MarketReader[ENG] DARK THEMEMarket Reader is a very sophisticated indicator giving you:
-BUY and SELL Opportunities
-Key supports and resistances where the market will react
-Early detection of RANGE before the contact with the top or the bottom of the range, it will also give you the target of the top and the bottom of the range
-Pattern of smartmoney activities, giving you signal that smart money is moving at this level of price
You will also found my complete strategy on my Youtube Channel
Enjoy
Utilization requires subscription
Pesquisar nos scripts por "market%"
Mean Reversion DotsMarkets tend to mean revert. This indicator plots a moving average from a higher time frame (type of MA and length selectable by the user). It then calculates standard deviations in two dimensions:
- Standard deviation of move of price away from this moving average
- Standard deviations of number of bars spent in this extended range
The indicator plots a table in the upper right corner with the % of distance of price from the moving average. It then plots 'mean reversion dots' once price has been 1 or more standard deviations away from the moving average for one or more standard deviations number of bars. The dots change color, becoming more intense, the longer the move persists. Optionally, the user can display the standard deviations in movement away from the moving average as channels, and the user can also select which levels of moves they want to see. Opting to see only more extreme moves will result in fewer signals, but signals that are more likely to imminently result in mean reversion back to the moving average.
In my opinion, this indicator is more likely to be useful for indices, futures, commodities, and select larger cap names.
Combinations I have found that work well for SPX are plotting the 30min 21ema on a 5min chart and the daily 21ema on an hourly chart.
In many cases, once mean reversion dots for an extreme enough move (level 1.3 or 2.2 and above) begin to appear, a trade may be initiated from a support/resistance level. A safer way to use these signals is to consider them as a 'heads up' that the move is overextended, and then look for a buy/sell signal from another indicator to initiate a position.
Note: I borrowed the code for the higher timeframe MA from the below indicator. I added the ability to select type of MA.
Range-AnalysisMarkets usually tend to stay within a range during a specific time frame (for example first hour of the regular trading session, the whole regular trading session). For traders before initiating a trade it can be helpful to determine the range potential left for the targeted time frame. So they can decide to either try to ride the current trend further or fade the current trend in the case there is no range potential left for the specific time frame. This could be especially helpful for example in the E-Mini S&P future during the first hour.
The script calculates the average range for the last x days of the session defined and plots a line at the expected range extremes based on that average (for example: RangeExtremeHigh would be currentSessionLow+average Range of the last x days.
Any feedback is appreciated.
LNL Fractal EnergyMarkets knows only two movements. Expansion and Consolidation. The price is either moving or it is consolidating. Fractal Energy will show you which move is about to happen. The funny thing is.. Fractal Energy will NOT tell you the direction of the potential move nor the time when the move will happen. It only shows whether the energy is accumulating or exhausting and which one of these moves are about to happen.
Fractal Energy Zones:
1. Gray = Neutral Energy, price will spend most of the time between the 0.60 and 0.30 ranges, if the FE is hanging around midline chop can be expected.
2. Pink = Energy Building (low compression), pink can produce solid moves but can turn in to a red or dark red which are way more powerful.
3. Red = Energy Building (yet to be released), once the FE colors red there is a high probability for a bigger than expected move.
4. Dark Red = Energy Building (high compression), dark red is rare and can be seen usually around earnings reports (explosive move can be expected).
5. Yellow = Energy Released = Exhaustion, everything ends at some point, yellow color represents the exhaustion of the move (the car ran out of gas).
6. Orange = Extreme Exhaustion, high probability for a sideways action or a reversal.
Tips & Tricks:
1. Importance of the Midline:
- Midline can be used as a target for the compressions. Once the FE reach the midpoint, the move is usually considered to be over.
2. Huge Gaps on earnings DO NOT COUNT:
- If the price heavily gaps up or gaps down, FE usually drops too steep with the gap thus signals after such moves can be ignored.
3. Fractal Energy Length & Time Frames:
- For the daily & weekly time frame length of 13 works nicely. But for the lower TF length 13 starts to lag behind the price a little. I would suggest using Length 15 for 30min to 4 hour and Length 17 - 20 for below 30min time frames.
4. Exhaustions:
- Exhaustions can be played too. Once the FE drops below 0.30 the price usually stays within the weekly expected move (great for iron condors), or non directional option strategies.. yellow/orange = price either reverse or stays at same levels for a few candles..
5. Combination of direction based studies with the magnitude based studies:
- Use the FE as a confirmation of your analysis from other (direction-based) trend or momentum indicators. Once you analyze your direction you can use Fractal Energy (magnitute-based) indicator to analyse whether there is a chance for a big move or not.
Hope it helps.
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment
## 1. Introduction
Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985).
The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014).
## 2. Theoretical Foundation
### 2.1 Momentum Theory and Market Anomalies
The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015).
### 2.2 Drawdown Analysis and Risk Management
Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators.
### 2.3 Adaptive Smoothing and Market Regimes
The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets.
## 3. Methodology
### 3.1 Core Algorithm Design
The RAMO algorithm consists of several interconnected components:
#### 3.1.1 Risk-Adjusted Momentum Calculation
The fundamental innovation of RAMO lies in its risk adjustment mechanism:
Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor)
Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05)
This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952).
#### 3.1.2 Multi-Algorithm Momentum Framework
RAMO supports three distinct momentum calculation methods:
1. Rate of Change: Traditional percentage-based momentum (Pring, 2002)
2. Price Momentum: Absolute price differences
3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997)
This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013).
### 3.2 Leading Indicator Components
#### 3.2.1 Momentum Acceleration Analysis
The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes:
Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n)
This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969).
#### 3.2.2 Linear Regression Prediction
RAMO incorporates linear regression-based prediction to project momentum values forward:
Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset)
This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes.
#### 3.2.3 Volume-Based Exhaustion Detection
The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns:
Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20)
This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989).
### 3.3 Statistical Normalization and Robustness
RAMO employs Z-score normalization with outlier protection to ensure statistical robustness:
Z_Score = (Value - Mean) / Standard_Deviation
Normalized_Value = max(-3.5, min(3.5, Z_Score))
This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions.
### 3.4 Adaptive Threshold Calculation
Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992):
Upper_Threshold = Mean + (Multiplier × Standard_Deviation)
Lower_Threshold = Mean - (Multiplier × Standard_Deviation)
This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992).
## 4. Implementation Details
### 4.1 Adaptive Smoothing Algorithm
The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility:
Volatility_Percentile = Percentrank(Volatility, 100)
Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100)
This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995).
### 4.2 Risk Environment Classification
RAMO classifies market conditions into three risk environments:
- Low Risk: Current_DD < 30% × Max_DD
- Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD
- High Risk: Current_DD ≥ 70% × Max_DD
This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014).
## 5. Signal Generation and Interpretation
### 5.1 Entry Signal Logic
RAMO generates enhanced entry signals through multiple confirmation layers:
1. Primary Signal: Crossover between indicator and signal line
2. Risk Filter: Confirmation of favorable risk environment for long positions
3. Leading Component: Early warning signals via acceleration analysis
4. Exhaustion Filter: Volume-based reversal detection
This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992).
### 5.2 Divergence Analysis
RAMO incorporates both traditional and leading divergence detection:
- Traditional Divergence: Price and indicator divergence over 3-5 periods
- Slope Divergence: Momentum slope versus price direction
- Acceleration Divergence: Changes in momentum acceleration
This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999).
## 6. Empirical Advantages and Applications
### 6.1 Risk-Adjusted Performance
The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980).
### 6.2 Regime Awareness
RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments.
### 6.3 Institutional Applications
The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013).
## 7. Limitations and Future Research
### 7.1 Parameter Sensitivity
Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions.
### 7.2 Market Microstructure Considerations
RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance.
### 7.3 Transaction Cost Integration
Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004).
## References
Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons.
Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press.
Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin.
Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985.
Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764.
Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press.
Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58.
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247.
Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84.
Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall.
Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291.
Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill.
Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082.
Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91.
Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance.
Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library.
Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill.
Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press.
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790.
Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314.
Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239.
Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Historical Volatility Markets oscillate from periods of low volatility to high volatility
and back. The author`s research indicates that after periods of
extremely low volatility, volatility tends to increase and price
may move sharply. This increase in volatility tends to correlate
with the beginning of short- to intermediate-term moves in price.
They have found that we can identify which markets are about to make
such a move by measuring the historical volatility and the application
of pattern recognition.
The indicator is calculating as the standard deviation of day-to-day
logarithmic closing price changes expressed as an annualized percentage.
ALEX - ATR Extensions + ADR + TableALEX - ATR Extensions + ADR + Table
Overview
The ALEX ATR Extensions indicator is a comprehensive volatility and momentum analysis tool that combines Average True Range (ATR), Average Daily Range (ADR), and moving average distance calculations in a single, customizable display. This indicator helps traders assess current price action relative to historical volatility and key moving averages, providing crucial context for risk management and trade planning.
Key Features
Multi-Metric Analysis
- ATR Percentage: Current ATR as a percentage of price for volatility assessment
- ADR Percentage: Average Daily Range as a percentage for typical daily movement
- Low of Day Distance: Distance from current price to daily low
- Moving Average Distance: ATR-normalized distance from 21 and 50 period moving averages
Flexible Moving Average Options
- Configurable MA Types: Choose between EMA or SMA for both 21 and 50 period averages
- Customizable Periods: Adjust moving average lengths to suit your trading style
- Daily Timeframe Data: Uses daily moving averages regardless of chart timeframe
ATR Extension Levels
- Dynamic Price Targets: Calculate extension levels based on ATR multiples from moving averages
- Visual Reference Lines: Optional overlay lines showing ATR extension targets
- Customizable Multipliers: Adjust ATR multipliers for different risk/reward scenarios
Smart Visual Alerts
- Color-Coded Distance Metrics: Automatic color changes based on distance thresholds
- Symbol Plotting: Customizable chart symbols when distance thresholds are exceeded
- Threshold-Based Alerts: Visual cues when price reaches significant ATR distances
Comprehensive Data Table
- Real-Time Metrics: Live updating table with all key measurements
- Customizable Display: Toggle individual metrics on/off based on preference
- Professional Styling: Adjustable colors, fonts, and transparency
How to Use
Volatility Assessment
- High ATR%: Indicates elevated volatility, larger position sizing considerations
- Low ATR%: Suggests compressed volatility, potential for expansion
- ADR% Comparison: Compare current day's range to historical average
Moving Average Analysis
- ATR Distance 21/50: Normalized distance showing how extended price is from key levels
- Positive Values: Price above moving average (bullish positioning)
- Negative Values: Price below moving average (bearish positioning)
- Color Changes: Automatic alerts when reaching threshold levels
Extension Target Planning
- ATR Extension Lines: Visual price targets based on volatility-adjusted projections
- Risk/Reward Planning: Use extension levels for profit target placement
- Breakout Confirmation: Extension levels can confirm breakout validity
Symbol Alert System
- Chart Symbols: Automatic plotting when distance thresholds are breached
- Customizable Triggers: Set your own threshold levels for alerts
- Visual Scanning: Quick identification of extended conditions across multiple charts
Settings
Display Controls
- Show ADR%: Toggle average daily range percentage display
- Show ATR%: Toggle average true range percentage display
- Show LoD Distance: Toggle low of day distance calculation
- Show LoD Price: Toggle actual low of day price display
- Show ATR Distance from 21/50 DMA: Toggle moving average distance metrics
- Show 21/50 DMA Price: Toggle actual moving average price display
- Show ATR Extension Levels: Toggle extension target display in table
Moving Average Configuration
- 21/50 DMA Type: Choose between EMA or SMA calculation methods
- 21/50 DMA Period: Customize moving average lengths
- ADR/ATR Length: Adjust calculation periods for range measurements
Color Thresholds
- Threshold Levels: Set distance levels for color changes (default 2.0 and 5.0)
- Custom Colors: Choose colors for different threshold breaches
- Separate 21/50 Settings: Independent color schemes for each moving average
Symbol Settings
- Show Char Symbol: Toggle symbol plotting for each moving average
- Custom Symbols: Choose any character for chart plotting
- Symbol Colors: Customize colors for visual distinction
- Threshold Levels: Set trigger points for symbol appearance
ATR Extension Lines
- Show Extension Lines: Toggle visual extension level lines
- ATR Multipliers: Customize extension distance (default 2.0x)
- Line Colors: Choose colors for extension level visualization
Table Customization
- Background Color: Adjust table transparency and color
- Text Color: Customize default text appearance
- Font Size: Choose from tiny to huge font options
Advanced Applications
Trend Strength Analysis
- Large ATR distances suggest strong trending moves
- Small ATR distances indicate potential consolidation or reversal zones
- Compare current readings to recent historical ranges
Risk Management
- Use ATR% for position sizing calculations
- Extension levels provide natural profit target zones
- Distance metrics help identify overextended conditions
Multi-Timeframe Context
- Apply to different timeframes for comprehensive analysis
- Daily data provides consistency across all chart intervals
- Combine with weekly/monthly analysis for broader context
Market Regime Identification
- High volatility periods: Increased ATR% readings
- Low volatility periods: Compressed ATR% readings
- Trending markets: Sustained high distance readings
- Consolidating markets: Low distance readings with frequent color changes
Best Practices
Volatility-Adjusted Trading
- Increase position sizes during low volatility periods
- Reduce position sizes during high volatility periods
- Use ATR% for stop-loss placement relative to normal market movement
Extension Level Usage
- Primary targets: 1.5-2.0x ATR extensions
- Secondary targets: 2.5-3.0x ATR extensions
- Avoid chasing prices beyond 3x ATR extensions
Threshold Optimization
- Backtest different threshold levels for your trading style
- Consider market conditions when setting alert levels
- Adjust thresholds based on instrument volatility characteristics
Integration Strategies
- Combine with momentum indicators for confirmation
- Use alongside support/resistance levels
- Incorporate into systematic trading approaches
Technical Specifications
- Compatible with Pine Script v6
- Uses daily timeframe data for consistency
- Optimized for real-time performance
- Works on all chart types and timeframes
- Supports all tradeable instruments
Ideal For
- Swing traders using daily charts
- Position traders seeking volatility context
- Day traders needing intraday reference levels
- Risk managers requiring volatility metrics
- Systematic traders building rule-based strategies
Disclaimer
This indicator is for educational and informational purposes only. It should not be used as the sole basis for trading decisions. Always combine with other forms of analysis, proper risk management techniques, and consider your individual trading plan and risk tolerance. Past performance does not guarantee future results.
Compatible with Pine Script v6 | Optimized for daily timeframe analysis | Works across all markets and instruments
NTL SCALP v2 with TP & SL (Absolute)🧠 NTL SCALP v2 with TP & SL (Absolute) – Smart Scalping Indicator
Author: NTL Team
Markets: Forex, Gold (XAUUSD), Crypto
Recommended Timeframes: 1–15 minutes (Optimal: M3, M5)
📌 Key Features:
Automatic BUY/SELL signals with clear entry points.
Displays TP1, TP2, TP3, TP4, and SL directly on the chart.
Win rate statistics panel showing hit rates for all targets.
Dynamic EMA bands to identify trend direction and key zones.
Noise filtering mechanism to avoid counter-trend entries.
Optimized for fast-paced scalping and precise reversal detection.
📊 Example:
Entry: BUY at 3376.72 → TP1 HIT, TP2 HIT, TP3 HIT...
Real-time win rate displayed: 97.1%
Total signals during session: 1274
⚙️ Customizable Settings:
TP and SL are calculated using absolute values (in price points).
Adjustable levels for SL and all 4 TPs to fit your strategy.
Easily integrated with alert systems or trading bots (EAs).
You can include the chart image as a reference to show its visual clarity and performance.
Let me know if you'd like me to help convert this into a Pine Script version or add more technical explanations.
BACAP PRICE STRUCTURE 21 EMA TREND21dma-STRUCTURE
Overview
The 21dma-STRUCTURE indicator is a sophisticated overlay indicator that visualizes price action relative to a triple 21-period exponential moving average structure. Originally developed by BalarezoCapital and enhanced by PrimeTrading, this indicator provides clear visual cues for trend direction and momentum through dynamic bar coloring and EMA structure analysis.
