labels
When a trade closes, a label shows:
Previous trade return in % (first value is the effective PL)
Green background for profit, Red for losses.
📑 Summary Table Overlay
This table appears in a corner of the chart (user-defined) and shows live performance data including:
Trade direction (yellow long, purple short)
Emojis: 💚 for current profit, 😡 for current loss
Total number of trades
Win rate
Max drawdown
Duration in days
Current trade profit/loss (absolute and %)
Cumulative PnL (absolute and %)
APR (Annualized Percentage Return)
Each metric is color-coded:
Green for strong results
Yellow/orange for average
Red/maroon for poor performance
You can select where this appears:
Top Left
Top Right
Bottom Left
Bottom Right (default)
📚 Interpretation of Key Metrics
Equity Multiplier: How many times initial capital has grown (e.g., “1.75x”)
Net Profit: Total gains including open positions
Max Drawdown: Largest peak-to-valley drop in strategy equity
APR: Annualized return calculated based on equity growth and days elapsed
Win Rate: % of profitable trades
PnL %: Percentage profit on the most recent trade
🧠 Advanced Logic & Safety Features
🛑 “Don’t Re-Enter” Filter
If a trade is closed due to StopLoss without a confirmed reversal, the strategy avoids re-entering in that same direction until conditions improve. This prevents false reversals and repetitive losses in sideways markets.
🧷 Equity Protection
No new trades are initiated if equity falls below initial_capital / 30. This avoids overleveraging or continuing to trade when capital preservation is critical.
Keep in mind that past results in no way guarantee future performance.
Eddie Bitcoin
Strong Economic Events Indicator (mtbr)This indicator is designed to help traders anticipate market reactions to key economic events and visualize trade levels directly on their TradingView charts. It is highly customizable, allowing precise planning for entries, take-profits, and stop-losses.
Key Features:
Multi-Event Support:
Supports dozens of economic events including ISM Services PMI, CPI, Core CPI, PPI, Non-Farm Payrolls, Unemployment Rate, Retail Sales, GDP, and major central bank rate decisions (Fed, ECB, BOE, BOJ, Australia, Brazil, Canada, China).
Custom Event Date and Time:
Manually set the year, month, day, hour, and minute of the event to match your chart and timezone, ensuring accurate alignment.
Forecast vs Actual Analysis:
Input the forecast and actual values. The indicator calculates the likely market direction (Buy/Sell/Neutral) according to historical market reactions for each event.
Dynamic Trade Levels:
Automatically plots:
Entry price
TP1, TP2, TP3 in pips relative to the entry
Stop Loss in pips relative to the entry
Levels are automatically adjusted based on the event's Buy/Sell direction.
Visual Chart Representation:
Entry: Blue line and label
TP1/TP2/TP3: Green lines and labels
Stop Loss: Red line and label
Event occurrence: Orange dashed vertical line
Informative Table Panel:
Displays at the bottom-right of the chart:
Event name
Entry price
TP1, TP2, TP3 values
Current market direction (Buy/Sell/Neutral)
Customizable Line Extension:
Extend the lines for visibility across multiple bars on the chart.
How to Use the Indicator:
Select the Asset:
Set the Asset to Trade input to the symbol you want to analyze (e.g., XAUUSD, EURUSD).
Choose the Economic Event:
Use the drop-down menu to select the event you want to track.
Set the Event Date and Time:
Input the year, month, day, hour, and minute of the event. This ensures the event lines and labels appear at the correct time on your chart.
Input Forecast and Actual Values:
Enter the forecasted value and the actual result of the event. The script will determine market direction based on historically observed reactions for that event.
Configure Entry and Pip Levels:
Set your Entry Price
Set pip distances for TP1, TP2, TP3, and Stop Loss
The script automatically adjusts the levels according to Buy or Sell direction.
View Levels and Status:
Once the event occurs (or on backtesting), the indicator will plot:
Entry, Take Profits, Stop Loss on the chart
Vertical line for event occurrence
Table summarizing levels and Buy/Sell status
Adjust Line Extension:
Use the Line Extension (bars) input to control how far the horizontal levels extend on the chart.
Example Scenario:
Event: PPI MoM
Forecast: 0.2
Actual: 0.9
The indicator identifies the correct market reaction (Sell for EURUSD) and plots the Entry, TP1, TP2, TP3, and Stop Loss accordingly.
Important Notes:
The indicator does not execute trades automatically; it is for analysis and visualization only.
Always combine the signals with your own risk management and analysis.
Ensure your chart is set to the correct timezone corresponding to the event’s time.
This description fully explains how to use the indicator, what it displays, and step-by-step guidance for beginners and experienced traders
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Institutional Analyst Board
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Jul 19
📊 Institutional Analyst Board – Smart Money Confluence Scanner for XAUUSD, Forex, Crypto
🔍 Overview
The Institutional Analyst Board is a complete multi-timeframe smart money toolkit designed for traders who demand clarity, confluence, and precision. It brings together institutional-grade metrics—Order Blocks (OB), Fair Value Gaps (FVG), Liquidity Sweeps, MACD/RSI bias, VWAP positioning, and Break of Structure (BoS)—into a single powerful visual dashboard.
This indicator is especially optimized for Gold (XAUUSD) but is also compatible with Crypto and Forex assets.
🧠 Key Features
✅ Multi-Timeframe Dashboard (5M / 15M / 1H)
✅ Order Block Detection with dynamic zones that extend until broken
✅ Fair Value Gap Detection with clear zone shading and border distinction
✅ MACD + RSI Confluence for momentum and bias alignment
✅ VWAP Positioning to identify premium/discount zones
✅ Liquidity Sweeps (internal/external range breaks)
✅ Killzone Highlighting (Asia / London / New York)
✅ Break of Structure (BoS) with advanced confluence filters
✅ Gold Bias Flags across timeframes (BUY / SELL / NEUTRAL)
✅ Dynamic Price Watermark with real-time data
✅ Fully customizable colors, transparencies, and text labels
🧠 How It Works
The Board uses institutional logic to analyze the chart in real time:
Metric Purpose
OB Zones Highlight potential smart money footprints where price is likely to react.
FVG Zones Identify imbalance areas between buyers and sellers—ideal for mean reversion entries.
MACD/RSI Confirm momentum direction and relative strength confluence.
VWAP Determine whether price is trading at a premium or discount.
Liquidity Sweeps Detect manipulative moves before major reversals.
BoS Mark potential trend reversals, filtered by institutional confluence.
Each signal is computed across 3 timeframes and visualized in a clean board that updates live. You’ll also see labels, alerts, and session overlays for maximum clarity.
📌 Ideal Use Case
This tool is perfect for:
Funded Challenge Traders (FTMO, MyForexFunds, etc.)
Gold scalpers and intraday traders
Crypto price action traders using BTC, ETH, SOL, etc.
Smart Money Concept (SMC) and ICT followers
⚙️ Customization Options
Toggle each module (OB, FVG, VWAP, MACD/RSI, etc.)
Set transparency and color for each zone type
Adjust Killzone timing (Asia, London, NY)
Control board position (Top/Bottom) and metric visibility
📈 Compatible Assets
✅ XAUUSD (optimized)
✅ Forex majors/minors
✅ Crypto pairs (BTC, ETH, SOL, etc.)
✅ Indices (GER40, NASDAQ, SPX with minor adaptation)
🛠️ Requirements
Use on TradingView v5
Set chart time to UTC+0 or UTC+3 for optimal Killzone accuracy
For crypto, redefine Killzone hours if needed (24/7 market)
🧠 Pro Tip
Pair this indicator with volume profile tools, CVD/Delta Flow, or Footprint overlays to build high-confidence trade setups with clear institutional confluence.
Pineify Signals and OverlaysIndicator Theoretical Basis
Pineify Signals and Overlays is an invite-only trend-following and reversal-detection toolkit that fuses four well-known concepts— Dow-Theory trend phases , a multi-pair EMA cloud, QQE momentum, and ATR-based risk management—into a single, weight-balanced engine. An optional multi-time-frame (MTF) filter aligns lower-time-frame signals with higher-time-frame structure, helping traders avoid counter-trend setups. All components can be toggled from the settings panel, and a beginner “One-Click” preset loads a conservative profile out of the box.
Why it’s a single script: The algorithm scores every bar on three orthogonal axes—trend, momentum, and volatility—then issues context-aware arrows and coloured clouds only when the axes agree within user-defined tolerances. This inter-locking logic cannot be reproduced by simply stacking independent indicators on a chart, hence the need for an integrated implementation.
Trend Confirmation
Trend Confirmation: This indicator presents two types of market trends: the primary trend and the secondary trend. The primary trend is the long - term direction of the market and can last for days or months; the secondary trend is the adjustment phase within the primary trend.
This indicator uses the EMA (Exponential Moving Average) and visualizes the trend phases through color filling. The judgment of the trend is that blue plus green indicates a bullish trend, and yellow plus red indicates a bearish trend.
The primary trend of this indicator is visualized by two sets of moving averages through color filling. These two sets of moving averages are used to describe the short - term and long - term trends in the market.
The short - period moving averages and the long - period moving averages each consist of 4 moving averages, with a total of 8 moving averages, representing the short - term fluctuations and trends of the market.
Trend Persistence: Once the primary trend is formed, it will persist for a period of time. This indicator judges based on the Dow Theory. Short - term market fluctuations do not necessarily reflect changes in the primary trend. Therefore, the judgment direction of the primary trend is visualized through color.
The Signals of Buying, Selling and Closing
In the primary trend, we can see signals of trend reversal. This indicator incorporates the "Consecutive Candles". The indicator mainly identifies the overbought or oversold state of the market through a series of consecutive conditions, so as to predict the reversal point. The core of this indicator is to identify a series of consecutive price movements in the market trend and determine whether the market is about to reverse based on this sequence. We visualize the turning points through buy and sell signals.
The trend confirmation system utilizes four pairs of Exponential Moving Averages (EMAs) creating dynamic cloud formations that visualize market direction. Short-period EMAs (5, 8, 20, 34) interact with longer-period EMAs (9, 13, 21, 50) to generate color-coded trend clouds . Blue and green clouds indicate bullish conditions, while yellow and red clouds signal bearish trends, providing immediate visual trend identification.
The presentation of buying and selling points, namely "Quantitative Qualitative Estimation", is a technical indicator that combines the concepts of the Relative Strength Index (RSI) and moving averages. It is used to evaluate market trends, overbought and oversold conditions, as well as potential trend reversal points. The oscillator has a relatively long smoothing period, making the indicator relatively stable, thus enabling the visualization of buy + and sell + signals for trading.
ATR Stop - Loss Line
ATR (Average True Range) is an indicator for measuring market volatility. By using the ATR value to set the stop - loss distance, the stop - loss level can be automatically adjusted according to market volatility, making the stop - loss more flexible.
Core principle
Trend-Cloud Engine
EMA Pairs (5, 8, 20, 34 vs 9, 13, 21, 50)—Two four-EMA sets form “fast” and “slow” envelopes. When the volume-weighted mean of the fast set sits above the slow set and both slopes are positive, the bar is tagged primary bullish; the inverse tags primary bearish. Cloud colours (blue/green vs yellow/red) mirror Dow Theory’s primary/secondary trend hierarchy.
Momentum & Exhaustion Layer
QQE Oscillator (RSI 14, factor 4.238) detects momentum extremes and smooths noise more than a raw RSI, making it better suited for multi-time-frame use.
Consecutive-Candle Counter (default 8) highlights potential exhaustion after extended unidirectional moves; reversal symbols appear only if QQE divergence also exists.
Volatility-Adjusted Risk Line
ATR Trailing Stop (ATR 21, dynamic multiplier) expands in high volatility and tightens in low volatility, offering an adaptive exit reference rather than a fixed-tick stop.
Multi-Time-Frame Confirmation
The script automatically chooses a higher aggregation (e.g., 4 × the chart timeframe) and requires primary-trend agreement before issuing “Long ▲+” or “Short ▼+” confirmations. This guards against false signals during counter-trend rebounds.
Recommended parameters
RSI Length: 14 (QQE calculation base)
QQE Factor: 4.238 (Fibonacci-based multiplier)
ATR Period: 21 (volatility measurement)
EMA Lengths: Configurable short (5,8,20,34) and long (9,13,21,50) periods
Consecutive Candles: Selectable count (8)
Multi-timeframe Filter: Filter is enabled by default, resulting in more accurate signals.
Filters
The multi-timeframe filter enhances signal reliability by confirming trends across higher timeframes. This prevents counter-trend trades by ensuring alignment between current chart timeframe and broader market direction. The filter automatically calculates appropriate higher timeframes for trend confirmation.
Signals & Alerts
The indicator system exports multiple alert signals, and you can easily alert for any signal.
Up Trend : Primary long signal appears
Long - ▲ : Buy signal appears
Long - ▲+ : Confirmation buy signal appears
Long - ● : Primary reversal signal appears
Long - ☓ : Secondary reversal signal appears
Down Trend : Primary short signal appears
Short - ▼ : Sell signal appears
Short - ▼+ : Confirmation sell signal appears
Short - ● : Primary reversal signal appears
Short - ☓ : Secondary reversal signal appears
Originality & Value for Traders
Integrated scoring logic ensures signals fire only when trend, momentum, and volatility metrics corroborate, reducing “indicator conflict”.
Auto-computed MTF pairs mean no manual timeframe juggling.
Weight-balanced QQE/EMA blend creates smoother trend clouds than standard MA crosses, yet remains more responsive than Keltner or Donchian approaches.
One-click beginner profile plus full parameter access supports both novice and advanced users.
