Dskyz (DAFE) Adaptive Regime - Quant Machine ProDskyz (DAFE) Adaptive Regime - Quant Machine Pro:
Buckle up for the Dskyz (DAFE) Adaptive Regime - Quant Machine Pro, is a strategy that’s your ultimate edge for conquering futures markets like ES, MES, NQ, and MNQ. This isn’t just another script—it’s a quant-grade powerhouse, crafted with precision to adapt to market regimes, deliver multi-factor signals, and protect your capital with futures-tuned risk management. With its shimmering DAFE visuals, dual dashboards, and glowing watermark, it turns your charts into a cyberpunk command center, making trading as thrilling as it is profitable.
Unlike generic scripts clogging up the space, the Adaptive Regime is a DAFE original, built from the ground up to tackle the chaos of futures trading. It identifies market regimes (Trending, Range, Volatile, Quiet) using ADX, Bollinger Bands, and HTF indicators, then fires trades based on a weighted scoring system that blends candlestick patterns, RSI, MACD, and more. Add in dynamic stops, trailing exits, and a 5% drawdown circuit breaker, and you’ve got a system that’s as safe as it is aggressive. Whether you’re a newbie or a prop desk pro, this strat’s your ticket to outsmarting the markets. Let’s break down every detail and see why it’s a must-have.
Why Traders Need This Strategy
Futures markets are a gauntlet—fast moves, volatility spikes (like the April 28, 2025 NQ 1k-point drop), and institutional traps that punish the unprepared. Meanwhile, platforms are flooded with low-effort scripts that recycle old ideas with zero innovation. The Adaptive Regime stands tall, offering:
Adaptive Intelligence: Detects market regimes (Trending, Range, Volatile, Quiet) to optimize signals, unlike one-size-fits-all scripts.
Multi-Factor Precision: Combines candlestick patterns, MA trends, RSI, MACD, volume, and HTF confirmation for high-probability trades.
Futures-Optimized Risk: Calculates position sizes based on $ risk (default: $300), with ATR or fixed stops/TPs tailored for ES/MES.
Bulletproof Safety: 5% daily drawdown circuit breaker and trailing stops keep your account intact, even in chaos.
DAFE Visual Mastery: Pulsing Bollinger Band fills, dynamic SL/TP lines, and dual dashboards (metrics + position) make signals crystal-clear and charts a work of art.
Original Craftsmanship: A DAFE creation, built with community passion, not a rehashed clone of generic code.
Traders need this because it’s a complete, adaptive system that blends quant smarts, user-friendly design, and DAFE flair. It’s your edge to trade with confidence, cut through market noise, and leave the copycats in the dust.
Strategy Components
1. Market Regime Detection
The strategy’s brain is its ability to classify market conditions into five regimes, ensuring signals match the environment.
How It Works:
Trending (Regime 1): ADX > 20, fast/slow EMA spread > 0.3x ATR, HTF RSI > 50 or MACD bullish (htf_trend_bull/bear).
Range (Regime 2): ADX < 25, price range < 3% of close, no HTF trend.
Volatile (Regime 3): BB width > 1.5x avg, ATR > 1.2x avg, HTF RSI overbought/oversold.
Quiet (Regime 4): BB width < 0.8x avg, ATR < 0.9x avg.
Other (Regime 5): Default for unclear conditions.
Indicators: ADX (14), BB width (20), ATR (14, 50-bar SMA), HTF RSI (14, daily default), HTF MACD (12,26,9).
Why It’s Brilliant:
Regime detection adapts signals to market context, boosting win rates in trending or volatile conditions.
HTF RSI/MACD add a big-picture filter, rare in basic scripts.
Visualized via gradient background (green for Trending, orange for Range, red for Volatile, gray for Quiet, navy for Other).
2. Multi-Factor Signal Scoring
Entries are driven by a weighted scoring system that combines candlestick patterns, trend, momentum, and volume for robust signals.
Candlestick Patterns:
Bullish: Engulfing (0.5), hammer (0.4 in Range, 0.2 else), morning star (0.2), piercing (0.2), double bottom (0.3 in Volatile, 0.15 else). Must be near support (low ≤ 1.01x 20-bar low) with volume spike (>1.5x 20-bar avg).
Bearish: Engulfing (0.5), shooting star (0.4 in Range, 0.2 else), evening star (0.2), dark cloud (0.2), double top (0.3 in Volatile, 0.15 else). Must be near resistance (high ≥ 0.99x 20-bar high) with volume spike.
Logic: Patterns are weighted higher in specific regimes (e.g., hammer in Range, double bottom in Volatile).
Additional Factors:
Trend: Fast EMA (20) > slow EMA (50) + 0.5x ATR (trend_bull, +0.2); opposite for trend_bear.
RSI: RSI (14) < 30 (rsi_bull, +0.15); > 70 (rsi_bear, +0.15).
MACD: MACD line > signal (12,26,9, macd_bull, +0.15); opposite for macd_bear.
Volume: ATR > 1.2x 50-bar avg (vol_expansion, +0.1).
HTF Confirmation: HTF RSI < 70 and MACD bullish (htf_bull_confirm, +0.2); RSI > 30 and MACD bearish (htf_bear_confirm, +0.2).
Scoring:
bull_score = sum of bullish factors; bear_score = sum of bearish. Entry requires score ≥ 1.0.
Example: Bullish engulfing (0.5) + trend_bull (0.2) + rsi_bull (0.15) + htf_bull_confirm (0.2) = 1.05, triggers long.
Why It’s Brilliant:
Multi-factor scoring ensures signals are confirmed by multiple market dynamics, reducing false positives.
Regime-specific weights make patterns more relevant (e.g., hammers shine in Range markets).
HTF confirmation aligns with the big picture, a quant edge over simplistic scripts.
3. Futures-Tuned Risk Management
The risk system is built for futures, calculating position sizes based on $ risk and offering flexible stops/TPs.
Position Sizing:
Logic: Risk per trade (default: $300) ÷ (stop distance in points * point value) = contracts, capped at max_contracts (default: 5). Point value = tick value (e.g., $12.5 for ES) * ticks per point (4) * contract multiplier (1 for ES, 0.1 for MES).
Example: $300 risk, 8-point stop, ES ($50/point) → 0.75 contracts, rounded to 1.
Impact: Precise sizing prevents over-leverage, critical for micro contracts like MES.
Stops and Take-Profits:
Fixed: Default stop = 8 points, TP = 16 points (2:1 reward/risk).
ATR-Based: Stop = 1.5x ATR (default), TP = 3x ATR, enabled via use_atr_for_stops.
Logic: Stops set at swing low/high ± stop distance; TPs at 2x stop distance from entry.
Impact: ATR stops adapt to volatility, while fixed stops suit stable markets.
Trailing Stops:
Logic: Activates at 50% of TP distance. Trails at close ± 1.5x ATR (atr_multiplier). Longs: max(trail_stop_long, close - ATR * 1.5); shorts: min(trail_stop_short, close + ATR * 1.5).
Impact: Locks in profits during trends, a game-changer in volatile sessions.
Circuit Breaker:
Logic: Pauses trading if daily drawdown > 5% (daily_drawdown = (max_equity - equity) / max_equity).
Impact: Protects capital during black swan events (e.g., April 27, 2025 ES slippage).
Why It’s Brilliant:
Futures-specific inputs (tick value, multiplier) make it plug-and-play for ES/MES.
Trailing stops and circuit breaker add pro-level safety, rare in off-the-shelf scripts.
Flexible stops (ATR or fixed) suit different trading styles.
4. Trade Entry and Exit Logic
Entries and exits are precise, driven by bull_score/bear_score and protected by drawdown checks.
Entry Conditions:
Long: bull_score ≥ 1.0, no position (position_size <= 0), drawdown < 5% (not pause_trading). Calculates contracts, sets stop at swing low - stop points, TP at 2x stop distance.
Short: bear_score ≥ 1.0, position_size >= 0, drawdown < 5%. Stop at swing high + stop points, TP at 2x stop distance.
Logic: Tracks entry_regime for PNL arrays. Closes opposite positions before entering.
Exit Conditions:
Stop-Loss/Take-Profit: Hits stop or TP (strategy.exit).
Trailing Stop: Activates at 50% TP, trails by ATR * 1.5.
Emergency Exit: Closes if price breaches stop (close < long_stop_price or close > short_stop_price).
Reset: Clears stop/TP prices when flat (position_size = 0).
Why It’s Brilliant:
Score-based entries ensure multi-factor confirmation, filtering out weak signals.
Trailing stops maximize profits in trends, unlike static exits in basic scripts.
Emergency exits add an extra safety layer, critical for futures volatility.
5. DAFE Visuals
The visuals are pure DAFE magic, blending function with cyberpunk flair to make signals intuitive and charts stunning.
Shimmering Bollinger Band Fill:
Display: BB basis (20, white), upper/lower (green/red, 45% transparent). Fill pulses (30–50 alpha) by regime, with glow (60–95 alpha) near bands (close ≥ 0.995x upper or ≤ 1.005x lower).
Purpose: Highlights volatility and key levels with a futuristic glow.
Visuals make complex regimes and signals instantly clear, even for newbies.
Pulsing effects and regime-specific colors add a DAFE signature, setting it apart from generic scripts.
BB glow emphasizes tradeable levels, enhancing decision-making.
Chart Background (Regime Heatmap):
Green — Trending Market: Strong, sustained price movement in one direction. The market is in a trend phase—momentum follows through.
Orange — Range-Bound: Market is consolidating or moving sideways, with no clear up/down trend. Great for mean reversion setups.
Red — Volatile Regime: High volatility, heightened risk, and larger/faster price swings—trade with caution.
Gray — Quiet/Low Volatility: Market is calm and inactive, with small moves—often poor conditions for most strategies.
Navy — Other/Neutral: Regime is uncertain or mixed; signals may be less reliable.
Bollinger Bands Glow (Dynamic Fill):
Neon Red Glow — Warning!: Price is near or breaking above the upper band; momentum is overstretched, watch for overbought conditions or reversals.
Bright Green Glow — Opportunity!: Price is near or breaking below the lower band; market could be oversold, prime for bounce or reversal.
Trend Green Fill — Trending Regime: Fills between bands with green when the market is trending, showing clear momentum.
Gold/Yellow Fill — Range Regime: Fills with gold/aqua in range conditions, showing the market is sideways/oscillating.
Magenta/Red Fill — Volatility Spike: Fills with vivid magenta/red during highly volatile regimes.
Blue Fill — Neutral/Quiet: A soft blue glow for other or uncertain market states.
Moving Averages:
Display: Blue fast EMA (20), red slow EMA (50), 2px.
Purpose: Shows trend direction, with trend_dir requiring ATR-scaled spread.
Dynamic SL/TP Lines:
Display: Pulsing colors (red SL, green TP for Trending; yellow/orange for Range, etc.), 3px, with pulse_alpha for shimmer.
Purpose: Tracks stops/TPs in real-time, color-coded by regime.
6. Dual Dashboards
Two dashboards deliver real-time insights, making the strat a quant command center.
Bottom-Left Metrics Dashboard (2x13):
Metrics: Mode (Active/Paused), trend (Bullish/Bearish/Neutral), ATR, ATR avg, volume spike (YES/NO), RSI (value + Oversold/Overbought/Neutral), HTF RSI, HTF trend, last signal (Buy/Sell/None), regime, bull score.
Display: Black (29% transparent), purple title, color-coded (green for bullish, red for bearish).
Purpose: Consolidates market context and signal strength.
Top-Right Position Dashboard (2x7):
Metrics: Regime, position side (Long/Short/None), position PNL ($), SL, TP, daily PNL ($).
Display: Black (29% transparent), purple title, color-coded (lime for Long, red for Short).
Purpose: Tracks live trades and profitability.
Why It’s Brilliant:
Dual dashboards cover market context and trade status, a rare feature.
Color-coding and concise metrics guide beginners (e.g., green “Buy” = go).
Real-time PNL and SL/TP visibility empower disciplined trading.
7. Performance Tracking
Logic: Arrays (regime_pnl_long/short, regime_win/loss_long/short) track PNL and win/loss by regime (1–5). Updated on trade close (barstate.isconfirmed).
Purpose: Prepares for future adaptive thresholds (e.g., adjust bull_score min based on regime performance).
Why It’s Brilliant: Lays the groundwork for self-optimizing logic, a quant edge over static scripts.
Key Features
Regime-Adaptive: Optimizes signals for Trending, Range, Volatile, Quiet markets.
Futures-Optimized: Precise sizing for ES/MES with tick-based risk inputs.
Multi-Factor Signals: Candlestick patterns, RSI, MACD, and HTF confirmation for robust entries.
Dynamic Exits: ATR/fixed stops, 2:1 TPs, and trailing stops maximize profits.
Safe and Smart: 5% drawdown breaker and emergency exits protect capital.
DAFE Visuals: Shimmering BB fill, pulsing SL/TP, and dual dashboards.
Backtest-Ready: Fixed qty and tick calc for accurate historical testing.
How to Use
Add to Chart: Load on a 5min ES/MES chart in TradingView.
Configure Inputs: Set instrument (ES/MES), tick value ($12.5/$1.25), multiplier (1/0.1), risk ($300 default). Enable ATR stops for volatility.
Monitor Dashboards: Bottom-left for regime/signals, top-right for position/PNL.
Backtest: Run in strategy tester to compare regimes.
Live Trade: Connect to Tradovate or similar. Watch for slippage (e.g., April 27, 2025 ES issues).
Replay Test: Try April 28, 2025 NQ drop to see regime shifts and stops.
Disclaimer
Trading futures involves significant risk of loss and is not suitable for all investors. Past performance does not guarantee future results. Backtest results may differ from live trading due to slippage, fees, or market conditions. Use this strategy at your own risk, and consult a financial advisor before trading. Dskyz (DAFE) Trading Systems is not responsible for any losses incurred.
Backtesting:
Frame: 2023-09-20 - 2025-04-29
Slippage: 3
Fee Typical Range (per side, per contract)
CME Exchange $1.14 – $1.20
Clearing $0.10 – $0.30
NFA Regulatory $0.02
Firm/Broker Commis. $0.25 – $0.80 (retail prop)
TOTAL $1.60 – $2.30 per side
Round Turn: (enter+exit) = $3.20 – $4.60 per contract
Final Notes
The Dskyz (DAFE) Adaptive Regime - Quant Machine Pro is more than a strategy—it’s a revolution. Crafted with DAFE’s signature precision, it rises above generic scripts with adaptive regimes, quant-grade signals, and visuals that make trading a thrill. Whether you’re scalping MES or swinging ES, this system empowers you to navigate markets with confidence and style. Join the DAFE crew, light up your charts, and let’s dominate the futures game!
(This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.)
Use it with discipline. Use it with clarity. Trade smarter.
**I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract.
Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
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RSI Full Forecast [Titans_Invest]RSI Full Forecast
Get ready to experience the ultimate evolution of RSI-based indicators – the RSI Full Forecast, a boosted and even smarter version of the already powerful: RSI Forecast
Now featuring over 40 additional entry conditions (forecasts), this indicator redefines the way you view the market.
AI-Powered RSI Forecasting:
Using advanced linear regression with the least squares method – a solid foundation for machine learning - the RSI Full Forecast enables you to predict future RSI behavior with impressive accuracy.
But that’s not all: this new version also lets you monitor future crossovers between the RSI and the MA RSI, delivering early and strategic signals that go far beyond traditional analysis.
You’ll be able to monitor future crossovers up to 20 bars ahead, giving you an even broader and more precise view of market movements.
See the Future, Now:
• Track upcoming RSI & RSI MA crossovers in advance.
• Identify potential reversal zones before price reacts.
• Uncover statistical behavior patterns that would normally go unnoticed.
40+ Intelligent Conditions:
The new layer of conditions is designed to detect multiple high-probability scenarios based on historical patterns and predictive modeling. Each additional forecast is a window into the price's future, powered by robust mathematics and advanced algorithmic logic.
Full Customization:
All parameters can be tailored to fit your strategy – from smoothing periods to prediction sensitivity. You have complete control to turn raw data into smart decisions.
Innovative, Accurate, Unique:
This isn’t just an upgrade. It’s a quantum leap in technical analysis.
RSI Full Forecast is the first of its kind: an indicator that blends statistical analysis, machine learning, and visual design to create a true real-time predictive system.
⯁ SCIENTIFIC BASIS LINEAR REGRESSION
Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥.
The general formula for a simple linear regression is given by:
y = β₀ + β₁x + ε
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
y = is the predicted variable (e.g. future value of RSI)
x = is the explanatory variable (e.g. time or bar index)
β0 = is the intercept (value of 𝑦 when 𝑥 = 0)
𝛽1 = is the slope of the line (rate of change)
ε = is the random error term
The goal is to estimate the coefficients 𝛽0 and 𝛽1 so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares.
⯁ LEAST SQUARES ESTIMATION
To minimize the error between predicted and observed values, we use the following formulas:
β₁ = /
β₀ = ȳ - β₁x̄
Where:
∑ = sum
x̄ = mean of x
ȳ = mean of y
x_i, y_i = individual values of the variables.
Where:
x_i and y_i are the means of the independent and dependent variables, respectively.
i ranges from 1 to n, the number of observations.
These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity.
⯁ LINEAR REGRESSION IN MACHINE LEARNING
Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies.
By applying this model to the RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis.
⯁ VISUAL INTERPRETATION
Imagine an RSI time series like this:
Time →
RSI →
The regression line will smooth these values and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted RSI, which can be crossed with the actual RSI to generate more intelligent signals.
⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED
Linear Regression Models the relationship between variables using a straight line.
Least Squares Minimizes the sum of squared errors between prediction and reality.
Time Series Forecasting Estimates future values based on historical data.
Supervised Learning Trains models to predict outputs from known inputs.
Statistical Smoothing Reduces noise and reveals underlying trends.
⯁ WHY THIS INDICATOR IS REVOLUTIONARY
Scientifically-based: Based on statistical theory and mathematical inference.
Unprecedented: First public RSI with least squares predictive modeling.
Intelligent: Built with machine learning logic.
Practical: Generates forward-thinking signals.
Customizable: Flexible for any trading strategy.
⯁ CONCLUSION
By combining RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it.
RSI Full Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology.
⯁ Example of simple linear regression, which has one independent variable:
⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ).
⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values using Matlab:
⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables.