Key Features
Triple EMA Structure
- 21 EMA High: Tracks the exponential moving average of high prices
- 21 EMA Close: Tracks the exponential moving average of closing prices
- 21 EMA Low: Tracks the exponential moving average of low prices
- Dynamic Cloud: Gray fill between high and low EMAs for visual structure reference
Smart Bar Coloring System
- Blue Bars: Price closes above all three EMAs (strong bullish momentum)
- Pink Bars: Daily high falls below the lowest EMA (strong bearish signal)
- Gray Bars: Neutral conditions or transitional phases
- Color Memory: Maintains previous color until new condition is met
Dynamic Center Line
- Trend-Following Color: Green when all EMAs are rising, red when all are falling
- Color Persistence: Maintains trend color during sideways movement
- Visual Clarity: Thicker center line for easy trend identification
Customizable Visual Elements
- Adjustable line thickness for all EMA plots
- Customizable colors for bullish and bearish conditions
- Configurable trend colors for uptrend and downtrend phases
- Optional bar color changes with toggle control
How to Use
Trend Identification
- Rising Green Center Line: All EMAs trending upward (bullish structure)
- Falling Red Center Line: All EMAs trending downward (bearish structure)
- Flat Center Line: Maintains last trend color during consolidation
Momentum Analysis
- Blue Bars: Strong bullish momentum with price above entire EMA structure
- Pink Bars: Strong bearish momentum with high below lowest EMA
- Gray Bars: Neutral or transitional momentum phases
Entry and Exit Signals
- Bullish Setup: Look for blue bars during green center line periods
- Bearish Setup: Look for pink bars during red center line periods
- Exit Consideration: Watch for color changes as potential momentum shifts
Structure Trading
- Support/Resistance: Use EMA cloud as dynamic support and resistance zones
- Breakout Confirmation: Bar color changes can confirm structure breakouts
- Trend Continuation: Color persistence suggests ongoing momentum
Settings
Visual Customization
- Change Bar Color: Toggle to enable/disable bar coloring
- Line Size: Adjust thickness of EMA lines (default: 3)
- Bullish Candle Color: Customize blue bar color
- Bearish Candle Color: Customize pink bar color
Trend Colors
- Uptrend Color: Color for rising EMA center line (default: green)
- Downtrend Color: Color for falling EMA center line (default: red)
- Cloud Color: Fill color between high and low EMAs (default: gray)
Advanced Features
Modified Bar Logic
Unlike traditional EMA systems, this indicator uses refined conditions:
- Bullish signals require close above ALL three EMAs
- Bearish signals require high below the LOWEST EMA
- Enhanced precision reduces false signals compared to single EMA systems
Trend Memory System
- Intelligent color persistence during sideways movement
- Reduces noise from minor EMA fluctuations
- Maintains trend context during consolidation periods
Performance Optimization
- Efficient calculation methods for real-time performance
- Clean visual design that doesn't clutter charts
- Compatible with all timeframes and instruments
Best Practices
Multi-Timeframe Analysis
- Use higher timeframes to identify overall trend direction
- Apply on multiple timeframes for confluence
- Combine with weekly/monthly charts for position trading
Risk Management
- Use bar color changes as early warning signals
- Consider position sizing based on EMA structure strength
- Set stops relative to EMA support/resistance levels
Combination Strategies
- Pair with volume indicators for confirmation
- Use alongside RSI or MACD for momentum confirmation
- Combine with key support/resistance levels
Market Context
- More effective in trending markets than choppy conditions
- Consider overall market environment and sector strength
- Adjust expectations during high volatility periods
Technical Specifications
- Based on 21-period exponential moving averages
- Uses Pine Script v6 for optimal performance
- Overlay indicator that works with any chart type
- Maximum 500 lines for clean performance
Ideal Applications
- Swing trading on daily charts
- Position trading on weekly charts
- Intraday momentum trading (adjust timeframe accordingly)
- Trend following strategies
- Structure-based trading approaches
Disclaimer
This indicator is for educational and informational purposes only. It should not be used as the sole basis for trading decisions. Always combine with other forms of analysis, proper risk management, and consider your individual trading plan and risk tolerance.
Compatible with Pine Script v6 | Works on all timeframes | Optimized for trending markets
Improved Stoch RSI + Supertrend Filter + ATR SL/TPThis custom indicator, "Improved Stoch RSI + Supertrend Filter + ATR SL/TP," is a powerful tool designed to generate high-probability trading signals in trending markets. It combines three technical indicators:
1. **Stochastic RSI** – Provides overbought and oversold signals by calculating the stochastic of the RSI, which helps identify momentum reversals.
2. **Supertrend Filter** – A trend-following indicator that filters signals to only trade in the direction of the current trend, reducing false signals and improving overall accuracy.
3. **ATR-based Stop-Loss and Take-Profit** – Uses the Average True Range (ATR) multiplied by a 1.5 factor to set dynamic stop-loss levels, and calculates take-profit levels based on a configurable Risk-Reward Ratio (default: 1.5).
**How it works:**
* When the %K line of the Stochastic RSI crosses above the %D line below the oversold level (default: 20), and the Supertrend indicates an uptrend, a **long trade signal** is generated.
* When the %K line of the Stochastic RSI crosses below the %D line above the overbought level (default: 80), and the Supertrend indicates a downtrend, a **short trade signal** is generated.
* Each trade signal comes with a plotted stop-loss and take-profit level based on the ATR, giving you predefined risk management points.
This indicator helps traders:
* Trade only with the prevailing trend
* Identify reversal points with high accuracy
* Manage risk consistently with ATR-based stop-loss and take-profit
It's suitable for all timeframes and can be used as a standalone system or to complement other trading strategies.
Universal Global SessionUniversal Global Session
This Script combines the world sessions of: Stocks, Forex, Bitcoin Kill Zones, strategic points, all configurable, in a single Script, to capitalize the opening and closing times of global exchanges as investment assets, becoming an Universal Global Session .
It is based on the great work of @oscarvs ( BITCOIN KILL ZONES v2 ) and the scripts of @ChrisMoody. Thank you Oscar and Chris for your excellent judgment and great work.
At the end of this writing you can find all the internet references of the extensive documentation that I present here. To maximize your benefits in the use of this Script, I recommend that you read the entire document to create an objective and practical criterion.
All the hours of the different exchanges are presented at GMT -6. In Market24hClock you can adjust it to your preferences.
After a deep investigation I have been able to show that the different world sessions reveal underlying investment cycles, where it is possible to find sustained changes in the nominal behavior of the trend before the passage from one session to another and in the natural overlaps between the sessions. These underlying movements generally occur 15 minutes before the start, close or overlap of the session, when the session properly starts and also 15 minutes after respectively. Therefore, this script is designed to highlight these particular trending behaviors. Try it, discover your own conclusions and let me know in the notes, thank you.
Foreign Exchange Market Hours
It is the schedule by which currency market participants can buy, sell, trade and speculate on currencies all over the world. It is open 24 hours a day during working days and closes on weekends, thanks to the fact that operations are carried out through a network of information systems, instead of physical exchanges that close at a certain time. It opens Monday morning at 8 am local time in Sydney —Australia— (which is equivalent to Sunday night at 7 pm, in New York City —United States—, according to Eastern Standard Time), and It closes at 5pm local time in New York City (which is equivalent to 6am Saturday morning in Sydney).
The Forex market is decentralized and driven by local sessions, where the hours of Forex trading are based on the opening range of each active country, becoming an efficient transfer mechanism for all participants. Four territories in particular stand out: Sydney, Tokyo, London and New York, where the highest volume of operations occurs when the sessions in London and New York overlap. Furthermore, Europe is complemented by major financial centers such as Paris, Frankfurt and Zurich. Each day of forex trading begins with the opening of Australia, then Asia, followed by Europe, and finally North America. As markets in one region close, another opens - or has already opened - and continues to trade in the currency market. The seven most traded currencies in the world are: the US dollar, the euro, the Japanese yen, the British pound, the Australian dollar, the Canadian dollar, and the New Zealand dollar.
Currencies are needed around the world for international trade, this means that operations are not dominated by a single exchange market, but rather involve a global network of brokers from around the world, such as banks, commercial companies, central banks, companies investment management, hedge funds, as well as retail forex brokers and global investors. Because this market operates in multiple time zones, it can be accessed at any time except during the weekend, therefore, there is continuously at least one open market and there are some hours of overlap between the closing of the market of one region and the opening of another. The international scope of currency trading means that there are always traders around the world making and satisfying demands for a particular currency.
The market involves a global network of exchanges and brokers from around the world, although time zones overlap, the generally accepted time zone for each region is as follows:
Sydney 5pm to 2am EST (10pm to 7am UTC)
London 3am to 12 noon EST (8pm to 5pm UTC)
New York 8am to 5pm EST (1pm to 10pm UTC)
Tokyo 7pm to 4am EST (12am to 9am UTC)
Trading Session
A financial asset trading session refers to a period of time that coincides with the daytime trading hours for a given location, it is a business day in the local financial market. This may vary according to the asset class and the country, therefore operators must know the hours of trading sessions for the securities and derivatives in which they are interested in trading. If investors can understand market hours and set proper targets, they will have a much greater chance of making a profit within a workable schedule.
Kill Zones
Kill zones are highly liquid events. Many different market participants often come together and perform around these events. The activity itself can be event-driven (margin calls or option exercise-related activity), portfolio management-driven (asset allocation rebalancing orders and closing buy-in), or institutionally driven (larger players needing liquidity to complete the size) or a combination of any of the three. This intense cross-current of activity at a very specific point in time often occurs near significant technical levels and the established trends emerging from these events often persist until the next Death Zone approaches or enters.
Kill Zones are evolving with time and the course of world history. Since the end of World War II, New York has slowly invaded London's place as the world center for commercial banking. So much so that during the latter part of the 20th century, New York was considered the new center of the financial universe. With the end of the cold war, that leadership appears to have shifted towards Europe and away from the United States. Furthermore, Japan has slowly lost its former dominance in the global economic landscape, while Beijing's has increased dramatically. Only time will tell how these death zones will evolve given the ever-changing political, economic, and socioeconomic influences of each region.
Financial Markets
New York
New York (NYSE Chicago, NASDAQ)
7:30 am - 2:00 pm
It is the second largest currency platform in the world, followed largely by foreign investors as it participates in 90% of all operations, where movements on the New York Stock Exchange (NYSE) can have an immediate effect (powerful) on the dollar, for example, when companies merge and acquisitions are finalized, the dollar can instantly gain or lose value.
A. Complementary Stock Exchanges
Brazil (BOVESPA - Brazilian Stock Exchange)
07:00 am - 02:55 pm
Canada (TSX - Toronto Stock Exchange)
07:30 am - 02:00 pm
New York (NYSE - New York Stock Exchange)
08:30 am - 03:00 pm
B. North American Trading Session
07:00 am - 03:00 pm
(from the beginning of the business day on NYSE and NASDAQ, until the end of the New York session)
New York, Chicago and Toronto (Canada) open the North American session. Characterized by the most aggressive trading within the markets, currency pairs show high volatility. As the US markets open, trading is still active in Europe, however trading volume generally decreases with the end of the European session and the overlap between the US and Europe.
C. Strategic Points
US main session starts in 1 hour
07:30 am
The euro tends to drop before the US session. The NYSE, CHX and TSX (Canada) trading sessions begin 1 hour after this strategic point. The North American session begins trading Forex at 07:00 am.
This constitutes the beginning of the overlap of the United States and the European market that spans from 07:00 am to 10:35 am, often called the best time to trade EUR / USD, it is the period of greatest liquidity for the main European currencies since it is where they have their widest daily ranges.
When New York opens at 07:00 am the most intense trading begins in both the US and European markets. The overlap of European and American trading sessions has 80% of the total average trading range for all currency pairs during US business hours and 70% of the total average trading range for all currency pairs during European business hours. The intersection of the US and European sessions are the most volatile overlapping hours of all.
Influential news and data for the USD are released between 07:30 am and 09:00 am and play the biggest role in the North American Session. These are the strategically most important moments of this activity period: 07:00 am, 08:00 am and 08:30 am.
The main session of operations in the United States and Canada begins
08:30 am
Start of main trading sessions in New York, Chicago and Toronto. The European session still overlaps the North American session and this is the time for large-scale unpredictable trading. The United States leads the market. It is difficult to interpret the news due to speculation. Trends develop very quickly and it is difficult to identify them, however trends (especially for the euro), which have developed during the overlap, often turn the other way when Europe exits the market.
Second hour of the US session and last hour of the European session
09:30 am
End of the European session
10:35 am
The trend of the euro will change rapidly after the end of the European session.
Last hour of the United States session
02:00 pm
Institutional clients and very large funds are very active during the first and last working hours of almost all stock exchanges, knowing this allows to better predict price movements in the opening and closing of large markets. Within the last trading hours of the secondary market session, a pullback can often be seen in the EUR / USD that continues until the opening of the Tokyo session. Generally it happens if there was an upward price movement before 04:00 pm - 05:00 pm.
End of the trade session in the United States
03:00 pm
D. Kill Zones
11:30 am - 1:30 pm
New York Kill Zone. The United States is still the world's largest economy, so by default, the New York opening carries a lot of weight and often comes with a huge injection of liquidity. In fact, most of the world's marketable assets are priced in US dollars, making political and economic activity within this region even more important. Because it is relatively late in the world's trading day, this Death Zone often sees violent price swings within its first hour, leading to the proven adage "never trust the first hour of trading in America. North.
---------------
London
London (LSE - London Stock Exchange)
02:00 am - 10:35 am
Britain dominates the currency markets around the world, and London is its main component. London, a central trading capital of the world, accounts for about 43% of world trade, many Forex trends often originate from London.
A. Complementary Stock Exchange
Dubai (DFM - Dubai Financial Market)
12:00 am - 03:50 am
Moscow (MOEX - Moscow Exchange)
12:30 am - 10:00 am
Germany (FWB - Frankfurt Stock Exchange)
01:00 am - 10:30 am
Afríca (JSE - Johannesburg Stock Exchange)
01:00 am - 09:00 am
Saudi Arabia (TADAWUL - Saudi Stock Exchange)
01:00 am - 06:00 am
Switzerland (SIX - Swiss Stock Exchange)
02:00 am - 10:30 am
B. European Trading Session
02:00 am - 11:00 am
(from the opening of the Frankfurt session to the close of the Order Book on the London Stock Exchange / Euronext)
It is a very liquid trading session, where trends are set that start during the first trading hours in Europe and generally continue until the beginning of the US session.
C. Middle East Trading Session
12:00 am - 06:00 am
(from the opening of the Dubai session to the end of the Riyadh session)
D. Strategic Points
European session begins
02:00 am
London, Frankfurt and Zurich Stock Exchange enter the market, overlap between Europe and Asia begins.
End of the Singapore and Asia sessions
03:00 am
The euro rises almost immediately or an hour after Singapore exits the market.
Middle East Oil Markets Completion Process
05:00 am
Operations are ending in the European-Asian market, at which time Dubai, Qatar and in another hour in Riyadh, which constitute the Middle East oil markets, are closing. Because oil trading is done in US dollars, and the region with the trading day coming to an end no longer needs the dollar, consequently, the euro tends to grow more frequently.
End of the Middle East trading session
06:00 am
E. Kill Zones
5:00 am - 7:00 am
London Kill Zone. Considered the center of the financial universe for more than 500 years, Europe still has a lot of influence in the banking world. Many older players use the European session to establish their positions. As such, the London Open often sees the most significant trend-setting activity on any trading day. In fact, it has been suggested that 80% of all weekly trends are set through the London Kill Zone on Tuesday.
F. Kill Zones (close)
2:00 pm - 4:00 pm
London Kill Zone (close).
---------------
Tokyo
Tokyo (JPX - Tokyo Stock Exchange)
06:00 pm - 12:00 am
It is the first Asian market to open, receiving most of the Asian trade, just ahead of Hong Kong and Singapore.
A. Complementary Stock Exchange
Singapore (SGX - Singapore Exchange)
07:00 pm - 03:00 am
Hong Kong (HKEx - Hong Kong Stock Exchange)
07:30 pm - 02:00 am
Shanghai (SSE - Shanghai Stock Exchange)
07:30 pm - 01:00 am
India (NSE - India National Stock Exchange)
09:45 pm - 04:00 am
B. Asian Trading Session
06:00 pm - 03:00 am
From the opening of the Tokyo session to the end of the Singapore session
The first major Asian market to open is Tokyo which has the largest market share and is the third largest Forex trading center in the world. Singapore opens in an hour, and then the Chinese markets: Shanghai and Hong Kong open 30 minutes later. With them, the trading volume increases and begins a large-scale operation in the Asia-Pacific region, offering more liquidity for the Asian-Pacific currencies and their crosses. When European countries open their doors, more liquidity will be offered to Asian and European crossings.
C. Strategic Points
Second hour of the Tokyo session
07:00 pm
This session also opens the Singapore market. The commercial dynamics grows in anticipation of the opening of the two largest Chinese markets in 30 minutes: Shanghai and Hong Kong, within these 30 minutes or just before the China session begins, the euro usually falls until the same moment of the opening of Shanghai and Hong Kong.
Second hour of the China session
08:30 pm
Hong Kong and Shanghai start trading and the euro usually grows for more than an hour. The EUR / USD pair mixes up as Asian exporters convert part of their earnings into both US dollars and euros.
Last hour of the Tokyo session
11:00 pm
End of the Tokyo session
12:00 am
If the euro has been actively declining up to this time, China will raise the euro after the Tokyo shutdown. Hong Kong, Shanghai and Singapore remain open and take matters into their own hands causing the growth of the euro. Asia is a huge commercial and industrial region with a large number of high-quality economic products and gigantic financial turnover, making the number of transactions on the stock exchanges huge during the Asian session. That is why traders, who entered the trade at the opening of the London session, should pay attention to their terminals when Asia exits the market.
End of the Shanghai session
01:00 am
The trade ends in Shanghai. This is the last trading hour of the Hong Kong session, during which market activity peaks.
D. Kill Zones
10:00 pm - 2:00 am
Asian Kill Zone. Considered the "Institutional" Zone, this zone represents both the launch pad for new trends as well as a recharge area for the post-American session. It is the beginning of a new day (or week) for the world and as such it makes sense that this zone often sets the tone for the remainder of the global business day. It is ideal to pay attention to the opening of Tokyo, Beijing and Sydney.
--------------
Sidney
Sydney (ASX - Australia Stock Exchange)
06:00 pm - 12:00 am
A. Complementary Stock Exchange
New Zealand (NZX - New Zealand Stock Exchange)
04:00 pm - 10:45 pm
It's where the global trading day officially begins. While it is the smallest of the megamarkets, it sees a lot of initial action when markets reopen Sunday afternoon as individual traders and financial institutions are trying to regroup after the long hiatus since Friday afternoon. On weekdays it constitutes the end of the current trading day where the change in the settlement date occurs.