Risk Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (Pineify) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Palgo Trading - Palgo🎯THE PALGO INDICATOR
The "Palgo Trading - Palgo" indicator, developed by PALGOTRADING is a sophisticated technical analysis tool designed to identify potential buy and sell signals by combining trend analysis with momentum and optional AI-driven sentiment assessment. This indicator provides a clear visual representation of potential trading opportunities directly on the price chart.
At its core, the Palgo indicator synthesizes information from well-established technical analysis concepts with statistical functions, and has optional AI Integration for social analysis of the asset using external data :
Supertrend: This indicator identifies the prevailing trend direction. A positive Supertrend value suggests an upward trend, while a negative value indicates a downward trend. The Palgo indicator utilizes a Supertrend with a customizable multiplier and a user-configurable Average True Range (ATR) length (defaulting to 21).
🛜Signal Generation Logic
The indicator generates buy and sell signals based on a calculated "final direction" value. This value is derived by combining the Supertrend direction and a modified RSI. The modification involves scaling the RSI output to a range of -0.5 to 0.5 and then further adjusting it.
The buy and sell conditions are as follows:
Buy Signal: A buy signal is triggered when the "final direction" crosses above a positive activation threshold while the current signal is not already bullish. Upon signal generation, a "Buy" label (colored green) appears below the bar, and initial Take Profit (TP) and Stop Loss (SL) levels are calculated and stored.
Sell Signal: Conversely, a sell signal is triggered when the "final direction" crosses below a negative activation threshold while the current signal is not already bearish. A "Sell" label (colored red) is plotted above the bar, and corresponding TP and SL levels are determined.
✅ Optimized Take-Profit / Stop-Loss
The Take-Profit (TP) & Stop-Loss (SL) signals are optimized with Kernel Density Estimation (KDE), the script uses KDE activated by gaussian function on previous pivot points and trains the model, then tries to estimate new pivot points early, to determine new TP / SL levels for the current signal. Kernel Density Estimation takes values of the previous confirmed pivots' RSI values, body size & more factors to determine their role. This indicator can generate up to 5 TP signals per signal.
📈 Signal Trail
Palgo also includes a "Signal Trail" that visually shows the market's momentum. This trail is like a dynamic line that follows the price.
When the market is in an uptrend and looking strong, you'll see a green trail.
When it's in a downtrend and looking weak, you'll see a red trail.
This trail helps you see if the market is currently aligned with Palgo's bullish (buy) or bearish (sell) signal. It also acts as a visual guide for potential support or resistance levels.
📊Backtesting Dashboard
The Palgo indicator includes an optional Backtesting Dashboard to help you understand its historical performance. This dashboard appears directly on your chart and provides a quick summary of how the indicator's signals have performed in the past.
Here's what you'll see on the dashboard:
Sensitivity: This shows the specific "Sensitivity" setting you've chosen for the indicator. This setting influences how often signals are generated.
Wins: This number tells you how many trades initiated by the Palgo indicator historically ended in profit (reached a Take-Profit target or closed profitably when the signal reversed).
Loss: This number indicates how many trades historically ended in a loss (hit the Stop-Loss).
Winrate: This is a very important metric, displayed as a percentage. It shows you the proportion of winning trades compared to the total number of trades (Wins / (Wins + Loss)). A higher winrate generally suggests a more effective strategy.
This dashboard is a valuable tool for reviewing the indicator's effectiveness with different settings and helping you make informed decisions about its use in your trading.
🤖AI Integration (Optional):
A unique feature of the Palgo indicator is the optional integration of Artificial Intelligence (AI) sentiment analysis. When the "Use AI" input is enabled, the indicator incorporates two additional user-defined inputs:
Impression Change %: This input represents the percentage change in overall market sentiment as assessed by an external AI.
Positivity Change: This input reflects the change in positive sentiment, also provided by an AI.
These AI inputs are combined to create an "AI Score," which then influences the "final direction" calculation. A positive AI Score amplifies the bullish signals and dampens bearish signals, while a negative AI Score has the opposite effect.
❓Why PALGO ?
All-in-One Analysis: Palgo combines trend, momentum, and advanced statistical analysis into one easy-to-use tool, giving you a complete picture without needing multiple indicators.
Dynamic Profit & Loss Management: Unlike many tools with fixed targets, Palgo's smart profit and stop-loss system adapts to the market using KDE. This helps you potentially capture more gains and limit losses effectively.
Optional AI Insights: For an extra edge, Palgo can tap into Artificial Intelligence (AI) to gauge overall market mood. If the AI sees a lot of positive buzz, it can strengthen buy signals; if it's negative, it can reinforce sell signals. This helps you trade with a better understanding of the market's pulse.
Clear and Customizable: Palgo is designed to be very visual. It changes the color of the price bars, adds clear "Buy" or "Sell" labels, and marks your profit and stop-loss points. You can also change the colors to suit your preference.
Palgo aims to be a comprehensive and adaptable trading tool, giving you clearer insights.
⚙️Visualizations and Customization
The Palgo indicator offers several visual cues to aid traders:
Bar Coloring: The price bars are colored green when the indicator identifies a bullish signal and red during a bearish signal.
Signal Labels: Clear "Buy" and "Sell" labels are plotted at the signal generation points.
Take Profit and Stop Loss Markers: Distinct shapes and labels indicate when the price reaches the calculated TP and SL levels.
Style Options: Users can customize the colors for bullish and bearish bars, text, and TP/SL markers within the indicator's settings.
Quantum State Superposition Indicator (QSSI)Quantum State Superposition Indicator (QSSI) - Where Physics Meets Finance
The Quantum Revolution in Market Analysis
After months of research into quantum mechanics and its applications to financial markets, I'm thrilled to present the Quantum State Superposition Indicator (QSSI) - a groundbreaking approach that models price action through the lens of quantum physics. This isn't just another technical indicator; it's a paradigm shift in how we understand market behavior.
The Theoretical Foundation
Quantum Superposition in Markets
In quantum mechanics, particles exist in multiple states simultaneously until observed. Similarly, markets exist in a superposition of potential states (bullish, bearish, neutral) until a significant volume event "collapses" the wave function into a definitive direction.
The mathematical framework:
Wave Function (Ψ): Represents the market's quantum state as a weighted sum of all possible states:
Ψ = Σ(αᵢ × Sᵢ)
Where αᵢ are probability amplitudes and Sᵢ are individual quantum states.
Probability Amplitudes: Calculated using the Born rule, normalized so Σ|αᵢ|² = 1
Observation Operator: Volume/Average Volume ratio determines observation strength
The Five Quantum States
Momentum State: Short-term price velocity (EMA of returns)
Mean Reversion State: Deviation from equilibrium (normalized z-score)
Volatility Expansion State: ATR relative to historical average
Trend Continuation State: Long-term price positioning
Chaos State: Volatility of volatility (market uncertainty)
Each state contributes to the overall wave function based on current market conditions.
Wave Function Collapse
When volume exceeds the observation threshold (default 1.5x average), the wave function "collapses," committing the market to a direction. This models how institutional volume forces markets out of uncertainty into trending states.
Collapse Detection Formula:
Collapse = Volume > (Threshold × Average Volume)
Direction = Sign(Ψ) at collapse moment
Advanced Quantum Concepts
Heisenberg Uncertainty Principle
The indicator calculates market uncertainty as the product of price and momentum
uncertainties:
ΔP × ΔM = ℏ (market uncertainty constant)
This manifests as dynamic uncertainty bands that widen during unstable periods.
Quantum Tunneling
Calculates the probability of price "tunneling" through resistance/support barriers:
P(tunnel) = e^(-2×|barrier_height|×√coherence_length)
Unlike classical technical analysis, this gives probability of breakouts before they occur.
Entanglement
Measures the quantum correlation between price and volume:
Entanglement = |Correlation(Price, Volume, lookback)|
High entanglement suggests coordinated institutional activity.
Decoherence
When market states lose quantum properties and behave classically:
Decoherence = 1 - Σ(amplitude²)
Indicates trend emergence from quantum uncertainty.
Visual Innovation
Probability Clouds
Three-tier probability distributions visualize market uncertainty:
Inner Cloud (68%): One standard deviation - most likely price range
Middle Cloud (95%): Two standard deviations - probable extremes
Outer Cloud (99.7%): Three standard deviations - tail risk zones
Cloud width directly represents market uncertainty - wider clouds signal higher entropy states.
Quantum State Visualization
Colored dots represent individual quantum states:
Green: Momentum state strength
Red: Mean reversion state strength
Yellow: Volatility state strength
Dot brightness indicates amplitude (influence) of each state.
Collapse Events
Aqua Diamonds (Above): Bullish collapse - upward commitment
Pink Diamonds (Below): Bearish collapse - downward commitment
These mark precise moments when markets exit superposition.
Implementation Details
Core Calculations
Feature Extraction: Normalize price returns, volume ratios, and volatility measures
State Calculation: Compute each quantum state's value
Amplitude Assignment: Weight states by market conditions and observation strength
Wave Function: Sum weighted states for final market quantum state
Visualization: Transform quantum values to price space for display
Performance Optimization
- Efficient array operations for state calculations
- Single-pass normalization algorithms
- Optimized correlation calculations for entanglement
- Smart label management to prevent visual clutter
Trading Applications:
Signal Generation
Bullish Signals:
- Positive wave function during collapse
- High tunneling probability at support
- Coherent market state with bullish bias
Bearish Signals:
- Negative wave function during collapse
- High tunneling probability at resistance
- Decoherent state transitioning bearish
Risk Management
Uncertainty-Based Position Sizing:
Narrow clouds: Normal position size
Wide clouds: Reduced position size
Extreme uncertainty: Stay flat
Quantum Stop Losses:
- Place stops outside probability clouds
- Adjust for Heisenberg uncertainty
- Respect quantum tunneling levels
Market Regime Recognition
Quantum Coherent (Superposed):
- Market in multiple states
- Avoid directional trades
- Prepare for collapse
Quantum Decoherent (Classical):
-Clear trend emergence
- Follow directional signals
- Traditional analysis applies
Advanced Features
Adaptive Dashboards
Quantum State Panel: Real-time wave function, dominant state, and coherence status
Performance Metrics: Win rate, signal frequency, and regime analysis
Information Guide: Comprehensive explanation of all quantum concepts
- All dashboards feature adjustable sizing for different screen resolutions.
Multi-Timeframe Quantum Analysis
The indicator adapts to any timeframe:
Scalping (1-5m): Short coherence length, sensitive thresholds
Day Trading (15m-1H): Balanced parameters
Swing Trading (4H-1D): Long coherence, stable states
Alert System
Sophisticated alerts for:
- Wave function collapse events
- Decoherence transitions
- High tunneling probability
- Strong entanglement detection
Originality & Innovation
This indicator introduces several firsts:
Quantum Superposition: First to model markets as quantum systems
Wave Function Collapse: Original volume-triggered state commitment
Tunneling Probability: Novel breakout prediction method
Entanglement Metrics: Unique price-volume quantum correlation
Probability Clouds: Revolutionary uncertainty visualization
Development Journey
Creating QSSI required:
- Deep study of quantum mechanics principles
- Translation of physics equations to market context
- Extensive backtesting across multiple markets
- UI/UX optimization for trader accessibility
- Performance optimization for real-time calculation
- The result bridges cutting-edge physics with practical trading.
Best Practices
Parameter Optimization
Quantum States (2-5):
- 2-3 for simple markets (forex majors)
- 4-5 for complex markets (indices, crypto)
Coherence Length (10-50):
- Lower for fast markets
- Higher for stable markets
Observation Threshold (1.0-3.0):
- Lower for active markets
- Higher for thin markets
Signal Confirmation
Always confirm quantum signals with:
- Market structure (support/resistance)
- Volume patterns
- Correlated assets
- Fundamental context
Risk Guidelines
- Never risk more than 2% per trade
- Respect probability cloud boundaries
- Exit on decoherence shifts
- Scale with confidence levels
Educational Value
QSSI teaches advanced concepts:
- Quantum mechanics applications
- Probability theory
- Non-linear dynamics
- Risk management
- Market microstructure
Perfect for traders seeking deeper market understanding.
Disclaimer
This indicator is for educational and research purposes only. While quantum mechanics provides a fascinating framework for market analysis, no indicator can predict future prices with certainty. The probabilistic nature of both quantum mechanics and markets means outcomes are inherently uncertain.
Always use proper risk management, conduct thorough analysis, and never risk more than you can afford to lose. Past performance does not guarantee future results.
Conclusion
The Quantum State Superposition Indicator represents a revolutionary approach to market analysis, bringing institutional-grade quantum modeling to retail traders. By viewing markets through the lens of quantum mechanics, we gain unique insights into uncertainty, probability, and state transitions that classical indicators miss.
Whether you're a physicist interested in finance or a trader seeking cutting-edge tools, QSSI opens new dimensions in market analysis.
"The market, like Schrödinger's cat, exists in multiple states until observed through volume."
* As you may have noticed, the past two indicators I've released (Lorentzian Classification and Quantum State Superposition) are designed with strategy implementation in mind. I'm currently developing a stable execution platform that's completely unique and moves away from traditional ATR-based position sizing and stop loss systems. I've found ATR-based approaches to be unreliable in volatile markets and regime transitions - they often lag behind actual market conditions and can lead to premature exits or oversized positions during volatility spikes.
The goal is to create something that adapts to market conditions in real-time using the quantum and relativistic principles we've been exploring. Hopefully I'll have something groundbreaking to share soon. Stay tuned!
Trade with quantum insight. Trade with QSSI .