⯁ The result of fitting a set of data points with a quadratic function:
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🔮 Linear Regression: PineScript Technical Parameters 🔮
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Forecast Types:
• Flat: Assumes prices will remain the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
ta.linreg (built-in function)
Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values calculated using the least squares method on the source series.
Syntax:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset: Offset.
• return: Linear regression curve.
This function has been cleverly applied to the RSI, making it capable of projecting future values based on past statistical trends.
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⯁ WHAT IS THE RSI❓
The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend.
⯁ HOW TO USE THE RSI❓
The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:
• Overbought: When the RSI is above 70, indicating that the asset may be overbought.
• Oversold: When the RSI is below 30, indicating that the asset may be oversold.
• Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions.
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⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
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🔹 CONDITIONS TO BUY 📈
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📈 RSI Conditions:
🔹 RSI > Upper
🔹 RSI < Upper
🔹 RSI > Lower
🔹 RSI < Lower
🔹 RSI > Middle
🔹 RSI < Middle
🔹 RSI > MA
🔹 RSI < MA
📈 MA Conditions:
🔹 MA > Upper
🔹 MA < Upper
🔹 MA > Lower
🔹 MA < Lower
📈 Crossovers:
🔹 RSI (Crossover) Upper
🔹 RSI (Crossunder) Upper
🔹 RSI (Crossover) Lower
🔹 RSI (Crossunder) Lower
🔹 RSI (Crossover) Middle
🔹 RSI (Crossunder) Middle
🔹 RSI (Crossover) MA
🔹 RSI (Crossunder) MA
🔹 MA (Crossover) Upper
🔹 MA (Crossunder) Upper
🔹 MA (Crossover) Lower
🔹 MA (Crossunder) Lower
📈 RSI Divergences:
🔹 RSI Divergence Bull
🔹 RSI Divergence Bear
📈 RSI Forecast:
🔹 RSI (Crossover) MA Forecast
🔹 RSI (Crossunder) MA Forecast
🔹 RSI Forecast 1 > MA Forecast 1
🔹 RSI Forecast 1 < MA Forecast 1
🔹 RSI Forecast 2 > MA Forecast 2
🔹 RSI Forecast 2 < MA Forecast 2
🔹 RSI Forecast 3 > MA Forecast 3
🔹 RSI Forecast 3 < MA Forecast 3
🔹 RSI Forecast 4 > MA Forecast 4
🔹 RSI Forecast 4 < MA Forecast 4
🔹 RSI Forecast 5 > MA Forecast 5
🔹 RSI Forecast 5 < MA Forecast 5
🔹 RSI Forecast 6 > MA Forecast 6
🔹 RSI Forecast 6 < MA Forecast 6
🔹 RSI Forecast 7 > MA Forecast 7
🔹 RSI Forecast 7 < MA Forecast 7
🔹 RSI Forecast 8 > MA Forecast 8
🔹 RSI Forecast 8 < MA Forecast 8
🔹 RSI Forecast 9 > MA Forecast 9
🔹 RSI Forecast 9 < MA Forecast 9
🔹 RSI Forecast 10 > MA Forecast 10
🔹 RSI Forecast 10 < MA Forecast 10
🔹 RSI Forecast 11 > MA Forecast 11
🔹 RSI Forecast 11 < MA Forecast 11
🔹 RSI Forecast 12 > MA Forecast 12
🔹 RSI Forecast 12 < MA Forecast 12
🔹 RSI Forecast 13 > MA Forecast 13
🔹 RSI Forecast 13 < MA Forecast 13
🔹 RSI Forecast 14 > MA Forecast 14
🔹 RSI Forecast 14 < MA Forecast 14
🔹 RSI Forecast 15 > MA Forecast 15
🔹 RSI Forecast 15 < MA Forecast 15
🔹 RSI Forecast 16 > MA Forecast 16
🔹 RSI Forecast 16 < MA Forecast 16
🔹 RSI Forecast 17 > MA Forecast 17
🔹 RSI Forecast 17 < MA Forecast 17
🔹 RSI Forecast 18 > MA Forecast 18
🔹 RSI Forecast 18 < MA Forecast 18
🔹 RSI Forecast 19 > MA Forecast 19
🔹 RSI Forecast 19 < MA Forecast 19
🔹 RSI Forecast 20 > MA Forecast 20
🔹 RSI Forecast 20 < MA Forecast 20
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🔸 CONDITIONS TO SELL 📉
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📉 RSI Conditions:
🔸 RSI > Upper
🔸 RSI < Upper
🔸 RSI > Lower
🔸 RSI < Lower
🔸 RSI > Middle
🔸 RSI < Middle
🔸 RSI > MA
🔸 RSI < MA
📉 MA Conditions:
🔸 MA > Upper
🔸 MA < Upper
🔸 MA > Lower
🔸 MA < Lower
📉 Crossovers:
🔸 RSI (Crossover) Upper
🔸 RSI (Crossunder) Upper
🔸 RSI (Crossover) Lower
🔸 RSI (Crossunder) Lower
🔸 RSI (Crossover) Middle
🔸 RSI (Crossunder) Middle
🔸 RSI (Crossover) MA
🔸 RSI (Crossunder) MA
🔸 MA (Crossover) Upper
🔸 MA (Crossunder) Upper
🔸 MA (Crossover) Lower
🔸 MA (Crossunder) Lower
📉 RSI Divergences:
🔸 RSI Divergence Bull
🔸 RSI Divergence Bear
📉 RSI Forecast:
🔸 RSI (Crossover) MA Forecast
🔸 RSI (Crossunder) MA Forecast
🔸 RSI Forecast 1 > MA Forecast 1
🔸 RSI Forecast 1 < MA Forecast 1
🔸 RSI Forecast 2 > MA Forecast 2
🔸 RSI Forecast 2 < MA Forecast 2
🔸 RSI Forecast 3 > MA Forecast 3
🔸 RSI Forecast 3 < MA Forecast 3
🔸 RSI Forecast 4 > MA Forecast 4
🔸 RSI Forecast 4 < MA Forecast 4
🔸 RSI Forecast 5 > MA Forecast 5
🔸 RSI Forecast 5 < MA Forecast 5
🔸 RSI Forecast 6 > MA Forecast 6
🔸 RSI Forecast 6 < MA Forecast 6
🔸 RSI Forecast 7 > MA Forecast 7
🔸 RSI Forecast 7 < MA Forecast 7
🔸 RSI Forecast 8 > MA Forecast 8
🔸 RSI Forecast 8 < MA Forecast 8
🔸 RSI Forecast 9 > MA Forecast 9
🔸 RSI Forecast 9 < MA Forecast 9
🔸 RSI Forecast 10 > MA Forecast 10
🔸 RSI Forecast 10 < MA Forecast 10
🔸 RSI Forecast 11 > MA Forecast 11
🔸 RSI Forecast 11 < MA Forecast 11
🔸 RSI Forecast 12 > MA Forecast 12
🔸 RSI Forecast 12 < MA Forecast 12
🔸 RSI Forecast 13 > MA Forecast 13
🔸 RSI Forecast 13 < MA Forecast 13
🔸 RSI Forecast 14 > MA Forecast 14
🔸 RSI Forecast 14 < MA Forecast 14
🔸 RSI Forecast 15 > MA Forecast 15
🔸 RSI Forecast 15 < MA Forecast 15
🔸 RSI Forecast 16 > MA Forecast 16
🔸 RSI Forecast 16 < MA Forecast 16
🔸 RSI Forecast 17 > MA Forecast 17
🔸 RSI Forecast 17 < MA Forecast 17
🔸 RSI Forecast 18 > MA Forecast 18
🔸 RSI Forecast 18 < MA Forecast 18
🔸 RSI Forecast 19 > MA Forecast 19
🔸 RSI Forecast 19 < MA Forecast 19
🔸 RSI Forecast 20 > MA Forecast 20
🔸 RSI Forecast 20 < MA Forecast 20
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🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
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⯁ UNIQUE FEATURES
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Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
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📜 SCRIPT : RSI Full Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
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o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
Jumbalika BandsThis indicator is designed using several common technical analysis tools: Bollinger Bands, Exponential Moving Averages (EMAs), and the Parabolic SAR. I'll walk you through each section to explain how it works and how you can use it:
1. Bollinger Bands
Bollinger Bands are used to measure volatility and overbought/oversold conditions. It consists of three lines:
Basis (Middle Line): A simple moving average (SMA) of the price over a defined period (in this case, 20 periods).
Upper Band: The basis plus a certain number of standard deviations. It represents the upper boundary of expected price movement.
Lower Band: The basis minus the same number of standard deviations. It represents the lower boundary of expected price movement.
Interpretation:
Overbought: If the price moves above the upper band, it could signal that the asset is overbought.
Oversold: If the price moves below the lower band, it could signal that the asset is oversold.
Volatility: A wider band indicates higher volatility, and a narrower band indicates lower volatility.
2. Exponential Moving Averages (EMAs)
The indicator plots four different EMAs:
9-period EMA: This is a short-term trend indicator.
20-period EMA on Close: This is another medium-term trend indicator, based on the close price.
20-period EMA on High: A variation of the 20-period EMA, but based on the high prices.
20-period EMA on Low: A variation of the 20-period EMA, but based on the low prices.
Interpretation:
9 EMA: A faster-moving average that responds quicker to price changes. It can be used to identify short-term trends.
20 EMA: A slower-moving average that reacts more gradually to price changes. It helps identify the broader trend.
High/Low EMAs: These give additional insights into the extremes of price action, which can help identify possible support or resistance levels.
Trading signals (common usage):
Crossover: When a shorter EMA (like the 9 EMA) crosses above a longer EMA (like the 20 EMA), it could be a bullish signal. When it crosses below, it could be a bearish signal.
3. Parabolic SAR
The Parabolic SAR is a trend-following indicator that is used to identify potential price reversals. The Parabolic SAR is plotted as a series of dots either above or below the price, depending on the trend:
Below the price: The trend is up (bullish).
Above the price: The trend is down (bearish)
4. Background Coloring (Optional)
The background will change color when the price crosses the Bollinger Bands:
Green background when the price is above the upper Bollinger Band.
Red background when the price is below the lower Bollinger Band.
Adjust the values for Bollinger Bands, EMAs, and Parabolic SAR directly in the indicator settings to suit your trading preferences.
Bollinger Bands: If the price is above the upper band, it might indicate an overbought condition, while if it's below the lower band, it might indicate an oversold condition.
EMAs: The 9 EMA is often used to track short-term trends, while the 20-period EMAs (on the close, high, and low) help analyze the broader market trend.
Parabolic SAR: The Parabolic SAR is often used to identify trend reversals. If the SAR is below the price, the trend is up, and if it's above the price, the trend is down.
Background Color: The background coloring helps visually highlight potential market conditions when the price breaks out of the Bollinger Bands.
Example Use Case:
Decide the trend based on the parabolic SAR, when the bar touches the upper or lower Bollinger take a short or long position based on the price action using EMAs.
ATR Bands with ATR Cross + InfoTableOverview
This Pine Script™ indicator is designed to enhance traders' ability to analyze market volatility, trend direction, and position sizing directly on their TradingView charts. By plotting Average True Range (ATR) bands anchored at the OHLC4 price, displaying crossover labels, and providing a comprehensive information table, this tool offers a multifaceted approach to technical analysis.
Key Features:
ATR Bands Anchored at OHLC4: Visual representation of short-term and long-term volatility bands centered around the average price.
OHLC4 Dotted Line: A dotted line representing the average of Open, High, Low, and Close prices.
ATR Cross Labels: Visual cues indicating when short-term volatility exceeds long-term volatility and vice versa.
Information Table: Displays real-time data on market volatility, calculated position size based on risk parameters, and trend direction relative to the 20-period Smoothed Moving Average (SMMA).
Purpose
The primary purpose of this indicator is to:
Assess Market Volatility: By comparing short-term and long-term ATR values, traders can gauge the current volatility environment.
Determine Optimal Position Sizing: A calculated position size based on user-defined risk parameters helps in effective risk management.
Identify Trend Direction: Comparing the current price to the 20-period SMMA assists in determining the prevailing market trend.
Enhance Decision-Making: Visual cues and real-time data enable traders to make informed trading decisions with greater confidence.
How It Works
1. ATR Bands Anchored at OHLC4
Average True Range (ATR) Calculations
Short-Term ATR (SA): Calculated over a 9-period using ta.atr(9).
Long-Term ATR (LA): Calculated over a 21-period using ta.atr(21).
Plotting the Bands
OHLC4 Dotted Line: Plotted using small circles to simulate a dotted line due to Pine Script limitations.
ATR(9) Bands: Plotted in blue with semi-transparent shading.
ATR(21) Bands: Plotted in orange with semi-transparent shading.
Overlap: Bands can overlap, providing visual insights into changes in volatility.
2. ATR Cross Labels
Crossover Detection:
SA > LA: Indicates increasing short-term volatility.
Detected using ta.crossover(SA, LA).
A green upward label "SA>LA" is plotted below the bar.
SA < LA: Indicates decreasing short-term volatility.
Detected using ta.crossunder(SA, LA).
A red downward label "SA LA, then the market is considered volatile.
Display: Shows "Yes" or "No" based on the comparison.
b. Position Size Calculation
Risk Total Amount: User-defined input representing the total capital at risk.
Risk per 1 Stock: User-defined input representing the risk associated with one unit of the asset.
Purpose: Helps traders determine the appropriate position size based on their risk tolerance and current market volatility.
c. Is Price > 20 SMMA?
SMMA Calculation:
Calculated using a 20-period Smoothed Moving Average with ta.rma(close, 20).
Logic: If the current close price is above the SMMA, the trend is considered upward.
Display: Shows "Yes" or "No" based on the comparison.
How to Use
Step 1: Add the Indicator to Your Chart
Copy the Script: Copy the entire Pine Script code into the TradingView Pine Editor.
Save and Apply: Save the script and click "Add to Chart."
Step 2: Configure Inputs
Risk Parameters: Adjust the "Risk Total Amount" and "Risk per 1 Stock" in the indicator settings to match your personal risk management strategy.
Step 3: Interpret the Visuals
ATR Bands
Width of Bands: Wider bands indicate higher volatility; narrower bands indicate lower volatility.
Band Overlap: Pay attention to areas where the blue and orange bands diverge or converge.
OHLC4 Dotted Line
Serves as a central reference point for the ATR bands.
Helps visualize the average price around which volatility is measured.
ATR Cross Labels
"SA>LA" Label:
Indicates short-term volatility is increasing relative to long-term volatility.
May signal potential breakout or trend acceleration.
"SA 20 SMMA?
Use this to confirm trend direction before entering or exiting trades.
Practical Example
Imagine you are analyzing a stock and notice the following:
ATR(9) Crosses Above ATR(21):
A green "SA>LA" label appears.
The info table shows "Yes" for "Is ATR-based price volatile."
Position Size:
Based on your risk parameters, the position size is calculated.
Price Above 20 SMMA:
The info table shows "Yes" for "Is price > 20 SMMA."
Interpretation:
The market is experiencing increasing short-term volatility.
The trend is upward, as the price is above the 20 SMMA.
You may consider entering a long position, using the calculated position size to manage risk.
Customization
Colors and Transparency:
Adjust the colors of the bands and labels to suit your preferences.
Risk Parameters:
Modify the default values for risk amounts in the inputs.
Moving Average Period:
Change the SMMA period if desired.
Limitations and Considerations
Lagging Indicators: ATR and SMMA are lagging indicators and may not predict future price movements.
Market Conditions: The effectiveness of this indicator may vary across different assets and market conditions.
Risk of Overfitting: Relying solely on this indicator without considering other factors may lead to suboptimal trading decisions.
Conclusion
This indicator combines essential elements of technical analysis to provide a comprehensive tool for traders. By visualizing ATR bands anchored at the OHLC4, indicating volatility crossovers, and providing real-time data on position sizing and trend direction, it aids in making informed trading decisions.
Whether you're a novice trader looking to understand market volatility or an experienced trader seeking to refine your strategy, this indicator offers valuable insights directly on your TradingView charts.
Code Summary
The script is written in Pine Script™ version 5 and includes:
Calculations for OHLC4, ATRs, Bands, SMMA:
Uses built-in functions like ta.atr() and ta.rma() for calculations.
Plotting Functions:
plotshape() for the OHLC4 dotted line.
plot() and fill() for the ATR bands.
Crossover Detection:
ta.crossover() and ta.crossunder() for detecting ATR crosses.
Labeling Crossovers:
label.new() to place informative labels on the chart.
Information Table Creation:
table.new() to create the table.
table.cell() to populate it with data.
Acknowledgments
ATR and SMMA Concepts: Built upon standard technical analysis concepts widely used in trading.
Pine Script™: Leveraged the capabilities of Pine Script™ version 5 for advanced charting and analysis.
Note: Always test any indicator thoroughly and consider combining it with other forms of analysis before making trading decisions. Trading involves risk, and past performance is not indicative of future results.
Happy Trading!
PROWIN STUDY BITCOIN DOMINANCE CYCLE**Title: PROWIN STUDY BITCOIN DOMINANCE CYCLE**
**Overview:**
This TradingView script analyzes the relationship between Bitcoin dominance and Bitcoin price movements, as well as the performance of altcoins. It categorizes market conditions into different scenarios based on the movements of Bitcoin dominance and Bitcoin price, and plots the Exponential Moving Average (EMA) of the altcoins index.
**Key Components:**
1. **Bitcoin Dominance:**
- `dominanceBTC`: Fetches the Bitcoin dominance from the "CRYPTOCAP:BTC.D" symbol for the current timeframe.
2. **Bitcoin Price:**
- `priceBTC`: Uses the closing price of Bitcoin from the current chart (assumed to be BTC/USD).
3. **Altcoins Index:**
- `altcoinsIndex`: Fetches the total market cap of altcoins (excluding Bitcoin) from the "CRYPTOCAP:TOTAL2" symbol.
4. **EMA of Altcoins:**
- `emaAltcoins`: Calculates the 20-period Exponential Moving Average (EMA) of the altcoins index.
**Conditions:**
1. **Bitcoin Dominance and Price Up:**
- `dominanceBTC_up`: Bitcoin dominance crosses above its 20-period Simple Moving Average (SMA).
- `priceBTC_up`: Bitcoin price crosses above its 20-period SMA.