B. Pacific Trading Session
04:00 pm - 12:00 am
(from the opening of the Wellington session to the end of the Sydney session)
Forex begins its business hours when Wellington (New Zealand Exchange) opens local time on Monday. Sydney (Australian Stock Exchange) opens in 2 hours. It is a session with a fairly low volatility, configuring itself as the calmest session of all. Strong movements appear when influential news is published and when the Pacific session overlaps the Asian Session.
C. Strategic Points
End of the Sydney session
12:00 am
---------------
Conclusions
The best time to trade is during overlaps in trading times between open markets. Overlaps equate to higher price ranges, creating greater opportunities.
Regarding press releases (news), it should be noted that these in the currency markets have the power to improve a normally slow trading period. When a major announcement is made regarding economic data, especially when it goes against the predicted forecast, the coin can lose or gain value in a matter of seconds. In general, the more economic growth a country produces, the more positive the economy is for international investors. Investment capital tends to flow to countries that are believed to have good growth prospects and subsequently good investment opportunities, leading to the strengthening of the country's exchange rate. Also, a country that has higher interest rates through its government bonds tends to attract investment capital as foreign investors seek high-yield opportunities. However, stable economic growth and attractive yields or interest rates are inextricably intertwined. It's important to take advantage of market overlaps and keep an eye out for press releases when setting up a trading schedule.
References:
www.investopedia.com
www.investopedia.com
www.investopedia.com
www.investopedia.com
market24hclock.com
market24hclock.com
GA - Momentum DivergencesGA Momentum Divergences Script highlights Trend Strength, Overbought-Oversold Conditions, Regular-Hidden Divergences. Besides, it shows the Buying-Selling Pressure.
The practical use of any Momentum Curve helps in the comprehension of:
Supply-Demand Absorption.
Thrusts and their shortening.
The reversing and the continuation of the trend.
True Strength of the Trend.
Price Strength.
Increase and Decrease in Buying-Selling Pressure.
You choose which curve to show, according to your needs. There are 2 groups of curves.
Momentum Curves
GA Momentum.
Commodity Channel Index ( CCI ).
Relative Strength Index ( RSI ).
Stochastic %K.
True Strength Indicator ( TSI ).
Money Flow Curves
GA Money Flow.
Chaikin Money Flow.
Money Flow Index.
Every Curve used in this script has 0 as center. This means that RSI and Stochastic Curves wave around 0 and not around 50.
Fractal Algorithm for Pivots and Divergences
GA Momentum script highlights Divergences. This is possible by the Fractal Calculation of Pivot Points .
The sensibility of the algorithm depends on the look back and on the look forward of pivot points . This means that it does not highlight every divergence. But it marks divergences according to settings.
Besides, the interpretation of those divergences depends on the experience of the trader.
This feature has a particular use for the purpose to simplify and optimize. Besides, it is a very important feature provided by the GA Money Flow script.
Regular and Hidden Divergences highlight the weakening and strengthening of the price behavior. They give an anticipation to price changing. Besides, they enforce the judgment on the condition that marks the price continuation.
The Fractal Algorithm can also mark a Channel. This happens enveloping the Curve between its marked pivot points .
Flags and lines mark Divergences in the Curve. GA Momentum Divergences highlights Regular Divergences and Hidden Divergences.
Price and Momentum, Volume and Money Flow
The GA Momentum script works with any marketplace. It uses price variations and volume variations, according to needs and market.
Every curve available in the script is a mathematical discretization of the market. But in those marketplaces that includes the volume you can use Money Flow Curves. Where the volume is missing the Money Flow Curves return zero. In this case, a Momentum Curve is the right choice because it uses the price variations.
GA Momentum and GA Money Flow are formulas built for this script. They include several peculiarities that are a privilege of other functions. This gives a better visual impact by their practical use.
TSI Curve or RSI Curve are the right choices to replace Money Flow Curves where the volume is not available. In the same way, RSI Curve can replace the TSI Curve for the Trend Strength. Then, the RSI Curve is universal. It works on any marketplace giving a lot of information, using it in the right way.
RSI is a slow curve. It waves above and below the middle line, according to the bullish and bearish trend . This is why it incorporates the Trend Strength in its calculation.
Instead, other choices give Faster Momentum Curves that give different advantages and peculiarities. The final result and purpose do not change.
Market Conditions
Overbought and Oversold Conditions could not cause the immediate reversing of the trend. The changing occurs according to Thrusts and their shortening.
This happens by one or more rebounds in the price action. Indeed, this marks hesitation to continue the advancing or the declining of the price.
The Momentum Curve can highlight the absorption of Supply Pressure and Supporting Demand. This precedes the Climactic Point so as a Thrust during the advancing or declining of the price.
True Strength and Money Flow curves follow the trend. They show where the trend is weakening or strengthening.
When these curves rise together with the trend, this confirms the trend. Instead, when these curves hesitate, they are marking a changing.
TSI and Money Flow have advantages. They show the continuation of the trend by its positive or negative value. Besides, they show the shortening of the trend. Moreover, the curve anticipates the shortening of the thrust.
Money Flow Curves highlights the prevailing of Buying Pressure of Selling Pressure. This is possible because their formulas includes the volume . But the TSI discretization that uses prices, works giving a fair result.
This returns an unconditional conclusion. The volume has a high relevance because of the correlation between effort and result. But despite this, the mathematical discretization of the market can work without it.
Short and Long Signal Lines
The GA Momentum plots 2 extra curves to support the market momentum interpretation. They are Exponential Moving Average applied to the momentum curve.
The Short Signal Line follows the main curve and it gives the first crossing for an entry signal. Of course, this is useful only when there are the right condition for an entry point.
Instead, the Long Signal Line exists to be a trending indicator. When the main curve is approaching it, rebounds, the shortening of the thrust, can mark a changing. Following the thrust, these curves become closer and closer for some waves. This becomes better visible by the plotting of the Histogram.
The Histogram shows the difference between the main curve and the Long Signal Line. The distance between those curves becomes relevant and helpful in many circumstances. This highlights the changing in the Strength or Weakness of the trend.
Short and Long Signal Curves can have a partial plotting. This reduces the impact of those curves on screen. The script can show them only when they give a relevant visual impact for the trading practice.
Coloring
GA Momentum Script colors curve and price bars. It highlights conditions where the price is Overbought or Oversold. But it highlights also divergences with labels and colored lines.
The script plots colors on bars with extended prices. Besides, the script plots colors on bars that are the ending of divergences
GA Momentum script colors the price bars using the same criteria applied to color curves. Color used on the Curve are the same used on the price bars.
True Strength Curve and Momentum Curves color price bars. This happens for the entire Trend Strength. Then the prevailing of the Buying Pressure or Bearish Pressure is also visible on bars. This occurs by the persistent green or red colors according to Pressure and Trend.
Alerts
GA Momentum provides 2 alerts for Bearish and Bullish Signals. Both uses the crossing of Short and Long Signals in the same direction.
Note: I restrict access to the tool.
Regards
Girolamo Aloe
Founder of Profiting Me
Enhanced Volume Trend Indicator with BB SqueezeEnhanced Volume Trend Indicator with BB Squeeze: Comprehensive Explanation
The visualization system allows traders to quickly scan multiple securities to identify high-probability setups without detailed analysis of each chart. The progression from squeeze to breakout, supported by volume trend confirmation, offers a systematic approach to identifying trading opportunities.
The script combines multiple technical analysis approaches into a comprehensive dashboard that helps traders make informed decisions by identifying high-probability setups while filtering out noise through its sophisticated confirmation requirements. It combines multiple technical analysis approaches into an integrated visual system that helps traders identify potential trading opportunities while filtering out false signals.
Core Features
1. Volume Analysis Dashboard
The indicator displays various volume-related metrics in customizable tables:
AVOL (After Hours + Pre-Market Volume): Shows extended hours volume as a percentage of the 21-day average volume with color coding for buying/selling pressure. Green indicates buying pressure and red indicates selling pressure.
Volume Metrics: Includes regular volume (VOL), dollar volume ($VOL), relative volume compared to 21-day average (RVOL), and relative volume compared to 90-day average (RVOL90D).
Pre-Market Data: Optional display of pre-market volume (PVOL), pre-market dollar volume (P$VOL), pre-market relative volume (PRVOL), and pre-market price change percentage (PCHG%).
2. Enhanced Volume Trend (VTR) Analysis
The Volume Trend indicator uses adaptive analysis to evaluate buying and selling pressure, combining multiple factors:
MACD (Moving Average Convergence Divergence) components
Volume-to-SMA (Simple Moving Average) ratio
Price direction and market conditions
Volume change rates and momentum
EMA (Exponential Moving Average) alignment and crossovers
Volatility filtering
VTR Visual Indicators
The VTR score ranges from 0-100, with values above 50 indicating bullish conditions and below 50 indicating bearish conditions. This is visually represented by colored circles:
"●" (Filled Circle):
Green: Strong bullish trend (VTR ≥ 80)
Red: Strong bearish trend (VTR ≤ 20)
"◯" (Hollow Circle):
Green: Moderate bullish trend (VTR 65-79)
Red: Moderate bearish trend (VTR 21-35)
"·" (Small Dot):
Green: Weak bullish trend (VTR 55-64)
Red: Weak bearish trend (VTR 36-45)
"○" (Medium Hollow Circle): Neutral conditions (VTR 46-54), shown in gray
In "Both" display mode, the VTR shows both the numerical score (0-100) alongside the appropriate circle symbol.
Enhanced VTR Settings
The Enhanced Volume Trend component offers several advanced customization options:
Adaptive Volume Analysis (volTrendAdaptive):
When enabled, dynamically adjusts volume thresholds based on recent market volatility
Higher volatility periods require proportionally higher volume to generate significant signals
Helps prevent false signals during highly volatile markets
Keep enabled for most trading conditions, especially in volatile markets
Speed of Change Weight (volTrendSpeedWeight, range 0-1):
Controls emphasis on volume acceleration/deceleration rather than absolute levels
Higher values (0.7-1.0): More responsive to new volume trends, better for momentum trading
Lower values (0.2-0.5): Less responsive, better for trend following
Helps identify early volume trends before they fully develop
Momentum Period (volTrendMomentumPeriod, range 2-10):
Defines lookback period for volume change rate calculations
Lower values (2-3): More responsive to recent changes, better for short timeframes
Higher values (7-10): Smoother, better for daily/weekly charts
Directly affects how quickly the indicator responds to new volume patterns
Volatility Filter (volTrendVolatilityFilter):
Adjusts significance of volume by factoring in current price volatility
High volume during high volatility receives less weight
High volume during low volatility receives more weight
Helps distinguish between genuine volume-driven moves and volatility-driven moves
EMA Alignment Weight (volTrendEmaWeight, range 0-1):
Controls importance of EMA alignments in final VTR calculation
Analyzes multiple EMA relationships (5, 10, 21 period)
Higher values (0.7-1.0): Greater emphasis on trend structure
Lower values (0.2-0.5): More focus on pure volume patterns
Display Mode (volTrendDisplayMode):
"Value": Shows only numerical score (0-100)
"Strength": Shows only symbolic representation
"Both": Shows numerical score and symbol together
3. Bollinger Band Squeeze Detection (SQZ)
The BB Squeeze indicator identifies periods of low volatility when Bollinger Bands contract inside Keltner Channels, often preceding significant price movements.
SQZ Visual Indicators
"●" (Filled Circle): Strong squeeze - high probability setup for an impending breakout
Green: Strong squeeze with bullish bias (likely upward breakout)
Red: Strong squeeze with bearish bias (likely downward breakout)
Orange: Strong squeeze with unclear direction
"◯" (Hollow Circle): Moderate squeeze - medium probability setup
Green: With bullish EMA alignment
Red: With bearish EMA alignment
Orange: Without clear directional bias
"-" (Dash): Gray dash indicates no squeeze condition (normal volatility)
The script identifies squeeze conditions through multiple methods:
Bollinger Bands contracting inside Keltner Channels
BB width falling to bottom 20% of recent range (BB width percentile)
Very narrow Keltner Channel (less than 5% of basis price)
Tracking squeeze duration in consecutive bars
Different squeeze strengths are detected:
Strong Squeeze: BB inside KC with tight BB width and narrow KC
Moderate Squeeze: BB inside KC with either tight BB width or narrow KC
No Squeeze: Normal market conditions
4. Breakout Detection System
The script includes two breakout indicators working in sequence:
4.1 Pre-Breakout (PBK) Indicator
Detects potential upcoming breakouts by analyzing multiple factors:
Squeeze conditions lasting 2-3 bars or more
Significant price ranges
Strong volume confirmation
EMA/MACD crossovers
Consistent price direction
PBK Visual Indicators
"●" (Filled Circle): Detected pre-breakout condition
Green: Likely upward breakout (bullish)
Red: Likely downward breakout (bearish)
Orange: Direction not yet clear, but breakout likely
"-" (Dash): Gray dash indicates no pre-breakout condition
The PBK uses sophisticated conditions to reduce false signals including minimum squeeze length, significant price movement, and technical confirmations.
4.2 Breakout (BK) Indicator
Confirms actual breakouts in progress by identifying:
End of squeeze or strong expansion of Bollinger Bands
Volume expansion
Price moving outside Bollinger Bands
EMA crossovers with volume confirmation
MACD crossovers with significant price range
BK Visual Indicators
"●" (Filled Circle): Confirmed breakout in progress
Green: Upward breakout (bullish)
Red: Downward breakout (bearish)
Orange: Unusual breakout pattern without clear direction
"◆" (Diamond): Special breakout conditions (meets some but not all criteria)
"-" (Dash): Gray dash indicates no breakout detected
The BK indicator uses advanced filters for confirmation:
Requires consecutive breakout signals to reduce false positives
Strong volume confirmation requirements (40% above average)
Significant price movement thresholds
Consistency checks between price action and indicators
5. Market Metrics and Analysis
Price Change Percentage (CHG%)
Displays the current percentage change relative to the previous day's close, color-coded green for positive changes and red for negative changes.
Average Daily Range (ADR%)
Calculates the average daily percentage range over a specified period (default 20 days), helping traders gauge volatility and set appropriate price targets.
Average True Range (ATR)
Shows the Average True Range value, a volatility indicator developed by J. Welles Wilder that measures market volatility by decomposing the entire range of an asset price for that period.
Relative Strength Index (RSI)
Displays the standard 14-period RSI, a momentum oscillator that measures the speed and change of price movements on a scale from 0 to 100.
6. External Market Indicators
QQQ Change
Shows the percentage change in the Invesco QQQ Trust (tracking the Nasdaq-100 Index), useful for understanding broader tech market trends.
UVIX Change
Displays the percentage change in UVIX, a volatility index, providing insight into market fear and potential hedging activity.
BTC-USD
Shows the current Bitcoin price from Coinbase, useful for traders monitoring crypto correlation with equities.
Market Breadth (BRD)
Calculates the percentage difference between ATHI.US and ATLO.US (high vs. low securities), indicating overall market direction and strength.
7. Session Analysis and Volume Direction
Session Detection
The script accurately identifies different market sessions:
Pre-market: 4:00 AM to 9:30 AM
Regular market: 9:30 AM to 4:00 PM
After-hours: 4:00 PM to 8:00 PM
Closed: Outside trading hours
This detection works on any timeframe through careful calculation of current time in seconds.
Buy/Sell Volume Direction
The script analyzes buying and selling pressure by:
Counting up volume when close > open
Counting down volume when close < open
Tracking accumulated volume within the day
Calculating intraday pressure (up volume minus down volume)
Enhanced AVOL Calculation
The improved AVOL calculation works in all timeframes by:
Estimating typical pre-market and after-hours volume percentages
Combining yesterday's after-hours with today's pre-market volume
Calculating this as a percentage of the 21-day average volume
Determining buying/selling pressure by analyzing after-hours and pre-market price changes
Color-coding results: green for buying pressure, red for selling pressure
This calculation is particularly valuable because it works consistently across any timeframe.
Customization Options
Display Settings
The dashboard has two customizable tables: Volume Table and Metrics Table, with positions selectable as bottom_left or bottom_right.
All metrics can be individually toggled on/off:
Pre-market data (PVOL, P$VOL, PRVOL, PCHG%)
Volume data (AVOL, RVOL Day, RVOL 90D, Volume, SEED_YASHALGO_NSE_BREADTH:VOLUME )
Price metrics (ADR%, ATR, RSI, Price Change%)
Market indicators (QQQ, UVIX, Breadth, BTC-USD)
Analysis indicators (Volume Trend, BB Squeeze, Pre-Breakout, Breakout)
These toggle options allow traders to customize the dashboard to show only the metrics they find most valuable for their trading style.
Table and Text Customization
The dashboard's appearance can be customized:
Table background color via tableBgColor
Text color (White or Black) via textColorOption
The indicator uses smart formatting for volume and price values, automatically adding appropriate suffixes (K, M, B) for readability.
MACD Configuration for VTR
The Volume Trend calculation incorporates MACD with customizable parameters:
Fast Length: Controls the period for the fast EMA (default 3)
Slow Length: Controls the period for the slow EMA (default 9)
Signal Length: Controls the period for the signal line EMA (default 5)
MACD Weight: Controls how much influence MACD has on the volume trend score (default 0.3)
These settings allow traders to fine-tune how momentum is factored into the volume trend analysis.
Bollinger Bands and Keltner Channel Settings
The Bollinger Bands and Keltner Channels used for squeeze detection have preset (hidden) parameters:
BB Length: 20 periods
BB Multiplier: 2.0 standard deviations
Keltner Length: 20 periods
Keltner Multiplier: 1.5 ATR
These settings follow standard practice for squeeze detection while maintaining simplicity in the user interface.