— Dskyz , for DAFE Trading Systems
Bullish and Bearish Breakout Alert for Gold Futures PullbackBelow is a Pine Script (version 6) for TradingView that includes both bullish and bearish breakout conditions for my intraday trading strategy on micro gold futures (MGC). The strategy focuses on scalping two-legged pullbacks to the 20 EMA or key levels with breakout confirmation, tailored for the Apex Trader Funding $300K challenge. The script accounts for the Daily Sentiment Index (DSI) at 87 (overbought, favoring pullbacks). It generates alerts for placing stop-limit orders for 175 MGC contracts, ensuring compliance with Apex’s rules ($7,500 trailing threshold, $20,000 profit target, 4:59 PM ET close).
Script Requirements
Version: Pine Script v6 (latest for TradingView, April 2025).
Purpose:
Bullish: Alert when price breaks above a rejection candle’s high after a two-legged pullback to the 20 EMA in a bullish trend (price above 20 EMA, VWAP, higher highs/lows).
Bearish: Alert when price breaks below a rejection candle’s low after a two-legged pullback to the 20 EMA in a bearish trend (price below 20 EMA, VWAP, lower highs/lows).
Context: 5-minute MGC chart, U.S. session (8:30 AM–12:00 PM ET), avoiding overbought breakouts above $3,450 (DSI 87).
Output: Alerts for stop-limit orders (e.g., “Buy: Stop=$3,377, Limit=$3,377.10” or “Sell: Stop=$3,447, Limit=$3,446.90”), quantity 175 MGC.
Apex Compliance: 175-contract limit, stop-losses, one-directional news trading, close by 4:59 PM ET.
How to Use the Script in TradingView
1. Add Script:
Open TradingView (tradingview.com).
Go to “Pine Editor” (bottom panel).
Copy the script from the content.
Click “Add to Chart” to apply to your MGC 5-minute chart .
2. Configure Chart:
Symbol: MGC (Micro Gold Futures, CME, via Tradovate/Apex data feed).
Timeframe: 5-minute (entries), 15-minute (trend confirmation, manually check).
Indicators: Script plots 20 EMA and VWAP; add RSI (14) and volume manually if needed .
3. Set Alerts:
Click the “Alert” icon (bell).
Add two alerts:
Bullish Breakout: Condition = “Bullish Breakout Alert for Gold Futures Pullback,” trigger = “Once Per Bar Close.”
Bearish Breakout: Condition = “Bearish Breakout Alert for Gold Futures Pullback,” trigger = “Once Per Bar Close.”
Customize messages (default provided) and set notifications (e.g., TradingView app, SMS).
Example: Bullish alert at $3,377 prompts “Stop=$3,377, Limit=$3,377.10, Quantity=175 MGC” .
4. Execute Orders:
Bullish:
Alert triggers (e.g., stop $3,377, limit $3,377.10).
In TradingView’s “Order Panel,” select “Stop-Limit,” set:
Stop Price: $3,377.
Limit Price: $3,377.10.
Quantity: 175 MGC.
Direction: Buy.
Confirm via Tradovate.
Add bracket order (OCO):
Stop-loss: Sell 175 at $3,376.20 (8 ticks, $1,400 risk).
Take-profit: Sell 87 at $3,378 (1:1), 88 at $3,379 (2:1) .
Bearish:
Alert triggers (e.g., stop $3,447, limit $3,446.90).
Select “Stop-Limit,” set:
Stop Price: $3,447.
Limit Price: $3,446.90.
Quantity: 175 MGC.
Direction: Sell.
Confirm via Tradovate.
Add bracket order:
Stop-loss: Buy 175 at $3,447.80 (8 ticks, $1,400 risk).
Take-profit: Buy 87 at $3,446 (1:1), 88 at $3,445 (2:1) .
5. Monitor:
Green triangles (bullish) or red triangles (bearish) confirm signals.
Avoid bullish entries above $3,450 (DSI 87, overbought) or bearish entries below $3,296 (support) .
Close trades by 4:59 PM ET (set 4:50 PM alert) .
Head & Shoulders Pattern (Zeiierman)█ Overview
The Head & Shoulders Pattern (Zeiierman) is an advanced pattern recognition tool that automatically detects and visualizes one of the most powerful reversal patterns in technical analysis — the classic Head & Shoulders and Inverse Head & Shoulders formations .
This indicator brings structure clarity directly onto the price chart, allowing traders to instantly spot potential major reversal zones without manually drawing or searching for patterns.
It doesn't just draw lines — it intelligently scans price action for symmetry, pivot behavior, and neckline structures — then projects realistic price targets based on the pattern's height.
⚪ In simple terms:
▸ Standard Head & Shoulders → Bearish Reversal Pattern
▸ Inverse Head & Shoulders → Bullish Reversal Pattern
▸ Target Projection → Estimated Move from Neckline Break
▸ Labels → Clear annotation of Left Shoulder, Head, and Right Shoulder
█ How It Works
The indicator combines multiple technical detection layers into a clean visual model:
⚪ Dynamic Pivot Engine
Automatically detects pivot highs and lows based on user-defined Period.
Longer Period = Broader, higher-confidence patterns
Shorter Period = Smaller, more frequent patterns
⚪ Pattern Detection Logic
Scans pivot structures in real-time to identify valid:
Bearish Head & Shoulders (H&S)
Bullish Inverse Head & Shoulders (iH&S)
Conditions include:
▸ Symmetry validation
▸ Head above (or below) Shoulders
▸ Neckline structure
▸ Minimum price conditions met
█ How to Use
⚪ Reversal Trading
Look for Head & Shoulders at the top of an uptrend
Look for Inverse Head & Shoulders at the bottom of a downtrend
⚪ What makes our tool truly unique is that it goes beyond the traditional textbook definition.
Our custom Head & Shoulders algorithm is built with flexibility and adaptability in mind. It dynamically responds to real-time price action, allowing it to detect valid patterns not only at major trend reversals — but also within trending environments.
That means you can spot Head & Shoulders formations at:
Consolidation zones
Trend continuation areas
Corrective phases within established trends
It doesn’t have to be the absolute top or bottom of a move — and that’s the real power of this tool. It adapts. It evolves. It finds structure where most indicators stay blind.
█ Common Real-World Stop Loss Strategies with Head & Shoulders Patterns
Not all Head & Shoulders patterns are created equal — and neither are the stop loss strategies used to trade them.
Depending on your trading style, risk tolerance, and market context — here are the 3 most common ways traders manage stop placement when trading Head & Shoulders (H&S) or Inverse Head & Shoulders (iH&S) patterns:
⚪ Conservative Stop Placement
Maximum Safety — Minimum Chance of Being Stopped Prematurely
Stop Placement:
Above the Head (Bearish H&S)
Below the Head (Bullish iH&S)
Pros: Safest approach. Provides maximum protection against false breakouts and noise.
Cons: Often results in very large stop losses, especially on bigger patterns or higher timeframes. Risk-to-Reward (RR) can be poor unless the target is far.
⚪ Aggressive Stop Placement
Tighter Risk — Faster Invalidations
Stop Placement:
Above the Right Shoulder (Bearish H&S)
Below the Right Shoulder (Bullish iH&S)
Pros: Smaller stop losses. Improved RR. Ideal for traders who want tighter control over risk.
Cons: Higher chance of getting stopped on retests or minor volatility around the neckline zone.
⚪ Neckline Reclaim Invalidation
Dynamic & Price-Action Based Exit
Stop Placement:
Exit the trade if price closes back above (bearish) or below (bullish) the neckline after breaking it.
Pros: Dynamic approach based on market behavior rather than static levels. Allows more flexibility.
Cons: Requires active trade management. Not suitable for fully automated or set-and-forget trading styles.
█ Why It's Useful
This is not a basic pattern drawing tool — it's a complete detection system built for traders who want to:
Automatically detect powerful reversal patterns
Avoid the subjectivity of manually drawing H&S structures
Trade with clear target projections
Identify high-probability reversal zones
Visually map structure shifts in real-time
█ Settings
Pivot Detection
Period → Number of bars used to scan for pivots (Higher = Bigger patterns)
Pattern Detection
Enable Bullish Head & Shoulders
Enable Bearish Head & Shoulders
Visualization
Customize Colors (Lines, Fills, Labels)
Enable/Disable Labels
Pattern Style: Closed / Open
Custom Label Colors
Target Projection
Enable/Disable Target Projection
Customize Target Colors
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Multi-Timeframe Liquidity Zones V6 (Table)Multi-Timeframe Liquidity Zones V6 (Table) Indicator: Functionality and Uses
Overview: The Multi-Timeframe Liquidity Zones V6 (Table) indicator is a technical analysis tool that highlights key volume-based support and resistance levels across multiple timeframes. It leverages volume profile concepts – specifically the Point of Control (POC) and Value Area High/Low (VAH/VAL) – to identify “liquidity zones” where trading activity was heaviest . Unlike a standard single-timeframe volume profile, this indicator compiles data from several timeframes (e.g. monthly, weekly, daily, intraday) and displays the results in a convenient table format on the chart. The goal is to give traders a consolidated view of important price levels (derived from volume concentrations) across different horizons, helping them plan trades with a broader market perspective.
Purpose and Functionality of the Indicator
Multi-Timeframe Analysis: The primary objective of this indicator is to simplify multi-timeframe analysis of volume distribution. Rather than manually checking volume profiles on separate charts for each timeframe, the tool automatically calculates the key levels for each selected timeframe and presents them together. This includes higher-level perspectives (like monthly or weekly volume hotspots) alongside shorter-term levels (daily or hourly), ensuring that traders don’t miss significant zones from any timeframe . By offering a broader perspective on support and resistance levels, multi-timeframe tools help improve risk management and signal confirmation , and this indicator is designed to provide that volume-based perspective at a glance.
Table Format Display: Multi-Timeframe Liquidity Zones V6 (Table) specifically presents the information as a table (as opposed to plotting lines on the chart). Each row in the table typically corresponds to a timeframe (for example, Monthly, Weekly, Daily, 4H, 1H, 30M, 15M), and the columns list the calculated POC, VAH, VAL, and possibly the average volume for that timeframe’s look-back period. By structuring the data in a table, traders can quickly read off the exact price levels of these liquidity zones without having to visually trace lines. This format makes it easy to compare levels across timeframes or note where multiple timeframes’ levels cluster near the same price – a sign of especially strong support/resistance. The indicator uses a user-defined number of bars or length of history for each timeframe to calculate these values (so you can adjust how far back it looks to define the volume profile for each period).
Objective: In summary, the functionality is geared toward identifying high-liquidity price zones across multiple time scales and presenting them clearly. These high-liquidity zones often coincide with areas where price reacts (stalls, reverses, or accelerates) because a lot of trading activity (hence, orders and volume) took place there in the past. The indicator’s objective is to alert the trader to those areas in advance. It effectively answers questions like: “Where are the major volume concentration levels on the 1-hour, daily, and weekly charts right now?” and “Are there overlapping volume-based support/resistance levels from different timeframes around the current price?” By compiling this information, the indicator helps traders incorporate context from multiple timeframes in their decision-making, without needing to flip through numerous charts.
Identifying Liquidity Zones with POC, VAH, and VAL
Liquidity Zones Defined: In market terms, a “liquidity zone” is an area of the chart where a significant amount of trading occurred, meaning high liquidity (many buyers and sellers exchanged volume there). These zones often act as support or resistance because past heavy trading indicates consensus or interest around those price levels. This indicator identifies liquidity zones through volume profile analysis on each timeframe’s recent price action. Essentially, it looks at the distribution of trading volume at different prices over the specified period and finds the value area – the range of prices that encompassed the majority of that volume (commonly around 70% of the total volume ). Within that value area, it pinpoints the Point of Control (POC), which is the single price level that had the highest traded volume (the peak of the volume profile) . The upper and lower boundaries of that high-volume range are marked as Value Area High (VAH) and Value Area Low (VAL) respectively . Together, the VAH and VAL define the liquidity zone where the market spent most of its time and volume, and POC highlights the most traded price in that zone.
• Point of Control (POC): The POC is the price level with the greatest volume traded for the given period. It represents the price at which the most liquidity was exchanged – effectively the market’s “center of gravity” for that timeframe’s trading activity . The indicator calculates the POC for each selected timeframe by scanning the volume at each price; the price with maximum volume is flagged as that timeframe’s POC. In the table, the POC might be highlighted or listed as a key level (sometimes traders color-code it or mark it for emphasis). Because so many positions were opened or closed at the POC, it often serves as a strong support/resistance. For example, if price falls to a major POC from above, traders expect buyers may step in there (since it was a popular buy/sell level historically), potentially causing a bounce. Conversely, if price breaks through a POC decisively, it may signal a significant shift in market acceptance.
• Value Area High (VAH) and Low (VAL): The VAH and VAL are the price boundaries of the value area, which is typically defined to contain about 70% of the total traded volume for the period . In other words, between VAH and VAL is where the “bulk” of trading occurred, and outside this range is where relatively less volume traded. The indicator derives VAH/VAL by accumulating volume from the highest-volume price (POC) outward until ~70% of volume is covered (this is a common method for volume profile value area). VAH is the top of this high-volume region and VAL is the bottom. These levels are important because they often act like support/resistance boundaries: when price is inside the value area, it’s in a high-liquidity zone and tends to oscillate between VAH and VAL; when price moves above VAH or below VAL, it’s leaving the high-volume zone, which can indicate a potential trend or imbalance (price entering a lower-liquidity area where it might move faster until finding the next liquidity zone). Traders watch VAH/VAL for signs of rejection or acceptance: for instance, a price rally that falters at VAH suggests that level is acting as resistance (sellers defending that high-volume area), whereas if price pushes above VAH, it may continue until the next timeframe’s zone or until it finds new interest. The Multi-Timeframe Liquidity Zones V6 indicator gives the VAH and VAL for each timeframe, essentially mapping out the upper and lower bounds of key liquidity zones at those scales.