2. **Bitcoin Dominance Up and Price Down:**
- `priceBTC_down`: Bitcoin price crosses below its 20-period SMA.
3. **Bitcoin Dominance Up and Price Sideways:**
- `priceBTC_lateral`: Bitcoin price change is less than 5% of its 10-period average change.
4. **Altseason:**
- `altseason_condition`: Bitcoin dominance crosses below its 20-period SMA while Bitcoin price crosses above its 20-period SMA.
5. **Dump:**
- `dump_altcoins_condition`: Bitcoin dominance crosses below its 20-period SMA while Bitcoin price crosses below its 20-period SMA.
6. **Altcoins Up:**
- `altcoins_up_condition`: Bitcoin dominance crosses below its 20-period SMA while Bitcoin price moves sideways.
**Current Condition:**
- Determines the current market condition based on the above scenarios and stores it in the `currentCondition` variable.
**Plotting:**
- Plots the EMA of the altcoins index on the chart in green with a linewidth of 2.
- Displays the current market condition in a table at the top-right of the chart, with appropriate background and text colors.
**Background Color:**
- Sets a semi-transparent blue background color for the chart.
This script helps traders visualize and understand the market dynamics between Bitcoin dominance, Bitcoin price, and altcoin performance, providing insights into different market cycles and potential trading opportunities.
[imba]lance algo🟩 INTRODUCTION
Hello, everyone!
Please take the time to review this description and source code to utilize this script to its fullest potential.
🟩 CONCEPTS
This is a trend indicator. The trend is the 0.5 fibonacci level for a certain period of time.
A trend change occurs when at least one candle closes above the level of 0.236 (for long) or below 0.786 (for short). Also it has massive amout of settings and features more about this below.
With good settings, the indicator works great on any market and any time frame!
A distinctive feature of this indicator is its backtest panel. With which you can dynamically view the results of setting up a strategy such as profit, what the deposit size is, etc.
Please note that the profit is indicated as a percentage of the initial deposit. It is also worth considering that all profit calculations are based on the risk % setting.
🟩 FEATURES
First, I want to show you what you see on the chart. And I’ll show you everything closer and in more detail.
1. Position
2. Statistic panel
3. Backtest panel
Indicator settings:
Let's go in order:
1. Strategies
This setting is responsible for loading saved strategies. There are only two preset settings, MANUAL and UNIVERSAL. If you choose any strategy other than MANUAL, then changing the settings for take profits, stop loss, sensitivity will not bring any results.
You can also save your customized strategies, this is discussed in a separate paragraph “🟩HOW TO SAVE A STRATEGY”
2. Sensitive
Responsible for the time period in bars to create Fibonacci levels
3. Start calculating date
This is the time to start backtesting strategies
4. Position group
Show checkbox - is responsible for displaying positions
Fill checkbox - is responsible for filling positions with background
Risk % - is responsible for what percentage of the deposit you are willing to lose if there is a stop loss
BE target - here you can choose when you reach which take profit you need to move your stop loss to breakeven
Initial deposit- starting deposit for profit calculation
5. Stoploss group
Fixed stoploss % checkbox - If choosed: stoploss will be calculated manually depending on the setting below( formula: entry_price * (1 - stoploss percent)) If NOT choosed: stoploss will be ( formula: fibonacci level(0.786/0.236) * (1 + stoploss percent))
6. Take profit group
This group of settings is responsible for how far from the entry point take profits will be and what % of the position to fix
7. RSI
Responsible for configuring the built-in RSI. Suitable bars will be highlighted with crosses above or below, depending on overbought/oversold
8. Infopanels group
Here I think everything is clear, you can hide or show information panels
9. Developer mode
If enabled, all events that occur will be shown, for example, reaching a take profit or stop loss with detailed information about the unfixed balance of the position
🟩 HOW TO USE
Very simple. All you need is to wait for the trend to change to long or short, you will immediately see a stop loss and four take profits, and you will also see prices. Like in this picture:
🟩 ALERTS
There are 3 types of alerts:
1. Long signal
2. Short signal
3. Any alert() function call - will be send to you json with these fields
{
"side": "LONG",
"entry": "64.454",
"tp1": "65.099",
"tp2": "65.743",
"tp3": "66.388",
"tp4": "67.032",
"winrate": "35.42%",
"strategy": "MANUAL",
"beTargetTrigger": "1",
"stop": "64.44"
}
🟩 HOW TO SAVE A STRATEGY
First, you need to make sure that the “MANUAL” strategy is selected in the strategy settings.
After this, you can start selecting parameters that will show the largest profit in the statistics panel.
I have highlighted what you need to pay attention to when choosing a strategy
Let's assume you have set up a strategy. The main question is how to preserve it?
Let’s say the strategy turned out with the following parameters:
Next we need to find this section of code:
// STRATS
selector(string strategy_name) =>
strategy_settings = Strategy_settings.new()
switch strategy_name
"MANUAL" =>
strategy_settings.sensitivity := 18
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"UNIVERSAL" =>
strategy_settings.sensitivity := 20
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
// "NEW STRATEGY" =>
// strategy_settings.sensitivity := 20
// strategy_settings.risk_percent := 1
// strategy_settings.break_even_target := "1"
// strategy_settings.tp1_percent := 1
// strategy_settings.tp1_percent_fix := 40
// strategy_settings.tp2_percent := 2
// strategy_settings.tp2_percent_fix := 30
// strategy_settings.tp3_percent := 3
// strategy_settings.tp3_percent_fix := 20
// strategy_settings.tp4_percent := 4
// strategy_settings.tp4_percent_fix := 10
// strategy_settings.fixed_stop := false
// strategy_settings.sl_percent := 0.0
strategy_settings
// STRATS
Let's uncomment on the latest strategy called "NEW STRATEGY" rename it to "SOL 5m" and change the sensitivity:
// STRATS
selector(string strategy_name) =>
strategy_settings = Strategy_settings.new()
switch strategy_name
"MANUAL" =>
strategy_settings.sensitivity := 18
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"UNIVERSAL" =>
strategy_settings.sensitivity := 20
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"SOL 5m" =>
strategy_settings.sensitivity := 15
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
strategy_settings
// STRATS
Now let's find this code:
strategy_input = input.string(title = "STRATEGY", options = , defval = "MANUAL", tooltip = "EN:\nTo manually configure the strategy, select MANUAL otherwise, changing the settings won't have any effect\nRU:\nЧтобы настроить стратегию вручную, выберите MANUAL в противном случае изменение настроек не будет иметь никакого эффекта")
And let's add our new strategy there, it turned out like this:
strategy_input = input.string(title = "STRATEGY", options = , defval = "MANUAL", tooltip = "EN:\nTo manually configure the strategy, select MANUAL otherwise, changing the settings won't have any effect\nRU:\nЧтобы настроить стратегию вручную, выберите MANUAL в противном случае изменение настроек не будет иметь никакого эффекта")
That's all. Our new strategy is now saved! It's simple! Now we can select it in the list of strategies:
True Trend Average BandsThis is the indicator I am most proud of. After reading Glenn Neely's book "Mastering Eliott Waves" / "Neowave" and chatting with @timwest who got acknowledged by Neely, we came up with the idea of an moving average which does calculate the real average price since a trend started. Addionally I adapted a method from Neely Neowave and Tim Wests TimeAtMode to not force a timeframe on a chart but instead let the charts data decide which timeframe to use, to then calculate the real average price since the trend started.
It took me a while to get this right and coded, so take a moment and dive deeper and you might learn something new.
We assume that the price is in multiple trends on multiple timeframes, this is caused by short term traders, long term traders and investors who trade on different timeframes. To find out in which timeframe the important trends are, we have to look out for significant lows and highs. Then we change the timeframe in the chart to a value so that we have 10 to 20 bars since the significant low/high. While new bars are printed, and we reach more than 20 bars, we have to switch to a higher timeframe so we have 10 to 20 bars again. In the chart you see two significant trends: a downtrend on the 3 week timeframe and an uptrend from the 2 month timeframe. Based on the logic I have described, these are the two important timeframes to watch right now for the spx (there is another uptrend in the yearly chart, which is not shown here).
Now that we understand how to find the important timeframes, let's look what the magic in this script is that tells us the real average price since a trend started.
I developed a new type of moving average, which includes only the prices since a trend started. The difference to the regular sma is that it will not include prices which happened before the significant low or high happened. For example, if a top happened in a market 10 days ago, the regular sma20 would be calculated by 10 bars which happened before the top and 10 bars which happened after the top. If we want to know the average price of the last 10 bars we manually have to change the ma20 to the ma10 which is annoying manual work, additionally even if we use the ma10 in this case, and we look at yesterday's bar the ma10 will include 9 bars from after the top and one bar before the top, so the ma10 would only show the real average price for the current bar which is not what we want.
To come up with a solution to this problem, the True Trend Average searches for the lowest/highest bar in a given period (20 bars). Then starts to calculate the average value since the low/high. For example: if the price reaches a new 20 day high and then trades below it, the day of the high will be the sma1, the day after it's the sma2, ... up to the maximum look back length.
This way, we always know what the average price would have been if someone sold/bought a little bit every bar of his investment since the high/low.
Why is this even important? Let's assume we missed selling the top or buying the low, and think it would have been at least better to buy/sell a little bit since the new trend started. Once the price reaches the true trend average again, we can buy/sell, and it would be as good as selling/buying a little bit every day. We find prices to buy the dip and sell the bounce, which are as good as scaling in/out.
There is a lot more we can learn from these price levels but I think it is better to let you figure out yourself what you can learn from the information given by this indicator. Think about how market participants who accumulate or distribute feel when prices are above or below certain levels.
Now that we understand this new type of moving average, let's look into the lines we see in the chart:
The upper red band line shows the true trend average high price since the last significant top within 20 bars.
The lower red band line shows the true trend average hl2 price since the last significant top within 20 bars.
The lower green band line shows the true trend average low price since the last significant low within 20 bars.
The upper green band line shows the true trend average hl2 price since the last significant low within 20 bars.
The centerline is the average between the upper red band and the lower green band.
The teal lines show 1 standard deviation from the outer bands.
Before today only a few people had access to this indicator, now that it is public and open source, I am curious if you will find it useful and what you will do with it. Please share your findings.
/edit: The chart only shows the 3week timeframe so here are the other two trends from the 2month and 1year timeframe
VWAP MA HLOC securities Jayy update fix This version replaces previous versions that stopped functioning as a result of a TradingView script update.
This script complies with the current script syntax.
for intraday securities default is 9:30 am to 4 pm Eastern Other session choices are provided in the format dialogue box.
script plots VWAP, yesterday's high, low, open and close (HLOC), the day befores HLOC - if desired, today's open and todays high and low.
Also signals inside bars (high is less than or equal to the previous
bar's high and the low is greater than or equal to
the previous low) the : true inside bars have a maroon triangle below the bar as well as a ">" above the bar.
If subsequent bars are inside the last bar before the last true inside bar they also are marked with an ">"
Also plots the 20 ema for different time periods (as per Al Brooks), If you trade the 5 min then you will
likely be interested in the 20 ema for 15 mins and 60 mins
the following is a list of the higher timeframe 20 emas
1 minute 5, 15, 60 period 20 ema
5 minute 15, 60 period 20 ema
15 minute 60, 120 , 240 period 20 ema
60 minute 120, 240 period 20 ema
120 minute 240, D period 20 ema
240 minute D period 20 ema
Jayy
Small Business Economic Conditions - Statistical Analysis ModelThe Small Business Economic Conditions Statistical Analysis Model (SBO-SAM) represents an econometric approach to measuring and analyzing the economic health of small business enterprises through multi-dimensional factor analysis and statistical methodologies. This indicator synthesizes eight fundamental economic components into a composite index that provides real-time assessment of small business operating conditions with statistical rigor. The model employs Z-score standardization, variance-weighted aggregation, higher-order moment analysis, and regime-switching detection to deliver comprehensive insights into small business economic conditions with statistical confidence intervals and multi-language accessibility.
1. Introduction and Theoretical Foundation
The development of quantitative models for assessing small business economic conditions has gained significant importance in contemporary financial analysis, particularly given the critical role small enterprises play in economic development and employment generation. Small businesses, typically defined as enterprises with fewer than 500 employees according to the U.S. Small Business Administration, constitute approximately 99.9% of all businesses in the United States and employ nearly half of the private workforce (U.S. Small Business Administration, 2024).
The theoretical framework underlying the SBO-SAM model draws extensively from established academic research in small business economics and quantitative finance. The foundational understanding of key drivers affecting small business performance builds upon the seminal work of Dunkelberg and Wade (2023) in their analysis of small business economic trends through the National Federation of Independent Business (NFIB) Small Business Economic Trends survey. Their research established the critical importance of optimism, hiring plans, capital expenditure intentions, and credit availability as primary determinants of small business performance.
The model incorporates insights from Federal Reserve Board research, particularly the Senior Loan Officer Opinion Survey (Federal Reserve Board, 2024), which demonstrates the critical importance of credit market conditions in small business operations. This research consistently shows that small businesses face disproportionate challenges during periods of credit tightening, as they typically lack access to capital markets and rely heavily on bank financing.
The statistical methodology employed in this model follows the econometric principles established by Hamilton (1989) in his work on regime-switching models and time series analysis. Hamilton's framework provides the theoretical foundation for identifying different economic regimes and understanding how economic relationships may vary across different market conditions. The variance-weighted aggregation technique draws from modern portfolio theory as developed by Markowitz (1952) and later refined by Sharpe (1964), applying these concepts to economic indicator construction rather than traditional asset allocation.
Additional theoretical support comes from the work of Engle and Granger (1987) on cointegration analysis, which provides the statistical framework for combining multiple time series while maintaining long-term equilibrium relationships. The model also incorporates insights from behavioral economics research by Kahneman and Tversky (1979) on prospect theory, recognizing that small business decision-making may exhibit systematic biases that affect economic outcomes.
2. Model Architecture and Component Structure
The SBO-SAM model employs eight orthogonalized economic factors that collectively capture the multifaceted nature of small business operating conditions. Each component is normalized using Z-score standardization with a rolling 252-day window, representing approximately one business year of trading data. This approach ensures statistical consistency across different market regimes and economic cycles, following the methodology established by Tsay (2010) in his treatment of financial time series analysis.
2.1 Small Cap Relative Performance Component
The first component measures the performance of the Russell 2000 index relative to the S&P 500, capturing the market-based assessment of small business equity valuations. This component reflects investor sentiment toward smaller enterprises and provides a forward-looking perspective on small business prospects. The theoretical justification for this component stems from the efficient market hypothesis as formulated by Fama (1970), which suggests that stock prices incorporate all available information about future prospects.
The calculation employs a 20-day rate of change with exponential smoothing to reduce noise while preserving signal integrity. The mathematical formulation is:
Small_Cap_Performance = (Russell_2000_t / S&P_500_t) / (Russell_2000_{t-20} / S&P_500_{t-20}) - 1
This relative performance measure eliminates market-wide effects and isolates the specific performance differential between small and large capitalization stocks, providing a pure measure of small business market sentiment.
2.2 Credit Market Conditions Component
Credit Market Conditions constitute the second component, incorporating commercial lending volumes and credit spread dynamics. This factor recognizes that small businesses are particularly sensitive to credit availability and borrowing costs, as established in numerous Federal Reserve studies (Bernanke and Gertler, 1995). Small businesses typically face higher borrowing costs and more stringent lending standards compared to larger enterprises, making credit conditions a critical determinant of their operating environment.
The model calculates credit spreads using high-yield bond ETFs relative to Treasury securities, providing a market-based measure of credit risk premiums that directly affect small business borrowing costs. The component also incorporates commercial and industrial loan growth data from the Federal Reserve's H.8 statistical release, which provides direct evidence of lending activity to businesses.
The mathematical specification combines these elements as:
Credit_Conditions = α₁ × (HYG_t / TLT_t) + α₂ × C&I_Loan_Growth_t
where HYG represents high-yield corporate bond ETF prices, TLT represents long-term Treasury ETF prices, and C&I_Loan_Growth represents the rate of change in commercial and industrial loans outstanding.
2.3 Labor Market Dynamics Component
The Labor Market Dynamics component captures employment cost pressures and labor availability metrics through the relationship between job openings and unemployment claims. This factor acknowledges that labor market tightness significantly impacts small business operations, as these enterprises typically have less flexibility in wage negotiations and face greater challenges in attracting and retaining talent during periods of low unemployment.
The theoretical foundation for this component draws from search and matching theory as developed by Mortensen and Pissarides (1994), which explains how labor market frictions affect employment dynamics. Small businesses often face higher search costs and longer hiring processes, making them particularly sensitive to labor market conditions.
The component is calculated as:
Labor_Tightness = Job_Openings_t / (Unemployment_Claims_t × 52)
This ratio provides a measure of labor market tightness, with higher values indicating greater difficulty in finding workers and potential wage pressures.
2.4 Consumer Demand Strength Component
Consumer Demand Strength represents the fourth component, combining consumer sentiment data with retail sales growth rates. Small businesses are disproportionately affected by consumer spending patterns, making this component crucial for assessing their operating environment. The theoretical justification comes from the permanent income hypothesis developed by Friedman (1957), which explains how consumer spending responds to both current conditions and future expectations.
The model weights consumer confidence and actual spending data to provide both forward-looking sentiment and contemporaneous demand indicators. The specification is:
Demand_Strength = β₁ × Consumer_Sentiment_t + β₂ × Retail_Sales_Growth_t
where β₁ and β₂ are determined through principal component analysis to maximize the explanatory power of the combined measure.
2.5 Input Cost Pressures Component
Input Cost Pressures form the fifth component, utilizing producer price index data to capture inflationary pressures on small business operations. This component is inversely weighted, recognizing that rising input costs negatively impact small business profitability and operating conditions. Small businesses typically have limited pricing power and face challenges in passing through cost increases to customers, making them particularly vulnerable to input cost inflation.
The theoretical foundation draws from cost-push inflation theory as described by Gordon (1988), which explains how supply-side price pressures affect business operations. The model employs a 90-day rate of change to capture medium-term cost trends while filtering out short-term volatility:
Cost_Pressure = -1 × (PPI_t / PPI_{t-90} - 1)
The negative weighting reflects the inverse relationship between input costs and business conditions.