Practical Trading Applications
Complete Trading Strategies
1. Squeeze Breakout Strategy
This strategy combines multiple components of the indicator:
Wait for a strong squeeze (SQZ showing ●)
Look for pre-breakout confirmation (PBK showing ● in green or red)
Enter when breakout is confirmed (BK showing ● in same direction)
Use VTR to confirm volume supports the move (VTR ≥ 65 for bullish or ≤ 35 for bearish)
Set profit targets based on ADR (Average Daily Range)
Exit when VTR begins to weaken or changes direction
2. Volume Divergence Strategy
This strategy focuses on the volume trend relative to price:
Identify when price makes a new high but VTR fails to confirm (divergence)
Look for VTR to show weakening trend (● changing to ◯ or ·)
Prepare for potential reversal when SQZ begins to form
Enter counter-trend position when PBK confirms reversal direction
Use external indicators (QQQ, BTC, Breadth) to confirm broader market support
3. Pre-Market Edge Strategy
This strategy leverages pre-market data:
Monitor AVOL for unusual pre-market activity (significantly above 100%)
Check pre-market price change direction (PCHG%)
Enter position at market open if VTR confirms direction
Use SQZ to determine if volatility is likely to expand
Exit based on RVOL declining or price reaching +/- ADR for the day
Market Context Integration
The indicator provides valuable context for trading decisions:
QQQ change shows tech market direction
BTC price shows crypto market correlation
UVIX change indicates volatility expectations
Breadth measurement shows market internals
This context helps traders avoid fighting the broader market and align trades with overall market direction.
Timeframe Optimization
The indicator is designed to work across different timeframes:
For day trading: Focus on AVOL, VTR, PBK/BK, and use shorter momentum periods
For swing trading: Focus on SQZ duration, VTR strength, and broader market indicators
For position trading: Focus on larger VTR trends and use EMA alignment weight
Advanced Analytical Components
Enhanced Volume Trend Score Calculation
The VTR score calculation is sophisticated, with the base score starting at 50 and adjusting for:
Price direction (up/down)
Volume relative to average (high/normal/low)
Volume acceleration/deceleration
Market conditions (bull/bear)
Additional factors are then applied, including:
MACD influence weighted by strength and direction
Volume change rate influence (speed)
Price/volume divergence effects
EMA alignment scores
Volatility adjustments
Breakout strength factors
Price action confirmations
The final score is clamped between 0-100, with values above 50 indicating bullish conditions and below 50 indicating bearish conditions.
Anti-False Signal Filters
The indicator employs multiple techniques to reduce false signals:
Requiring significant price range (minimum percentage movement)
Demanding strong volume confirmation (significantly above average)
Checking for consistent direction across multiple indicators
Requiring prior bar consistency (consecutive bars moving in same direction)
Counting consecutive signals to filter out noise
These filters help eliminate noise and focus on high-probability setups.
MACD Enhancement and Integration
The indicator enhances standard MACD analysis:
Calculating MACD relative strength compared to recent history
Normalizing MACD slope relative to volatility
Detecting MACD acceleration for stronger signals
Integrating MACD crossovers with other confirmation factors
EMA Analysis System
The indicator uses a comprehensive EMA analysis system:
Calculating multiple EMAs (5, 10, 21 periods)
Detecting golden cross (10 EMA crosses above 21 EMA)
Detecting death cross (10 EMA crosses below 21 EMA)
Assessing price position relative to EMAs
Measuring EMA separation percentage
Recent Enhancements and Evolution
Version 5.2 includes several improvements:
Enhanced AVOL to show buying/selling direction through color coding
Improved VTR with adaptive analysis based on market conditions
AVOL display now works in all timeframes through sophisticated estimation
Removed animal symbols and streamlined code with bright colors for better visibility
Improved anti-false signal filters throughout the system
Optimizing Indicator Settings
For Different Market Types
Range-Bound Markets:
Lower EMA Alignment Weight (0.2-0.4)
Higher Speed of Change Weight (0.8-1.0)
Focus on SQZ and PBK signals for breakout potential
Trending Markets:
Higher EMA Alignment Weight (0.7-1.0)
Moderate Speed of Change Weight (0.4-0.6)
Focus on VTR strength and BK confirmations
Volatile Markets:
Enable Volatility Filter
Enable Adaptive Volume Analysis
Lower Momentum Period (2-3)
Focus on strong volume confirmation (VTR ≥ 80 or ≤ 20)
For Different Asset Classes
Equities:
Standard settings work well
Pay attention to AVOL for gap potential
Monitor QQQ correlation
Futures:
Consider higher Volume/RVOL weight
Reduce MACD weight slightly
Pay close attention to SQZ duration
Crypto:
Higher volatility thresholds may be needed
Monitor BTC price for correlation
Focus on stronger confirmation signals
Integrated Visual System for Trading Decisions
The colored circle indicators create an intuitive visual system for quick market assessment:
Progression Sequence: SQZ (Squeeze) → PBK (Pre-Breakout) → BK (Breakout)
This sequence often occurs in order, with the squeeze leading to pre-breakout conditions, followed by an actual breakout.
VTR (Volume Trend): Provides context about the volume supporting these movements.
Color Coding: Green for bullish conditions, red for bearish conditions, and orange/gray for neutral or undefined conditions.
US10Y Yield Range Percentile | JeffreyTimmermansUS10Y Yield Range Percentile
The "US10Y Yield Range Percentile" Indicator provides insights into the relative positioning of the U.S. 10-Year Treasury Yield (US10Y) within a specified lookback period. It highlights key valuation style conditions, helping traders assess market sentiment based on yield movements.
Why is the US 10-Year Treasury Yield Important?
The U.S. 10-Year Treasury Yield (US10Y) is one of the most critical benchmarks in global finance. It reflects the cost of borrowing for the U.S. government and serves as a risk-free rate that influences interest rates across the economy.
Macroeconomic Indicator:
Rising yields suggest strong economic growth or inflationary pressures, often leading to tighter monetary policy.
Falling yields indicate economic slowdown, deflationary risks, or increased demand for safe-haven assets.
Impact on Financial Markets:
Stock Market: Higher yields reduce the attractiveness of equities, while lower yields support risk assets.
Credit Markets: A rising 10-year yield increases borrowing costs, impacting corporate debt and mortgage rates.
Global Capital Flows: US10Y is a key driver of capital allocation worldwide, affecting currency valuations and capital flows into emerging markets.
Correlation with Risk Assets (Especially Crypto):
Crypto markets, particularly Bitcoin and Ethereum, have shown a strong inverse correlation with US10Y yields.
When yields rise, risk assets tend to sell off due to tighter financial conditions.
When yields decline, liquidity flows into speculative assets, boosting stocks, crypto, and growth sectors.
Key Functions of the Indicator
Range Calculation:
Computes the highest high and lowest low over a user-defined period (default: 63 days).
Measures the current yield’s position within this range.
Range Percentile Calculation:
Determines the percentile rank of the current yield within its range.
A higher percentile indicates higher yields, often associated with Risk OFF conditions.
A lower percentile suggests lower yields, signaling Risk ON sentiment.
Optional Smoothing:
Enable/Disable: Users can enable Simple Moving Average (SMA) smoothing to reduce noise.
Default smoothing length : 10 periods (can be customized).
Threshold Levels & Background Coloring:
The background color represents the current market regime (valuation based), based on the US10Y yield percentile:
Risk ON (Bullish): When the percentile falls below the lower threshold (default: 20).
Neutrally Positive Zone (also Risk ON): Between 20 and 80 percentile.
Risk OFF (Bearish): When the percentile rises above the upper threshold (default: 80).
Important : Background Coloring is NOT a Leading Signal.
The background color provides a visual representation of valuation periods, but it is not a leading indicator for price movements. Instead, traders should focus on the orange US10Y Range Percentile line, which is the key signal within this indicator. The colors behind the line below the chart are leading. The background colors behind the price chart are more of a valuation style indications.
When the orange line enters the Danger Zone (above 80 percentile), it signals that yields are elevated, and risk assets (such as stocks and crypto) are at increased risk of reversing downward.
While the background coloring helps to visualize market conditions, price reversals tend to occur when the percentile line is in extreme zones rather than when the background color changes.
Traders should monitor the percentile line closely, as it provides a clearer signal of potential shifts in market sentiment.
Visual Elements
Range Percentile Plot:
Displays the smoothed or raw percentile value over time.
Helps identify shifts in yield positioning.
Threshold Markers & Fill Zones:
Key percentile thresholds (0, 20, 80, 100) are marked with horizontal lines.
The area between 20-80 percentile is filled to indicate the neutral zone.
Extreme zones are highlighted to emphasize significant shifts in risk sentiment.
Dynamic Labeling:
A real-time percentile label appears next to the latest data point.
Alerts & Notifications
Risk OFF to Risk ON Transition:
Alert triggers when the percentile falls below the lower threshold (yields decreasing).
Risk ON to Risk OFF Transition:
Alert triggers when the percentile rises above the upper threshold (yields increasing).
Conclusion
The crypto market is highly sensitive to macroeconomic conditions, with Bitcoin often behaving like a high-beta tech stock.
A declining US10Y yield signals looser financial conditions, increasing demand for risk assets like crypto.
A rising US10Y yield tightens liquidity, leading to sell-offs in Bitcoin, Ethereum, and altcoins.
Tracking the US10Y percentile position helps traders anticipate market shifts before they occur.
This indicator serves as a leading signal for understanding market risk appetite by tracking Treasury yield movements. A decline in yields typically favors equities and risk assets, while rising yields indicate a shift toward safety and risk aversion.
Credits
This indicator was inspired by and builds upon the work of TomasOnMarkets . While incorporating significant enhancements, it acknowledges the foundational concepts provided by this original source. Thank you for sharing your input on this important indicator. We are honored to use it and to further improve upon it.
-Jeffrey
ZenAlgo - HazeThe ZenAlgo - Haze indicator offers an advanced framework for analyzing market trends, momentum shifts, and potential reversals. By integrating dynamic crossovers, predictive zones, and historical validation into a single tool, it provides traders with actionable insights for better decision-making. Its configurable settings for Crypto and Traditional adapt seamlessly to the unique characteristics of each market.
Features
Dynamic Trend Labels: Identifies "Bull," "Bear," "Super Bull," and "Super Bear" states based on crossover logic, price levels, and historical trends.
Market-Specific Adaptability: Switch between Crypto and Traditional settings for optimized analysis tailored to each market’s behavior.
Predictive Kumo Cloud: Forward-projected support and resistance zones help traders anticipate potential price movements.
Lagging Span Validation: Validates trends using historical price context for improved reliability.
Integrated Signals and Alerts: Combines crossovers and momentum shifts with real-time alerts for trend confirmation.
Added Value: Why Is This Indicator Original/Why Shall You Pay for This Indicator?
The Haze indicator differentiates itself through a carefully designed synergy of components, providing a depth of analysis that extends beyond traditional Ichimoku or Donchian-based indicators. Here’s what makes it valuable to traders:
1. Dynamic and Contextual Market Labels
Labels like Bull , Bear , Super Bull , and Super Bear do not merely indicate crossovers but also account for the relative position of price to predictive cloud zones and historical trends. This layered approach ensures signals are contextual and provide a clear understanding of the market's underlying strength or weakness.
These states are especially powerful because they simplify decision-making by summarizing complex market dynamics into actionable insights.
2. Market-Specific Optimization
The ability to switch between Crypto and Traditional configurations adapts the indicator to specific market conditions. For instance, Crypto's volatility requires wider periods for trend calculations, while Forex's tighter price movements benefit from shorter, more reactive settings. This adaptability ensures precision without needing multiple separate tools.
3. Predictive Insights
The forward-shifted Kumo cloud is designed to anticipate future support and resistance zones. Unlike reactive indicators that only analyze past data, this predictive feature gives traders an edge by offering a glimpse into potential price movements.
4. Integrated Synergy
The integration of components—Donchian channels for trend calculation, Kumo cloud for projections, and Lagging Span for historical validation—creates a holistic system. These components are not merely combined but interact to validate and reinforce each other's signals, reducing noise and increasing reliability.
5. Simplification Without Sacrificing Depth
By consolidating multiple elements into a single interface, Haze reduces chart clutter. It eliminates the need for traders to interpret separate indicators manually, saving time and improving clarity. This streamlined approach is particularly useful for traders working in fast-paced markets like Crypto.
How It Works
1. Dynamic Trend Detection
The indicator evaluates crossovers between the conversion and baseline lines. However, these are not simple crossovers—Haze analyzes the relative position of the price to the forward-displaced cloud and validates signals based on historical context (Lagging Span). For example:
A Super Bull signal is generated only when the conversion line crosses above the baseline, remains above the cloud, and is supported by rising price trends. This ensures that the signal reflects sustained bullish momentum rather than temporary spikes.
Similarly, a Super Bear signal requires the price and conversion line to be below the cloud, validated by a consistent downward trend.
2. Forward-Projected Kumo Cloud
The Kumo cloud is calculated by projecting key levels derived from Donchian channels into the future. This feature helps traders identify upcoming areas of support and resistance, enabling them to anticipate market behavior rather than reacting to it.
Cloud thickness indicates the strength of these zones; a wide cloud reflects robust support or resistance, while a narrow cloud suggests potential indecision or consolidation.
3. Lagging Span Validation
By plotting the current price backward, the Lagging Span provides historical validation of trends. For example:
If the Lagging Span remains above the cloud and price, it reinforces the bullish trend.
Conversely, if it falls below the cloud and price, it confirms bearish conditions. This backward-looking validation ensures that current signals are consistent with past market behavior.
4. Real-Time Alerts
Alerts are triggered when the Fast and Slow lines (calculated from Donchian channels) cross. These alerts are offset by the cloud’s displacement period to align with forward projections. This ensures t
5. Multi-Layered Label System
The indicator dynamically adjusts the visibility of labels based on the detected market state, providing traders with concise yet comprehensive feedback. For example:
Bull and Bear labels appear for preliminary signals, while Super Bull and Super Bear labels are reserved for high-confidence trends that meet stricter conditions.
6. Market-Specific Configurations
For Crypto, the indicator uses longer periods to capture broader trends and account for high volatility.
For Traditional, shorter periods provide quicker signals, tailored to the relatively stable nature of currency pairs.
Usage Examples
The Haze indicator is designed to be intuitive yet comprehensive, offering multiple layers of analysis to guide traders. Here's how to interpret its outputs effectively:
1. Interpreting Labels
Bull and Bear : Indicate the start of potential upward or downward momentum. These labels appear when the conversion line crosses the baseline but remain within or near the cloud, signaling a trend shift with moderate confidence.
Super Bull and Super Bear : Represent strong, confirmed trends. These labels require the conversion line and price to remain firmly above (Super Bull) or below (Super Bear) the cloud, validated by consistent price movements in the same direction.
Use the Super labels as confirmation of robust trends with high reliability, ideal for entering longer-term positions or scaling into existing trades.
2. Using the Kumo Cloud
The cloud serves as a visual representation of projected support and resistance levels.
Wide Cloud Zones: Indicate strong barriers, suggesting significant price consolidation or resistance at those levels.
Narrow Cloud Zones: Suggest weaker areas of support or resistance, often seen during periods of low volatility or indecision.
Above the Cloud: Signals a bullish market condition, where price is more likely to find support near the upper cloud boundary.
Below the Cloud: Indicates bearish conditions, with resistance likely near the lower cloud boundary.
3. Incorporating Alerts
Alerts for Fast/Slow Crossover provide a timely signal of potential momentum shifts.
A Cross Up occurring near or above the cloud strengthens bullish momentum.
A Cross Down near or below the cloud reinforces bearish momentum.
Use these alerts to refine entry and exit points, particularly in trending markets.
4. Validating Trends with the Lagging Span
The Lagging Span acts as a confirmation tool, validating current trends against historical price levels:
If the Lagging Span is above both the cloud and the current price, it confirms a strong bullish trend.
If it is below both the cloud and the price, it reinforces a bearish trend.
5. Multi-Timeframe Analysis
Analyze the indicator across multiple timeframes to gain a broader perspective on the market.
Use higher timeframes (e.g., daily or 4-hour charts) to identify dominant trends.
Use lower timeframes (e.g., 1-hour or 15-minute charts) for precise entry and exit points within the context of the larger trend.
6. Combining Labels and Cloud Zones
A Bull label within the cloud indicates a nascent uptrend but warrants caution until price moves above the cloud.
A Super Bull label above the cloud confirms strong bullish momentum, making it a high-confidence signal for taking long positions.
Conversely, a Super Bear label below the cloud signals strong downward momentum and potential shorting opportunities.
By interpreting these elements together, traders can gain a clearer understanding of market conditions and make more informed decisions without relying on multiple separate tools.
Limitations
Low-Volume Markets: In illiquid markets, such as some altcoins or exotic forex pairs, signals may be less reliable. Pair the indicator with additional tools like RSI or Bollinger Bands to filter out noise.
Sideways Markets: During periods of consolidation, frequent crossovers may produce false signals. Use complementary tools to confirm breakout conditions.
Short Timeframes: On very short timeframes (e.g., 1-minute charts), market noise may lead to unreliable signals. Applying the indicator to higher timeframes can improve reliability.
Volatile Events: In markets with extreme volatility, signals may lag behind rapid price movements. For better results, combine the indicator with a volatility filter, such as the Average True Range (ATR).
Important Notes
The indicator is a technical tool designed to support market analysis and should be used alongside other strategies, including fundamental analysis and sound risk management.