How the Indicator Identifies These: Under the hood, the indicator likely uses historical price and volume data for each timeframe’s lookback window. For each timeframe (say the last 20 weekly bars for a weekly profile, last 100 daily bars for a daily profile, etc.), it constructs a volume profile (a histogram of volume at each price). From that distribution, it finds the POC (highest volume bin) and calculates VAH/VAL around it. The output is a set of numbers (price levels) that mark where those zones lie. In practice, if using the Lines version of this indicator, those levels are drawn as horizontal lines on the chart and labeled by timeframe (e.g., a line at 1.2345 labeled “D POC” for Daily POC) . In the Table version, those values are instead listed in text form. Either way, the identification process is the same – it’s finding the high-volume price regions on each timeframe and calling them out. By doing this for multiple timeframes concurrently, the indicator reveals how these liquidity zones from different periods relate to each other. For example, you might discover that a daily-chart value area overlaps with a weekly-chart POC, creating a particularly strong zone of interest. This kind of insight is hard to get from a single timeframe analysis alone.
Volume Profile Data Across Multiple Timeframes
Multiple Timeframes in One View: One of the biggest advantages of this indicator is the ability to see volume profile information from various timeframes side by side. Traders often perform multiple timeframe analysis to get a fuller picture — for instance, checking monthly or weekly levels for long-term context while planning a trade on a 4-hour chart. This indicator automates that process for volume-based levels. The table will typically list each chosen timeframe (which could be preset or user-selected). For each timeframe, you get the POC, VAH, VAL, and possibly an average volume metric. The “average volume” likely refers to the average volume per bar or the average volume traded over the profile’s duration for that timeframe, which gives a sense of how significant that period’s activity is. For example, a weekly profile might show an average volume of say 500k per week, versus a daily profile average of 80k per day – indicating the scale of trading on weekly vs daily. High average volume on a timeframe means its liquidity zones were formed with a lot of participation, possibly making them more reliable support/resistance. By comparing these, traders can gauge which timeframes had unusually high or low activity recently. The table format makes such comparisons straightforward.
Identification of Confluence: Because all the data is presented together, traders can quickly spot confluence or overlaps between timeframes. If two different timeframes show liquidity zones at similar price levels, that price becomes extremely noteworthy. For instance, suppose the indicator shows: a 1-hour POC at 1.1300, a 4-hour VAL at 1.1280, and a daily VAL at 1.1290. These are all in a tight range – effectively indicating a multi-timeframe liquidity zone around 1.1280–1.1300. A trader seeing this cluster in the table will recognize that as a strong support area, since multiple profiles from intraday to daily all suggest heavy trading interest there. Similarly, overlaps of VAH (resistance zone) from different timeframes could signal a strong ceiling. The multi-timeframe view prevents a trader from, say, going long into a major weekly POC above, or shorting when there’s a huge monthly value-area low just below – situations where awareness of higher timeframe volume structure can make the difference between a good and bad trade.
User Customization: The indicator is flexible in that you can typically adjust which timeframes to include and how many bars to use for each timeframe’s calculation. For example, one might configure it to calculate monthly levels using the past 12 monthly bars (1 year of data), weekly levels using the past 20 weeks, daily using 100 days, etc., depending on preference. By tuning the “bars count” or period length , the trader can focus on recent liquidity zones or incorporate more history if desired. Shorter lookback might catch more recent shifts in volume distribution (important if the market structure changed recently), while longer lookback gives more established levels. This customization ensures the indicator’s output can be tailored to different trading styles (short-term vs swing vs long-term investing). Regardless of settings, the multi-timeframe table allows simultaneous visibility of the chosen timeframes’ volume landscape. This comprehensive view is the core strength: it consolidates data that normally requires flipping through multiple charts.
Using the Liquidity Zones Data for Trading Decisions
Traders can use the information from the MTF Liquidity Zones V6 (Table) indicator in several practical ways to enhance their decision-making:
• Identify Support and Resistance: Each liquidity zone acts as a potential support or resistance area. For example, if the table shows a daily VAH at a certain level above the current price, that level might serve as resistance if the price rallies up to it (since it marks the top of a high-volume region where sellers might step in). Conversely, a weekly VAL below current price could act as support on a dip. By noting these levels in the table, a trader planning an entry or exit can anticipate where the price might stall or reverse. Essentially, you get a map of high-interest price levels from different timeframes, which you can mark on your trading chart for guidance.
• Plan Entries and Exits Around Key Levels: Many traders incorporate volume profile levels into their strategies, for instance: buying near VAL (betting that the value area will hold and price will revert upward), or selling/shorting near VAH (expecting the top of value to hold as resistance), or trading breakouts when price moves outside the value area. With the multi-timeframe table, one can refine these tactics by also considering higher timeframe levels. Suppose you see that on the 1-hour chart the price is just above its 1H POC, but the table indicates that just slightly above, there’s also the daily POC. You might delay a long entry until price clears that daily POC, because that could be a stronger intraday barrier. Or if you intend to take profit on a long trade, you might choose a target just below a weekly VAH since price may struggle to climb past that on the first attempt. The indicator thus acts as a guide for precision in entry/exit decisions, aligning them with where liquidity is high.
• Gauge Trend Strength and Directional Bias: By observing where current price is relative to these volume zones, traders can infer certain market conditions. For instance, if price is trading above the VAH of multiple timeframes’ value areas, it suggests the market is in a more bullish or overextended territory (price accepted above prior value), whereas if price is below multiple VALs, it’s in bearish or undervalued territory relative to recent history. If the price stays around a POC, it indicates consolidation or equilibrium (market comfortable at that price). Traders can use this context for bias – e.g., if price is above the weekly VAH, you might lean bullish but watch for potential pullbacks to that VAH level (now a support). If price is below the monthly VAL, you might avoid longs until it re-enters that value area. In essence, the liquidity zones provide context of value vs. price: is price trading within the high-volume areas (implying range-bound behavior) or outside them (implying a breakout or trending move)? This can prevent chasing trades at poor locations.
• Combine with Other Indicators/Analysis: It’s generally advised to not use any single indicator in isolation, and this holds true here. The liquidity zones from this indicator are best used alongside price action or other technical signals for confirmation . For example, if a bullish candlestick reversal pattern forms right at a confluence of a 4H VAL and Daily POC, that’s a stronger buy signal than the pattern alone. Or if an oscillator shows overbought exactly as price hits a weekly VAH, it adds conviction to a possible short. The indicator’s table basically gives you a shortlist of critical price levels; you can then watch how price behaves at those levels (via candlesticks, order flow, etc.) to make the final trade decision. Traders might set alerts for when price approaches one of the listed levels, or they might drop down to a lower timeframe to fine-tune an entry once a key zone is reached. By integrating this volume-based insight with trend analysis, chart patterns, or momentum indicators, one can make more informed and high-probability decisions rather than trading in the dark.
• Risk Management and Stop Placement: High-liquidity zones can also inform stop-loss placement. Ideally, you want your stop on the other side of a strong support/resistance. If you go long near a VAL, you might place your stop just below the VAL (since a move beyond that suggests the high-volume zone didn’t hold). If you short near a VAH, a stop just above the VAH or POC could be logical. Moreover, if multiple timeframes show overlapping zones, a stop beyond all of them could be even safer (albeit at the cost of a wider stop). The indicator helps identify those spots. It also warns you of where not to put a stop – for example, placing a stop-loss right at a POC might be unwise because price could gravitate to that POC repeatedly (due to its magnetic effect as a high-volume price). Instead, a trader might choose a stop beyond the far side of the value area. By using the table’s information, you can align your risk management with areas of high liquidity, reducing the chance of being whipsawed by normal volatility around heavily traded levels .
Benefits of the Multi-Timeframe Liquidity Zones Indicator
Using the Multi-Timeframe Liquidity Zones V6 (Table) indicator offers several key benefits for traders, ultimately aiming to streamline analysis and improve decision quality:
• Consolidated Key Levels: It provides a clear, consolidated view of crucial volume-driven levels from multiple timeframes all at once . This saves time and ensures you always account for major support/resistance zones that come from higher or lower timeframe volume clusters. You won’t accidentally overlook a significant weekly level while focused on a 15-minute chart, for example.
• Enhanced Multi-Timeframe Insight: By aligning information from long-term and short-term periods, the indicator helps traders see the “bigger picture” while still operating on their preferred timeframe. This multi-scale awareness can improve trade timing and confidence. You’re effectively doing multi-timeframe analysis with volume profiles in an efficient manner, which can confirm or caution your trade ideas (e.g., a trend looks strong on the 1H, but the table shows a huge monthly VAH just overhead – a reason to be cautious or take profit early).
• Improved Decision Making and Precision: Knowing where liquidity zones lie allows for more precise entries, exits, and stop placements. Traders can make informed decisions such as waiting for a pullback to a value area before entering, or taking profits before price hits a major POC from a higher timeframe. These decisions are grounded in objectively important price levels, potentially leading to higher probability trades and better risk-reward setups. It essentially enhances your strategy by adding a layer of volume context – you’re trading with an awareness of where the market’s interest is heaviest.
• Volume-Based Confirmation: Price alone can sometimes be deceptive, but volume tells the true story of participation. The liquidity zones indicator provides volume-based confirmation of support/resistance. If a price level is identified by this tool, it’s because significant volume happened there – adding weight to that level’s importance. This can help filter out false support/resistance levels that aren’t backed by volume. In other words, it highlights high-quality levels that many traders (and possibly institutions) have shown interest in.
• Adaptable to Different Trading Styles: Whether one is a scalper looking at intraday (15M, 5M charts) or a swing trader focusing on daily/weekly, the indicator can be configured to those needs. You choose which timeframes and how much data to consider. This means the concept of liquidity zones can be applied universally – from spotting intraday pivot levels with volume, to seeing long-term value zones on an investment. The consistent methodology of POC/VAH/VAL across scales provides a common framework to analyze any market and timeframe.
• Informed Risk Management: As discussed, the knowledge of multi-timeframe volume zones aids in risk management. By placing stops beyond major liquidity areas or avoiding trades that run into strong volume walls, traders can reduce the likelihood of whipsaw losses. It’s an extra layer of defense to ensure your trade plan accounts for where the market has historically found lots of interest (hence likely friction). This level of informed planning can be the difference between a well-managed trade and an avoidable loss.
In conclusion, the Multi-Timeframe Liquidity Zones V6 (Table) indicator serves as a powerful analytical aid, giving traders a structured view of where price is likely to encounter support or resistance based on volume concentrations across timeframes. Its functionality centers on identifying those liquidity zones (via POC, VAH, VAL) and presenting them in an easy-to-read format, while its ultimate purpose is to help traders make more informed decisions. By integrating this tool into their workflow, traders can more confidently navigate price action, knowing the objective volume-based landmarks that lie ahead. Remember that while these volume levels often coincide with strong S/R zones, it’s best to use them in conjunction with other technical or fundamental analysis for confirmation . When used appropriately, the indicator can streamline multi-timeframe analysis and enhance your overall trading strategy , giving you an edge in identifying where the market’s liquidity (and opportunity) resides.
Simple APF Strategy Backtesting [The Quant Science]Simple backtesting strategy for the quantitative indicator Autocorrelation Price Forecasting. This is a Buy & Sell strategy that operates exclusively with long orders. It opens long positions and generates profit based on the future price forecast provided by the indicator. It's particularly suitable for trend-following trading strategies or directional markets with an established trend.
Main functions
1. Cycle Detection: Utilize autocorrelation to identify repetitive market behaviors and cycles.
2. Forecasting for Backtesting: Simulate trades and assess the profitability of various strategies based on future price predictions.
Logic
The strategy works as follow:
Entry Condition: Go long if the hypothetical gain exceeds the threshold gain (configurable by user interface).
Position Management: Sets a take-profit level based on the future price.
Position Sizing: Automatically calculates the order size as a percentage of the equity.
No Stop-Loss: this strategy doesn't includes any stop loss.
Example Use Case
A trader analyzes a dayli period using 7 historical bars for autocorrelation.
Sets a threshold gain of 20 points using a 5% of the equity for each trade.
Evaluates the effectiveness of a long-only strategy in this period to assess its profitability and risk-adjusted performance.
User Interface
Length: Set the length of the data used in the autocorrelation price forecasting model.
Thresold Gain: Minimum value to be considered for opening trades based on future price forecast.
Order Size: percentage size of the equity used for each single trade.
Strategy Limit
This strategy does not use a stop loss. If the price continues to drop and the future price forecast is incorrect, the trader may incur a loss or have their capital locked in the losing trade.
Disclaimer!
This is a simple template. Use the code as a starting point rather than a finished solution. The script does not include important parameters, so use it solely for educational purposes or as a boilerplate.
Wick Size in USD with 10-Bar AverageWick Size in USD with 10-Bar Average
Version: 1.0
Author: QCodeTrader
🔍 Overview
This indicator converts the price wicks of your candlestick chart into USD values based on ticks, providing both raw and smoothed data via a 10-bar simple moving average. It helps traders visualize the monetary impact of price extremes, making it easier to assess volatility, potential risk, and plan appropriate stop loss levels.
⚙️ Key Features
Tick-Based Calculation:
Converts wick sizes into ticks (using a fixed tick size of 0.01, typical for stocks) and then into USD using a customizable tick value.