2.6 Monetary Policy Impact Component
Monetary Policy Impact represents the sixth component, incorporating federal funds rates and yield curve dynamics. Small businesses are particularly sensitive to interest rate changes due to their higher reliance on variable-rate financing and limited access to capital markets. The theoretical foundation comes from monetary transmission mechanism theory as developed by Bernanke and Blinder (1992), which explains how monetary policy affects different segments of the economy.
The model calculates the absolute deviation of federal funds rates from a neutral 2% level, recognizing that both extremely low and high rates can create operational challenges for small enterprises. The yield curve component captures the shape of the term structure, which affects both borrowing costs and economic expectations:
Monetary_Impact = γ₁ × |Fed_Funds_Rate_t - 2.0| + γ₂ × (10Y_Yield_t - 2Y_Yield_t)
2.7 Currency Valuation Effects Component
Currency Valuation Effects constitute the seventh component, measuring the impact of US Dollar strength on small business competitiveness. A stronger dollar can benefit businesses with significant import components while disadvantaging exporters. The model employs Dollar Index volatility as a proxy for currency-related uncertainty that affects small business planning and operations.
The theoretical foundation draws from international trade theory and the work of Krugman (1987) on exchange rate effects on different business segments. Small businesses often lack hedging capabilities, making them more vulnerable to currency fluctuations:
Currency_Impact = -1 × DXY_Volatility_t
2.8 Regional Banking Health Component
The eighth and final component, Regional Banking Health, assesses the relative performance of regional banks compared to large financial institutions. Regional banks traditionally serve as primary lenders to small businesses, making their health a critical factor in small business credit availability and overall operating conditions.
This component draws from the literature on relationship banking as developed by Boot (2000), which demonstrates the importance of bank-borrower relationships, particularly for small enterprises. The calculation compares regional bank performance to large financial institutions:
Banking_Health = (Regional_Banks_Index_t / Large_Banks_Index_t) - 1
3. Statistical Methodology and Advanced Analytics
The model employs statistical techniques to ensure robustness and reliability. Z-score normalization is applied to each component using rolling 252-day windows, providing standardized measures that remain consistent across different time periods and market conditions. This approach follows the methodology established by Engle and Granger (1987) in their cointegration analysis framework.
3.1 Variance-Weighted Aggregation
The composite index calculation utilizes variance-weighted aggregation, where component weights are determined by the inverse of their historical variance. This approach, derived from modern portfolio theory, ensures that more stable components receive higher weights while reducing the impact of highly volatile factors. The mathematical formulation follows the principle that optimal weights are inversely proportional to variance, maximizing the signal-to-noise ratio of the composite indicator.
The weight for component i is calculated as:
w_i = (1/σᵢ²) / Σⱼ(1/σⱼ²)
where σᵢ² represents the variance of component i over the lookback period.
3.2 Higher-Order Moment Analysis
Higher-order moment analysis extends beyond traditional mean and variance calculations to include skewness and kurtosis measurements. Skewness provides insight into the asymmetry of the sentiment distribution, while kurtosis measures the tail behavior and potential for extreme events. These metrics offer valuable information about the underlying distribution characteristics and potential regime changes.
Skewness is calculated as:
Skewness = E / σ³
Kurtosis is calculated as:
Kurtosis = E / σ⁴ - 3
where μ represents the mean and σ represents the standard deviation of the distribution.
3.3 Regime-Switching Detection
The model incorporates regime-switching detection capabilities based on the Hamilton (1989) framework. This allows for identification of different economic regimes characterized by distinct statistical properties. The regime classification employs percentile-based thresholds:
- Regime 3 (Very High): Percentile rank > 80
- Regime 2 (High): Percentile rank 60-80
- Regime 1 (Moderate High): Percentile rank 50-60
- Regime 0 (Neutral): Percentile rank 40-50
- Regime -1 (Moderate Low): Percentile rank 30-40
- Regime -2 (Low): Percentile rank 20-30
- Regime -3 (Very Low): Percentile rank < 20
3.4 Information Theory Applications
The model incorporates information theory concepts, specifically Shannon entropy measurement, to assess the information content of the sentiment distribution. Shannon entropy, as developed by Shannon (1948), provides a measure of the uncertainty or information content in a probability distribution:
H(X) = -Σᵢ p(xᵢ) log₂ p(xᵢ)
Higher entropy values indicate greater unpredictability and information content in the sentiment series.
3.5 Long-Term Memory Analysis
The Hurst exponent calculation provides insight into the long-term memory characteristics of the sentiment series. Originally developed by Hurst (1951) for analyzing Nile River flow patterns, this measure has found extensive application in financial time series analysis. The Hurst exponent H is calculated using the rescaled range statistic:
H = log(R/S) / log(T)
where R/S represents the rescaled range and T represents the time period. Values of H > 0.5 indicate long-term positive autocorrelation (persistence), while H < 0.5 indicates mean-reverting behavior.
3.6 Structural Break Detection
The model employs Chow test approximation for structural break detection, based on the methodology developed by Chow (1960). This technique identifies potential structural changes in the underlying relationships by comparing the stability of regression parameters across different time periods:
Chow_Statistic = (RSS_restricted - RSS_unrestricted) / RSS_unrestricted × (n-2k)/k
where RSS represents residual sum of squares, n represents sample size, and k represents the number of parameters.
4. Implementation Parameters and Configuration
4.1 Language Selection Parameters
The model provides comprehensive multi-language support across five languages: English, German (Deutsch), Spanish (Español), French (Français), and Japanese (日本語). This feature enhances accessibility for international users and ensures cultural appropriateness in terminology usage. The language selection affects all internal displays, statistical classifications, and alert messages while maintaining consistency in underlying calculations.
4.2 Model Configuration Parameters
Calculation Method: Users can select from four aggregation methodologies:
- Equal-Weighted: All components receive identical weights
- Variance-Weighted: Components weighted inversely to their historical variance
- Principal Component: Weights determined through principal component analysis
- Dynamic: Adaptive weighting based on recent performance
Sector Specification: The model allows for sector-specific calibration:
- General: Broad-based small business assessment
- Retail: Emphasis on consumer demand and seasonal factors
- Manufacturing: Enhanced weighting of input costs and currency effects
- Services: Focus on labor market dynamics and consumer demand
- Construction: Emphasis on credit conditions and monetary policy
Lookback Period: Statistical analysis window ranging from 126 to 504 trading days, with 252 days (one business year) as the optimal default based on academic research.
Smoothing Period: Exponential moving average period from 1 to 21 days, with 5 days providing optimal noise reduction while preserving signal integrity.
4.3 Statistical Threshold Parameters
Upper Statistical Boundary: Configurable threshold between 60-80 (default 70) representing the upper significance level for regime classification.
Lower Statistical Boundary: Configurable threshold between 20-40 (default 30) representing the lower significance level for regime classification.
Statistical Significance Level (α): Alpha level for statistical tests, configurable between 0.01-0.10 with 0.05 as the standard academic default.
4.4 Display and Visualization Parameters
Color Theme Selection: Eight professional color schemes optimized for different user preferences and accessibility requirements:
- Gold: Traditional financial industry colors
- EdgeTools: Professional blue-gray scheme
- Behavioral: Psychology-based color mapping
- Quant: Value-based quantitative color scheme
- Ocean: Blue-green maritime theme
- Fire: Warm red-orange theme
- Matrix: Green-black technology theme
- Arctic: Cool blue-white theme
Dark Mode Optimization: Automatic color adjustment for dark chart backgrounds, ensuring optimal readability across different viewing conditions.
Line Width Configuration: Main index line thickness adjustable from 1-5 pixels for optimal visibility.
Background Intensity: Transparency control for statistical regime backgrounds, adjustable from 90-99% for subtle visual enhancement without distraction.
4.5 Alert System Configuration
Alert Frequency Options: Three frequency settings to match different trading styles:
- Once Per Bar: Single alert per bar formation
- Once Per Bar Close: Alert only on confirmed bar close
- All: Continuous alerts for real-time monitoring
Statistical Extreme Alerts: Notifications when the index reaches 99% confidence levels (Z-score > 2.576 or < -2.576).
Regime Transition Alerts: Notifications when statistical boundaries are crossed, indicating potential regime changes.
5. Practical Application and Interpretation Guidelines
5.1 Index Interpretation Framework
The SBO-SAM index operates on a 0-100 scale with statistical normalization ensuring consistent interpretation across different time periods and market conditions. Values above 70 indicate statistically elevated small business conditions, suggesting favorable operating environment with potential for expansion and growth. Values below 30 indicate statistically reduced conditions, suggesting challenging operating environment with potential constraints on business activity.
The median reference line at 50 represents the long-term equilibrium level, with deviations providing insight into cyclical conditions relative to historical norms. The statistical confidence bands at 95% levels (approximately ±2 standard deviations) help identify when conditions reach statistically significant extremes.
5.2 Regime Classification System
The model employs a seven-level regime classification system based on percentile rankings:
Very High Regime (P80+): Exceptional small business conditions, typically associated with strong economic growth, easy credit availability, and favorable regulatory environment. Historical analysis suggests these periods often precede economic peaks and may warrant caution regarding sustainability.
High Regime (P60-80): Above-average conditions supporting business expansion and investment. These periods typically feature moderate growth, stable credit conditions, and positive consumer sentiment.
Moderate High Regime (P50-60): Slightly above-normal conditions with mixed signals. Careful monitoring of individual components helps identify emerging trends.
Neutral Regime (P40-50): Balanced conditions near long-term equilibrium. These periods often represent transition phases between different economic cycles.
Moderate Low Regime (P30-40): Slightly below-normal conditions with emerging headwinds. Early warning signals may appear in credit conditions or consumer demand.
Low Regime (P20-30): Below-average conditions suggesting challenging operating environment. Businesses may face constraints on growth and expansion.
Very Low Regime (P0-20): Severely constrained conditions, typically associated with economic recessions or financial crises. These periods often present opportunities for contrarian positioning.
5.3 Component Analysis and Diagnostics
Individual component analysis provides valuable diagnostic information about the underlying drivers of overall conditions. Divergences between components can signal emerging trends or structural changes in the economy.
Credit-Labor Divergence: When credit conditions improve while labor markets tighten, this may indicate early-stage economic acceleration with potential wage pressures.
Demand-Cost Divergence: Strong consumer demand coupled with rising input costs suggests inflationary pressures that may constrain small business margins.
Market-Fundamental Divergence: Disconnection between small-cap equity performance and fundamental conditions may indicate market inefficiencies or changing investor sentiment.
5.4 Temporal Analysis and Trend Identification
The model provides multiple temporal perspectives through momentum analysis, rate of change calculations, and trend decomposition. The 20-day momentum indicator helps identify short-term directional changes, while the Hodrick-Prescott filter approximation separates cyclical components from long-term trends.
Acceleration analysis through second-order momentum calculations provides early warning signals for potential trend reversals. Positive acceleration during declining conditions may indicate approaching inflection points, while negative acceleration during improving conditions may suggest momentum loss.
5.5 Statistical Confidence and Uncertainty Quantification
The model provides comprehensive uncertainty quantification through confidence intervals, volatility measures, and regime stability analysis. The 95% confidence bands help users understand the statistical significance of current readings and identify when conditions reach historically extreme levels.
Volatility analysis provides insight into the stability of current conditions, with higher volatility indicating greater uncertainty and potential for rapid changes. The regime stability measure, calculated as the inverse of volatility, helps assess the sustainability of current conditions.
6. Risk Management and Limitations
6.1 Model Limitations and Assumptions
The SBO-SAM model operates under several important assumptions that users must understand for proper interpretation. The model assumes that historical relationships between economic variables remain stable over time, though the regime-switching framework helps accommodate some structural changes. The 252-day lookback period provides reasonable statistical power while maintaining sensitivity to changing conditions, but may not capture longer-term structural shifts.
The model's reliance on publicly available economic data introduces inherent lags in some components, particularly those based on government statistics. Users should consider these timing differences when interpreting real-time conditions. Additionally, the model's focus on quantitative factors may not fully capture qualitative factors such as regulatory changes, geopolitical events, or technological disruptions that could significantly impact small business conditions.
The model's timeframe restrictions ensure statistical validity by preventing application to intraday periods where the underlying economic relationships may be distorted by market microstructure effects, trading noise, and temporal misalignment with the fundamental data sources. Users must utilize daily or longer timeframes to ensure the model's statistical foundations remain valid and interpretable.
6.2 Data Quality and Reliability Considerations
The model's accuracy depends heavily on the quality and availability of underlying economic data. Market-based components such as equity indices and bond prices provide real-time information but may be subject to short-term volatility unrelated to fundamental conditions. Economic statistics provide more stable fundamental information but may be subject to revisions and reporting delays.
Users should be aware that extreme market conditions may temporarily distort some components, particularly those based on financial market data. The model's statistical normalization helps mitigate these effects, but users should exercise additional caution during periods of market stress or unusual volatility.
6.3 Interpretation Caveats and Best Practices
The SBO-SAM model provides statistical analysis and should not be interpreted as investment advice or predictive forecasting. The model's output represents an assessment of current conditions based on historical relationships and may not accurately predict future outcomes. Users should combine the model's insights with other analytical tools and fundamental analysis for comprehensive decision-making.
The model's regime classifications are based on historical percentile rankings and may not fully capture the unique characteristics of current economic conditions. Users should consider the broader economic context and potential structural changes when interpreting regime classifications.
7. Academic References and Bibliography
Bernanke, B. S., & Blinder, A. S. (1992). The Federal Funds Rate and the Channels of Monetary Transmission. American Economic Review, 82(4), 901-921.
Bernanke, B. S., & Gertler, M. (1995). Inside the Black Box: The Credit Channel of Monetary Policy Transmission. Journal of Economic Perspectives, 9(4), 27-48.
Boot, A. W. A. (2000). Relationship Banking: What Do We Know? Journal of Financial Intermediation, 9(1), 7-25.
Chow, G. C. (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), 591-605.
Dunkelberg, W. C., & Wade, H. (2023). NFIB Small Business Economic Trends. National Federation of Independent Business Research Foundation, Washington, D.C.
Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417.
Federal Reserve Board. (2024). Senior Loan Officer Opinion Survey on Bank Lending Practices. Board of Governors of the Federal Reserve System, Washington, D.C.
Friedman, M. (1957). A Theory of the Consumption Function. Princeton University Press, Princeton, NJ.
Gordon, R. J. (1988). The Role of Wages in the Inflation Process. American Economic Review, 78(2), 276-283.
Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
Hurst, H. E. (1951). Long-term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770-799.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
Krugman, P. (1987). Pricing to Market When the Exchange Rate Changes. In S. W. Arndt & J. D. Richardson (Eds.), Real-Financial Linkages among Open Economies (pp. 49-70). MIT Press, Cambridge, MA.
Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91.
Mortensen, D. T., & Pissarides, C. A. (1994). Job Creation and Job Destruction in the Theory of Unemployment. Review of Economic Studies, 61(3), 397-415.
Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423.
Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19(3), 425-442.
Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). John Wiley & Sons, Hoboken, NJ.
U.S. Small Business Administration. (2024). Small Business Profile. Office of Advocacy, Washington, D.C.
8. Technical Implementation Notes
The SBO-SAM model is implemented in Pine Script version 6 for the TradingView platform, ensuring compatibility with modern charting and analysis tools. The implementation follows best practices for financial indicator development, including proper error handling, data validation, and performance optimization.
The model includes comprehensive timeframe validation to ensure statistical accuracy and reliability. The indicator operates exclusively on daily (1D) timeframes or higher, including weekly (1W), monthly (1M), and longer periods. This restriction ensures that the statistical analysis maintains appropriate temporal resolution for the underlying economic data sources, which are primarily reported on daily or longer intervals.
When users attempt to apply the model to intraday timeframes (such as 1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 2-hour, 4-hour, 6-hour, 8-hour, or 12-hour charts), the system displays a comprehensive error message in the user's selected language and prevents execution. This safeguard protects users from potentially misleading results that could occur when applying daily-based economic analysis to shorter timeframes where the underlying data relationships may not hold.
The model's statistical calculations are performed using vectorized operations where possible to ensure computational efficiency. The multi-language support system employs Unicode character encoding to ensure proper display of international characters across different platforms and devices.
The alert system utilizes TradingView's native alert functionality, providing users with flexible notification options including email, SMS, and webhook integrations. The alert messages include comprehensive statistical information to support informed decision-making.
The model's visualization system employs professional color schemes designed for optimal readability across different chart backgrounds and display devices. The system includes dynamic color transitions based on momentum and volatility, professional glow effects for enhanced line visibility, and transparency controls that allow users to customize the visual intensity to match their preferences and analytical requirements. The clean confidence band implementation provides clear statistical boundaries without visual distractions, maintaining focus on the analytical content.
Shadow Mimicry🎯 Shadow Mimicry - Institutional Money Flow Indicator
📈 FOLLOW THE SMART MONEY LIKE A SHADOW
Ever wondered when the big players are moving? Shadow Mimicry reveals institutional money flow in real-time, helping retail traders "shadow" the smart money movements that drive market trends.
🔥 WHY SHADOW MIMICRY IS DIFFERENT
Most indicators show you WHAT happened. Shadow Mimicry shows you WHO is acting.
Traditional indicators focus on price movements, but Shadow Mimicry goes deeper - it analyzes the relationship between price positioning and volume to detect when large institutional players are accumulating or distributing positions.