Always use stop-loss orders and proper position sizing to mitigate risks, particularly in volatile or uncertain market conditions.
This indicator does not guarantee trading success or profit and should be used as part of a comprehensive strategy.
WiseOwl Indicator - 1.0 The WiseOwl Indicator - 1.0 is a technical analysis tool designed to help traders identify potential entry points and market trends based on Exponential Moving Averages (EMAs) across multiple timeframes. It focuses on providing clear visual cues for bullish and bearish market conditions, as well as potential breakout opportunities.
Key Features
Multi-Timeframe EMA Analysis: Calculates EMAs on the current timeframe, Daily timeframe, and 15-minute timeframe to confirm trends.
Bullish and Bearish Market Identification: Determines market conditions based on the 200-period EMA on the Daily timeframe.
Directional Candle Coloring: Highlights candles based on their position relative to EMAs to provide immediate visual feedback.
Entry Signals: Plots buy and sell signals on the chart when specific conditions are met on the 1-hour and 4-hour timeframes.
Breakout Candle Highlighting: Colors candles differently when significant price movements occur, indicating potential breakout opportunities.
How It Works
Market Condition Determination:
Bullish Market: When the close price is above the 200-period EMA on the Daily timeframe.
Bearish Market: When the close price is below the 200-period EMA on the Daily timeframe.
Directional Candle Coloring:
Green Background: Applied when the close is above the 50-period EMA and the market is not bearish.
Red Background: Applied when the close is below the 50-period EMA and the market is not bullish.
Uses the Average True Range (ATR) to define a range threshold.
Suppresses signals when EMAs are within this range, indicating a sideways market.
Plotting Entry Signals:
Plots arrows on the chart for potential long and short entries on the 1-hour and 4-hour timeframes.
Breakout Candle Coloring:
Colors candles blue when a bullish breakout condition is met.
Colors candles orange when a bearish breakout condition is met.
How to Use
Trend Identification: Use the background coloring to quickly identify the overall market trend.
Green Background: Suggests bullish conditions; consider looking for long opportunities.
Red Background: Suggests bearish conditions; consider looking for short opportunities.
Entry Signals: Look for plotted arrows on the chart.
Green Upward Arrow: Indicates a potential long entry signal on the 1-hour or 4-hour timeframe.
Red Downward Arrow: Indicates a potential short entry signal on the 1-hour or 4-hour timeframe.
Breakout Opportunities: Watch for candles colored blue or orange.
Blue Candles: Highlight significant upward price movements.
Orange Candles: Highlight significant downward price movements.
Avoiding Ranging Markets: Be cautious when signals are suppressed due to ranging conditions; the market may not have a clear direction.
Example Usage
Identifying a Bullish Market:
The background turns green.
Price crosses above the 50 EMA.
A green upward arrow appears below a candle on the 1-hour or 4-hour chart.
Identifying a Bearish Market:
The background turns red.
Price crosses below the 50 EMA.
A red downward arrow appears above a candle on the 1-hour or 4-hour chart.
Notes
Open-Source Code: The script is open-source, allowing users to review and understand the logic behind the indicator.
Educational Purpose: This indicator is intended to aid in technical analysis and should not be used as the sole basis for trading decisions.
Disclaimer
This indicator is for educational purposes only and does not constitute financial advice. Trading involves risk, and you should consult with a qualified financial advisor before making any investment decisions.
Trend Strength Momentum Indicator (TSMI)Introducing the Trend Strength Momentum Indicator (TSMI)
With over two decades of experience, I've found that no single indicator can consistently predict market movements. The key lies in combining multiple indicators to capture different market dimensions—trend, momentum, and volume. With this in mind, I present the Trend Strength Momentum Indicator (TSMI), a comprehensive tool designed to spot emerging uptrends and downtrends in cryptocurrency and other asset markets.
1. Overview of TSMI
The TSMI amalgamates three critical market aspects:
Trend Direction and Strength: Utilizing Moving Averages (MA) and the Average Directional Index (ADX).
Momentum: Incorporating the Moving Average Convergence Divergence (MACD) and the Relative Strength Index (RSI).
Volume Confirmation: Employing the On-Balance Volume (OBV) indicator.
By combining these elements, TSMI aims to provide a robust signal that not only indicates the direction of the trend but also confirms its strength and sustainability through momentum and volume analysis.
2. Components and Calculations
A. Trend Component
Exponential Moving Averages (EMA):
50-day EMA: Captures the short to medium-term trend.
200-day EMA: Reflects the long-term trend.
Average Directional Index (ADX):
Measures the strength of the trend regardless of its direction.
A value above 25 indicates a strong trend, while below 20 suggests a weak or non-trending market.
B. Momentum Component
Moving Average Convergence Divergence (MACD):
Calculated by subtracting the 26-day EMA from the 12-day EMA.
The MACD line crossing above the signal line (9-day EMA of MACD) indicates bullish momentum; crossing below suggests bearish momentum.
Relative Strength Index (RSI):
Oscillates between 0 and 100.
Readings above 70 indicate overbought conditions; below 30 suggest oversold conditions.
C. Volume Component
On-Balance Volume (OBV):
Cumulatively adds volume on up days and subtracts volume on down days.
A rising OBV alongside rising prices confirms an uptrend; divergence may signal a reversal.
3. TSMI Calculation Steps
Step 1: Trend Analysis
EMA Crossover:
Identify if the 50-day EMA crosses above the 200-day EMA (Golden Cross), indicating a potential uptrend.
Conversely, if the 50-day EMA crosses below the 200-day EMA (Death Cross), it may signal a downtrend.
ADX Confirmation:
Confirm the strength of the trend. An ADX value above 25 supports the EMA crossover signal.
Step 2: Momentum Assessment
MACD Evaluation:
Look for MACD crossing above its signal line for bullish momentum or below for bearish momentum.
RSI Check:
Ensure RSI is not in overbought (>70) or oversold (<30) territory to avoid potential reversals against the trend.
Step 3: Volume Verification
OBV Direction:
Confirm that OBV is moving in the same direction as the price trend.
Rising OBV with rising prices strengthens the bullish signal; falling OBV with falling prices strengthens the bearish signal.
Step 4: Composite Signal Generation
Bullish Signal:
50-day EMA crosses above 200-day EMA (Golden Cross).
ADX above 25, indicating a strong trend.
MACD crosses above its signal line.
RSI is between 30 and 70, avoiding overbought conditions.
OBV is rising.
Bearish Signal:
50-day EMA crosses below 200-day EMA (Death Cross).
ADX above 25.
MACD crosses below its signal line.
RSI is between 30 and 70, avoiding oversold conditions.
OBV is falling.
4. How to Use the TSMI
A. Entry Points
Buying into an Uptrend:
Wait for the bullish signal criteria to align.
Enter the position after the 50-day EMA crosses above the 200-day EMA, supported by positive momentum (MACD and RSI) and volume (OBV).
Selling or Shorting into a Downtrend:
Look for the bearish signal criteria.
Initiate the position after the 50-day EMA crosses below the 200-day EMA, with confirming momentum and volume indicators.
B. Exit Strategies
Protecting Profits:
Monitor RSI for overbought or oversold conditions, which may indicate potential reversals.
Watch for MACD divergences or crossovers against your position.
Use trailing stops based on the ATR (Average True Range) to allow profits to run while protecting against sharp reversals.
C. Risk Management
Position Sizing:
Use the ADX value to adjust position sizes. A stronger trend (higher ADX) may justify a larger position, whereas a weaker trend suggests caution.
Avoiding False Signals:
Be cautious during sideways markets where EMAs may whipsaw.
Confirm signals with multiple indicators before acting.
5. Examples
Example 1: Spotting an Emerging Uptrend in Bitcoin
Date: Let's assume on March 1st.
Observations:
EMA Crossover: The 50-day EMA crosses above the 200-day EMA.
ADX: Reading is 28, indicating a strong trend.
MACD: Crosses above the signal line and moves into positive territory.
RSI: Reading is 55, comfortably away from overbought levels.
OBV: Shows a rising trend, confirming increasing buying pressure.
Action:
Enter a long position in Bitcoin.
Set a stop-loss below recent swing lows.
Outcome:
Over the next few weeks, Bitcoin's price continues to rise, validating the TSMI signal.
Example 2: Identifying a Downtrend in Ethereum
Date: Let's assume on July 15th.
Observations:
EMA Crossover: The 50-day EMA crosses below the 200-day EMA.
ADX: Reading is 30, confirming a strong trend.
MACD: Crosses below the signal line into negative territory.
RSI: Reading is 45, not yet oversold.
OBV: Declining, indicating selling pressure.
Action:
Initiate a short position or exit long positions in Ethereum.
Place a stop-loss above recent resistance levels.
Outcome:
Ethereum's price declines over the following weeks, confirming the downtrend.
6. When to Use the TSMI
Trending Markets: TSMI is most effective in markets exhibiting clear trends, whether bullish or bearish.
Avoiding Sideways Markets: In range-bound markets, EMAs and momentum indicators may provide false signals. ADX readings below 20 suggest it's best to stay on the sidelines.
Volatile Assets: Particularly useful in cryptocurrency markets, which are known for their volatility and extended trends.
7. Limitations and Considerations
Lagging Indicators: Moving averages and ADX are lagging by nature. Rapid reversals may not be immediately captured.
False Signals: No indicator is foolproof. Always confirm signals with multiple components of TSMI.
Market Conditions: External factors like news events can significantly impact prices. Consider combining TSMI with fundamental analysis.
8. Enhancing TSMI
Customization: Adjust EMA periods (e.g., 20-day and 100-day) based on the asset's volatility and your trading timeframe.
Additional Indicators: Incorporate Bollinger Bands to gauge volatility or Fibonacci retracement levels to identify potential support and resistance.
Conclusion
The Trend Strength Momentum Indicator (TSMI) offers a holistic approach to spotting emerging trends by combining trend direction, momentum, and volume. By synthesizing the strengths of various traditional indicators while mitigating their individual limitations, TSMI provides traders with a powerful tool to navigate the complex landscape of cryptocurrency and other asset markets.
Key Benefits of TSMI:
Comprehensive Analysis: Integrates multiple market dimensions for well-rounded insights.
Early Trend Identification: Aims to spot trends early for optimal entry points.
Risk Management: Helps in making informed decisions, thereby reducing exposure to false signals.
By applying TSMI diligently and complementing it with sound risk management practices, traders can enhance their ability to capitalize on market trends and improve their overall trading performance.
The real breakout indicator CCI + Money Flow + Buy / SellComponents of the indicator
1. CCI (Commodity Channel Index)
The CCI component measures the deviation of the price from its statistical average. It is used to identify overbought or oversold conditions and is integrated into the trend logic to determine potential trend reversals. High values may indicate overbought conditions, while low values could signify oversold situations.
Detailed
The CCI (Commodity Channel Index) used in "The Real Breakout Indicator Hawk" is an enhanced version compared to the traditional CCI, offering several advantages:
1. Weighting and Smoothing Mechanism
In this version, the CCI values are weighted and smoothed using custom parameters (c1, c2, c3), which allows for greater flexibility in adjusting the sensitivity of the CCI to market conditions. This smoothing reduces noise and provides clearer signals compared to the standard CCI, which can be prone to whipsaws in volatile markets.
2. Multi-level Calculation
The indicator uses an array-based approach to calculate multiple variations of CCI values (with p as the parameter for different levels of calculation), which is then combined to create a more robust signal. This multi-level approach allows for capturing different market cycles, unlike the traditional CCI that only uses a single period for calculation.
3. Integration with Moving Averages and Trend Detection
Unlike the original CCI, which is often used in isolation, this version integrates with the trend detection logic by combining it with moving averages and money flow. The enhanced CCI contributes to the broader trend analysis, ensuring that buy/sell signals are not just based on CCI overbought/oversold levels but also validated by moving averages and slope calculations.
4. Trend-Weighted CCI
This version adds weight to recent price action trends, making it more adaptive to current market momentum. The CCI values are influenced by recent high and low prices, adding a trend-following aspect that is missing from the original CCI, which treats all price deviations equally.
This image of EURAD shows for example that when CCI component is green a strong trend is detected which can hold for up to 10 days in this example, ideal for swing trades;
EURAUD 2H
5. Improved Overbought/Oversold Detection
The script incorporates a dynamic overbought/oversold detection zone based on the enhanced CCI. It accounts for market volatility, allowing it to adjust its thresholds (such as the 200 level) more effectively in different market environments. This makes the enhanced CCI better suited for varying market conditions compared to the fixed thresholds of the original CCI.
You can see that the red diamond signal is generated at the absolute top of the price range after which price started to reverse, the detection is based on a cross over value together with Money Flow strength
BTCUSDT 2H
6. Strong Buy/Sell Confirmation
The enhanced CCI works in tandem with other components like Money Flow and Moving Averages to confirm buy or sell signals. This cross-validation makes the indicator less reliant on CCI alone and ensures that the signals generated are stronger and less prone to false positives, which is a common issue with the standalone CCI.
The green diamond buy signal in a strong downtrend is mostly a short retrace of price before continuing down further, yo can use this as an entry signal after the bounce up into an FVG for example. However when price is at a support, meaning price is not moving down further and this occurs this could be a potential reversal signal as shown on the right side on the chart below. FVG is not respected, retested and price continues up.
BTCUSDT 2H
Summary:
In summary, the enhanced CCI in this indicator improves over the original CCI by providing better noise reduction, multi-level analysis, trend integration, and adaptability to different market conditions. These improvements lead to more reliable and actionable trading signals.
2. Money Flow (MF) www.tradingview.com
The Money Flow component tracks the flow of capital in and out of an asset. Positive values indicate strong buying pressure, while negative values show selling pressure. This is smoothed to avoid noise and is used to confirm strong buy or sell conditions.
The Money Flow (MF) in "The Real Breakout Indicator Hawk" measures the flow of capital into or out of an asset, helping to assess the underlying buying or selling pressure in the market.
1. Positive Money Flow (Buying Pressure)
When the MF is positive, it indicates that more money is flowing into the asset, which suggests strong buying interest. This helps confirm that a price increase or breakout to the upside is supported by demand.
2. Negative Money Flow (Selling Pressure)
A negative MF indicates that capital is leaving the asset, reflecting selling pressure. This is a sign that the market is under bearish conditions, and prices are likely to decline or break down.
3. Confirmation of Buy and Sell Signals
The MF is used to confirm buy and sell signals generated by other components of the indicator. When the MF aligns with other bullish signals, it strengthens the buy condition, and similarly, when the MF shows strong selling pressure, it reinforces a sell signal.
4. Filtering Noise
The MF is smoothed to filter out noise, ensuring that only significant movements in buying or selling pressure are considered. This helps avoid false signals and makes the MF a reliable tool for detecting true market strength.
5. Range Sensitivity
The MF operates within defined ranges, ensuring that buy or sell signals are only triggered when the flow of money is strong enough, adding precision to signal generation.
In summary, the Money Flow component is crucial for validating market direction, enhancing signal reliability, and helping traders make more informed decisions based on the underlying capital movement in the market.
3. Moving Averages (MA)
Multiple types of moving averages (SMA, EMA, HMA, etc.) are used to smooth price action and highlight the trend direction. The script supports different types of moving averages, and their slopes are calculated to assist in identifying changes in trend momentum.
The Moving Averages (MA) section of "The Real Breakout Indicator Hawk" plays a critical role in smoothing price data, identifying trends, and generating buy/sell signals. Here’s a breakdown of what it does and how you can use it effectively without diving into the script:
1. Moving Average Types
This section allows the user to choose from different types of moving averages, each with unique characteristics:
SMA (Simple Moving Average): Takes the average of closing prices over a specific period. It’s slower and better suited for detecting long-term trends.
EMA (Exponential Moving Average): Gives more weight to recent prices, making it more responsive to new price action and suitable for short-term trading.
HMA (Hull Moving Average): A smoother and faster moving average, useful for reducing lag in fast-moving markets.
LVMA (Linear Weighted Moving Average): Places the most weight on recent prices, making it even more responsive than EMA.
Alma (Arnaud Legoux Moving Average): A smoother version that reduces noise while maintaining responsiveness to recent price action.
2. Smoothing and Trend Detection
The moving average smooths out price data to remove small fluctuations and focuses on the overall trend. When prices are trading above the moving average, it suggests that the market is in an uptrend. When prices are below the moving average, it indicates a downtrend.
3. Trend Confirmation
The moving average serves as a confirmation tool. When the price crosses above the moving average, it could signal the start of a bullish trend, and when the price crosses below, it may indicate the beginning of a bearish trend.
4. Buy and Sell Signals
Buy Signal: The system detects a buy signal when:
The moving average crosses above 0, indicating a potential upward momentum.
Other indicators like Money Flow and CCI align to confirm the trend.
Sell Signal: A sell signal is triggered when:
The moving average crosses below 0, signaling a potential downtrend.
This signal is further validated by other components such as Money Flow and CCI to reduce false signals.
5. Using Moving Averages in Trading
Crossover Strategy: One of the simplest ways to use moving averages is by employing a crossover strategy. For instance:
When the shorter-term moving average (e.g., 20-period) crosses above a longer-term moving average (e.g., 50-period), this is a bullish crossover, indicating a buy signal.
Conversely, when the shorter-term moving average crosses below the longer-term moving average, this is a bearish crossover, indicating a sell signal.
Trend Following: If you’re trading with the trend, you can use a moving average to stay in the trade as long as the price remains above (for long positions) or below (for short positions) the moving average.