10-Bar Moving Average:
Smooths out the wick values over the last 10 bars, giving you a clearer view of average wick behavior.
Bullish/Bearish Visual Cues:
The chart background automatically highlights bullish candles in green and bearish candles in red for quick visual assessment.
Stop Loss Optimization:
The indicator highlights long wick sizes, which can help you set more accurate stop loss levels. Even when the price moves in your favor, long wicks may indicate potential reversals—allowing you to account for this risk when planning your stop losses.
User-Friendly Customization:
Easily adjust the USD value per tick through the settings to tailor the indicator to your specific instrument.
📊 How It Works
Wick Calculation:
The indicator calculates the upper and lower wicks by measuring the distance between the candle’s high/low and its body (open/close).
Conversion to Ticks & USD:
These wick sizes are first converted from price points to ticks (dividing by a fixed tick size of 0.01) and then multiplied by the user-defined tick value to convert the measurement into USD.
Smoothing Data:
A 10-bar simple moving average is computed for both the upper and lower wick values, providing smoothed data that helps identify trends and deviations.
Visual Representation:
Columns display the raw wick sizes in USD.
Lines indicate the 10-bar moving averages.
Background Color shifts between green (bullish) and red (bearish) based on candle type.
⚡ How to Use
Add the Indicator:
Apply it to your chart to begin visualizing wick sizes in monetary terms.
Customize Settings:
Adjust the Tick Value in USD in the settings to match your instrument’s tick value.
(Note: The tick size is fixed at 0.01, which is standard for many stocks.)
Optimize Your Stop Loss:
Analyze the raw and averaged wick values to understand volatility. Long wicks—even when the price moves in your favor—may indicate potential reversals. This insight can help you set more accurate stop loss levels to protect your gains.
Analyze:
Use the indicator’s data to gauge market volatility and assess the significance of price movements, aiding in more informed trading decisions.
This indicator is perfect for traders looking to understand the impact of extreme price movements in monetary terms, optimize stop loss levels, and effectively manage risk across stocks and other instruments with similar tick structures.
Supertrend pro+ (Adaptive ATR) Supertrend Pro+ (Adaptive ATR) - Param Approach
By SKP
Overview
This advanced Supertrend Pro+ strategy improves on the classic Supertrend indicator by integrating an Adaptive ATR, ensuring dynamic volatility adjustments for more accurate trend detection. This strategy filters out false signals using ADX trend strength validation and volume confirmation, making it a powerful tool for trend-following traders.
Key Features
✔ Adaptive ATR Calculation - Dynamically adjusts to market volatility for more reliable Supertrend signals.
✔ ADX Trend Filter - Ensures trades occur only in strong trending markets, avoiding false breakouts.
✔ Volume Confirmation - Prevents trading in low-liquidity conditions by verifying volume strength.
✔ Multi-Timeframe Analysis - Displays Supertrend trends from different timeframes for enhanced trade confidence.
✔ Trailing Stop & Take Profit Options - Allows flexible risk management with stop-loss and profit-targeting mechanisms.
✔ Custom Alerts for Trade Signals - Alerts trigger on confirmed Supertrend buy/sell signals and potential trend shifts.
✔ Max Drawdown Protection - Automatically closes trades if equity drops beyond a set percentage, preventing excessive losses.
How It Works
Adaptive ATR Calculation
Instead of using a fixed ATR, this strategy calculates an adaptive ATR based on a longer-term ATR baseline.
If volatility increases, the ATR expands dynamically, ensuring stop-losses and Supertrend calculations adjust accordingly.
Supertrend Confirmation
Uses an enhanced Supertrend algorithm with adaptive ATR to determine trend direction.
If price crosses above the trendline, it signals a bullish reversal (Buy Signal).
If price crosses below the trendline, it signals a bearish reversal (Sell Signal).
ADX Trend Strength Filter
Trades are only taken when ADX is above the threshold, ensuring entry in strong trending markets.
Volume Confirmation
Uses a relative volume filter to ensure sufficient liquidity before entering trades.
Helps avoid false breakouts in low-volume conditions.
Risk Management
Trailing Stop Loss - Automatically moves the stop as price moves in favor of the trade.
Manual Stop Loss & Take Profit - Allows precise percentage-based exit points.
Max Drawdown Protection - Closes all trades if equity falls below a set threshold, reducing risk.
Multi-Timeframe Supertrend Table
Displays Supertrend signals across different timeframes (1 min, 5 min, 15 min, 1 hour, Daily)
Helps traders align their entries with higher timeframe trends for better accuracy.
Custom Alerts
Alerts notify when a new buy/sell signal appears.
Extra early warning alerts indicate potential trade setups before confirmation.
How to Use
📌 For trend-following traders:
Focus on entries in the direction of the higher timeframes.
Only enter when ADX is trending and volume confirms liquidity.
📌 For scalpers:
Use shorter timeframes (1m, 5m, 15m) for quick trades.
Adjust the ATR multiplier and Adaptive ATR sensitivity for tighter stops.
📌 For swing traders:
Use longer timeframes (1H, Daily) for more stable trends.
Enable trailing stop loss to lock in profits as the trend progresses.
Inputs & Customization
ATR Period & Adaptive ATR Sensitivity
Supertrend Multiplier
ADX Filter & Threshold
Volume Confirmation Settings
Stop Loss & Take Profit Options
Multi-Timeframe Supertrend Display
Custom Alerts
[3Commas] HA & MAHA & MA
🔷What it does: This tool is designed to test a trend-following strategy using Heikin Ashi candles and moving averages. It enters trades after pullbacks, aiming to let profits run once the risk-to-reward ratio reaches 1:1 while securing the position.
🔷Who is it for: It is ideal for traders looking to compare final results using fixed versus dynamic take profits by adjusting parameters and trade direction—a concept applicable to most trading strategies.
🔷How does it work: We use moving averages to define the market trend, then wait for opposite Heikin Ashi candles to form against it. Once these candles reverse in favor of the trend, we enter the trade, using the last swing created by the pullback as the stop loss. By applying the breakeven ratio, we protect the trade and let it run, using the slower moving average as a trailing stop.
A buy signal is generated when:
The previous candle is bearish (ha_bear ), indicating a pullback.
The fast moving average (ma1) is above the slow moving average (ma2), confirming an uptrend.
The current candle is bullish (ha_bull), showing trend continuation.
The Heikin Ashi close is above the fast moving average (ma1), reinforcing the bullish bias.
The real price close is above the open (close > open), ensuring bullish momentum in actual price data.
The signal is confirmed on the closed candle (barstate.isconfirmed) to avoid premature signals.
dir is undefined (na(dir)), preventing repeated signals in the same direction.
A sell signal is generated when:
The previous candle is bullish (ha_bull ), indicating a temporary upward move before a potential reversal.
The fast moving average (ma1) is below the slow moving average (ma2), confirming a downtrend.
The current candle is bearish (ha_bear), showing trend continuation to the downside.
The Heikin Ashi close is below the fast moving average (ma1), reinforcing bearish pressure.
The real price close is below the open (close < open), confirming bearish momentum in actual price data.
The signal is confirmed after the candle closes (barstate.isconfirmed), avoiding premature entries.
dir is undefined (na(dir)), preventing consecutive signals in the same direction.
In simple terms, this setup looks for trend continuation after a pullback, confirming entries with both Heikin Ashi and real price action, supported by moving average alignment to avoid false signals.
If the price reaches a 1:1 risk-to-reward ratio, the stop will be moved to the entry point. However, if the slow moving average surpasses this level, it will become the new exit point, acting as a trailing stop
🔷Why It’s Unique
Easily visualizes the benefits of using risk-to-reward ratios when trading instead of fixed percentages.
Provides a simple and straightforward approach to trading, embracing the "keep it simple" concept.
Offers clear visualization of DCA Bot entry and exit points based on user preferences.
Includes an option to review the message format before sending signals to bots, with compatibility for multi-pair and futures contract pairs.
🔷 Considerations Before Using the Indicator
⚠️Very important: The indicator must be used on charts with real price data, such as Japanese candlesticks, line charts, etc. Do not use it on Heikin Ashi charts, as this may lead to unrealistic results.
🔸Since this is a trend-following strategy, use it on timeframes above 4 hours, where market noise is reduced and trends are clearer. Also, carefully review the statistics before using it, focusing on pairs that tend to have long periods of well-defined trends.
🔸Disadvantages:
False Signals in Ranges: Consolidating markets can generate unreliable signals.
Lagging Indicator: Being based on moving averages, it may react late to sudden price movements.
🔸Advantages:
Trend Focused: Simplifies the identification of trending markets.
Noise Reduction: Uses Heikin Ashi candles to identify trend continuation after pullbacks.
Broad Applicability: Suitable for forex, crypto, stocks, and commodities.
🔸The strategy provides a systematic way to analyze markets but does not guarantee successful outcomes. Use it as an additional tool rather than relying solely on an automated system.
Trading results depend on various factors, including market conditions, trader discipline, and risk management. Past performance does not ensure future success, so always approach the market cautiously.
🔸Risk Management: Define stop-loss levels, position sizes, and profit targets before entering any trade. Be prepared for potential losses and ensure your approach aligns with your overall trading plan.
🔷 STRATEGY PROPERTIES
Symbol: BINANCE:BTCUSDT (Spot).
Timeframe: 4h.
Test Period: All historical data available.
Initial Capital: 10000 USDT.
Order Size per Trade: 1% of Capital, you can use a higher value e.g. 5%, be cautious that the Max Drawdown does not exceed 10%, as it would indicate a very risky trading approach.
Commission: Binance commission 0.1%, adjust according to the exchange being used, lower numbers will generate unrealistic results. By using low values e.g. 5%, it allows us to adapt over time and check the functioning of the strategy.
Slippage: 5 ticks, for pairs with low liquidity or very large orders, this number should be increased as the order may not be filled at the desired level.
Margin for Long and Short Positions: 100%.
Indicator Settings: Default Configuration.
MA1 Length: 9.
MA2 Length: 18.
MA Calculations: EMA.
Take Profit Ratio: Disable. Ratio 1:4.
Breakeven Ratio: Enable, Ratio 1:1.
Strategy: Long & Short.
🔷 STRATEGY RESULTS
⚠️Remember, past results do not guarantee future performance.
Net Profit: +324.88 USDT (+3.25%).
Max Drawdown: -81.18 USDT (-0.78%).
Total Closed Trades: 672.
Percent Profitable: 35.57%.
Profit Factor: 1.347.
Average Trade: +0.48 USDT (+0.48%).
Average # Bars in Trades: 13.
🔷 HOW TO USE
🔸 Adjust Settings:
The default values—MA1 (9) and MA2 (18) with EMA calculation—generally work well. However, you can increase these values, such as 20 and 40, to better identify stronger trends.
🔸 Choose a Symbol that Typically Trends:
Select an asset that tends to form clear trends. Keep in mind that the Strategy Tester results may show poor performance for certain assets, making them less suitable for sending signals to bots.
🔸 Experiment with Ratios:
Test different take profit and breakeven ratios to compare various scenarios—especially to observe how the strategy performs when only the trade is protected.
🔸This is an example of how protecting the trade works: once the price moves in favor of the position with a 1:1 risk-to-reward ratio, the stop loss is moved to the entry price. If the Slow MA surpasses this level, it will act as a trailing stop, aiming to follow the trend and maximize potential gains.
🔸In contrast, in this example, for the same trade, if we set a take profit at a 1:3 risk-to-reward ratio—which is generally considered a good risk-reward relationship—we can see how a significant portion of the upward move is left on the table.
🔸Results Review:
It is important to check the Max Drawdown. This value should ideally not exceed 10% of your capital. Consider adjusting the trade size to ensure this threshold is not surpassed.
Remember to include the correct values for commission and slippage according to the symbol and exchange where you are conducting the tests. Otherwise, the results will not be realistic.
If you are satisfied with the results, you may consider automating your trades. However, it is strongly recommended to use a small amount of capital or a demo account to test proper execution before committing real funds.
🔸Create alerts to trigger the DCA Bot:
Verify Messages: Ensure the message matches the one specified by the DCA Bot.
Multi-Pair Configuration: For multi-pair setups, enable the option to add the symbol in the correct format.
Signal Settings: Enable whether you want to receive long or short signals (Entry | TP | SL), copy and paste the the messages for the DCA Bots configured.
Alert Setup:
When creating an alert, set the condition to the indicator and choose "alert() function call only.
Enter any desired Alert Name.
Open the Notifications tab, enable Webhook URL, and paste the Webhook URL.
For more details, refer to the section: "How to use TradingView Custom Signals".
Finalize Alerts: Click Create, you're done! Alerts will now be sent automatically in the correct format.
🔷 INDICATOR SETTINGS
MA 1: Fast MA Length
MA 2: Slow MA Length
MA Calc: MA's Calculations (SMA,EMA, RMA,WMA)
TP Ratio: This is the take profit ratio relative to the stop loss, where the trade will be closed in profit.
BE Ratio: This is the breakeven ratio relative to the stop loss, where the stop loss will be updated to breakeven or if the MA2 is greater than this level.
Strategy: Order Type direction in which trades are executed.
Use Custom Test Period: When enabled signals only works in the selected time window. If disabled it will use all historical data available on the chart.
Test Start and End: Once the Custom Test Period is enabled, here you select the start and end date that you want to analyze.
Check Messages: Enable the table to review the messages to be sent to the bot.
Entry | TP | SL: Enable this options to send Buy Entry, Take Profit (TP), and Stop Loss (SL) signals.