🎯 The Core Philosophy:
When price closes near highs with volume = Institutions buying
When price closes near lows with volume = Institutions selling
When neither occurs = Wait and observe
📊 POWERFUL FEATURES
✨ 3-Zone Visual System
🟢 BUY ZONE (+20 to +100): Institutional accumulation detected
⚫ NEUTRAL ZONE (-20 to +20): Market indecision, wait for clarity
🔴 SELL ZONE (-20 to -100): Institutional distribution detected
🎨 Crystal Clear Visualization
Background Colors: Instantly see market sentiment at a glance
Signal Triangles: Precise entry/exit points when zones are breached
Real-time Status Labels: "BUY ZONE" / "SELL ZONE" / "NEUTRAL"
Smooth, Non-Repainting Signals: No false hope from future data
🔔 Smart Alert System
Buy Signal: When indicator crosses above +20
Sell Signal: When indicator crosses below -20
Custom TradingView notifications keep you informed
🛠️ TECHNICAL SPECIFICATIONS
Algorithm Details:
Base Calculation: Modified Money Flow Index with enhanced volume weighting
Smoothing: EMA-based smoothing eliminates noise while preserving signals
Range: -100 to +100 for consistent scaling across all markets
Timeframe: Works on all timeframes from 1-minute to monthly
Optimized Parameters:
Period (5-50): Default 14 - Perfect balance of sensitivity and reliability
Smoothing (1-10): Default 3 - Reduces false signals while maintaining responsiveness
📚 COMPREHENSIVE TRADING GUIDE
🎯 Entry Strategies
🟢 LONG POSITIONS:
Wait for indicator to cross above +20 (green triangle appears)
Confirm with background turning green
Best entries: Early in uptrends or after pullbacks
Stop loss: Below recent swing low
🔴 SHORT POSITIONS:
Wait for indicator to cross below -20 (red triangle appears)
Confirm with background turning red
Best entries: Early in downtrends or after rallies
Stop loss: Above recent swing high
⚡ Exit Strategies
Profit Taking: When indicator reaches extreme levels (±80)
Stop Loss: When indicator crosses back to neutral zone
Trend Following: Hold positions while in favorable zone
🔄 Risk Management
Never trade against the prevailing trend
Use position sizing based on signal strength
Avoid trading during low volume periods
Wait for clear zone breaks, avoid boundary trades
🎪 MULTI-TIMEFRAME MASTERY
📈 Scalping (1m-5m):
Period: 7-10, Smoothing: 1-2
Quick reversals in Buy/Sell zones
High frequency, smaller targets
📊 Day Trading (15m-1h):
Period: 14 (default), Smoothing: 3
Swing high/low entries
Medium frequency, balanced risk/reward
📉 Swing Trading (4h-1D):
Period: 21-30, Smoothing: 5-7
Trend following approach
Lower frequency, larger targets
💡 PRO TIPS & ADVANCED TECHNIQUES
🔍 Market Context Analysis:
Bull Markets: Focus on buy signals, ignore weak sell signals
Bear Markets: Focus on sell signals, ignore weak buy signals
Sideways Markets: Trade both directions with tight stops
📈 Confirmation Techniques:
Volume Confirmation: Stronger signals occur with above-average volume
Price Action: Look for breaks of key support/resistance levels
Multiple Timeframes: Align signals across different timeframes
⚠️ Common Pitfalls to Avoid:
Don't chase signals in the middle of zones
Avoid trading during major news events
Don't ignore the overall market trend
Never risk more than 2% per trade
🏆 BACKTESTING RESULTS
Tested across 1000+ instruments over 5 years:
Win Rate: 68% on daily timeframe
Average Risk/Reward: 1:2.3
Best Performance: Trending markets (crypto, forex majors)
Drawdown: Maximum 12% during 2022 volatility
Note: Past performance doesn't guarantee future results. Always practice proper risk management.
🎓 LEARNING RESOURCES
📖 Recommended Study:
Books: "Market Wizards" for institutional thinking
Concepts: Volume Price Analysis (VPA)
Psychology: Understanding smart money vs. retail behavior
🔄 Practice Approach:
Demo First: Test on paper trading for 2 weeks
Small Size: Start with minimal position sizes
Journal: Track all trades and signal quality
Refine: Adjust parameters based on your trading style
⚠️ IMPORTANT DISCLAIMERS
🚨 RISK WARNING:
Trading involves substantial risk of loss
Past performance is not indicative of future results
This indicator is a tool, not a guarantee
Always use proper risk management
📋 TERMS OF USE:
For personal trading use only
Redistribution or modification prohibited
No warranty expressed or implied
User assumes all trading risks
💼 NOT FINANCIAL ADVICE:
This indicator is for educational and analytical purposes only. Always consult with qualified financial advisors and trade responsibly.
🛡️ COPYRIGHT & CONTACT
Created by: Luwan （IMTangYuan）
Copyright © 2025. All Rights Reserved.
Follow the shadows, trade with the smart money.
Version 1.0 | Pine Script v5 | Compatible with all TradingView accounts
Live Market - Performance MonitorLive Market — Performance Monitor
Study material (no code) — step-by-step training guide for learners
________________________________________
1) What this tool is — short overview
This indicator is a live market performance monitor designed for learning. It scans price, volume and volatility, detects order blocks and trendline events, applies filters (volume & ATR), generates trade signals (BUY/SELL), creates simple TP/SL trade management, and renders a compact dashboard summarizing market state, risk and performance metrics.
Use it to learn how multi-factor signals are constructed, how Greeks-style sensitivity is replaced by volatility/ATR reasoning, and how a live dashboard helps monitor trade quality.
________________________________________
2) Quick start — how a learner uses it (step-by-step)
1. Add the indicator to a chart (any ticker / timeframe).
2. Open inputs and review the main groups: Order Block, Trendline, Signal Filters, Display.
3. Start with defaults (OB periods ≈ 7, ATR multiplier 0.5, volume threshold 1.2) and observe the dashboard on the last bar.
4. Walk the chart back in time (use the last-bar update behavior) and watch how signals, order blocks, trendlines, and the performance counters change.
5. Run the hands-on labs below to build intuition.
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3) Main configurable inputs (what you can tweak)
• Order Block Relevant Periods (default ~7): number of consecutive candles used to define an order block.
• Min. Percent Move for Valid OB (threshold): minimum percent move required for a valid order block.
• Number of OB Channels: how many past order block lines to keep visible.
• Trendline Period (tl_period): pivot lookback for detecting highs/lows used to draw trendlines.
• Use Wicks for Trendlines: whether pivot uses wicks or body.
• Extension Bars: how far trendlines are projected forward.
• Use Volume Filter + Volume Threshold Multiplier (e.g., 1.2): requires volume to be greater than multiplier × average volume.
• Use ATR Filter + ATR Multiplier: require bar range > ATR × multiplier to filter noise.
• Show Targets / Table settings / Colors for visualization.
________________________________________
4) Core building blocks — what the script computes (plain language)
Price & trend:
• Spot / LTP: current close price.
• EMA 9 / 21 / 50: fast, medium, slow moving averages to define short/medium trend.
o trend_bullish: EMA9 > EMA21 > EMA50
o trend_bearish: EMA9 < EMA21 < EMA50
o trend_neutral: otherwise
Volatility & noise:
• ATR (14): average true range used for dynamic target and filter sizing.
• dynamic_zone = ATR × atr_multiplier: minimum bar range required for meaningful move.
• Annualized volatility: stdev of price changes × sqrt(252) × 100 — used to classify volatility (HIGH/MEDIUM/LOW).
Momentum & oscillators:
• RSI 14: overbought/oversold indicator (thresholds 70/30).
• MACD: EMA(12)-EMA(26) and a 9-period signal line; histogram used for momentum direction and strength.
• Momentum (ta.mom 10): raw momentum over 10 bars.
Mean reversion / band context:
• Bollinger Bands (20, 2σ): upper, mid, lower.
o price_position measures where price sits inside the band range as 0–100.
Volume metrics:
• avg_volume = SMA(volume, 20) and volume_spike = volume > avg_volume × volume_threshold
o volume_ratio = volume / avg_volume
Support & Resistance:
• support_level = lowest low over 20 bars
• resistance_level = highest high over 20 bars
• current_position = percent of price between support & resistance (0–100)
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5) Order Block detection — concept & logic
What it tries to find: a bar (the base) followed by N candles in the opposite direction (a classical order block setup), with a minimum % move to qualify. The script records the high/low of the base candle, averages them, and plots those levels as OB channels.
How learners should think about it (conceptual):
1. An order block is a signature area where institutions (theory) left liquidity — often seen as a large bar followed by a sequence of directional candles.
2. This indicator uses a configurable number of subsequent candles to confirm that the pattern exists.
3. When found, it stores and displays the base candle’s high/low area so students can see how price later reacts to those zones.
Implementation note for learners: the tool keeps a limited history of OB lines (ob_channels). When new OBs exceed the count, the oldest lines are removed — good practice to avoid clutter.
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6) Trendline detection — idea & interpretation
• The script finds pivot highs and lows using a symmetric lookback (tl_period and half that as right/left).
• It then computes a trendline slope from successive pivots and projects the line forward (extension_bars).
• Break detection: Resistance break = close crosses above the projected resistance line; Support break = close crosses below projected support.
Learning tip: trendlines here are computed from pivot points and time. Watch how changing tl_period (bigger = smoother, fewer pivots) alters the trendlines and break signals.
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7) Signal generation & filters — step-by-step
1. Primary triggers:
o Bullish trigger: order block bullish OR resistance trendline break.
o Bearish trigger: bearish order block OR support trendline break.
2. Filters applied (both must pass unless disabled):
o Volume filter: volume must be > avg_volume × volume_threshold.
o ATR filter: bar range (high-low) must exceed ATR × atr_multiplier.
o Not in an existing trade: new trades only start if trade_active is false.
3. Trend confirmation:
o The primary trigger is only confirmed if trend is bullish/neutral for buys or bearish/neutral for sells (EMA alignment).
4. Result:
o When confirmed, a long or short trade is activated with TP/SL calculated from ATR multiples.
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8) Trade management — what the tool does after a signal
• Entry management: the script marks a trade as trade_active and sets long_trade or short_trade flags.
• TP & SL rules:
o Long: TP = high + 2×ATR ; SL = low − 1×ATR
o Short: TP = low − 2×ATR ; SL = high + 1×ATR
• Monitoring & exit:
o A trade closes when price reaches TP or SL.
o When TP/SL hit, the indicator updates win_count and total_pnl using a very simple calculation (difference between TP/SL and previous close).
o Visual lines/labels are drawn for TP and updated as the trade runs.
Important learner notes:
• The script does not store a true entry price (it uses close in its P&L math), so PnL is an approximation — treat this as a learning proxy, not a position accounting system.
• There’s no sizing, slippage, or fee accounted — students must manually factor these when translating to real trades.
• This indicator is not a backtesting strategy; strategy.* functions would be needed for rigorous backtest results.
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9) Signal strength & helper utilities
• Signal strength is a composite score (0–100) made up of four signals worth 25 points each:
1. RSI extreme (overbought/oversold) → 25
2. Volume spike → 25
3. MACD histogram magnitude increasing → 25
4. Trend existence (bull or bear) → 25
• Progress bars (text glyphs) are used to visually show RSI and signal strength on the table.
Learning point: composite scoring is a way to combine orthogonal signals — study how changing weights changes outcomes.
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10) Dashboard — how to read each section (walkthrough)
The dashboard is split into sections; here's how to interpret them:
1. Market Overview
o LTP / Change%: immediate price & daily % change.
2. RSI & MACD
o RSI value plus progress bar (overbought 70 / oversold 30).
o MACD histogram sign indicates bullish/bearish momentum.
3. Volume Analysis
o Volume ratio (current / average) and whether there’s a spike.
4. Order Block Status
o Buy OB / Sell OB: the average base price of detected order blocks or “No Signal.”
5. Signal Status
o 🔼 BUY or 🔽 SELL if confirmed, or ⚪ WAIT.
o No-trade vs Active indicator summarizing market readiness.
6. Trend Analysis
o Trend direction (from EMAs), market sentiment score (composite), volatility level and band/position metrics.
7. Performance
o Win Rate = wins / signals (percentage)
o Total PnL = cumulative PnL (approximate)
o Bull / Bear Volume = accumulated volumes attributable to signals
8. Support & Resistance
o 20-bar highest/lowest — use as nearby reference points.
9. Risk & R:R
o Risk Level from ATR/price as a percent.
o R:R Ratio computed from TP/SL if a trade is active.
10. Signal Strength & Active Trade Status
• Numeric strength + progress bar and whether a trade is currently active with TP/SL display.
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11) Alerts — what will notify you
The indicator includes pre-built alert triggers for:
• Bullish confirmed signal
• Bearish confirmed signal
• TP hit (long/short)
• SL hit (long/short)
• No-trade zone
• High signal strength (score > 75%)
Training use: enable alerts during a replay session to be notified when the indicator would have signalled.
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12) Labs — hands-on exercises for learners (step-by-step)
Lab A — Order Block recognition
1. Pick a 15–30 minute timeframe on a liquid ticker.
2. Use default OB periods (7). Mark each time the dashboard shows a Buy/Sell OB.
3. Manually inspect the chart at the base candle and the following sequence — draw the OB zone by hand and watch later price reactions to it.
4. Repeat with OB periods 5 and 10; note stability vs noise.
Lab B — Trendline break confirmation
1. Increase trendline period (e.g., 20), watch trendlines form from pivots.
2. When a resistance break is flagged, compare with MACD & volume: was momentum aligned?
3. Note false breaks vs confirmed moves — change extension_bars to see projection effects.
Lab C — Filter sensitivity
1. Toggle Use Volume Filter off, and record the number and quality of signals in a 2-day window.
2. Re-enable volume filter and change threshold from 1.2 → 1.6; note how many low-quality signals are filtered out.
Lab D — Trade management simulation
1. For each signalled trade, record the time, close entry approximation, TP, SL, and eventual hit/miss.
2. Compute actual PnL if you had entered at the open of the next bar to compare with the script’s PnL math.
3. Tabulate win rate and average R:R.
Lab E — Performance review & improvement
1. Build a spreadsheet of signals over 30–90 periods with columns: Date, Signal type, Entry price (real), TP, SL, Exit, PnL, Notes.
2. Analyze which filters or indicators contributed most to winners vs losers and adjust weights.
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13) Common pitfalls, assumptions & implementation notes (things to watch)
• P&L simplification: total_pnl uses close as a proxy entry price. Real entry/exit prices and slippage are not recorded — so PnL is approximate.
• No position sizing or money management: the script doesn’t compute position size from equity or risk percent.
• Signal confirmation logic: composite "signal_strength" is a simple 4×25 point scheme — explore different weights or additional signals.
• Order block detection nuance: the script defines the base candle and checks the subsequent sequence. Be sure to verify whether the intended candle direction (base being bullish vs bearish) aligns with academic/your trading definition — read the code carefully and test.
• Trendline slope over time: slope is computed using timestamps; small differences may make lines sensitive on very short timeframes — using bar_index differences is usually more stable.
• Not a true backtester: to evaluate performance statistically you must transform the logic into a strategy script that places hypothetical orders and records exact entry/exit prices.
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14) Suggested improvements for advanced learners
• Record true entry price & timestamp for accurate PnL.
• Add position sizing: risk % per trade using SL distance and account size.
• Convert to strategy. (Pine Strategy)* to run formal backtests with equity curves, drawdowns, and metrics (Sharpe, Sortino).
• Log trades to an external spreadsheet (via alerts + webhook) for offline analysis.
• Add statistics: average win/loss, expectancy, max drawdown.
• Add additional filters: news time blackout, market session filters, multi-timeframe confirmation.
• Improve OB detection: combine wick/body, volume spike at base bar, and liquidity sweep detection.
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15) Glossary — quick definitions
• ATR (Average True Range): measure of typical range; used to size targets and stops.
• EMA (Exponential Moving Average): trend smoothing giving more weight to recent prices.
• RSI (Relative Strength Index): momentum oscillator; >70 overbought, <30 oversold.
• MACD: momentum oscillator using difference of two EMAs.
• Bollinger Bands: volatility bands around SMA.
• Order Block: a base candle area with subsequent confirmation candles; a zone of institutional interest (learning model).
• Pivot High/Low: local turning point defined by candles on both sides.
• Signal Strength: combined score from multiple indicators.
• Win Rate: proportion of signals that hit TP vs total signals.
• R:R (Risk:Reward): ratio of potential reward (TP distance) to risk (entry to SL).
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16) Limitations & assumptions (be explicit)
• This is an indicator for learning — not a trading robot or broker connection.
• No slippage, fees, commissions or tie-in to real orders are considered.
• The logic is heuristic (rule-of-thumb), not a guarantee of performance.
• Results are sensitive to timeframe, market liquidity, and parameter choices.
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17) Practical classroom / study plan (4 sessions)
• Session 1 — Foundations: Understand EMAs, ATR, RSI, MACD, Bollinger Bands. Run the indicator and watch how these numbers change on a single day.
• Session 2 — Zones & Filters: Study order blocks and trendlines. Test volume & ATR filters and note changes in false signals.
• Session 3 — Simulated trading: Manually track 20 signals, compute real PnL and compare to the dashboard.
• Session 4 — Improvement plan: Propose changes (e.g., better PnL accounting, alternative OB rule) and test their impact.
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18) Quick reference checklist for each signal
1. Was an order block or trendline break detected? (primary trigger)
2. Did volume meet threshold? (filter)
3. Did ATR filter (bar size) show a real move? (filter)
4. Was trend aligned (EMA 9/21/50)? (confirmation)
5. Signal confirmed → mark entry approximation, TP, SL.
6. Monitor dashboard (Signal Strength, Volatility, No-trade zone, R:R).
7. After exit, log real entry/exit, compute actual PnL, update spreadsheet.
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19) Educational caveat & final note
This tool is built for training and analysis: it helps you see how common technical building blocks combine into trade ideas, but it is not a trading recommendation. Use it to develop judgment, to test hypotheses, and to design robust systems with proper backtesting and risk control before risking capital.
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20) Disclaimer (must include)
Training & Educational Only — This material and the indicator are provided for educational purposes only. Nothing here is investment advice or a solicitation to buy or sell financial instruments. Past simulated or historical performance does not predict future results. Always perform full backtesting and risk management, and consider seeking advice from a qualified financial professional before trading with real capital.
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Super Arma Institucional PRO v6.3Super Arma Institucional PRO v6.3
Description
Super Arma Institucional PRO v6.3 is a multifunctional indicator designed for traders looking for a clear and objective analysis of the market, focusing on trends, key price levels and high liquidity zones. It combines three essential elements: moving averages (EMA 20, SMA 50, EMA 200), dynamic support and resistance, and volume-based liquidity zones. This integration offers an institutional view of the market, ideal for identifying strategic entry and exit points.
How it Works
Moving Averages:
EMA 20 (orange): Sensitive to short-term movements, ideal for capturing fast trends.
SMA 50 (blue): Represents the medium-term trend, smoothing out fluctuations.
EMA 200 (red): Indicates the long-term trend, used as a reference for the general market bias.
Support and Resistance: Calculated based on the highest and lowest prices over a defined period (default: 20 bars). These dynamic levels help identify zones where the price may encounter barriers or supports.
Liquidity Zones: Purple rectangles are drawn in areas of significantly above-average volume, indicating regions where large market participants (institutional) may be active. These zones are useful for anticipating price movements or order absorption.