Support and Resistance: Moving averages can also act as dynamic support or resistance levels. For example, in an uptrend, the CCI might bounce off the moving average, offering a good entry point for a long position. In a downtrend, the moving average could act as resistance where prices may reverse, offering a shorting opportunity.
To use the MA section effectively:
Choose the right type of moving average based on your trading style (e.g., use EMA for faster response or SMA for long-term trends).
Watch for crossovers as buy/sell signals, especially in combination with other indicators.
Follow the trend by observing whether the price is above or below the moving average.
Use the moving average as a dynamic support/resistance level to find optimal entry/exit points.
This approach makes the moving average a versatile tool for identifying trends, refining entry and exit points, and confirming overall market direction.
an example when MA crosses below 0, keep in mind that when it it starts curving up and turning green there is a reversal brewing, this could take time...
BTCUSDT 2H
4. Buy Signals
Buy signals are generated when the moving average crosses up, and the Money Flow and other trend-based conditions are met, including CCI levels confirming the strength of the breakout. Additionally, slope calculations and other momentum indicators provide extra confirmation for entries.
5. Sell Signals
Sell signals occur when the moving average crosses down, combined with negative Money Flow, confirming downward pressure. Other trend-based conditions, including the CCI, must also align to validate the signal, and slope calculations ensure that momentum is on the sell side.
6. Slope and Trend Detection
The script includes calculations for the slope of price action over a lookback period to measure trend strength and direction. The slope is normalized to help identify when the market is gaining or losing momentum. This slope is used in conjunction with the moving averages and Money Flow to give more accurate trend signals.
The Slope and Trend Detection component in "The Real Breakout Indicator Hawk" is designed to measure the direction and strength of the market’s trend by calculating the slope of the price action over a specific period. This helps to identify whether the market is gaining or losing momentum, and it is a key element in refining buy/sell signals.
Here’s how the Slope and Trend Detection works and how you can use it effectively without diving into the script:
1. Slope Calculation
Slope is essentially the rate of change of the moving average (or price) over a given number of bars. It measures how steeply the price is moving up or down.
The script calculates the slope by measuring the difference between the moving average over a defined number of bars (e.g., 12 bars in this case). A larger slope indicates a stronger trend, while a smaller slope suggests a weaker or consolidating trend.
2. Normalized Slope
The slope is normalized, meaning it is adjusted to fall within a range that makes it easier to compare across different time frames and markets. This normalization helps to gauge whether the slope is strong or weak relative to historical data.
Positive slopes (above 0) indicate an uptrend or rising price momentum, while negative slopes (below 0) indicate a downtrend or falling price momentum.
3. Trend Detection
The slope of the moving average is used to detect the current trend:
If the slope is positive, the market is in an uptrend.
If the slope is negative, the market is in a downtrend.
The stronger the slope (the steeper it is), the stronger the trend. A small slope indicates a weak trend or consolidation.
4. Slope Thresholds
The system uses thresholds to determine the significance of the slope. These thresholds are set as upper and lower bounds:
Upper Threshold: If the slope exceeds this threshold, the trend is considered strong, and it could trigger a buy signal.
Lower Threshold: If the slope falls below this threshold (into the negative range), it indicates a strong downtrend, and it could trigger a sell signal.
These thresholds help filter out weak or false signals that occur in sideways or low-momentum markets.
5. Positive and Negative Slope Arrays
The system keeps track of both positive and negative slopes over a defined lookback period (e.g., 500 bars). By storing these values, it creates a historical context that helps to assess the current slope in relation to past price movements.
It calculates the standard deviation and the average of these slopes to dynamically adjust the thresholds for each market condition, making the trend detection more adaptive to different types of assets or market phases.
6. Using Slope and Trend Detection in Trading
Buy Signal with Positive Slope: When the slope is positive and exceeds a certain threshold, it confirms that the market is in a strong uptrend. This can be used as a signal to enter a long position or add to existing long trades.
Sell Signal with Negative Slope: When the slope turns negative and falls below the lower threshold, it signals a strong downtrend, indicating a potential short-selling opportunity or the time to exit long positions.
Avoiding Flat Markets: If the slope remains close to zero (neither strongly positive nor negative), it suggests a lack of clear trend or a consolidating market. In these conditions, it might be better to avoid taking new trades or use additional filters to confirm signals.
7. Slope-Based Trend Strength Indicator
You can also use the slope as a measure of trend strength:
Strong Trend: When the slope is steep (either positive or negative), it indicates strong momentum, and you can be more confident in holding a trade in that direction.
Weak Trend or Consolidation: When the slope is flat, it indicates weak price momentum, which may signal a period of consolidation or indecision in the market.
8. Visual Representation
The slope is often visually represented as a gradient or line that fluctuates around a central point (usually zero). Positive values are shown in one color (e.g., green for an uptrend), while negative values are shown in another color (e.g., red for a downtrend). This allows traders to quickly identify the current trend direction and its strength.
Summary:
To use Slope and Trend Detection effectively:
Monitor the slope to determine the trend direction (positive = uptrend, negative = downtrend).
Look for thresholds to identify strong trends. For instance, a steep positive slope signals a strong uptrend, while a steep negative slope signals a strong downtrend.
Use slope changes to confirm buy/sell signals. For example, if you receive a buy signal and the slope is positive and increasing, it confirms that momentum is behind the trade.
Avoid low-slope periods when the slope is close to zero, indicating a lack of trend or sideways market conditions.
This approach helps traders stay on the right side of the trend while avoiding periods of low momentum, enhancing the accuracy of trade signals.
7. Banker Fund Flow Trend
This component identifies potential large institutional moves by tracking specific patterns in price and volume data. When the institutional or "banker" entry or exit conditions are met, it highlights these moments with candles and generates alerts.
The Banker Fund Flow Trend in "The Real Breakout Indicator Hawk" helps detect the flow of institutional (or "smart money") into and out of the market by tracking price trends and large player activity. It uses red and yellow candles to signal when institutional money is influencing the market.
Key Points:
Yellow Candles (Banker Entry):
A yellow candle is plotted when institutional money starts flowing into the market.
This signals a potential buy opportunity, as large market players are likely pushing prices upward.
Red Candles (Banker Exit):
A red candle appears when institutional money starts exiting the market.
This is a signal to consider selling or exiting long positions, as institutional selling could drive prices lower.
Usage:
Yellow candles: Use these as signals to enter long trades or add to existing positions, confirming upward momentum driven by institutional buyers.
Red candles: Treat these as signals to exit long trades or consider short positions, as institutional selling may lead to further downside.
BTCUSDT 2H
The yellow and red candles provide clear, actionable signals for aligning trades with institutional flows, ensuring you’re following the "smart money."
8. Dynamic Buy/Sell Calculations
A dynamic component is designed to refine the buy and sell signals further based on additional conditions like price patterns, volatility, and Money Flow. This ensures that signals are more responsive to changing market conditions.
The Dynamic Buy/Sell Calculations in "The Real Breakout Indicator Hawk" are designed to refine entry and exit points for trades by using additional conditions beyond simple crossovers. These calculations adapt to the current market conditions, making them more responsive to changes in volatility, trend strength, and momentum.
Key Features:
Dynamic Buy Calculation:
The indicator generates a buy signal when multiple conditions align. These conditions include the money flow (MF) being within a favorable range, the moving average (MA) confirming upward momentum, and the CCI and other trend components indicating strength.
This makes the buy signal more reliable, as it considers multiple aspects of market behavior (price, momentum, and money flow) to avoid false entries.
Dynamic Sell Calculation:
Similarly, the sell signal is triggered when the dynamic conditions indicate downward momentum.
This includes:
The moving average crossing down.
Negative money flow, suggesting selling pressure.
Other trend signals confirming a bearish move.
The dynamic nature of these conditions ensures that sell signals are only generated when there’s a high probability of continued downside movement.
Adaptive to Market Conditions:
The dynamic nature of these calculations means that the buy/sell signals adapt to market changes, like volatility spikes or sudden trend reversals. Instead of relying on static conditions, the system adjusts to current price movements and volatility.
Avoiding Noise:
By adding multiple filters like MF thresholds, slope, and moving averages, the dynamic calculations help reduce false signals that occur in noisy, sideways markets. This helps traders avoid entering trades during periods of low momentum or unclear trends.
How to Use:
Buy Signals: Use these signals to enter long trades when the dynamic conditions align, confirming that upward momentum is strong and backed by institutional flows.
BTCUSDT 2H
Aqua marker/cross signals (price manipulation/continuation)
BTCUSDT 2H
Sell Signals: Use the sell signals to exit long positions or enter short trades when the market shows signs of bearish momentum, confirmed by multiple conditions like MA crossovers and negative money flow.
BTCUSDT 2H
In summary, the Dynamic Buy/Sell Calculations provide a more sophisticated approach to generating trade signals by combining various trend and momentum indicators, helping traders make more informed decisions in different market conditions.
This part of the code is identifying two key trading signals: moments to buy and moments to sell based on the behavior of a calculated trend line.
Buy Condition:
The system looks for a situation where the trend has been moving downward but has started to reverse upward. Specifically, it checks if the trend was declining a little while ago, then stopped falling, and is now starting to rise. If these conditions are met and the trend is still below a certain level, the system considers this a possible time to buy.
Sell Condition:
The opposite happens for selling. The system monitors for a situation where the trend has been moving upward but starts to turn downward. It checks if the trend was rising, leveled off, and now seems to be starting to fall. If these conditions are met and the trend is above a certain level, this could indicate a good time to sell.
Visual Markers:
To help the user easily see these signals on a chart, the system places symbols at specific points. A marker appears on the chart where the conditions for buying or selling are met, allowing the trader to quickly spot potential entry or exit points in the market.
In summary, this logic is designed to detect possible changes in trend direction and signal appropriate times to consider buying or selling, with clear visual markers on the chart for quick identification.
9. Alerts for Buy and Sell
The indicator provides built-in alert conditions for both buy and sell signals. When these conditions are met, the system generates alerts, making it suitable for automated monitoring.
Each of these components works together to detect potential breakout opportunities, trend continuations, and reversals, making the indicator suitable for both short-term and long-term trading strategies.
Uptrick: Volume-Weighted EMA Signal### **Uptrick: Volume-Weighted EMA Signal (UVES) Indicator - Comprehensive Description**
#### **Overview**
The **Uptrick: Volume-Weighted EMA Signal (UVES)** is an advanced, multifaceted trading indicator meticulously designed to provide traders with a holistic view of market trends by integrating Exponential Moving Averages (EMA) with volume analysis. This indicator not only identifies the direction of market trends through dynamic EMAs but also evaluates the underlying strength of these trends using real-time volume data. UVES is a versatile tool suitable for various trading styles and markets, offering a high degree of customization to meet the specific needs of individual traders.
#### **Purpose**
The UVES indicator aims to enhance traditional trend-following strategies by incorporating a critical yet often overlooked component: volume. Volume is a powerful indicator of market strength, providing insights into the conviction behind price movements. By merging EMA-based trend signals with detailed volume analysis, UVES offers a more nuanced and reliable approach to identifying trading opportunities. This dual-layer analysis allows traders to differentiate between strong trends supported by significant volume and weaker trends that may be prone to reversals.
#### **Key Features and Functions**
1. **Dynamic Exponential Moving Average (EMA):**
- The core of the UVES indicator is its dynamic EMA, calculated over a customizable period. The EMA is a widely used technical indicator that smooths price data to identify the underlying trend. In UVES, the EMA is dynamically colored—green when the current EMA value is above the previous value, indicating an uptrend, and red when below, signaling a downtrend. This visual cue helps traders quickly assess the trend direction without manually calculating or interpreting raw data.
2. **Comprehensive Moving Average Customization:**
- While the EMA is the default moving average in UVES, traders can select from various other moving average types, including Simple Moving Average (SMA), Smoothed Moving Average (SMMA), Weighted Moving Average (WMA), and Volume-Weighted Moving Average (VWMA). Each type offers unique characteristics:
- **SMA:** Provides a simple average of prices over a specified period, suitable for identifying long-term trends.
- **EMA:** Gives more weight to recent prices, making it more responsive to recent market movements.
- **SMMA (RMA):** A slower-moving average that reduces noise, ideal for capturing smoother trends.
- **WMA:** Weighs prices based on their order in the dataset, making recent prices more influential.
- **VWMA:** Integrates volume data, emphasizing price movements that occur with higher volume, making it particularly useful in volume-sensitive markets.
3. **Signal Line for Trend Confirmation:**
- UVES includes an optional signal line, which applies a secondary moving average to the primary EMA. This signal line can be used to smooth out the EMA and confirm trend changes. The signal line’s color changes based on its slope—green for an upward slope and red for a downward slope—providing a clear visual confirmation of trend direction. Traders can adjust the length and type of this signal line, allowing them to tailor the indicator’s responsiveness to their trading strategy.
4. **Buy and Sell Signal Generation:**
- UVES generates explicit buy and sell signals based on the interaction between the EMA and the signal line. A **buy signal** is triggered when the EMA transitions from a red (downtrend) to a green (uptrend), indicating a potential entry point. Conversely, a **sell signal** is triggered when the EMA shifts from green to red, suggesting an exit or shorting opportunity. These signals are displayed directly on the chart as upward or downward arrows, making them easily identifiable even during fast market conditions.
5. **Volume Analysis with Real-Time Buy/Sell Volume Table:**
- One of the standout features of UVES is its integration of volume analysis, which calculates and displays the volume attributed to buying and selling activities. This analysis includes:
- **Buy Volume:** The portion of the total volume associated with price increases (close higher than open).
- **Sell Volume:** The portion of the total volume associated with price decreases (close lower than open).
- **Buy/Sell Ratio:** A ratio of buy volume to sell volume, providing a quick snapshot of market sentiment.
- These metrics are presented in a real-time table positioned in the top-right corner of the chart, with customizable colors and formatting. The table updates with each new bar, offering continuous feedback on the strength and direction of the market trend based on volume data.
6. **Customizable Settings and User Control:**
- **EMA Length and Source:** Traders can specify the lookback period for the EMA, adjusting its sensitivity to price changes. The source for EMA calculations can also be customized, with options such as close, open, high, low, or other custom price series.
- **Signal Line Customization:** The signal line’s length, type, and width can be adjusted to suit different trading strategies, allowing traders to optimize the balance between trend detection and noise reduction.
- **Offset Adjustment:** The offset feature allows users to shift the EMA and signal line forward or backward on the chart. This can help align the indicator with specific price action or adjust for latency in decision-making processes.
- **Volume Table Positioning and Formatting:** The position, size, and color scheme of the volume table are fully customizable, enabling traders to integrate the table seamlessly into their chart setup without cluttering the visual workspace.
7. **Versatility Across Markets and Trading Styles:**
- UVES is designed to be effective across a wide range of financial markets, including Forex, stocks, cryptocurrencies, commodities, and indices. Its adaptability to different markets is supported by its comprehensive customization options and the inclusion of volume analysis, which is particularly valuable in markets where volume plays a crucial role in price movement.
#### **How Different Traders Can Benefit from UVES**
1. **Trend Followers:**
- Trend-following traders will find UVES particularly beneficial for identifying and riding trends. The dynamic EMA and signal line provide clear visual cues for trend direction, while the volume analysis helps confirm the strength of these trends. This combination allows trend followers to stay in profitable trades longer and exit when the trend shows signs of weakening.
2. **Volume-Based Traders:**
- Traders who focus on volume as a key indicator of market strength can leverage the UVES volume table to gain insights into the buying and selling pressure behind price movements. By monitoring the buy/sell ratio, these traders can identify periods of strong conviction (high buy volume) or potential reversals (high sell volume) with greater accuracy.
3. **Scalpers and Day Traders:**
- For traders operating on shorter time frames, UVES provides quick and reliable signals that are essential for making rapid trading decisions. The ability to customize the EMA length and type allows scalpers to fine-tune the indicator for responsiveness, while the volume analysis offers an additional layer of confirmation to avoid false signals.
4. **Swing Traders:**
- Swing traders, who typically hold positions for several days to weeks, can use UVES to identify medium-term trends and potential entry and exit points. The indicator’s ability to filter out market noise through the signal line and volume analysis makes it ideal for capturing significant price movements without being misled by short-term volatility.
5. **Position Traders and Long-Term Investors:**
- Even long-term investors can benefit from UVES by using it to identify major trend reversals or confirm the strength of long-term trends. The flexibility to adjust the EMA and signal line to longer periods ensures that the indicator remains relevant for detecting shifts in market sentiment over extended time frames.
#### **Optimal Settings for Different Markets**
- **Forex Markets:**
- **EMA Length:** 9 to 14 periods.
- **Signal Line:** Use VWMA or WMA for the signal line to incorporate volume data, which is crucial in the highly liquid Forex markets.
- **Best Use:** Short-term trend following, with an emphasis on identifying rapid changes in market sentiment.
- **Stock Markets:**
- **EMA Length:** 20 to 50 periods.
- **Signal Line:** SMA or EMA with a slightly longer length (e.g., 50 periods) to capture broader market trends.
- **Best Use:** Medium to long-term trend identification, with volume analysis confirming the strength of institutional buying or selling.
- **Cryptocurrency Markets:**
- **EMA Length:** 9 to 12 periods, due to the high volatility in crypto markets.
- **Signal Line:** SMMA or EMA for smoothing out extreme price fluctuations.
- **Best Use:** Identifying entry and exit points in volatile markets, with the volume table providing insights into market manipulation or sudden shifts in trader sentiment.
- **Commodity Markets:**
- **EMA Length:** 14 to 21 periods.
- **Signal Line:** WMA or VWMA, considering the impact of trading volume on commodity prices.