Deal Entry and Deal Exit : Copy and paste the message for the deal start signal and close order at Market Price of the DCA Bot. This is the message that will be sent with the alert to the Bot, you must verify that it is the same as the bot so that it can process properly so that it executes and starts the trade.
DCA Bot Multi-Pair: You must activate it if you want to use the signals in a DCA Bot Multi-pair in the text box you must enter (using the correct format) the symbol in which you are creating the alert, you can check the format of each symbol when you create the bot.
👨🏻💻💭 We hope this tool helps enhance your trading. Your feedback is invaluable, so feel free to share any suggestions for improvements or new features you'd like to see implemented.
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The information and publications within the 3Commas TradingView account are not meant to be and do not constitute financial, investment, trading, or other types of advice or recommendations supplied or endorsed by 3Commas and any of the parties acting on behalf of 3Commas, including its employees, contractors, ambassadors, etc.
Enhanced Bollinger Bands Strategy with SL/TP// Title: Enhanced Bollinger Bands Strategy with SL/TP
// Description:
// This strategy is based on the classic Bollinger Bands indicator and incorporates Stop Loss (SL) and Take Profit (TP) levels for automated trading. It identifies potential long and short entry points based on price crossing the lower and upper Bollinger Bands, respectively. The strategy allows users to customize several parameters to suit different market conditions and risk tolerances.
// Key Features:
// * **Bollinger Bands:** Uses Simple Moving Average (SMA) as the basis and calculates upper and lower bands based on a user-defined standard deviation multiplier.
// * **Customizable Parameters:** Offers extensive customization, including SMA length, standard deviation multiplier, Stop Loss (SL) in pips, and Take Profit (TP) in pips.
// * **Long/Short Position Control:** Allows users to independently enable or disable long and short positions.
// * **Stop Loss and Take Profit:** Implements Stop Loss and Take Profit levels based on pip values to manage risk and secure profits. Entry prices are set to the band levels on signals.
// * **Visualizations:** Provides options to display Bollinger Bands and entry signals on the chart for easy analysis.
// Strategy Logic:
// 1. **Bollinger Bands Calculation:** The strategy calculates the Bollinger Bands using the specified SMA length and standard deviation multiplier.
// 2. **Entry Conditions:**
// * **Long Entry:** Enters a long position when the closing price crosses above the lower Bollinger Band and the `Enable Long Positions` setting is enabled.
// * **Short Entry:** Enters a short position when the closing price crosses below the upper Bollinger Band and the `Enable Short Positions` setting is enabled.
// 3. **Exit Conditions:**
// * **Stop Loss:** Exits the position if the price reaches the Stop Loss level, calculated based on the input `Stop Loss (Pips)`.
// * **Take Profit:** Exits the position if the price reaches the Take Profit level, calculated based on the input `Take Profit (Pips)`.
// Input Parameters:
// * **SMA Length (length):** The length of the Simple Moving Average used to calculate the Bollinger Bands (default: 20).
// * **Standard Deviation Multiplier (mult):** The multiplier applied to the standard deviation to determine the width of the Bollinger Bands (default: 2.0).
// * **Enable Long Positions (enableLong):** A boolean value to enable or disable long positions (default: true).
// * **Enable Short Positions (enableShort):** A boolean value to enable or disable short positions (default: true).
// * **Pip Value (pipValue):** The value of a pip for the traded instrument. This is crucial for accurate Stop Loss and Take Profit calculations (default: 0.0001 for most currency pairs). **Important: Adjust this value to match the specific instrument you are trading.**
// * **Stop Loss (Pips) (slPips):** The Stop Loss level in pips (default: 10).
// * **Take Profit (Pips) (tpPips):** The Take Profit level in pips (default: 20).
// * **Show Bollinger Bands (showBands):** A boolean value to show or hide the Bollinger Bands on the chart (default: true).
// * **Show Entry Signals (showSignals):** A boolean value to show or hide entry signals on the chart (default: true).
// How to Use:
// 1. Add the strategy to your TradingView chart.
// 2. Adjust the input parameters to optimize the strategy for your chosen instrument and timeframe. Pay close attention to the `Pip Value`.
// 3. Backtest the strategy over different periods to evaluate its performance.
// 4. Use the `Enable Long Positions` and `Enable Short Positions` settings to customize the strategy for specific market conditions (e.g., only long positions in an uptrend).
// Important Notes and Disclaimers:
// * **Backtesting Results:** Past performance is not indicative of future results. Backtesting results can be affected by various factors, including market volatility, slippage, and transaction costs.
// * **Risk Management:** This strategy is provided for informational and educational purposes only and should not be considered financial advice. Always use proper risk management techniques when trading. Adjust Stop Loss and Take Profit levels according to your risk tolerance.
// * **Slippage:** The strategy takes into account slippage by specifying a slippage parameter on the `strategy` declaration. However, real-world slippage may vary.
// * **Market Conditions:** The performance of this strategy can vary significantly depending on market conditions. It may perform well in trending markets but poorly in ranging or choppy markets.
// * **Pip Value Accuracy:** **Ensure the `Pip Value` is correctly set for the specific instrument you are trading. Incorrect pip value will result in incorrect stop loss and take profit placement.** This is critical.
// * **Broker Compatibility:** The strategy's performance may vary depending on your broker's execution policies and fees.
// * **Disclaimer:** I am not a financial advisor, and this script is not financial advice. Use this strategy at your own risk. I am not responsible for any losses incurred while using this strategy.
Briss Thorn XtremeStrategy Description: Briss Thorn Xtreme
The Briss Thorn Xtreme is an innovative trading strategy designed to identify and capitalize on opportunities in the forex market through advanced technical analysis and dynamic risk management. This strategy combines calculations based on RSI and ATR with time and day filters, providing customized signals and real-time alerts via Discord. Ideal for traders seeking a structured and highly customizable methodology, Briss Thorn Xtreme integrates enhanced visual tools for efficient trade management.
Key Features:
RSI and ATR-Based Signals: Utilizes smoothed RSI and ATR calculations to identify trends and measure volatility, allowing for more precise detection of buy and sell opportunities.
Dynamic Stop-Loss (SL) and Take-Profit (TP) Levels: Automatically calculates SL and TP levels based on market volatility, dynamically adjusting to optimize risk management.
Advanced Discord Integration: Sends detailed alerts to your Discord channel, including information such as the asset, signal time, entry price, and SL/TP levels, facilitating real-time decision-making.
Complete Customization: Allows users to adjust key parameters such as RSI periods, smoothing factors, liquidity thresholds, trading schedules, and operation days, adapting to different trading styles and market conditions.
Enhanced Chart Visualization: Includes visual elements like candle color changes based on trend, colored boxes for SL and TP, and a summary table of recent trades, enabling quick market interpretation.
Day and Time Operation Filters: Enables selection of specific days of the week and time slots during which signals are generated, optimizing market exposure and avoiding periods of low liquidity or unwanted high volatility.
Trade Summary: Displays a summary of the last three trades directly on the chart, indicating whether TP or SL was reached, aiding in strategy performance evaluation.
Customizable Alert Messages: Allows customization of messages sent to Discord for buy and sell signals, tailoring them to your specific preferences and requirements.
Additional Visual Tools: Highlights the operational range on the chart during permitted trading hours and colors candles based on the current trend (bullish, bearish, or neutral), enhancing visibility and decision-making.
How the Strategy Works:
Technical Indicators Calculation:
- RSI (Relative Strength Index) : Calculates RSI with a defined period and smooths it using an Exponential Moving Average (EMA) to obtain a more stable and reliable signal.
- ATR (Average True Range) : Calculates ATR adjusted by a rapid liquidity factor to measure the current market volatility, thereby determining the strength of the trend.
Generating Buy and Sell Signals:
- Buy Signal: A buy signal is generated when the liquidity index surpasses the short liquidity level, indicating potential accumulation and an upward trend.
- Sell Signal: A sell signal is generated when the liquidity index falls below the long liquidity level, indicating potential distribution and a downward trend.
- Operation Conditions: Signals are only generated on selected days and times, avoiding periods of low liquidity or unwanted high volatility.
Dynamic SL and TP Levels Calculation:
- Stop-Loss (SL) and Take-Profit (TP): SL and TP levels are calculated based on the entry price and a defined number of ticks, automatically adjusting to market volatility to optimize risk management.
- SL and TP Visualization: Colored boxes are drawn on the chart for a clear visual reference of SL and TP levels, facilitating trade management.
Automatic Execution and Alerts:
- Order Execution: Upon signal generation, the strategy automatically executes a market order (buy or sell).
- Discord Alerts: Detailed alerts are sent to the configured Discord channel, providing essential information for swift decision-making, including asset, signal time, entry price, current volatility (ATR), and trend direction.
Trade Management and Monitoring:
- Trade Summary: A table on the chart displays a summary of the last three trades (Today, Yesterday, Day Before Yesterday), indicating whether TP or SL was reached, allowing real-time performance evaluation.
- Automatic Trade Closure: The strategy automatically closes trades upon reaching the established SL or TP levels, ensuring efficient risk management and preventing excessive losses.
Additional Visualization:
- Candle Coloring by Trend: Candles are colored based on the current trend (bullish, bearish, or neutral), facilitating quick identification of market direction.
- Operational Range Highlighting: The chart background is colored during permitted trading hours, highlighting active periods of the strategy and enhancing trade visibility.
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Strategy Properties (Important)
This backtest is conducted on M17 EURUSD using the following backtesting properties:
Initial Capital: $1000
Order Size: 1% of capital
Commission: $0.20 per order
Slippage: 1 tick
Pyramiding: 1 order
Price Verification for Limit Orders: 0 ticks
Recalculate on Order Execution: Enabled
Recalculate on Every Tick: Enabled
Recalculate After Order Execution: Enabled
Bar Magnifier for Backtesting Precision: Enabled
These properties ensure a realistic preview of the backtesting system. Note that default properties may vary for different reasons:
Order Size: It is essential to calculate the contract size according to the traded asset and desired risk level.
Commission and Slippage: These costs may vary depending on the market and instrument; there is no default value that guarantees realistic results.
All users are strongly recommended to adjust the properties within the script settings to align them with their trading accounts and platforms, ensuring that strategy results are realistic.
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Backtesting Results:
- Net Profit: $327.90 (32.79%)
- Total Closed Trades: 162
- Profit Percentage: 35.80%
- Profit Factor: 1.298
- Maximum Drawdown: $146.70 (10.27%)
- Average per Trade: $2.02 (0.02%)
- Average Bars per Trade: 22
These results were obtained under the mentioned conditions and properties, providing an overview of the strategy's historical performance.
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Interpretation of Results:
- The strategy has demonstrated profitability over the analyzed period, albeit with a success rate of 32.79%, indicating that success depends on a favorable risk-reward ratio.
- The profit factor of 1.298 suggests that total gains exceed total losses by this proportion.
- It is crucial to consider the maximum drawdown of 10.27% when evaluating the strategy's suitability to your risk tolerance.
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Risk Warning:
Trading with leveraged financial instruments involves a high level of risk and may not be suitable for all investors. Before deciding to trade, you should carefully consider your investment objectives, level of experience, and risk tolerance. Past performance does not guarantee future results. It is essential to perform additional testing and adjust the strategy according to your needs.
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What Makes This Strategy Original?
Unique RSI and Liquidity Focus: Unlike conventional strategies, Briss Thorn Xtreme focuses on combining RSI analysis with liquidity parameters to reflect institutional activity and macroeconomic events that may influence the market.
Advanced Technological Integration: The combination of automatic execution and customized alerts via Discord provides an efficient and modern tool for active traders.
Customization and Adaptability: The wide range of adjustable parameters allows the strategy to adapt to different assets, time zones, and trading styles, offering flexibility and complete user control.
Enhanced Visual Tools: Integrated visual elements, such as candle coloring, SL/TP boxes, and summary tables, facilitate quick market interpretation and informed decision-making.
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Additional Considerations
Continuous Testing and Optimization: Users are advised to perform additional backtests and optimize parameters based on their own observations and requirements.
Complementary Analysis: Use this strategy in conjunction with other indicators and fundamental analysis tools to reinforce decision-making and confirm generated signals.
Rigorous Risk Management: Ensure that SL and TP levels, as well as position sizes, are aligned with your risk management plan to avoid excessive losses.
Updates and Support: I am committed to providing updates and improvements based on community feedback. For inquiries or suggestions, feel free to contact me.
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Example Configuration
Assuming you want to use the strategy with the following parameters:
Discord Webhook: Your unique Discord Webhook
RSI Period: 6
RSI Smoothing Factor: 5
Rapid Liquidity Factor: 5
Liquidity Threshold: 5
SL Ticks: 100
TP Ticks: 250
SL/TP Box Width: 25 bars
Trading Days: Monday, Tuesday, Wednesday, Thursday, Friday
Trading Hours: Start at 8:00, End at 11:00
Simulated Initial Capital: $1000
Risk per Trade in Simulation: 1% of capital
Slippage and Commissions in Simulation: 1 tick slippage and $0.20 commission per trade
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Conclusion
The Briss Thorn Xtreme strategy offers an innovative approach by combining advanced technical analysis with dynamic risk management and modern technological tools. Its original and adaptable design makes it a valuable tool for traders looking to diversify their methods and capitalize on opportunities based on less conventional patterns. Ready for immediate implementation in TradingView, this strategy can enhance your trading arsenal and contribute to a more informed and structured approach in your operations.
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Final Disclaimer:
Financial markets are volatile and can present significant risks. This strategy should be used as part of a comprehensive trading approach and does not guarantee positive results. It is always advisable to consult with a professional financial advisor before making investment decisions.