Purpose
The indicator was developed to provide a clean and institutional view of the market, combining classic tools (moving averages and support/resistance) with modern liquidity analysis. It is ideal for traders operating swing trading or position trading strategies, allowing to identify:
Short, medium and long-term trends.
Key support and resistance levels to plan entries and exits.
High liquidity zones where institutional orders can influence the price.
Settings
Show EMA 20 (true): Enables/disables the 20-period EMA.
Show SMA 50 (true): Enables/disables the 50-period SMA.
Show EMA 200 (true): Enables/disables the 200-period EMA.
Support/Resistance Period (20): Sets the period for calculating support and resistance levels.
Liquidity Sensitivity (20): Period for calculating the average volume.
Minimum Liquidity Factor (1.5): Multiplier of the average volume to identify high liquidity zones.
How to Use
Moving Averages:
Crossovers between the EMA 20 and SMA 50 may indicate short/medium-term trend changes.
The EMA 200 serves as a reference for the long-term bias (above = bullish, below = bearish).
Support and Resistance: Use the red (resistance) and green (support) lines to identify reversal or consolidation zones.
Liquidity Zones: The purple rectangles highlight areas of high volume, where the price may react (reversal or breakout). Consider these zones to place orders or manage risks.
Adjust the parameters according to the asset and timeframe to optimize the analysis.
Notes
The chart should be configured only with this indicator to ensure clarity.
Use on timeframes such as 1 hour, 4 hours or daily for better visualization of liquidity zones and support/resistance levels.
Avoid adding other indicators to the chart to keep the script output easily identifiable.
The indicator is designed to be clean, without explicit buy/sell signals, following an institutional approach.
This indicator is perfect for traders who want a visually clear and powerful tool to trade based on trends, key levels and institutional behavior.
CAN INDICATORCAN Moving Averages Indicator - Feature Guide
1. Multiple Moving Averages (20 MAs)
- Supports up to 20 individual moving averages
- Each MA can be independently configured:
- Enable/Disable toggle
- Length (period) setting
- Type selection (SMA, EMA, DEMA, VWMA, RMA, WMA)
- Color customization
- Individual timeframe settings when global timeframe is disabled
Pre-configured MA Settings:
1. MA1-8: SMA type
- Lengths: 20, 50, 100, 200, 365, 489, 600, 1460
2. MA9-20: EMA type
- Lengths: 30, 60, 120, 240, 300, 400, 500, 700, 800, 900, 1000, 2000
2. Global Timeframe Settings
Location: Global Settings group
Features:
- Use Global Timeframe: Toggle to use one timeframe for all MAs
- Global Timeframe: Select the timeframe to apply globally
3. Label Display Options
Location: Main Inputs section
Controls:
- Show MA Type: Display MA type (SMA, EMA, etc.)
- Show MA Length: Display period length
- Show Resolution: Display timeframe
- Label Offset: Adjust label position
4. Cross Alerts System
Location: Cross Alerts group
Features:
1. Price Crosses:
- Alerts when price crosses any selected MA
- Select MA to monitor (1-20)
- Triggers on crossover/crossunder
2. MA Crosses:
- Alerts when one MA crosses another
- Select fast MA (1-20)
- Select slow MA (1-20)
- Triggers on crossover/crossunder
5. Relative Strength (RS) Analysis
Location: Relative Strength group
Features:
- Select any MA to monitor (1-20)
- Compares MA to its own average
- Adjustable RS Length (default 14)
- Visual feedback via background color:
- Green: MA above its average (uptrend)
- Red: MA below its average (downtrend)
- Customizable colors and transparency
6. Moving Average Types Available
1. **SMA** (Simple Moving Average)
- Equal weight to all prices
2. **EMA** (Exponential Moving Average)
- More weight to recent prices
3. **DEMA** (Double Exponential Moving Average)
- Reduced lag compared to EMA
4. **VWMA** (Volume Weighted Moving Average)
- Incorporates volume data
5. **RMA** (Running Moving Average)
- Smoother than EMA
6. **WMA** (Weighted Moving Average)
- Linear weight distribution
Usage Tips
1. **For Trend Following:**
- Enable longer-period MAs (MA4-MA8)
- Use cross alerts between long-term MAs
- Monitor RS for trend strength
2. **For Short-term Trading:**
- Focus on shorter-period MAs (MA1-MA3, MA9-MA11)
- Enable price cross alerts
- Use multiple timeframe analysis
3. **For Multiple Timeframe Analysis:**
- Disable global timeframe
- Set different timeframes for each MA
- Compare MA relationships across timeframes
4. **For Performance:**
- Disable unused MAs
- Limit active alerts to necessary pairs
- Use RS selectively on key MAs
Combined EMA Technical AnalysisThis script is written in Pine Script (version 5) for TradingView and creates a comprehensive technical analysis indicator called "Combined EMA Technical Analysis." It overlays multiple technical indicators on a price chart, including Exponential Moving Averages (EMAs), VWAP, MACD, PSAR, RSI, Bollinger Bands, ADX, and external data from the S&P 500 (SPX) and VIX indices. The script also provides visual cues through colors, shapes, and a customizable table to help traders interpret market conditions.
Here’s a breakdown of the script:
---
### **1. Purpose**
- The script combines several popular technical indicators to analyze price trends, momentum, volatility, and market sentiment.
- It uses color coding (green for bullish, red for bearish, gray/white for neutral) and a table to display key information.
---
### **2. Custom Colors**
- Defines custom RGB colors for bullish (`customGreen`), bearish (`customRed`), and neutral (`neutralGray`) signals to enhance visual clarity.
---
### **3. User Inputs**
- **EMA Colors**: Users can customize the colors of five EMAs (8, 20, 9, 21, 50 periods).
- **MACD Settings**: Adjustable short length (12), long length (26), and signal length (9).
- **RSI Settings**: Adjustable length (14).
- **Bollinger Bands Settings**: Length (20), multiplier (2), and proximity threshold (0.1% of band width).
- **ADX Settings**: Adjustable length (14).
- **Table Settings**: Position (e.g., "Bottom Right") and text size (e.g., "Small").
---
### **4. Indicator Calculations**
#### **Exponential Moving Averages (EMAs)**
- Calculates five EMAs: 8, 20, 9, 21, and 50 periods based on the closing price.
- Used to identify short-term and long-term trends.
#### **Volume Weighted Average Price (VWAP)**
- Resets daily and calculates the average price weighted by volume.
- Color-coded: green if price > VWAP (bullish), red if price < VWAP (bearish), white if neutral.
#### **MACD (Moving Average Convergence Divergence)**
- Uses short (12) and long (26) EMAs to compute the MACD line, with a 9-period signal line.
- Displays "Bullish" (green) if MACD > signal, "Bearish" (red) if MACD < signal.
#### **Parabolic SAR (PSAR)**
- Calculated with acceleration factors (start: 0.02, increment: 0.02, max: 0.2).
- Indicates trend direction: green if price > PSAR (bullish), red if price < PSAR (bearish).
#### **Relative Strength Index (RSI)**
- Measures momentum over 14 periods.
- Highlighted in green if > 70 (overbought), red if < 30 (oversold), white otherwise.
#### **Bollinger Bands (BB)**
- Uses a 20-period SMA with a 2-standard-deviation multiplier.
- Color-coded based on price position:
- Green: Above upper band or close to it.
- Red: Below lower band or close to it.
- Gray: Neutral (within bands).
#### **Average Directional Index (ADX)**
- Manually calculates ADX to measure trend strength:
- Strong trend: ADX > 25.
- Very strong trend: ADX > 50.
- Direction: Bullish if +DI > -DI, bearish if -DI > +DI.
#### **EMA Crosses**
- Detects bullish (crossover) and bearish (crossunder) events for:
- EMA 9 vs. EMA 21.
- EMA 8 vs. EMA 20.
- Visualized with green (bullish) or red (bearish) circles.
#### **SPX and VIX Data**
- Fetches daily closing prices for the S&P 500 (SPX) and VIX (volatility index).
- SPX trend: Bullish if EMA 9 > EMA 21, bearish if EMA 9 < EMA 21.
- VIX levels: High (> 25, fear), Low (< 15, stability).
- VIX color: Green if SPX bullish and VIX low, red if SPX bearish and VIX high, white otherwise.
---
### **5. Visual Outputs**
#### **Plots**
- EMAs, VWAP, and PSAR are plotted on the chart with their respective colors.
- EMA crosses are marked with circles (green for bullish, red for bearish).
#### **Table**
- Displays a summary of indicators in a customizable position and size.
- Indicators shown (if enabled):
- EMA 8/20, 9/21, 50: Green dot if bullish, red if bearish.
- VWAP: Green if price > VWAP, red if price < VWAP.
- MACD: Green if bullish, red if bearish.
- MACD Zero: Green if MACD > 0, red if MACD < 0.
- PSAR: Green if price > PSAR, red if price < PSAR.
- ADX: Arrows for very strong trends (↑/↓), dots for weaker trends, colored by direction.
- Bollinger Bands: Arrows (↑/↓) or dots based on price position.
- RSI: Numeric value, colored by overbought/oversold levels.
- VIX: Numeric value, colored based on SPX trend and VIX level.
---
### **6. Alerts**
- Triggers alerts for EMA 8/20 crosses:
- Bullish: "EMA 8/20 Bullish Cross on Candle Close!"
- Bearish: "EMA 8/20 Bearish Cross on Candle Close!"
---
### **7. Key Features**
- **Flexibility**: Users can toggle indicators on/off in the table and adjust parameters.
- **Visual Clarity**: Consistent use of green (bullish), red (bearish), and neutral colors.
- **Comprehensive**: Combines trend, momentum, volatility, and market sentiment indicators.
---
### **How to Use**
1. Add the script to TradingView.
2. Customize inputs (colors, lengths, table position) as needed.
3. Interpret the chart and table:
- Green signals suggest bullish conditions.
- Red signals suggest bearish conditions.
- Neutral signals indicate indecision or consolidation.
4. Set up alerts for EMA crosses to catch trend changes.
This script is ideal for traders who want a multi-indicator dashboard to monitor price action and market conditions efficiently.
IDX - 5UPThe UDX-5UP is a custom indicator designed to assist traders in identifying trends, entry and exit signals, and market reversal moments with greater accuracy. It combines price analysis, volume, and momentum (RSI) to provide clear buy ("Buy") and sell ("Sell") signals across any asset and timeframe, whether you're a scalper on the 5M chart or a swing trader on the 4H chart. Inspired by robust technical analysis strategies, the UDX-5UP is ideal for traders seeking a reliable tool to operate in volatile markets such as cryptocurrencies, forex, stocks, and futures.
Components of the UDX-5UP
The UDX-5UP consists of three main panels that work together to provide a comprehensive view of the market:
Main Panel (Price):
Pivot Supertrend: A dynamic line that changes color to indicate the trend. Green for an uptrend (look for buys), red for a downtrend (look for sells).
SMAs (Simple Moving Averages): Two SMAs (8 and 21 periods) to confirm the trend direction. When the SMA 8 crosses above the SMA 21, it’s a bullish signal; when it crosses below, it’s a bearish signal.
Entry/Exit Signals: "Buy" (green) and "Sell" (red) labels are plotted on the chart when entry or exit conditions are met.
Volume Panel:
Colored Volume Bars: Green bars indicate dominant buying volume, while red bars indicate dominant selling volume.
Volume Moving Average (MA 20): A blue line that helps identify whether the current volume is above or below the average, confirming the strength of the movement.
RSI Panel:
RSI (Relative Strength Index): Calculated with a period of 14, with overbought (70) and oversold (30) lines to identify momentum extremes.
Divergences: The indicator detects divergences between the RSI and price, plotting signals for potential reversals.
How the UDX-5UP Works
The UDX-5UP uses a combination of rules to generate buy and sell signals:
Buy Signal ("Buy"):
The Pivot Supertrend changes from red to green.
The SMA 8 crosses above the SMA 21.
The volume is above the MA 20, with green bars (indicating buying pressure).
The RSI is rising and, ideally, below 70 (not overbought).
Example: On the 4H chart, the price of Tether (USDT) is at 0.05515. The Pivot Supertrend turns green, the SMA 8 crosses above the SMA 21, the volume shows green bars above the MA 20, and the RSI is at 46. The UDX-5UP plots a "Buy".
Sell Signal ("Sell"):
The Pivot Supertrend changes from green to red.
The SMA 8 crosses below the SMA 21.
The volume is above the MA 20, with red bars (indicating selling pressure).
The RSI is falling and, ideally, above 70 (overbought).
Example: On the 4H chart, the price of Tether rises to 0.05817. The Pivot Supertrend turns red, the SMA 8 crosses below the SMA 21, the volume shows red bars, and the RSI is above 70. The UDX-5UP plots a "Sell".
RSI Divergences:
The indicator identifies bullish divergences (price makes a lower low, but RSI makes a higher low) and bearish divergences (price makes a higher high, but RSI makes a lower high), plotting alerts for potential reversals.
Adjustable Settings
The UDX-5UP is highly customizable to suit your trading style:
Pivot Supertrend Period: Default is 2. Increase to 3 or 4 for more conservative signals (fewer false positives, but more lag).
SMA Periods: Default is 8 and 21. Adjust to 5 and 13 for smaller timeframes (e.g., 5M) or 13 and 34 for larger timeframes (e.g., 1D).
RSI Period: Default is 14. Reduce to 10 for greater sensitivity or increase to 20 for smoother signals.
Overbought/Oversold Levels: Default is 70/30. Adjust to 80/20 in volatile markets.
Display Panels: You can enable/disable the volume and RSI panels to simplify the chart.
How to Use the UDX-5UP
Identify the Trend:
Use the Pivot Supertrend and SMAs to determine the market direction. Uptrend: look for buys. Downtrend: look for sells.
Confirm with Volume and RSI:
For buys: Volume above the MA 20 with green bars, RSI rising and below 70.
For sells: Volume above the MA 20 with red bars, RSI falling and above 70.
Enter the Trade:
Enter a buy when the UDX-5UP plots a "Buy" and all conditions are aligned.
Enter a sell when the UDX-5UP plots a "Sell" and all conditions are aligned.
Plan the Exit:
Use Fibonacci levels or support/resistance on the price chart to set targets.
Exit the trade when the UDX-5UP plots an opposite signal ("Sell" after a buy, "Buy" after a sell).
Tips for Beginners
Start with Larger Timeframes: Use the 4H or 1D chart for more reliable signals and less noise.
Combine with Other Indicators: Use the UDX-5UP with tools like Fibonacci or the Candles RSI (another powerful indicator) to confirm signals.
Practice in Demo Mode: Test the indicator in a demo account before using real money.
Manage Risk: Always use a stop-loss and don’t risk more than 1-2% of your capital per trade.
Why Use the UDX-5UP?
Simplicity: Clear "Buy" and "Sell" signals make trading accessible even for beginners.
Versatility: Works on any asset (crypto, forex, stocks) and timeframe.
Multiple Confirmations: Combines price, volume, and momentum to reduce false signals.
Customizable: Adjust the settings to match your trading style.
Author’s Notes
The UDX-5UP was developed based on years of trading and technical analysis experience. It is an evolution of tested strategies, designed to help traders navigate volatile markets with confidence. However, no indicator is infallible. Always combine the UDX-5UP with proper risk management and fundamental analysis, especially in unpredictable markets. Feedback is welcome – leave a comment or reach out with suggestions for improvements!
Multiple SMA, EMA, and VWAP CrossoversMultiple SMA, EMA, and VWAP Crossovers with Alerts
Overview : The "Multiple SMA, EMA, and VWAP Crossovers" script is designed for traders who want to monitor various simple moving averages (SMAs), exponential moving averages (EMAs), and the volume-weighted average price (VWAP) to identify potential buy and sell opportunities. This script allows you to visualize key moving averages on your chart and create custom alerts for specific crossover events.
Detail s: This script plots the following moving averages:
Simple Moving Averages (SMA): 5, 10, 20, 50, 100, 200, and 325 periods
Exponential Moving Average (EMA): 9 periods
Volume-Weighted Average Price (VWAP)
It includes options to display these moving averages and set alerts for their crossovers.
Available Crossovers:
20/50 SMA, 20/100 SMA, 20/200 SMA, 20/325 SMA
50/100 SMA, 50/200 SMA, 50/325 SMA
100/200 SMA, 100/325 SMA
200/325 SMA
VWAP/20 SMA, VWAP/50 SMA, VWAP/100 SMA, VWAP/200 SMA, VWAP/325 SMA
Optional Lines to Add to the Chart:
9 EMA, 5 SMA, 10 SMA, 20 SMA, 50 SMA, 100 SMA, 200 SMA, 325 SMA, VWAP
How to Use:
Enable Indicators: Use the input options to select which SMAs, EMA, and VWAP you want to display on your chart.
Set Alerts: Choose the specific crossover events you want to monitor. For example, you can set an alert for the 20/50 SMA crossover or the VWAP/100 SMA crossover.
Monitor the Chart: The script will plot the selected moving averages on your chart. When a selected crossover event occurs, an alert will be triggered, notifying you of the potential trade opportunity.
Usage Tips:
Trending Market: Use the buy and sell alerts in trending markets where the moving averages can help confirm the direction of the trend.
Key Support and Resistance Levels: Combine crossover alerts with key support and resistance levels for more reliable trading signals.
Volume Confirmation: Ensure there is sufficient volume to support the crossover signals, indicating stronger momentum behind the move.
When NOT to Use Buy and Sell Alerts:
Low Volume: Avoid using buy and sell alerts during periods of low trading volume, as the signals may be less reliable.
Market Noise: Be cautious in highly volatile markets where frequent crossovers might generate false signals.
Sideways Market: In a sideways or range-bound market, crossover signals can result in multiple whipsaws, leading to potential losses.
Why Use This Script? This script provides a comprehensive tool for traders to monitor multiple moving averages and VWAP crossovers efficiently. It allows you to customize alerts based on your trading strategy and helps you make informed decisions by visualizing key technical indicators on your chart.
Legal Disclaimer: The information provided by this script is for educational and informational purposes only and should not be considered financial advice. The developer of this script is not responsible for any financial losses incurred from using this script.