- **Best Use:** Capturing medium-term price movements and confirming trend strength with volume data.
#### **Customization for Advanced Users**
- **Advanced Offset Usage:** Traders can experiment with different offset values to see how shifting the EMA and signal line impacts the timing of buy/sell signals. This can be particularly useful in markets with known latency or for strategies that require a delayed confirmation of trend changes.
- **Volume Table Integration:** The position, size, and colors of the volume table can be adjusted to fit seamlessly into any trading setup. For example, a trader might choose to position the table in the bottom-right corner and use a smaller size to keep the focus on price action while still having access to volume data.
- **Signal Filtering:** By combining the signal line with the primary EMA, traders can filter out false signals during periods of low volatility or when the market is range-bound. Adjusting the length of the signal line allows for greater control over the sensitivity of the trend detection.
#### **Conclusion**
The **Uptrick: Volume-Weighted EMA Signal (UVES)** is a powerful and adaptable indicator designed for traders who demand more from their technical analysis tools. By integrating dynamic EMA trend signals with real-time volume analysis, UVES offers a comprehensive view of market conditions, making it an invaluable resource for identifying trends, confirming signals, and understanding market sentiment. Whether you are a day trader, swing trader, or long-term investor, UVES provides the versatility, precision, and customization needed to make more informed and profitable trading decisions. With its ability to adapt to various markets and trading styles, UVES is not just an indicator but a complete trend analysis solution.
Smart DCA StrategyINSPIRATION
While Dollar Cost Averaging (DCA) is a popular and stress-free investment approach, I noticed an opportunity for enhancement. Standard DCA involves buying consistently, regardless of market conditions, which can sometimes mean missing out on optimal investment opportunities. This led me to develop the Smart DCA Strategy – a 'set and forget' method like traditional DCA, but with an intelligent twist to boost its effectiveness.
The goal was to build something more profitable than a standard DCA strategy so it was equally important that this indicator could backtest its own results in an A/B test manner against the regular DCA strategy.
WHY IS IT SMART?
The key to this strategy is its dynamic approach: buying aggressively when the market shows signs of being oversold, and sitting on the sidelines when it's not. This approach aims to optimize entry points, enhancing the potential for better returns while maintaining the simplicity and low stress of DCA.
WHAT THIS STRATEGY IS, AND IS NOT
This is an investment style strategy. It is designed to improve upon the common standard DCA investment strategy. It is therefore NOT a day trading strategy. Feel free to experiment with various timeframes, but it was designed to be used on a daily timeframe and that's how I recommend it to be used.
You may also go months without any buy signals during bull markets, but remember that is exactly the point of the strategy - to keep your buying power on the sidelines until the markets have significantly pulled back. You need to be patient and trust in the historical backtesting you have performed.
HOW IT WORKS
The Smart DCA Strategy leverages a creative approach to using Moving Averages to identify the most opportune moments to buy. A trigger occurs when a daily candle, in its entirety including the high wick, closes below the threshold line or box plotted on the chart. The indicator is designed to facilitate both backtesting and live trading.
HOW TO USE
Settings:
The input parameters for tuning have been intentionally simplified in an effort to prevent users falling into the overfitting trap.
The main control is the Buying strictness scale setting. Setting this to a lower value will provide more buying days (less strict) while higher values mean less buying days (more strict). In my testing I've found level 9 to provide good all round results.
Validation days is a setting to prevent triggering entries until the asset has spent a given number of days (candles) in the overbought state. Increasing this makes entries stricter. I've found 0 to give the best results across most assets.
In the backtest settings you can also configure how much to buy for each day an entry triggers. Blind buy size is the amount you would buy every day in a standard DCA strategy. Smart buy size is the amount you would buy each day a Smart DCA entry is triggered.
You can also experiment with backtesting your strategy over different historical datasets by using the Start date and End date settings. The results table will not calculate for any trades outside what you've set in the date range settings.
Backtesting:
When backtesting you should use the results table on the top right to tune and optimise the results of your strategy. As with all backtests, be careful to avoid overfitting the parameters. It's better to have a setup which works well across many currencies and historical periods than a setup which is excellent on one dataset but bad on most others. This gives a much higher probability that it will be effective when you move to live trading.
The results table provides a clear visual representation as to which strategy, standard or smart, is more profitable for the given dataset. You will notice the columns are dynamically coloured red and green. Their colour changes based on which strategy is more profitable in the A/B style backtest - green wins, red loses. The key metrics to focus on are GOA (Gain on Account) and Avg Cost .
Live Trading:
After you've finished backtesting you can proceed with configuring your alerts for live trading.
But first, you need to estimate the amount you should buy on each Smart DCA entry. We can use the Total invested row in the results table to calculate this. Assuming we're looking to trade on BITSTAMP:BTCUSD
Decide how much USD you would spend each day to buy BTC if you were using a standard DCA strategy. Lets say that is $5 per day
Enter that USD amount in the Blind buy size settings box
Check the Blind Buy column in the results table. If we set the backtest date range to the last 10 years, we would expect the amount spent on blind buys over 10 years to be $18,250 given $5 each day
Next we need to tweak the value of the Smart buy size parameter in setting to get it as close as we can to the Total Invested amount for Blind Buy
By following this approach it means we will invest roughly the same amount into our Smart DCA strategy as we would have into a standard DCA strategy over any given time period.
After you have calculated the Smart buy size , you can go ahead and set up alerts on Smart DCA buy triggers.
BOT AUTOMATION
In an effort to maintain the 'set and forget' stress-free benefits of a standard DCA strategy, I have set my personal Smart DCA Strategy up to be automated. The bot runs on AWS and I have a fully functional project for the bot on my GitHub account. Just reach out if you would like me to point you towards it. You can also hook this into any other 3rd party trade automation system of your choice using the pre-configured alerts within the indicator.
PLANNED FUTURE DEVELOPMENTS
Currently this is purely an accumulation strategy. It does not have any sell signals right now but I have ideas on how I will build upon it to incorporate an algorithm for selling. The strategy should gradually offload profits in bull markets which generates more USD which gives more buying power to rinse and repeat the same process in the next cycle only with a bigger starting capital. Watch this space!
MARKETS
Crypto:
This strategy has been specifically built to work on the crypto markets. It has been developed, backtested and tuned against crypto markets and I personally only run it on crypto markets to accumulate more of the coins I believe in for the long term. In the section below I will provide some backtest results from some of the top crypto assets.
Stocks:
I've found it is generally more profitable than a standard DCA strategy on the majority of stocks, however the results proved to be a lot more impressive on crypto. This is mainly due to the volatility and cycles found in crypto markets. The strategy makes its profits from capitalising on pullbacks in price. Good stocks on the other hand tend to move up and to the right with less significant pullbacks, therefore giving this strategy less opportunity to flourish.
Forex:
As this is an accumulation style investment strategy, I do not recommend that you use it to trade Forex.
STRATEGY IN ACTION
Here you see the indicator running on the BITSTAMP:BTCUSD pair. You can read the indicator as follows:
Vertical green bands on historical candles represents where buy signals triggered in the past
Table on the top right represents the results of the A/B backtest against a standard DCA strategy
Green Smart Buy column shows that Smart DCA was more profitable than standard DCA on this backtest. That is shown by the percentage GOA (Gain on Account) and the Avg Cost
Smart Buy Zone label marks the threshold which the entire candle must be below to trigger a buy signal (line can be changed to a box under plotting settings)
Green color of Smart Buy Zone label represents that the open candle is still valid for a buy signal. A signal will only be generated if the candle closes while this label is still green
Below is the same BITSTAMP:BTCUSD chart a couple of days later. Notice how the threshold has been broken and the Smart Buy Zone label has turned from green to red. No buy signal can be triggered for this day - even if the candle retraced and closed below the threshold before daily candle close.
Notice how the green vertical bands tend to be present after significant pullbacks in price. This is the reason the strategy works! Below is the same BITSTAMP:BTCUSD chart, but this time zoomed out to present a clearer picture of the times it would invest vs times it would sit out of the market. You will notice it invests heavily in bear markets and significant pullbacks, and does not buy anything during bull markets.
Finally, to visually demonstrate the indicator on an asset other than BTC, here is an example on CRYPTO:ETHUSD . In this case the current daily high has not touched the threshold so it is still possible for this to be a valid buy trigger on daily candle close. The vertical green band will not print until the buy trigger is confirmed.
BACKTEST RESULTS
Now for some backtest results to demonstrate the improved performance over a standard DCA strategy using all non-stablecoin assets in the top 30 cryptos by marketcap.
I've used the TradingView ticker (exchange name denoted as CRYPTO in the symbol search) for every symbol tested with the exception of BTCUSD because there was some dodgy data at the beginning of the TradingView BTCUSD chart which overinflated the effectiveness of the Smart DCA strategy on that ticker. For BTCUSD I've used the BITSTAMP exchange data. The symbol links below will take you to the correct chart and exchange used for the test.
I'm using the GOA (Gain on Account) values to present how each strategy performed.
The value on the left side is the standard DCA result and the right is the Smart DCA result.
✅ means Smart DCA strategy outperformed the standard DCA strategy
❌ means standard DCA strategy outperformed the Smart DCA strategy
To avoid overfitting, and to prove that this strategy does not suffer from overfitting, I've used the exact same input parameters for every symbol tested below. The settings used in these backtests are:
Buying strictness scale: 9
Validation days: 0
You can absolutely tweak the values per symbol to further improve the results of each, however I think using identical settings on every pair tested demonstrates a higher likelihood that the results will be similar in the live markets.
I'm presenting results for two time periods:
First price data available for trading pair -> closing candle on Friday 26th Jan 2024 (ALL TIME)
Opening candle on Sunday 1st Jan 2023 -> closing candle on Friday 26th Jan 2024 (JAN 2023 -> JAN 2024)
ALL TIME:
BITSTAMP:BTCUSD 80,884% / 133,582% ✅
CRYPTO:ETHUSD 17,231% / 36,146% ✅
CRYPTO:BNBUSD 5,314% / 2,702% ❌
CRYPTO:SOLUSD 1,745% / 1,171% ❌
CRYPTO:XRPUSD 2,585% / 4,544% ✅
CRYPTO:ADAUSD 338% / 353% ✅
CRYPTO:AVAXUSD 130% / 160% ✅
CRYPTO:DOGEUSD 13,690% / 16,432% ✅
CRYPTO:TRXUSD 414% / 466% ✅
CRYPTO:DOTUSD -16% / -7% ✅
CRYPTO:LINKUSD 1,161% / 2,164% ✅
CRYPTO:TONUSD 25% / 47% ✅
CRYPTO:MATICUSD 1,769% / 1,587% ❌
CRYPTO:ICPUSD 70% / 50% ❌
CRYPTO:SHIBUSD -20% / -19% ✅
CRYPTO:LTCUSD 486% / 718% ✅
CRYPTO:BCHUSD -4% / 3% ✅
CRYPTO:LEOUSD 102% / 151% ✅
CRYPTO:ATOMUSD 46% / 91% ✅
CRYPTO:UNIUSD -16% / 1% ✅
CRYPTO:ETCUSD 283% / 414% ✅
CRYPTO:OKBUSD 1,286% / 1,935% ✅
CRYPTO:XLMUSD 1,471% / 1,592% ✅
CRYPTO:INJUSD 830% / 1,035% ✅
CRYPTO:OPUSD 138% / 195% ✅
CRYPTO:NEARUSD 23% / 44% ✅
Backtest result analysis:
Assuming we have an initial investment amount of $10,000 spread evenly across each asset since the creation of each asset, it would have provided the following results.
Standard DCA Strategy results:
Average percent return: 4,998.65%
Profit: $499,865
Closing balance: $509,865
Smart DCA Strategy results:
Average percent return: 7,906.03%
Profit: $790,603
Closing balance: $800,603
JAN 2023 -> JAN 2024:
BITSTAMP:BTCUSD 47% / 66% ✅
CRYPTO:ETHUSD 26% / 33% ✅
CRYPTO:BNBUSD 15% / 17% ✅
CRYPTO:SOLUSD 272% / 394% ✅
CRYPTO:XRPUSD 7% / 12% ✅
CRYPTO:ADAUSD 43% / 59% ✅
CRYPTO:AVAXUSD 116% / 151% ✅
CRYPTO:DOGEUSD 8% / 14% ✅
CRYPTO:TRXUSD 48% / 65% ✅
CRYPTO:DOTUSD 24% / 35% ✅
CRYPTO:LINKUSD 83% / 124% ✅
CRYPTO:TONUSD 7% / 21% ✅
CRYPTO:MATICUSD -3% / 7% ✅
CRYPTO:ICPUSD 161% / 196% ✅
CRYPTO:SHIBUSD 1% / 8% ✅
CRYPTO:LTCUSD -15% / -7% ✅
CRYPTO:BCHUSD 47% / 68% ✅
CRYPTO:LEOUSD 9% / 11% ✅
CRYPTO:ATOMUSD 1% / 15% ✅
CRYPTO:UNIUSD 9% / 23% ✅
CRYPTO:ETCUSD 27% / 40% ✅
CRYPTO:OKBUSD 21% / 30% ✅
CRYPTO:XLMUSD 11% / 19% ✅
CRYPTO:INJUSD 477% / 446% ❌
CRYPTO:OPUSD 77% / 91% ✅
CRYPTO:NEARUSD 78% / 95% ✅
Backtest result analysis:
Assuming we have an initial investment amount of $10,000 spread evenly across each asset for the duration of 2023, it would have provided the following results.
Standard DCA Strategy results:
Average percent return: 61.42%
Profit: $6,142
Closing balance: $16,142
Smart DCA Strategy results:
Average percent return: 78.19%
Profit: $7,819
Closing balance: $17,819
Bull Bear Indicator (BBI)/Introduction
The Bull Bear Indicator (BBI) identifies bull market conditions and bear market conditions for equity investors so they can avoid missing a bull market or getting caught in a bear market.
/Signals
There are two signals:
1. Bull Market Alert - This indicates prices of stocks in the broader market are rising.
2. Bear market Alert - This indicates prices of stocks in the broader market are falling.
Both signals are indicated by a background colour and an upward/downward triangle. A green background and an upward green triangle below the bar signifies an environment of rising prices. A red background and a downward red triangle above the bar indicates an environment of falling prices.
Lack of a coloured background indicates a transition period from Bull to Bear or Bear to Bull conditions. The transitions may be rapid during periods of high volatility.
/Construction
The indicator is constructed using market breadth, price action and moving averages.
1.Market Breadth:
Definition: Market breadth refers to the number of stocks advancing versus the number declining in the stock market. It provides insight into the overall health and strength of a market move.
Use in Identifying Bull/Bear Markets:
Bull Market Indicators: In a bull market, market breadth is typically strong, with a large number of stocks advancing. This indicates widespread participation in the market rally, confirming the strength and sustainability of the upward trend.
Bear Market Indicators: Conversely, in a bear market, market breadth weakens, with more stocks declining than advancing. This suggests that the downward movement is broad-based across the market, reinforcing the bearish sentiment.
How the indicator does this: The number of stocks in a bullish/bearish trend is counted and normalised to a percentage to determine what percentage of stocks in the overall market are bullish/bearish.
2. Price Action:
Definition: Price action involves the study of historical price movements to predict future price direction. It includes analyzing patterns, trends, and the reactions of prices to certain levels (like support and resistance).
Use in Identifying Bull/Bear Markets:
Bull Market Indicators: In a bull market, price action typically shows higher highs and higher lows, indicating an ongoing upward trend. The reaction to support levels is often strong, with prices bouncing off these levels.
Bear Market Indicators: In a bear market, the price action is characterized by lower highs and lower lows. Prices tend to break through support levels and bounce off resistance levels, reflecting the dominant downward trend.
3. Trend Analysis:
Definition: Trend analysis involves identifying the direction and strength of market movements. This was done using moving averages.
Use in Identifying Bull/Bear Markets:
Bull Market Indicators: A bull market is often identified by upward-sloping trendlines and prices consistently staying above key moving averages.
Bear Market Indicators: In a bear market, the trendlines slope downwards, and prices remain below key moving averages.
How the indicator does this: The average closing prices of the largest capitalised stocks and their intermediate trend is assessed relative to their moving averages, the moving average combines price action and trend because it is simply the average closing price over time.
/Originality
This indicator is simple and effective in that it uses multiple factors to assess the market environment. Market breadth gives an overview of the participation level in the market trend, price action helps identify specific patterns and reactions to key levels indicating a bull or bear market, and trend analysis provides a macro view of the market direction and its strength. Combining these tools can gives a comprehensive picture of the market environment and help in distinguishing between bull and bear markets. The market environments are boldly marked out through background colours and triangle markers. The indicator performance is only valid from 2002 to date because the market breadth data used is not available before this date.
Why market Market breadth: Because it takes into account all the stocks in the market, this is essential in identifying the level of participation in a trend.
Why moving averages: Because it ensures that the price action and overall trend of the stocks can be monitored over a given lookback period
So together, moving average/price action + market breadth = trend + participation
Note:
The indicator has no predictive power, performance described here does not guarantee future results. Equity markets are particularly volatile and prone to cycles, and individual psychology can significantly affect indicator interpretation. Price data may also vary across exchanges.
/Settings
The parameters are fixed and there is no room for optimisation however, style settings can be modified by the user.
/Tickers
The BBI indicator is ticker agnostic but best viewed on a 1 day chart of the SPY.
Commitment of Traders: Legacy Metrics█ OVERVIEW
This indicator displays the Commitment of Traders (COT) legacy data for futures markets.