Pivot High/Low [s3]This is a technical analysis tool that identifies significant price pivot points (highs and lows) in the market. It looks for both major and minor pivot points, which can help traders identify potential support and resistance levels, trend reversals, and breakout opportunities.
How Pivot Points Are Calculated:
The indicator uses a straightforward "higher than everything around it" or "lower than everything around it" approach:
For Pivot Highs:
- The indicator looks at a specific bar and compares it to bars before and after it
- For a major pivot high: It checks 50 bars to the left and 20 bars to the right
- If the bar's high price is higher than ALL bars within this range, it's marked as a pivot high
- Think of it like a mountain peak - it needs to be the highest point compared to everything around it
For Pivot Lows:
- Same concept but reversed - looking for valleys instead of peaks
- Checks the same ranges (50 left, 20 right)
- The bar's low price must be lower than ALL surrounding bars
- Like finding the bottom of a valley - it needs to be the lowest point in the area
Key Features:
1. Two types of pivot points:
- Major pivots (using longer lookback periods of 50 bars left, 20 bars right)
- Minor pivots (using half the lookback periods - 25 left, 10 right)
2. Visual elements:
- Triangle markers above/below bars for pivot points
- Dotted lines extending from pivot points
- Color coding: Green for lows (support), Red for highs (resistance)
- Major pivots are more prominent than minor pivots
3. Customizable alerts for:
- Formation of new pivot points
- Breakouts above/below pivot levels
Trading Applications:
1. Support and Resistance:
- Major pivot levels act as strong support (lows) and resistance (highs)
- Multiple touches of these levels increase their significance
- Minor pivots can indicate intermediate support/resistance levels
2. Trend Analysis:
- Higher highs and higher lows = Uptrend
- Lower highs and lower lows = Downtrend
- Breaking of major pivot levels can signal trend changes
3. Entry/Exit Signals:
- Long entries: When price bounces off major pivot lows
- Short entries: When price rejects from major pivot highs
- Take profits: At opposite pivot levels
- Stop losses: Just beyond the entry pivot level
4. Breakout Trading:
- Breaking above major pivot highs suggests bullish momentum
- Breaking below major pivot lows suggests bearish momentum
- Use the alert system to catch breakouts early
Settings Customization:
- Adjust lookback periods based on your timeframe
- Toggle visibility of markers and lines
- Customize colors for better visibility
- Enable/disable specific types of alerts
Risk Management Tips:
1. Don't rely solely on pivot points - combine with other indicators
2. Wait for confirmation of bounces/rejections before entering trades
3. Use proper position sizing based on stop loss placement
4. Consider market context and overall trend when trading pivot levels
This indicator is particularly useful for swing traders and position traders who focus on key market turning points and trend changes. It helps identify significant price levels where the market has previously shown reaction, making it valuable for both trend following and counter-trend strategies.
Quantify [Entry Model] | FractalystWhat’s the indicator’s purpose and functionality?
Quantify is a machine learning entry model designed to help traders identify high-probability setups to refine their strategies.
➙ Simply pick your bias, select your entry timeframes, and let Quantify handle the rest for you.
Can the indicator be applied to any market approach/trading strategy?
Absolutely, all trading strategies share one fundamental element: Directional Bias
Once you’ve determined the market bias using your own personal approach, whether it’s through technical analysis or fundamental analysis, select the trend direction in the Quantify user inputs.
The algorithm will then adjust its calculations to provide optimal entry levels aligned with your chosen bias. This involves analyzing historical patterns to identify setups with the highest potential expected values, ensuring your setups are aligned with the selected direction.
Can the indicator be used for different timeframes or trading styles?
Yes, regardless of the timeframe you’d like to take your entries, the indicator adapts to your trading style.
Whether you’re a swing trader, scalper, or even a position trader, the algorithm dynamically evaluates market conditions across your chosen timeframe.
How can this indicator help me to refine my trading strategy?
1. Focus on Positive Expected Value
• The indicator evaluates every setup to ensure it has a positive expected value, helping you focus only on trades that statistically favor long-term profitability.
2. Adapt to Market Conditions
• By analyzing real-time market behavior and historical patterns, the algorithm adjusts its calculations to match current conditions, keeping your strategy relevant and adaptable.
3. Eliminate Emotional Bias
• With clear probabilities, expected values, and data-driven insights, the indicator removes guesswork and helps you avoid emotional decisions that can damage your edge.
4. Optimize Entry Levels
• The indicator identifies optimal entry levels based on your selected bias and timeframes, improving robustness in your trades.
5. Enhance Risk Management
• Using tools like the Kelly Criterion, the indicator suggests optimal position sizes and risk levels, ensuring that your strategy maintains consistency and discipline.
6. Avoid Overtrading
• By highlighting only high-potential setups, the indicator keeps you focused on quality over quantity, helping you refine your strategy and avoid unnecessary losses.
How can I get started to use the indicator for my entries?
1. Set Your Market Bias
• Determine whether the market trend is Bullish or Bearish using your own approach.
• Select the corresponding bias in the indicator’s user inputs to align it with your analysis.
2. Choose Your Entry Timeframes
• Specify the timeframes you want to focus on for trade entries.
• The indicator will dynamically analyze these timeframes to provide optimal setups.
3. Let the Algorithm Analyze
• Quantify evaluates historical data and real-time price action to calculate probabilities and expected values.
• It highlights setups with the highest potential based on your selected bias and timeframes.
4. Refine Your Entries
• Use the insights provided—entry levels, probabilities, and risk calculations—to align your trades with a math-driven edge.
• Avoid overtrading by focusing only on setups with positive expected value.
5. Adapt to Market Conditions
• The indicator continuously adapts to real-time market behavior, ensuring its recommendations stay relevant and precise as conditions change.
How does the indicator calculate the current range?
The indicator calculates the current range by analyzing swing points from the very first bar on your charts to the latest available bar it identifies external liquidity levels, also known as BSLQ (buy-side liquidity levels) and SSLQ (sell-side liquidity levels).
What's the purpose of these levels? What are the underlying calculations?
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Pivot levels.
3. Identifying Discount and Premium Zones.
4. Importance of Risk-Reward in Premium and Discount Ranges
How does the script calculate probabilities?
The script calculates the probability of each liquidity level individually. Here's the breakdown:
1. Upon the formation of a new range, the script waits for the price to reach and tap into pivot level level. Status: "■" - Inactive
2. Once pivot level is tapped into, the pivot status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
4. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
What does the multi-timeframe functionality offer?
You can incorporate up to 4 higher timeframe probabilities directly into the table.
This feature allows you to analyze the probabilities of buyside and sellside liquidity across multiple timeframes, without the need to manually switch between them.
By viewing these higher timeframe probabilities in one place, traders can spot larger market trends and refine their entries and exits with a better understanding of the overall market context.
What are the multi-timeframe underlying calculations?
The script uses the same calculations (mentioned above) and uses security function to request the data such as price levels, bar time, probabilities and booleans from the user-input timeframe.
How does the Indicator Identifies Positive Expected Values?
Quantify instantly calculates whether a trade setup has the potential to generate positive expected value (EV).
To determine a positive EV setup, the indicator uses the formula:
EV = ( P(Win) × R(Win) ) − ( P(Loss) × R(Loss))
where:
- P(Win) is the probability of a winning trade.
- R(Win) is the reward or return for a winning trade, determined by the current risk-to-reward ratio (RR).
- P(Loss) is the probability of a losing trade.
- R(Loss) is the loss incurred per losing trade, typically assumed to be -1.
By calculating these values based on historical data and the current trading setup, the indicator helps you understand whether your trade has a positive expected value.
How can I know that the setup I'm going to trade with has a positive EV?
If the indicator detects that the adjusted pivot and buy/sell side probabilities have generated positive expected value (EV) in historical data, the risk-to-reward (RR) label within the range box will be colored blue and red .
If the setup does not produce positive EV, the RR label will appear gray.
This indicates that even the risk-to-reward ratio is greater than 1:1, the setup is not likely to yield a positive EV because, according to historical data, the number of losses outweighs the number of wins relative to the RR gain per winning trade.
What is the confidence level in the indicator, and how is it determined?
The confidence level in the indicator reflects the reliability of the probabilities calculated based on historical data. It is determined by the sample size of the probabilities used in the calculations. A larger sample size generally increases the confidence level, indicating that the probabilities are more reliable and consistent with past performance.
How does the confidence level affect the risk-to-reward (RR) label?
The confidence level (★) is visually represented alongside the probability label. A higher confidence level indicates that the probabilities used to determine the RR label are based on a larger and more reliable sample size.
How can traders use the confidence level to make better trading decisions?
Traders can use the confidence level to gauge the reliability of the probabilities and expected value (EV) calculations provided by the indicator. A confidence level above 95% is considered statistically significant and indicates that the historical data supporting the probabilities is robust. This high confidence level suggests that the probabilities are reliable and that the indicator’s recommendations are more likely to be accurate.
In data science and statistics, a confidence level above 95% generally means that there is less than a 5% chance that the observed results are due to random variation. This threshold is widely accepted in research and industry as a marker of statistical significance. Studies such as those published in the Journal of Statistical Software and the American Statistical Association support this threshold, emphasizing that a confidence level above 95% provides a strong assurance of data reliability and validity.
Conversely, a confidence level below 95% indicates that the sample size may be insufficient and that the data might be less reliable. In such cases, traders should approach the indicator’s recommendations with caution and consider additional factors or further analysis before making trading decisions.
How does the sample size affect the confidence level, and how does it relate to my TradingView plan?
The sample size for calculating the confidence level is directly influenced by the amount of historical data available on your charts. A larger sample size typically leads to more reliable probabilities and higher confidence levels.
Here’s how the TradingView plans affect your data access:
Essential Plan
The Essential Plan provides basic data access with a limited amount of historical data. This can lead to smaller sample sizes and lower confidence levels, which may weaken the robustness of your probability calculations. Suitable for casual traders who do not require extensive historical analysis.
Plus Plan
The Plus Plan offers more historical data than the Essential Plan, allowing for larger sample sizes and more accurate confidence levels. This enhancement improves the reliability of indicator calculations. This plan is ideal for more active traders looking to refine their strategies with better data.
Premium Plan
The Premium Plan grants access to extensive historical data, enabling the largest sample sizes and the highest confidence levels. This plan provides the most reliable data for accurate calculations, with up to 20,000 historical bars available for analysis. It is designed for serious traders who need comprehensive data for in-depth market analysis.
PRO+ Plans
The PRO+ Plans offer the most extensive historical data, allowing for the largest sample sizes and the highest confidence levels. These plans are tailored for professional traders who require advanced features and significant historical data to support their trading strategies effectively.
For many traders, the Premium Plan offers a good balance of affordability and sufficient sample size for accurate confidence levels.
What is the HTF probability table and how does it work?
The HTF (Higher Time Frame) probability table is a feature that allows you to view buy and sellside probabilities and their status from timeframes higher than your current chart timeframe.
Here’s how it works:
Data Request: The table requests and retrieves data from user-defined higher timeframes (HTFs) that you select.
Probability Display: It displays the buy and sellside probabilities for each of these HTFs, providing insights into the likelihood of price movements based on higher timeframe data.
Detailed Tooltips: The table includes detailed tooltips for each timeframe, offering additional context and explanations to help you understand the data better.
What do the different colors in the HTF probability table indicate?
The colors in the HTF probability table provide visual cues about the expected value (EV) of trading setups based on higher timeframe probabilities:
Blue: Suggests that entering a long position from the HTF user-defined pivot point, targeting buyside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Red: Indicates that entering a short position from the HTF user-defined pivot point, targeting sellside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Gray: Shows that neither long nor short trades from the HTF user-defined pivot point are expected to generate positive EV, suggesting that trading these setups may not be favorable.
What machine learning techniques are used in Quantify?
Quantify offers two main machine learning approaches:
1. Adaptive Learning (Fixed Sample Size): The algorithm learns from the entire dataset without resampling, maintaining a stable model that adapts to the latest market conditions.
2. Bootstrap Resampling: This method creates multiple subsets of the historical data, allowing the model to train on varying sample sizes. This technique enhances the robustness of predictions by ensuring that the model is not overfitting to a single dataset.
How does machine learning affect the expected value calculations in Quantify?
Machine learning plays a key role in improving the accuracy of expected value (EV) calculations. By analyzing historical price action, liquidity hits, and market bias patterns, the model continuously adjusts its understanding of risk and reward, allowing the expected value to reflect the most likely market movements. This results in more precise EV predictions, helping traders focus on setups that maximize profitability.
What is the Kelly Criterion, and how does it work in Quantify?
The Kelly Criterion is a mathematical formula used to determine the optimal position size for each trade, maximizing long-term growth while minimizing the risk of large drawdowns. It calculates the percentage of your portfolio to risk on a trade based on the probability of winning and the expected payoff.
Quantify integrates this with user-defined inputs to dynamically calculate the most effective position size in percentage, aligning with the trader’s risk tolerance and desired exposure.
How does Quantify use the Kelly Criterion in practice?
Quantify uses the Kelly Criterion to optimize position sizing based on the following factors:
1. Confidence Level: The model assesses the confidence level in the trade setup based on historical data and sample size. A higher confidence level increases the suggested position size because the trade has a higher probability of success.
2. Max Allowed Drawdown (User-Defined): Traders can set their preferred maximum allowed drawdown, which dictates how much loss is acceptable before reducing position size or stopping trading. Quantify uses this input to ensure that risk exposure aligns with the trader’s risk tolerance.
3. Probabilities: Quantify calculates the probabilities of success for each trade setup. The higher the probability of a successful trade (based on historical price action and liquidity levels), the larger the position size suggested by the Kelly Criterion.