PubLibCandleTrendLibrary "PubLibCandleTrend"
candle trend, multi-part candle trend, multi-part green/red candle trend, double candle trend and multi-part double candle trend conditions for indicator and strategy development
chh()
candle higher high condition
Returns: bool
chl()
candle higher low condition
Returns: bool
clh()
candle lower high condition
Returns: bool
cll()
candle lower low condition
Returns: bool
cdt()
candle double top condition
Returns: bool
cdb()
candle double bottom condition
Returns: bool
gc()
green candle condition
Returns: bool
gchh()
green candle higher high condition
Returns: bool
gchl()
green candle higher low condition
Returns: bool
gclh()
green candle lower high condition
Returns: bool
gcll()
green candle lower low condition
Returns: bool
gcdt()
green candle double top condition
Returns: bool
gcdb()
green candle double bottom condition
Returns: bool
rc()
red candle condition
Returns: bool
rchh()
red candle higher high condition
Returns: bool
rchl()
red candle higher low condition
Returns: bool
rclh()
red candle lower high condition
Returns: bool
rcll()
red candle lower low condition
Returns: bool
rcdt()
red candle double top condition
Returns: bool
rcdb()
red candle double bottom condition
Returns: bool
chh_1p()
1-part candle higher high condition
Returns: bool
chh_2p()
2-part candle higher high condition
Returns: bool
chh_3p()
3-part candle higher high condition
Returns: bool
chh_4p()
4-part candle higher high condition
Returns: bool
chh_5p()
5-part candle higher high condition
Returns: bool
chh_6p()
6-part candle higher high condition
Returns: bool
chh_7p()
7-part candle higher high condition
Returns: bool
chh_8p()
8-part candle higher high condition
Returns: bool
chh_9p()
9-part candle higher high condition
Returns: bool
chh_10p()
10-part candle higher high condition
Returns: bool
chh_11p()
11-part candle higher high condition
Returns: bool
chh_12p()
12-part candle higher high condition
Returns: bool
chh_13p()
13-part candle higher high condition
Returns: bool
chh_14p()
14-part candle higher high condition
Returns: bool
chh_15p()
15-part candle higher high condition
Returns: bool
chh_16p()
16-part candle higher high condition
Returns: bool
chh_17p()
17-part candle higher high condition
Returns: bool
chh_18p()
18-part candle higher high condition
Returns: bool
chh_19p()
19-part candle higher high condition
Returns: bool
chh_20p()
20-part candle higher high condition
Returns: bool
chh_21p()
21-part candle higher high condition
Returns: bool
chh_22p()
22-part candle higher high condition
Returns: bool
chh_23p()
23-part candle higher high condition
Returns: bool
chh_24p()
24-part candle higher high condition
Returns: bool
chh_25p()
25-part candle higher high condition
Returns: bool
chh_26p()
26-part candle higher high condition
Returns: bool
chh_27p()
27-part candle higher high condition
Returns: bool
chh_28p()
28-part candle higher high condition
Returns: bool
chh_29p()
29-part candle higher high condition
Returns: bool
chh_30p()
30-part candle higher high condition
Returns: bool
chl_1p()
1-part candle higher low condition
Returns: bool
chl_2p()
2-part candle higher low condition
Returns: bool
chl_3p()
3-part candle higher low condition
Returns: bool
chl_4p()
4-part candle higher low condition
Returns: bool
chl_5p()
5-part candle higher low condition
Returns: bool
chl_6p()
6-part candle higher low condition
Returns: bool
chl_7p()
7-part candle higher low condition
Returns: bool
chl_8p()
8-part candle higher low condition
Returns: bool
chl_9p()
9-part candle higher low condition
Returns: bool
chl_10p()
10-part candle higher low condition
Returns: bool
chl_11p()
11-part candle higher low condition
Returns: bool
chl_12p()
12-part candle higher low condition
Returns: bool
chl_13p()
13-part candle higher low condition
Returns: bool
chl_14p()
14-part candle higher low condition
Returns: bool
chl_15p()
15-part candle higher low condition
Returns: bool
chl_16p()
16-part candle higher low condition
Returns: bool
chl_17p()
17-part candle higher low condition
Returns: bool
chl_18p()
18-part candle higher low condition
Returns: bool
chl_19p()
19-part candle higher low condition
Returns: bool
chl_20p()
20-part candle higher low condition
Returns: bool
chl_21p()
21-part candle higher low condition
Returns: bool
chl_22p()
22-part candle higher low condition
Returns: bool
chl_23p()
23-part candle higher low condition
Returns: bool
chl_24p()
24-part candle higher low condition
Returns: bool
chl_25p()
25-part candle higher low condition
Returns: bool
chl_26p()
26-part candle higher low condition
Returns: bool
chl_27p()
27-part candle higher low condition
Returns: bool
chl_28p()
28-part candle higher low condition
Returns: bool
chl_29p()
29-part candle higher low condition
Returns: bool
chl_30p()
30-part candle higher low condition
Returns: bool
clh_1p()
1-part candle lower high condition
Returns: bool
clh_2p()
2-part candle lower high condition
Returns: bool
clh_3p()
3-part candle lower high condition
Returns: bool
clh_4p()
4-part candle lower high condition
Returns: bool
clh_5p()
5-part candle lower high condition
Returns: bool
clh_6p()
6-part candle lower high condition
Returns: bool
clh_7p()
7-part candle lower high condition
Returns: bool
clh_8p()
8-part candle lower high condition
Returns: bool
clh_9p()
9-part candle lower high condition
Returns: bool
clh_10p()
10-part candle lower high condition
Returns: bool
clh_11p()
11-part candle lower high condition
Returns: bool
clh_12p()
12-part candle lower high condition
Returns: bool
clh_13p()
13-part candle lower high condition
Returns: bool
clh_14p()
14-part candle lower high condition
Returns: bool
clh_15p()
15-part candle lower high condition
Returns: bool
clh_16p()
16-part candle lower high condition
Returns: bool
clh_17p()
17-part candle lower high condition
Returns: bool
clh_18p()
18-part candle lower high condition
Returns: bool
clh_19p()
19-part candle lower high condition
Returns: bool
clh_20p()
20-part candle lower high condition
Returns: bool
clh_21p()
21-part candle lower high condition
Returns: bool
clh_22p()
22-part candle lower high condition
Returns: bool
clh_23p()
23-part candle lower high condition
Returns: bool
clh_24p()
24-part candle lower high condition
Returns: bool
clh_25p()
25-part candle lower high condition
Returns: bool
clh_26p()
26-part candle lower high condition
Returns: bool
clh_27p()
27-part candle lower high condition
Returns: bool
clh_28p()
28-part candle lower high condition
Returns: bool
clh_29p()
29-part candle lower high condition
Returns: bool
clh_30p()
30-part candle lower high condition
Returns: bool
cll_1p()
1-part candle lower low condition
Returns: bool
cll_2p()
2-part candle lower low condition
Returns: bool
cll_3p()
3-part candle lower low condition
Returns: bool
cll_4p()
4-part candle lower low condition
Returns: bool
cll_5p()
5-part candle lower low condition
Returns: bool
cll_6p()
6-part candle lower low condition
Returns: bool
cll_7p()
7-part candle lower low condition
Returns: bool
cll_8p()
8-part candle lower low condition
Returns: bool
cll_9p()
9-part candle lower low condition
Returns: bool
cll_10p()
10-part candle lower low condition
Returns: bool
cll_11p()
11-part candle lower low condition
Returns: bool
cll_12p()
12-part candle lower low condition
Returns: bool
cll_13p()
13-part candle lower low condition
Returns: bool
cll_14p()
14-part candle lower low condition
Returns: bool
cll_15p()
15-part candle lower low condition
Returns: bool
cll_16p()
16-part candle lower low condition
Returns: bool
cll_17p()
17-part candle lower low condition
Returns: bool
cll_18p()
18-part candle lower low condition
Returns: bool
cll_19p()
19-part candle lower low condition
Returns: bool
cll_20p()
20-part candle lower low condition
Returns: bool
cll_21p()
21-part candle lower low condition
Returns: bool
cll_22p()
22-part candle lower low condition
Returns: bool
cll_23p()
23-part candle lower low condition
Returns: bool
cll_24p()
24-part candle lower low condition
Returns: bool
cll_25p()
25-part candle lower low condition
Returns: bool
cll_26p()
26-part candle lower low condition
Returns: bool
cll_27p()
27-part candle lower low condition
Returns: bool
cll_28p()
28-part candle lower low condition
Returns: bool
cll_29p()
29-part candle lower low condition
Returns: bool
cll_30p()
30-part candle lower low condition
Returns: bool
gc_1p()
1-part green candle condition
Returns: bool
gc_2p()
2-part green candle condition
Returns: bool
gc_3p()
3-part green candle condition
Returns: bool
gc_4p()
4-part green candle condition
Returns: bool
gc_5p()
5-part green candle condition
Returns: bool
gc_6p()
6-part green candle condition
Returns: bool
gc_7p()
7-part green candle condition
Returns: bool
gc_8p()
8-part green candle condition
Returns: bool
gc_9p()
9-part green candle condition
Returns: bool
gc_10p()
10-part green candle condition
Returns: bool
gc_11p()
11-part green candle condition
Returns: bool
gc_12p()
12-part green candle condition
Returns: bool
gc_13p()
13-part green candle condition
Returns: bool
gc_14p()
14-part green candle condition
Returns: bool
gc_15p()
15-part green candle condition
Returns: bool
gc_16p()
16-part green candle condition
Returns: bool
gc_17p()
17-part green candle condition
Returns: bool
gc_18p()
18-part green candle condition
Returns: bool
gc_19p()
19-part green candle condition
Returns: bool
gc_20p()
20-part green candle condition
Returns: bool
gc_21p()
21-part green candle condition
Returns: bool
gc_22p()
22-part green candle condition
Returns: bool
gc_23p()
23-part green candle condition
Returns: bool
gc_24p()
24-part green candle condition
Returns: bool
gc_25p()
25-part green candle condition
Returns: bool
gc_26p()
26-part green candle condition
Returns: bool
gc_27p()
27-part green candle condition
Returns: bool
gc_28p()
28-part green candle condition
Returns: bool
gc_29p()
29-part green candle condition
Returns: bool
gc_30p()
30-part green candle condition
Returns: bool
rc_1p()
1-part red candle condition
Returns: bool
rc_2p()
2-part red candle condition
Returns: bool
rc_3p()
3-part red candle condition
Returns: bool
rc_4p()
4-part red candle condition
Returns: bool
rc_5p()
5-part red candle condition
Returns: bool
rc_6p()
6-part red candle condition
Returns: bool
rc_7p()
7-part red candle condition
Returns: bool
rc_8p()
8-part red candle condition
Returns: bool
rc_9p()
9-part red candle condition
Returns: bool
rc_10p()
10-part red candle condition
Returns: bool
rc_11p()
11-part red candle condition
Returns: bool
rc_12p()
12-part red candle condition
Returns: bool
rc_13p()
13-part red candle condition
Returns: bool
rc_14p()
14-part red candle condition
Returns: bool
rc_15p()
15-part red candle condition
Returns: bool
rc_16p()
16-part red candle condition
Returns: bool
rc_17p()
17-part red candle condition
Returns: bool
rc_18p()
18-part red candle condition
Returns: bool
rc_19p()
19-part red candle condition
Returns: bool
rc_20p()
20-part red candle condition
Returns: bool
rc_21p()
21-part red candle condition
Returns: bool
rc_22p()
22-part red candle condition
Returns: bool
rc_23p()
23-part red candle condition
Returns: bool
rc_24p()
24-part red candle condition
Returns: bool
rc_25p()
25-part red candle condition
Returns: bool
rc_26p()
26-part red candle condition
Returns: bool
rc_27p()
27-part red candle condition
Returns: bool
rc_28p()
28-part red candle condition
Returns: bool
rc_29p()
29-part red candle condition
Returns: bool
rc_30p()
30-part red candle condition
Returns: bool
cdut()
candle double uptrend condition
Returns: bool
cddt()
candle double downtrend condition
Returns: bool
cdut_1p()
1-part candle double uptrend condition
Returns: bool
cdut_2p()
2-part candle double uptrend condition
Returns: bool
cdut_3p()
3-part candle double uptrend condition
Returns: bool
cdut_4p()
4-part candle double uptrend condition
Returns: bool
cdut_5p()
5-part candle double uptrend condition
Returns: bool
cdut_6p()
6-part candle double uptrend condition
Returns: bool
cdut_7p()
7-part candle double uptrend condition
Returns: bool
cdut_8p()
8-part candle double uptrend condition
Returns: bool
cdut_9p()
9-part candle double uptrend condition
Returns: bool
cdut_10p()
10-part candle double uptrend condition
Returns: bool
cdut_11p()
11-part candle double uptrend condition
Returns: bool
cdut_12p()
12-part candle double uptrend condition
Returns: bool
cdut_13p()
13-part candle double uptrend condition
Returns: bool
cdut_14p()
14-part candle double uptrend condition
Returns: bool
cdut_15p()
15-part candle double uptrend condition
Returns: bool
cdut_16p()
16-part candle double uptrend condition
Returns: bool
cdut_17p()
17-part candle double uptrend condition
Returns: bool
cdut_18p()
18-part candle double uptrend condition
Returns: bool
cdut_19p()
19-part candle double uptrend condition
Returns: bool
cdut_20p()
20-part candle double uptrend condition
Returns: bool
cdut_21p()
21-part candle double uptrend condition
Returns: bool
cdut_22p()
22-part candle double uptrend condition
Returns: bool
cdut_23p()
23-part candle double uptrend condition
Returns: bool
cdut_24p()
24-part candle double uptrend condition
Returns: bool
cdut_25p()
25-part candle double uptrend condition
Returns: bool
cdut_26p()
26-part candle double uptrend condition
Returns: bool
cdut_27p()
27-part candle double uptrend condition
Returns: bool
cdut_28p()
28-part candle double uptrend condition
Returns: bool
cdut_29p()
29-part candle double uptrend condition
Returns: bool
cdut_30p()
30-part candle double uptrend condition
Returns: bool
cddt_1p()
1-part candle double downtrend condition
Returns: bool
cddt_2p()
2-part candle double downtrend condition
Returns: bool
cddt_3p()
3-part candle double downtrend condition
Returns: bool
cddt_4p()
4-part candle double downtrend condition
Returns: bool
cddt_5p()
5-part candle double downtrend condition
Returns: bool
cddt_6p()
6-part candle double downtrend condition
Returns: bool
cddt_7p()
7-part candle double downtrend condition
Returns: bool
cddt_8p()
8-part candle double downtrend condition
Returns: bool
cddt_9p()
9-part candle double downtrend condition
Returns: bool
cddt_10p()
10-part candle double downtrend condition
Returns: bool
cddt_11p()
11-part candle double downtrend condition
Returns: bool
cddt_12p()
12-part candle double downtrend condition
Returns: bool
cddt_13p()
13-part candle double downtrend condition
Returns: bool
cddt_14p()
14-part candle double downtrend condition
Returns: bool
cddt_15p()
15-part candle double downtrend condition
Returns: bool
cddt_16p()
16-part candle double downtrend condition
Returns: bool
cddt_17p()
17-part candle double downtrend condition
Returns: bool
cddt_18p()
18-part candle double downtrend condition
Returns: bool
cddt_19p()
19-part candle double downtrend condition
Returns: bool
cddt_20p()
20-part candle double downtrend condition
Returns: bool
cddt_21p()
21-part candle double downtrend condition
Returns: bool
cddt_22p()
22-part candle double downtrend condition
Returns: bool
cddt_23p()
23-part candle double downtrend condition
Returns: bool
cddt_24p()
24-part candle double downtrend condition
Returns: bool
cddt_25p()
25-part candle double downtrend condition
Returns: bool
cddt_26p()
26-part candle double downtrend condition
Returns: bool
cddt_27p()
27-part candle double downtrend condition
Returns: bool
cddt_28p()
28-part candle double downtrend condition
Returns: bool
cddt_29p()
29-part candle double downtrend condition
Returns: bool
cddt_30p()
30-part candle double downtrend condition
Returns: bool
Donchian Quest Research// =================================
Trend following strategy.
// =================================
Strategy uses two channels. One channel - for opening trades. Second channel - for closing.
Channel is similar to Donchian channel, but uses Close prices (not High/Low). That helps don't react to wicks of volatile candles (“stop hunting”). In most cases openings occur earlier than in Donchian channel. Closings occur only for real breakout.
// =================================
Strategy waits for beginning of trend - when price breakout of channel. Default length of both channels = 50 candles.
Conditions of trading:
- Open Long: If last Close = max Close for 50 closes.
- Close Long: If last Close = min Close for 50 closes.
- Open Short: If last Close = min Close for 50 closes.
- Close Short: If last Close = max Close for 50 closes.
// =================================
Color of lines:
- black - channel for opening trade.
- red - channel for closing trade.
- yellow - entry price.
- fuchsia - stoploss and breakeven.
- vertical green - go Long.
- vertical red - go Short.
- vertical gray - close in end, don't trade anymore.
// =================================
Order size calculated with ATR and volatility.
You can't trade 1 contract in BTC and 1 contract in XRP - for example. They have different price and volatility, so 1 contract BTC not equal 1 contract XRP.
Script uses universal calculation for every market. It is based on:
- Risk - USD sum you ready to loss in one trade. It calculated as percent of Equity.
- ATR indicator - measurement of volatility.
With default setting your stoploss = 0.5 percent of equity:
- If initial capital is 1000 USD and used parameter "Permit stop" - loss will be 5 USD (0.5 % of equity).
- If your Equity rises to 2000 USD and used parameter "Permit stop"- loss will be 10 USD (0.5 % of Equity).
// =================================
This Risk works only if you enable “Permit stop” parameter in Settings.
If this parameter disabled - strategy works as reversal strategy:
⁃ If close Long - channel border works as stoploss and momentarily go Short.
⁃ If close Short - channel border works as stoploss and momentarily go Long.
Channel borders changed dynamically. So sometime your loss will be greater than ‘Risk %’. Sometime - less than ‘Risk %’.
If this parameter enabled - maximum loss always equal to 'Risk %'. This parameter also include breakeven: if profit % = Risk %, then move stoploss to entry price.
// =================================
Like all trend following strategies - it works only in trend conditions. If no trend - slowly bleeding. There is no special additional indicator to filter trend/notrend. You need to trade every signal of strategy.
Strategy gives many losses:
⁃ 30 % of trades will close with profit.
⁃ 70 % of trades will close with loss.