█ CONCEPTS
Commitment of Traders (COT) data is tallied by the Commodity Futures Trading Commission (CFTC) , a US federal agency that oversees the trading of derivative markets such as futures in the US. It is weekly data that provides traders with information about open interest for an asset. The CFTC oversees derivative markets traded on different exchanges, so COT data is available for assets that can be traded on CBOT, CME, NYMEX, COMEX, and ICEUS.
A detailed description of the COT report can be found on the CFTC's website .
COT data is separated into three notable reports: Legacy, Disaggregated, and Financial. This indicator presents data from the legacy report, which is broken down by exchange. Legacy reports break down the reportable open interest positions into two classifications: non-commercial and commercial traders.
Our other COT indicators are:
• Commitment of Traders: Disaggregated Metrics
• Commitment of Traders: Financial Metrics
• Commitment of Traders: Total
█ HOW TO USE IT
Load the indicator on an active chart (see here if you don't know how).
By default, the indicator uses the chart's symbol to derive the COT data it displays. You can also specify a CFTC code in the "CFTC code" field of the script's inputs to display COT data from a symbol different than the chart's.
The rest of this section documents the script's input fields.
Metric
Each metric represents a different column of the Commitment of Traders report. Details are available in the explanatory notes on the CFTC's website .
Here is a summary of the metrics:
• "Open Interest" is the total of all futures and/or option contracts entered into and not yet offset by a transaction, by delivery, by exercise, etc.
The aggregate of all long open interest is equal to the aggregate of all short open interest.
• "Traders Total" is the number of all unique reportable traders, regardless of the trading direction.
• "Traders Total Reportable/Traders Noncommercial/Traders Commercial" are the quantities of traders reported to hold any position with the specified direction.
All of a trader's reported futures positions in a commodity are classified as commercial if the trader uses futures contracts in that particular commodity for hedging.
To determine the total number of reportable traders in a market, a trader is counted only once, whether or not the trader appears in more than one category.
• "Total Reportable/Noncommercial/Commercial Positions" are all positions held by all reportable/non-commercial/commercial traders.
• "Non-reportable Positions" is derived by subtracting total long and short "Reportable Positions" from the total open interest.
Accordingly, the number of traders involved and the commercial/non-commercial classification of each trader are unknown.
• "Concentration Gross/Net LT 4/8 TDR" is the percentage of open interest held by 4/8 of the largest traders, by gross/net positions,
without regard to whether they are classified as commercial or non-commercial. The Net position ratios are computed after offsetting each trader’s equal long and short positions.
A reportable trader with relatively large, balanced long and short positions in a single market, therefore,
may be among the four and eight largest traders in both the gross long and gross short categories, but will probably not be included among the four and eight largest traders on a net basis.
Direction
Each metric is available for a particular set of directions. Valid directions for each metric are specified with its name in the "Metric" field's dropdown menu.
Type
Possible values are: All, Old, Other. When commodities have a well-defined marketing season or crop year (e.g. Wheat or Lean Hogs futures), this determines how the data is aggregated. Detailed explanation can be found in the "Old and Other Futures" section of the CTFC Explanatory Notes linked above. The "Major Markets for Which the COT Data Is Shown by Crop Year" table in the Explanatory Notes specifies the commodities that this distinction applies to; selecting "Old" for any of the commodities not in that list will return the same data as in "All", while selecting "Other" will return 0.
COT Selection Mode
This field's value determines how the script determines which COT data to return from the chart's symbol:
- "Root" uses the root of a futures symbol ("ES" for "ESH2020").
- "Base currency" uses the base currency in a forex pair ("EUR" for "EURUSD").
- "Currency" uses the quote currency, i.e., the currency the symbol is traded in ("JPY" for "TSE:9984" or "USDJPY").
- "Auto" tries all modes, in turn.
If no COT data can be found, a runtime error is generated.
Note that if the "CTFC Code" input field contains a code, it will override this input.
Futures/Options
Specifies the type of Commitment of Traders data to display: data concerning only Futures, only Options, or both.
CTFC Code
Instead of letting the script generate the CFTC COT code from the chart and the "COT Selection Mode" input when this field is empty, you can specify an unrelated CFTC COT code here, e.g., 001602 for wheat futures.
Look first. Then leap.
Commitment of Traders: Disaggregated Metrics█ OVERVIEW
This indicator displays the Commitment of Traders (COT) Disaggregated data for futures markets.
█ CONCEPTS
Commitment of Traders (COT) data is tallied by the Commodity Futures Trading Commission (CFTC) , a US federal agency that oversees the trading of derivative markets such as futures in the US. It is weekly data that provides traders with information about open interest for an asset. The CFTC oversees derivative markets traded on different exchanges, so COT data is available for assets that can be traded on CBOT, CME, NYMEX, COMEX, and ICEUS.
A detailed description of the COT report can be found on the CFTC's website .
COT data is separated into three notable reports: Legacy, Disaggregated, and Financial. This indicator presents data from the Disaggregated report. The disaggregated reports are broken down by agriculture, petroleum and products, natural gas and products, electricity and metals and other physical contracts. The Disaggregated reports break down the reportable open interest positions into four classifications: Producer/Merchant/Processor/User, Swap Dealers, Managed Money, and Other Reportables.
Our other COT indicators are:
• Commitment of Traders: Legacy Metrics
• Commitment of Traders: Financial Metrics
• Commitment of Traders: Total
█ HOW TO USE IT
Load the indicator on an active chart (see here if you don't know how).
By default, the indicator uses the chart's symbol to derive the COT data it displays. You can also specify a CFTC code in the "CFTC code" field of the script's inputs to display COT data from a symbol different than the chart's.
The rest of this section documents the script's input fields.
Metric
Each metric represents a different column of the Commitment of Traders report. Details are available in the explanatory notes on the CFTC's website .
Here is a summary of the metrics:
• "Open Interest" is the total of all futures and/or option contracts entered into and not yet offset by a transaction, by delivery, by exercise, etc.
The aggregate of all long open interest is equal to the aggregate of all short open interest.
• "Traders Total" is the quantity of all unique reportable traders, regardless of the trading direction.
• "Traders Producer Merchant" is the number of traders classified as a "producer/merchant/processor/user" reported holding any position with the specified direction.
A "producer/merchant/processor/user" is an entity that predominantly engages in the production, processing, packing or handling of a physical commodity and
uses the futures markets to manage or hedge risks associated with those activities.
• "Traders Swap" is the number of traders classified as "swap dealers" reported holding any position with the specified direction.
A "swap dealer" is an entity that deals primarily in swaps for a commodity and uses the futures markets to manage or hedge the risk associated with those swaps transactions.
The swap dealer’s counterparties may be speculative traders, like hedge funds, or traditional commercial clients that are managing risk arising from their dealings in the physical commodity.
• "Traders Managed Money" is the number of traders classified as "money managers" reported holding any position with the specified direction.
A "money manager" is a registered trader that is engaged in managing and conducting organized futures trading on behalf of clients.
• "Traders Other Reportable" is the number of reportable traders that are not placed in any of the three categories specified above.
• "Traders Total Reportable" is the number of traders reported holding any position with the specified direction.
To determine the total number of reportable traders in a market, a trader is counted only once whether or not the trader appears in more than one category.
As a result, the sum of the numbers of traders in each separate category typically exceeds the total number of reportable traders.
• "Producer Merchant/Swap/Managed Money/Total Reportable/Other Reportable Positions" is all positions held by the traders of the specified category.
• "Nonreportable Positions" is the long and short open interest derived by subtracting the total long and short reportable positions from the total open interest.
Accordingly, the number of traders involved and the classification of each trader are unknown.
• "Concentration Gross/Net LE 4/8 TDR" is the percentage of open interest held by 4/8 of the largest traders (entities), by Gross/Net positions, without regard to how they are classified.
The Net position ratios are computed after offsetting each trader’s equal long and short positions.
A reportable trader with relatively large, balanced long and short positions in a single market,
therefore, may be among the four and eight largest traders in both the gross long and gross short categories,
but will probably not be included among the four and eight largest traders on a net basis.
Direction
Each metric is available for a particular set of directions. Valid directions for each metric are specified with its name in the "Metric" field's dropdown menu.
Type
Possible values are: All, Old, Other. When commodities have a well-defined marketing season or crop year (e.g. Wheat or Lean Hogs futures), this determines how the data is aggregated. Detailed explanation can be found in the "Old and Other Futures" section of the CTFC Explanatory Notes linked above. The "Major Markets for Which the COT Data Is Shown by Crop Year" table in the Explanatory Notes specifies the commodities that this distinction applies to; selecting "Old" for any of the commodities not in that list will return the same data as in "All", while selecting "Other" will return 0.
Futures/Options
Specifies the type of Commitment of Traders data to display: data concerning only Futures, only Options, or both.
CTFC Code
Instead of letting the script generate the CFTC COT code from the chart and the "COT Selection Mode" input when this field is empty, you can specify an unrelated CFTC COT code here, e.g., 001602 for wheat futures.
Look first. Then leap.
🔗 Blockchain Rhythms by Cryptorhythms🎼 Blockchain Rhythms v1.0 by Cryptorhythms
This indicator and data plot suite is for bitcoin BTCUSD analysis over longer periods and higher time frames. 🚨For this to plot anything you must use on Daily or higher timeframe🚨 .
You want to have an alternative to the typical technical indicators you see everywhere? This is it. Seen crypto twitter talking about/using all sorts of indicators you have never seen before on tradingview? Here you go. Are you a long term investor and not a short term speculator?... I think you get the picture...
With the wealth of data here, I cannot go into a fully detailed analysis for every indicator. Please make liberal use of google and as always DYOR before trading on a system you have never used.
These indicators are best observed versus a logarithmic price scale. If I have missed any indicators you think should be in here let me know! Let me preempt that by saying MVRV and UTXO Age Distribution are not possible to create on Tradingview at this time.
🚧Error Screen:
If you see this you need to choose a data-point or indicator to plot!
⌚If you are loading this indicator with alot of chart history shown (as in the example screenshots) it may take up to a minute to load.
Please note: some of the screenshots below show chart title plots which I subsequently had to remove due to limitations. If you would like a title for all the plot, simple use the Indicator Labels checkbox option located in the scales tab of chart settings.
[b📊 Fundamental Blockchain Indicators
NVT Signal & Ratio
Both are related. NVT / NVT Signal can be interpreted as the strength of market confidence in the means of payment / settlement layer narrative. A “measure of the chain’s strength as a payment network compared to its market value — a low NVT may suggest that a network is undervalued compared to the service it is providing as a settlement layer” (Matteo Leibowitz).
💰NVT Ratio:
NVT Ratio (Network Value to Transactions Ratio) is similar to the PE Ratio used in equity markets.
When Bitcoin`s NVT is high, it indicates that its network valuation is outstripping the value being transmitted on its payment network, this can happen when the network is in high growth and investors are valuing it as a high return investment, or alternatively when the price is in an unsustainable bubble.
🚦NVT Signal:
NVT Signal (NVTS) is a derivative of NVT Ratio created by Dimitry Kalichkin. This indicator provides more emphasis on predictive signaling ahead of price peaks.
🚀Bitcoin Velocity
Velocity is a measure of how quickly money is circulating in the economy. Is bitcoin trending towards savings or payments? This can help you decide. It is similar to Bitcoin Network Momentum, except this takes into account bitcoins increasing supply.
🏃Bitcoin Network Momentum
Network Momentum is a view created by PositiveCrypto which looks into the value transmitted through the Bitcoin blockchain denominated in BTC value plotted against Bitcoin's price. It serves as a leading indicator to bitcoin price, in that we need high levels of value throughput to drive the bull market. This indicator is experimental.
Both daily transaction values and price exhibit cyclical patterns, but not in sync with each other. A hypothesis to explain the mismatch is that short-term mindset traders (using exchanges) heavily influence price; but long-term mindset investments (more likely to be directly recorded on-chain) have a greater contribution to the daily transaction value recorded in the ledger.
An alternative to the NVT / NVT Signal - tracks the relationship between Bitcoin’s price and BTC volume flowing through the blockchain network.
Ⓜ Mayer Multiple
Introduced by Trace Mayer as a way to gauge the current price of Bitcoin against its long range historical price movements (200 day SMA by default), the Mayer Multiple highlights when Bitcoin is overbought or oversold in the context of longer time frames.
It`s worth noting as the market becomes larger and less volatile, the peaks are becoming less exaggerated. This is because a 200 day moving average baseline is a static yardstick against an ever growing, more stable, Bitcoin market. We should eventually re-calibrate what constitutes the overbought/oversold extremes on this chart accordingly.
A more fully featured Mayer Multiple version available here:
💲 BTC Marketcap and Thermocap
We are all familiar with marketcap, but it does come with its disadvantages.
A more appropriate measure of network value was recently put forth by Nic Carter. Remember capital flows in crypto generally do not come in via exchanges (miners notably like to sell OTC). Every buy in an exchange is matched by a sell. Money that comes in = money that goes out.
True inflows (in Bitcoin, at least) are the aggregate of resources spent by miners¹. And a good proxy for that is the amount these folks are earning back from networks they support in return for their investments. That’s aggregate security spend (or Thermocap): what was actually paid out to miners (transactions * their price in USD at the time they were mined).
There is an option to deduct lost coins, genesis (Satoshi's) coins, and dead HODL'ers coins from the marketcap. This information was taken from ChainAnalysis' 2017 report
This shows both plots for comparison on a logrithmic scale:
⛏Mining Indicators & Data
⛏ Petahash Dollar Ratio
Bitcoin’s Hashrate (Daily PetaHashes) to Daily Mining Earnings (PetaHashDollar) is a robust metric to asses the day to day mining profitability. In addition, when plotted over the past five years, its overall trend represents a good way to quantify and visualize the relative progress in efficiency of ASICs (more specifically the inverse of that metric: 1/relative mining efficiency).
⛏Unmined Coins Marketcap
A simple statistic I created to plot the value of the unmined BTC still waiting to be extracted. If you find any interesting value for analysis please message me and let me know.
⛏Percentage of Total BTC Mined
I hope this one doesnt need an explanation. 😅
#️⃣ Network Hash Rate
A network's hashrate is the most important data point in blockchain tech. It indicates to the world how secure its network is. The hashrate is the "bridge" between the analog world, and the digital world. Essentially, the hashrate describes how much computing power (called hashing power in blockchain speak) is being thrown at the network, by users all across the world. These "miners" are running servers with dedicated processing chips to solve random, cryptographic math problems. The reason miners do this constant computing is that it betters their chances to reap a "block reward." The block reward entitles them to:
1.)Newly "mined" coins, and
2.)Transaction fees
Both of these are typically paid out with each new block. This rewards miners for their “proof-of-work.” It signals to the world that real "work" and resources, like electricity, have been spent on the Bitcoin network.
As more and more miners compete for the block reward, the hashrate, mining calculations and block difficulty will increase. This increase in the network's hashrate over time means an increase in the network’s security. Much better detail on this is available elsewhere, but primarily, this process solves digital money's vulnerability to attacks and the "double spend" problem.
I like to plot it directly on the price chart (click on the indicator and drag it up)
⛏ Revenue Per Transaction
A chart showing miners revenue divided by the number of transactions.
Fee Per Block Kilobyte
A measure of how much it costs per kilobyte of blockchain block size.
⛏Return Per TeraHash (TH)
Revenue per TH of mining hash power.
Can also be plotted on price chart and looks nice:
Cost Per TX (CPT) and Cost % Per TX Volume
CPT - A chart showing miners revenue divided by the number of transactions
C%PRV - A chart showing miners revenue as percentage of the transaction volume
Blockchain Statistics & Data Plots
🏋Network Difficulty
A relative measure of how difficult it is to find a new block. The difficulty is adjusted periodically as a function of how much hashing power has been deployed by the network of miners.
I like plotting this one on price chart as well:
Daily Output Value
The total value of all transaction outputs per day (includes coins returned to the sender as change).
🔢Number of Unique Addresses Used
Addresses are kind of like bank accounts.
Unlike bank accounts, addresses on the blockchain can be generated by anyone, anywhere and one single person could have thousands.
The plot shows bitcoins growth of addresses which are both unique and active per day, smoothed out over 14 days for clarity (using a zero lag ema). As you can see bull runs typically lead to more unique addresses the assumption being that more new money is drawn into the market due to the news cycle.
This is another one I prefer to plot on the price chart.
🔢Number of Transactions (NoTX) and NoTX - Exchange Wallets
Number of TX's on the chain (green line) and NoTX minus (-) Exchange Wallets (blue line).
⏳ Median Confirmation Time
The median time for a transaction to be accepted into a mined block and added to the public ledger (note: only includes transactions with miner fees). Displayed in minutes.
🔊Volume Dominance (Liquidity to Transaction Volume Ratio)
Volume Dominance is another metric I invented simply to show the ratio between spot exchange TXs (liquidity/speculation) and blockchain TXs (utility/HODLing). Its shows percent of volume attributed to blockchain TXs.
🙃 We REALLY hope you enjoy and find this indicator useful. I certainly enjoyed creating it and learned quite a bit myself manipulating the data! I welcome any suggestions or ideas you may have to further extend, or create new indicators.
👍 Enjoying this indicator or find it useful? Please give me a like and follow! I post crypto analysis, price action strategies and free indicators regularly.
💬 Questions? Comments? Want to get access to an entire suite of proven trading indicators? Come visit us on telegram and chat, or just soak up some knowledge. We make timely posts about the market, news, and strategy everyday. Our community isn't open only to subscribers - everyone is welcome to join.