What is a trailing stoploss, and how does it work in Quantify?
A trailing stoploss is a dynamic risk management tool that moves with the price as the market trend continues in the trader’s favor. Unlike a fixed take profit, which stays at a set level, the trailing stoploss automatically adjusts itself as the market moves, locking in profits as the price advances.
In Quantify, the trailing stoploss is enhanced by incorporating market structure liquidity levels (explain above). This ensures that the stoploss adjusts intelligently based on key price levels, allowing the trader to stay in the trade as long as the trend remains intact, while also protecting profits if the market reverses.
Why would a trader prefer a trailing stoploss based on liquidity levels instead of a fixed take-profit level?
Traders who use trailing stoplosses based on liquidity levels prefer this method because:
1. Market-Driven Flexibility: The stoploss follows the market structure rather than being static at a pre-defined level. This means the stoploss is less likely to be hit by small market fluctuations or false reversals. The stoploss remains adaptive, moving as the market moves.
2. Riding the Trend: Traders can capture more profit during a sustained trend because the trailing stop will adjust only when the trend starts to reverse significantly, based on key liquidity levels. This allows them to hold positions longer without prematurely locking in profits.
3. Avoiding Premature Exits: Fixed stoploss levels may exit a trade too early in volatile markets, while liquidity-based trailing stoploss levels respect the natural flow of price action, preventing the trader from exiting too soon during pullbacks or minor retracements.
🎲 Becoming the House: Gaining an Edge Over the Market
In American roulette, the casino has a 5.26% edge due to the presence of the 0 and 00 pockets. On even-money bets, players face a 47.37% chance of winning, while true 50/50 odds would require a 50% chance. This edge—the gap between the payout odds and the true probabilities—ensures that, statistically, the casino will always win over time, even if individual players win occasionally.
From a Trader’s Perspective
In trading, your edge comes from identifying and executing setups with a positive expected value (EV). For example:
• If you identify a setup with a 55.48% chance of winning and a 1:1 risk-to-reward (RR) ratio, your trade has a statistical advantage over a neutral (50/50) probability.
This edge works in your favor when applied consistently across a series of trades, just as the casino’s edge ensures profitability across thousands of spins.
🎰 Applying the Concept to Trading
Like casinos leverage their mathematical edge in games of chance, you can achieve long-term success in trading by focusing on setups with positive EV and managing your trades systematically. Here’s how:
1. Probability Advantage: Prioritize trades where the probability of success (win rate) exceeds the breakeven rate for your chosen risk-to-reward ratio.
• Example: With a 1:1 RR, you need a win rate above 50% to achieve positive EV.
2. Risk-to-Reward Ratio (RR): Even with a win rate below 50%, you can gain an edge by increasing your RR (e.g., a 40% win rate with a 2:1 RR still has positive EV).
3. Consistency and Discipline: Just as casinos profit by sticking to their mathematical advantage over thousands of spins, traders must rely on their edge across many trades, avoiding emotional decisions or overleveraging.
By targeting favorable probabilities and managing trades effectively, you “become the house” in your trading. This approach allows you to leverage statistical advantages to enhance your overall performance and achieve sustainable profitability.
What Makes the Quantify Indicator Original?
1. Data-Driven Edge
Unlike traditional indicators that rely on static formulas, Quantify leverages probability-based analysis and machine learning. It calculates expected value (EV) and confidence levels to help traders identify setups with a true statistical edge.
2. Integration of Market Structure
Quantify uses market structure liquidity levels to dynamically adapt. It identifies key zones like swing highs/lows and liquidity traps, enabling users to align entries and exits with where the market is most likely to react. This bridges the gap between price action analysis and quantitative trading.
3. Sophisticated Risk Management
The Kelly Criterion implementation is unique. Quantify allows traders to input their maximum allowed drawdown, dynamically adjusting risk exposure to maintain optimal position sizing. This ensures risk is scientifically controlled while maximizing potential growth.
4. Multi-Timeframe and Liquidity-Based Trailing Stops
The indicator doesn’t just suggest fixed profit-taking levels. It offers market structure-based trailing stop-loss functionality, letting traders ride trends as long as liquidity and probabilities favor the position, which is rare in most tools.
5. Customizable Bias and Adaptive Learning
• Directional Bias: Traders can set a bullish or bearish bias, and the indicator recalculates probabilities to align with the trader’s market outlook.
• Adaptive Learning: The machine learning model adapts to changes in data (via resampling or bootstrap methods), ensuring that predictions stay relevant in evolving markets.
6. Positive EV Focus
The focus on positive EV setups differentiates it from reactive indicators. It shifts trading from chasing signals to acting on setups that statistically favor profitability, akin to how professional quant funds operate.
7. User Empowerment
Through features like customizable timeframes, real-time probability updates, and visualization tools, Quantify empowers users to make data-informed decisions.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
FXC NQ Opening Range Breakout Strategy V2.4Mechanical Strategy that trades breakouts on NQ futures on the 15min timeframe during the NYSE session. It's designed to manage Apex and Top Step accounts with the lowest risk possible.
Risk Disclaimer:
Past results as well as strategy tester reports do not indicate future performance. Guarantees do not exist in trading. By using this strategy you risk losing all your money.
Important:
It only trades on Monday, Wednesday and Friday and takes usually only 1 trade per trading day.
It works on the 15min timeframe only.
The settings are optimised already for NQ but feel free to change them.
How it works:
Every selected trading day it measures the range of the first 15min candle after the NYSE open. As soon as price closes above on the 15min timeframe, it will trade the breakout targeting a set risk to reward ratio. SL on the opposite side of the range. It will trail the SL after a set amount of points and uses a buffer of the set amount of points to trail it.
Settings:
Opening Range Time : This is the time of the day in hours and minutes when the strategy starts looking for trades. It's in the EST/ NY Timezone and set to 9:30-09:45 by default
because that's the NYSE open.
Session Time : This is the time of the day in hours and minutes until the strategy trades. It's in the EST/ NY Timezone and set to 09:45-14:45 by default.
because that's what gave the best results in backtesting. Open trades will get closed automatically once the end of the session is reached. No matter if win or loss. This is just to prevent holding positions over night.
Session Border This setting is to select the border color in which the session box will be plotted.
Opening Range Box This setting is to select the fill color of the opening range box.
Opening Range Border This setting is to select the border color of the session box.
Trade Timeframe This setting determines on which timeframe candle has to close outside the opening range box in order to take a trade. It's set to 15min by default because this is what worked by far the best in backtests and live trading.
Stop Loss Buffer in Points: This is simply the buffer in points that is added to the SL for safety reasons. If you have it on 0, the SL will be at the exact price of the opposite side of the range. By default it's set to 0 pips because this is what delivered the best results in backtests.
Profit Target Factor: This is simply the total SL size in points multiplied by x.
Example: If you put 2, you get a 1:2 Risk to Reward Ratio. By Default it's set to 4 because this gave the best results in backtests, because trades always get closed either by trailing SL or because the end of the session is reached.
Use Trailing Stop Loss: This setting is to enable/ disable the trailing stop loss. It's enabled by default because this is a fundamental part of the strategy.
Trailing Stop Buffer: This setting determines after how many points in profit the trailing SL will be activated.
Risk Type: You can chose either between Fixed USD Amount, Risk per Trade in % or Fixed Contract Size. By default it's set to fixed contract size.
Risk Amount (USD or Contracts): This setting is to set how many USD or how many contracts you want to risk per trade. Make sure to check which risk type you have selected before you chose the risk amount.
Use Limit Orders If enabled, the strategy will place a pending order x points from the current price, instead of a market order. Limit orders are enabled by default for a better performance. Important: It doesn't actually place a limit order. The strategy will just wait for a pullback and then enter with a market order. It's more like a hidden limit order.
Limit Order Distance (points): If you have limit orders enabled, this setting determines how many points from the current price the limit order will be placed.
Trading Days: These checkboxes are to select on which week days the strategy has to trade. Thursday is disabled by default because backtests have shown that Thursday is the least profitable day
Backtest Settings:
For the backtest the commissions ere set to 0.35 USD per mini contract which is the highest amount Tradeovate charges. Margin was not accounted for because typically on Apex accounts you can use way more contracts than you need for the extremely low max drawdown. Margin would be important on personal accounts but even there typically it's not an issue at all especially because this strategy runs on the 15min timeframe so it won't use a lot of contracts anyways.
What makes it unique:
This script is unique because it's designed to be used on Apex and Top Step accounts with extremely strict drawdown rules.
The strategy is optimised to be traded with a fixed contract size instead of using % risk. The reason for that is that the drawdown rules of these Futures Prop Accounts are very strict and the fact that the smallest trade-able contract size is 1.
Why the source code is hidden:
The source code is hidden because I invested a lot of time and money into developing this strategy and optimising it with paid 3rd party software. Also since I use it myself on my Apex accounts and prop firms don't allow copy trading I don't want it to be used by too many traders.
Skeleton Key LiteSkeleton Key Lite Strategy
Note : Every input, except for the API Alerts, depends on an external indicator to provide the necessary values for the strategy to function.
Definitions
Strategy Direction: The trading direction (long or short) as determined by an external source, such as an indicator.
Threshold Conditions:
- Enter Condition: Defines the condition for entering a trade.
- Exit Condition: Defines the condition for exiting a trade.
Stop Loss (SL):
- Trail SL: A trailing stop loss, dynamically updated during the trade.
- Basic SL: A static stop loss level.
- Emergency SL (ER SL): A fallback stop loss for extreme conditions.
- Max SL: The maximum risk tolerance in stop loss.
- Limit SL: A predefined stop loss that is executed as a limit order.
Take Profit (TP):
- Max TP: The maximum profit target for a trade.
- Limit TP: A predefined take profit level executed as a limit order.
API Alerts:
- API Entry: JSON-based configuration for sending entry signals.
- API Exit: JSON-based configuration for sending exit signals.
Broad Concept
The Skeleton Key Lite strategy script is designed to provide a generalized framework for orchestrating trade execution based on external indicators. It allows QuantAlchemy and others to encapsulate strategies into indicators, which can then be backtested and automated using this strategy script.
Inputs
Note : All inputs are dependent on external indicators for values except for the API Alerts.
Strategy Direction:
- Source: Direction signal from an external indicator.
- Options: `LONG` (`1`), `SHORT` (`-1`).
Trade Conditions:
- Enter: Source input, trigger for entry condition.
- Exit: Source input, trigger for exit condition.
Stops and Take Profits:
- Trail SL: Enable/disable dynamic trailing stop loss.
- Basic SL: Enable/disable static stop loss.
- Emergency SL: Enable/disable emergency stop loss.
- Max SL: Enable/disable maximum risk stop loss.
- Max TP: Enable/disable maximum take profit.
- Limit SL: Enable/disable predefined stop loss executed as a limit order.
- Limit TP: Enable/disable predefined take profit executed as a limit order.
Alerts:
- API Entry: Configurable JSON message for entry signals.
- API Exit: Configurable JSON message for exit signals.
How It Works
Trade Logic:
- Conditions for entering and exiting trades are evaluated based on the selected input sources.
Stop Loss and Take Profit Management:
- Multiple stop loss types (trailing, basic, emergency, etc.) and take profit levels are calculated dynamically during the trade entry. Trailing stop loss is updated during the trade based on the selected input.
API Alerts:
- Alerts are triggered using customizable JSON messages, which can be integrated with external trading systems or APIs.
Trade Execution:
- Enter: Initiates a new trade if entry conditions are met and there is no open position.
- Exit: Closes all trades if exit conditions are met or stop loss/take profit thresholds are hit.
Key Features
Customizable: Fully configurable entry and exit conditions based on external indicators.
Encapsulation: Integrates seamlessly with indicators, allowing strategies to be developed as indicator-based signals.
Comprehensive Risk Management:
- Multiple stop loss and take profit options.
- Emergency stop loss for unexpected conditions.
API Integration: Alerts are designed to interface with external systems for automation and monitoring.
Plots
The script plots key variables on the chart for better visualization:
Enter and Exit Signals:
- `enter`: Displays when the entry condition is triggered.
- `exit`: Displays when the exit condition is triggered.
Risk Management Levels:
- `trailSL`: Current trailing stop loss level.
- `basicSL`: Static stop loss level.
- `erSL`: Emergency stop loss level.
- `maxSL`: Maximum risk stop loss level.
Profit Management Levels:
- `maxTP`: Maximum take profit level.
- `limitTP`: Limit-based take profit level.
Limit Orders:
- `limitSL`: Limit-based stop loss level.
- `limitTP`: Limit-based take profit level.
Proposed Interpretations
Entry and Exit Points:
- Use the plotted signals (`enter`, `exit`) to analyze the trade entry and exit points visually.
Risk and Profit Levels:
- Monitor the stop loss (`SL`) and take profit (`TP`) levels to assess trade performance.
Dynamic Trail SL:
- Observe the `trailSL` to evaluate how the trailing stop adapts during the trade.
Limitations
Dependence on Indicators:
- This script relies on external indicators to provide signals for strategy execution.
No Indicator Included:
- Users must integrate an appropriate indicator for source inputs.
Back-Test Constraints:
- Back-testing results depend on the accuracy and design of the integrated indicators.
Final Thoughts
The Skeleton Key Lite strategy by QuantAlchemy provides a robust framework for automated trading by leveraging indicator-based signals. Its flexibility and comprehensive risk management make it a valuable tool for traders seeking to implement and backtest custom strategies.
Disclaimer
This script is for educational purposes only. Trading involves risk, and past performance does not guarantee future results. Use at your own discretion and risk.