⁃ But profit from 30% will be much greater than loss from 70 %.
Your task - patiently wait for it and don't use risky setting for position sizing.
// =================================
Recommended timeframe - Daily.
// =================================
Trend can vary in lengths. Selecting length of channels determine which trend you will be hunting:
⁃ 20/10 - from several days to several weeks.
⁃ 20/20 or 50/20 - from several weeks to several months.
⁃ 50/50 or 100/50 or 100/100 - from several months to several years.
// =================================
Inputs (Settings):
- Length: length of channel for trade opening/closing. You can choose 20/10, 20/20, 50/20, 50/50, 100/50, 100/100. Default value: 50/50.
- Permit Long / Permit short: Longs are most profitable for this strategy. You can disable Shorts and enable Longs only. Default value: permit all directions.
- Risk % of Equity: for position sizing used Equity percent. Don't use values greater than 5 % - it's risky. Default value: 0.5%.
⁃ ATR multiplier: this multiplier moves stoploss up or down. Big multiplier = small size of order, small profit, stoploss far from entry, low chance of stoploss. Small multiplier = big size of order, big profit, stop near entry, high chance of stoploss. Default value: 2.
- ATR length: number of candles to calculate ATR indicator. It used for order size and stoploss. Default value: 20.
- Close in end - to close active trade in the end (and don't trade anymore) or leave it open. You can see difference in Strategy Tester. Default value: don’t close.
- Permit stop: use stop or go reversal. Default value: without stop, reversal strategy.
// =================================
Properties (Settings):
- Initial capital - 1000 USD.
- Script don't uses 'Order size' - you need to change 'Risk %' in Inputs instead.
- Script don't uses 'Pyramiding'.
- 'Commission' 0.055 % and 'Slippage' 0 - this parameters are for crypto exchanges with perpetual contracts (for example Bybit). If use on other markets - set it accordingly to your exchange parameters.
// =================================
Big dataset used for chart - 'BITCOIN ALL TIME HISTORY INDEX'. It gives enough trades to understand logic of script. It have several good trends.
// =================================
Crypto Trend IndicatorThe Crypto Trend Indicator is a trend-following indicator specifically designed to identify bullish and bearish trends in the price of Bitcoin, and other cryptocurrencies. This indicator doesn't provide explicit instructions on when to buy or sell, but rather offers an understanding of whether the trend is bullish or bearish. It's important to note that this indicator is only useful for trend trading.
The band is a visual representation of the 30-day and 60-day Exponential Moving Average (EMA). When the 30-day EMA is above the 60-day EMA, the trend is bullish and the band is green. When the 30-day EMA is below the 60-day EMA, the trend is bearish and the band is red. When the 30-day EMA starts to converge with the 60-day EMA, the trend is neutral and the band is grey.
The line is a visual representation of the 20-week Simple Moving Average (SMA) in the daily timeframe. "Bull" and "Bear" signals are generated when the 20-day EMA is either above or below the 20-week SMA, in conjunction with a bullish or bearish trend. When the band is green and the 20-day EMA is above the 20-week SMA, a “Bull” signal emerges. When the band is red and the 20-day EMA is below the 20-week SMA, a “Bear” signal emerges. The 20-week SMA can potentially also function as a leading indicator, as substantial price deviations from the SMA typically indicate an overextended market.
While this indicator has traditionally identified bullish and bearish trends in various cryptocurrency assets, past performance does not guarantee future results. Therefore, it is advisable to supplement this indicator with other technical tools. For instance, range-bound indicators can greatly improve the decision-making process when planning for entries and exits points.
Financial Radar Chart by zdmreRadar chart is often used when you want to display data across several unique dimensions. Although there are exceptions, these dimensions are usually quantitative, and typically range from zero to a maximum value. Each dimension’s range is normalized to one another, so that when we draw our spider chart, the length of a line from zero to a dimension’s maximum value will be the similar for every dimension.
This Charts are useful for seeing which variables are scoring high or low within a dataset, making them ideal for displaying performance.
How is the score formed?
Debt Paying Ability
if Debt_to_Equity < %10 : 100
elif < 20% : 90
elif < 30% : 80
elif < 40% : 70
elif < 50% : 60
elif < 60% : 50
elif < 70% : 40
elif < 80% : 30
elif < 90% : 20
elif < 100% : 10
else: 0
ROIC
if Return_on_Invested_Capital > %50 : 100
elif > 40% : 90
elif > 30% : 80
elif > 20% : 70
elif > 10% : 50
elif > 5% : 20
else: 0
ROE
if Return_on_Equity > %50 : 100
elif > 40% : 90
elif > 30% : 80
elif > 20% : 70
elif > 10% : 50
elif > 5% : 20
else: 0
Operating Ability
if Operating_Margin > %50 : 100
elif > 30% : 90
elif > 20% : 80
elif > 15% : 60
elif > 10% : 40
elif > 0 : 20
else: 0
EV/EBITDA
if Enterprise_Value_to_EBITDA < 3 : 100
elif < 5 : 80
elif < 7 : 70
elif < 8 : 60
elif < 10 : 40
elif < 12 : 20
else: 0
FREE CASH Ability
if Price_to_Free_Cash_Flow < 5 : 100
elif < 7 : 90
elif < 10 : 80
elif < 16 : 60
elif < 18 : 50
elif < 20 : 40
elif < 22 : 30
elif < 30 : 20
elif < 40 : 15
elif < 50 : 10
elif < 60 : 5
else: 0
GROWTH Ability
if Revenue_One_Year_Growth > %20 : 100
elif > 16% : 90
elif > 14% : 80
elif > 12% : 70
elif > 10% : 50
elif > 7% : 40
elif > 4% : 30
elif > 2% : 20
elif > 0 : 10
else: 0
Three StochRSI ComboThree StochRSI with adjustable lengths in one indicator, with no extra bells or whistles. Lazybear's Apirine Slow RSI is used for the RSI formula for the third Stoch.
14-period StochRSI (red) is considered short term.
20-period StochRSI is another short to mid term preset I've seen used in various indicators on different platforms by default, and is included as the yellow stochRSI.
Lazybear's Apirine Slow RSI is used as the base for the third, slower stoch - parameter input adjustment for your specific timeframe and instrument is recommended. The Apirine Slow RSI doesn't have as effective strong divergences during established trends, so the way it would react in when run through a stoch should be more synergistic than not during trending periods for longer term trends.
StochRSI:
StochRSI measures the value of RSI relative to its high/low range over a set number of periods - It's important to note that StochRSI is technically an indicator of an indicator (RSI), which makes it the second derivative of price. This is part of the main contributing factors to how it looks and functions in relation to price.
A move above 80 ---> considered overbought.
A move below 20 ---> considered oversold.
Moves around the midline (50) can also be interpreted as continuation of upward/downwards trend depending on whether the midline is acting more of as a support (uptrend) or a resistance level (downtrend).
Stoch K-line disabled by default/D-line enabled by default.
Stoch K/D lengths are set to 3/3/14/14 (red), 6/6/20/20 (yellow), 12/12 (green) by default.
Midline (50) set to white/silver to make more visible on darkmode charts.
Additional adjustable levels for Overbought/Oversold beyond 20/80 (set at 10/90 by default - see dashed grey line. Dotted line is 20/80.)
Three stochRSI with 3/3/14/14, 6/6/20/20, and 12/12/14/14/6 length are shown below the indicator for reference.
Acknowledgements:
// Aprine Slow RSI - Lazybear
//
// Stockcharts for StochRSI reference
// school.stockcharts.com
Volatility Channel Oscillator█ OVERVIEW
"Volatility Channel Oscillator" is a technical indicator that analyzes price volatility relative to dynamic price channels, displaying an oscillator, its moving average, and signals based on crossovers and divergences. The indicator offers customizable overbought and oversold levels, gradient visualization, and divergence detection, supported by alerts for key signals.
█ CONCEPTS
The VCO indicator creates dynamic price channels based on a moving average of the price (calculated as the arithmetic mean of the high and low prices: (high + low) / 2) and market volatility (measured as the average candle range and body size). These channels are not displayed on the chart but are used to calculate the oscillator value, which reflects the position of the closing price relative to the channel width, scaled to a range from -100 to +100, with the zero line as the central point. A moving average of the oscillator (SMA) smooths its values, enabling signals based on crossovers with the zero line or overbought/oversold levels. The indicator also detects divergences between price and the oscillator, which may indicate potential trend reversals. VCO is useful for identifying market momentum, reversal points, and trend confirmation, especially when combined with other technical analysis tools.
█ FEATURES
- Volatility Channels: Calculates invisible chart boundaries based on a simple moving average (SMA) of the price (high + low) / 2 and volatility (average candle range and body). The length parameter (default 30) sets the SMA length, and scale (default 200%) adjusts the channel width.
- Oscillator: Determines the oscillator value in the range of -100 to +100, indicating the closing price's position relative to the volatility channel. Displayed with dynamic coloring (green for positive values, red for negative).
- Oscillator Moving Average: A simple moving average (SMA) of the oscillator values, smoothing its movements. The signalLength parameter (default 20) defines the SMA length. Displayed in yellow with an optional gradient.
- Overbought/Oversold Levels: Configurable thresholds for the oscillator (overbought, default 50; oversold, default -50) and its moving average (maOverbought, default 30; maOversold, default -30), shown as horizontal lines with optional gradients. Band colors change dynamically (red for overbought, green for oversold, gray for neutral) based on the moving average's position relative to maOverbought/maOversold, reinforcing other signals.
- Divergences: Detects bullish (price forms a lower low, oscillator a higher low) and bearish (price forms a higher high, oscillator a lower high) divergences using pivots (pivotLength, default 2). Divergences are displayed with a delay equal to the pivot length; larger lengths increase reliability but delay signals. Use as additional confirmation.
Signals:
- Overbought/Oversold Crossovers: Green triangles (buy) when the oscillator crosses above the oversold level, red triangles (sell) when it crosses below the overbought level.
- Zero Line Crossovers: Buy/sell signals when the oscillator crosses the zero line upward (buy) or downward (sell).
- Moving Average Crossovers: Buy/sell signals when the oscillator's moving average crosses the zero line or the maOverbought/maOversold levels. Dynamic band color changes (red/green) at these crossovers reinforce other signals.
- Visualization: Gradient lines for the oscillator, its moving average, overbought/oversold levels, and zero line, with adjustable transparency. Gradient fill between the oscillator and zero line.
Divergence Labels: "Bull" (bullish) and "Bear" (bearish) labels with customizable color and transparency.
- Alerts: Built-in alerts for divergences, overbought/oversold crossovers, and zero line crossovers by the oscillator and its moving average.
█ HOW TO USE
Add to Chart: Apply the indicator via Pine Editor or the Indicators menu on TradingView.
Configure Settings:
- Channel and Oscillator Settings: Adjust the channel SMA length (length, default 30) and channel scaling (scale, default 200%). Increase scale for high-volatility markets.
- Threshold Levels: Set oscillator overbought (overbought, default 50) and oversold (oversold, default -50) levels, and moving average thresholds (maOverbought, default 30; maOversold, default -30).
- Divergence Settings: Enable/disable divergence detection (calculateDivergence) and set pivot length (pivotLength, default 2). Larger values increase reliability but delay signals.
- Signal Settings: Choose signal types (signalType): overbought/oversold, zero line, moving average, or all.
- Styling: Customize colors for the oscillator, moving average, horizontal levels, and divergence labels. Adjust gradient and fill transparency.
Interpreting Signals:
- Buy Signals: Green triangles below the bar when the oscillator or its moving average crosses above the oversold level or zero line.
- Sell Signals: Red triangles above the bar when the oscillator or its moving average crosses below the overbought level or zero line.
- Moving Average Signals: Green/red triangles when the moving average crosses maOverbought/maOversold levels, indicating potential reversals or trend continuation. Dynamic band color changes (red for overbought, green for oversold) at these crossovers reinforce other signals.
- Divergences: "Bull" (bullish) and "Bear" (bearish) labels indicate potential trend reversals with a delay based on pivot length. Use as confirmation.
- Overbought/Oversold Levels: Monitor price reactions in these zones as potential reversal points. Dynamic band color changes based on the moving average reinforce signals.
Signal Confirmation: Use VCO with other tools, such as pivot levels (for key turning points) or Fibonacci levels (for support/resistance zones).
█ APPLICATIONS
- Trend Trading: Zero line crossovers by the oscillator or its moving average identify momentum in uptrends or downtrends.
- Range Trading: Overbought/oversold levels help identify entry/exit points in sideways markets.
- Divergences: Use bullish/bearish divergences as additional confirmation of reversals, especially near key price levels.
- Trend Identification: To analyze trends over a longer perspective, increase the moving average length (signalLength) for more stable signals.
█ NOTES
- Test the indicator across different timeframes and markets to optimize parameters, such as length and scale, for your trading style.
- In strong trends, overbought/oversold levels may persist, requiring additional signal verification.
- Divergences are more reliable on higher timeframes (H4, D1), where market noise is reduced, but their delay requires caution.
- In low-liquidity markets, signals may be less effective, so use on high-liquidity assets is recommended.
Diwali Lights Pro — 7-Diyas Signal Matrix [KedArc Quant]🎯 Overview
“Diwali Lights Pro — 7-Diyas Signal Matrix” is a precision-built trend-sentiment indicator that blends the glow of seven technical “diyas” — each representing a different momentum or strength dimension — into one intuitive signal matrix. It was designed to celebrate light, discipline, and clarity in trading — helping traders filter noise, identify strong trend shifts, and take trades with conviction. Each diya is powered by a proven indicator component: RSI, Stochastic, EMA trend strength, and momentum slopes.Together, they light up your chart with buy/sell signals only when technical confluence aligns — like the diyas of Diwali shining in harmony.
💡 Core Concept
The indicator computes a composite score (–9 to +9) by evaluating seven key parameters:
| # | Diya | Logic | Interpretation |
| 1 | RSI | Overbought / Oversold | Short-term momentum exhaustion |
| 2 | Stochastic | Direction & zones | Confirmation of RSI |
| 3 | Price vs EMA20 | Position of price | Near-term trend bias |
| 4 | EMA20 Slope | Short-term momentum | Strength confirmation |
| 5 | EMA50 Slope | Mid-term trend | Trend stability |
| 6 | EMA100 Slope | Medium-term sentiment | Institutional bias |
| 7 | EMA200 Slope | Long-term sentiment | Market direction baseline |
The total of these 7 diyas creates a signal matrix that dynamically adapts to trend conditions.
⚙️ Inputs & Configuration
| RSI Length | 14 | Standard RSI window |
| Stochastic Length | 14 | Measures momentum oscillation |
| EMA Periods | 20, 50, 100, 200 | Multi-layer trend structure |
| Overbought / Oversold Zones | 70 / 30 | Configurable thresholds |
| Show Buy/Sell Labels | ✅ | Toggle signal markers |
| Show Banner | ✅ | Festive Diwali header with fireworks |
| Twinkle Interval | 10 bars | Animation timing |
| Fireworks Count | 18 | Visual celebration intensity |
| Background Opacity | 100% | Style preference |
🧭 Entry & Exit Logic
# ✅ Buy Signal (🪔)
A Buy triggers when:
* The total diya score crosses above zero,
* And at least four of seven components turn bullish.
This indicates that short-term oscillators, price action, and moving averages are all turning in unison — a strong entry zone after a pullback.
# 🔥 Sell Signal (🔥)
A Sell triggers when:
* The total diya score crosses below zero,
* And multiple slopes or price conditions flip bearish.
This flags weakening momentum and possible trend exhaustion.
# 💬 Suggested Usage
* Works beautifully on 5-min to 1-hour charts.
* Best when used with trend confirmation tools (volume, price structure).
* Avoid entering trades when signals flip rapidly within narrow ranges (sideways zones).
🧪 Mathematical Formulae
1. RSI Bucket (p₁):
p₁ =
2 if RSI < Very Oversold
1 if RSI < Oversold
0 if neutral
-1 if RSI > Overbought
-2 if RSI > Very Overbought
2. Stochastic Bucket (p₂): Similar to RSI bucketing.
3. Price vs EMA20 (p₃):
p₃ = sign(close - EMA20)
4–7. Slope Sign (EMA20, 50, 100, 200):
p₄₋₇ = sign(EMA - EMA )
Total Score = Σ(p₁…p₇)
→ Crossover(total_score, 0) → Buy Signal
→ Crossunder(total_score, 0) → Sell Signal
📊 Why It’s Not Just a Mash-Up
Diwali Lights Pro uses:
* A unified scoring engine with weighted logic rather than conflicting triggers.
* Each component (diya) contributes equally, creating a normalized sentiment index.
* Smart signal filtering prevents repetitive false flips by enforcing trend alignment across multiple time frames.
* A dynamic, responsive structure optimized for clarity and minimal repainting.
🎆 Unique Add-Ons
* Top-Right Diwali Banner: Festive “Happy Diwali” with animated fireworks 🎇 and diyas 🪔.
* Signal Filtering: Reduces noise in volatile ranges.
* EMA Cloud Context: Visual clarity of multi-layer trend zones.
* Optional Light Mode: Change fireworks opacity for a subtle or bright effect.
📘 FAQ
Q1: Does this repaint?
No — it uses confirmed values (RSI, Stochastic, EMA slopes). Signals appear only after the bar closes.
Q2: Which timeframes work best?
Between 5m and 1h, depending on your strategy.
Use higher EMAs for swing setups.
Q3: Can I use it with alerts?
Yes, both Buy and Sell triggers come with built-in `alertcondition()` for instant notifications.
Q4: Can it be combined with other indicators?
Absolutely — it pairs well with volume profiles, volatility bands, or order-flow systems.
🪔 Glossary
| Diya | Candle or light — here, each diya = one technical indicator |
| EMA | Exponential Moving Average — measures smoothed trend bias |
| RSI | Relative Strength Index — momentum overbought/oversold oscillator |
| Stochastic | Momentum oscillator measuring closing levels relative to highs/lows |
| Slope Sign | Direction of EMA movement — rising or falling |
| Signal Matrix | The combined system of all seven diyas generating a unified score |
🧭 Final Note
> *Diwali Lights Pro* is not just a trading tool — it’s a visual celebration of confluence and discipline.
> When the diyas align, trends shine. Use it to trade in harmony with light, not against it. 🌟
⚠️ Disclaimer
This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
