Automated Scalping Signals with TP/SL Indicator [QuantAlgo]🟢 Overview
The Automated Scalping Signals with Take Profit & Stop Loss Indicator is a multi-timeframe trading system that combines market structure analysis with directional bias filtering to identify potential scalping opportunities. It detects Points of Interest (POI) including Fair Value Gaps (FVG) and Order Blocks (OB) while cross-referencing entries with higher timeframe exponential moving average positioning to create systematic entry conditions.
The indicator features adaptive timeframe calculations that automatically scale analysis periods based on your chart timeframe, maintaining consistent analytical relationships across different trading sessions. It provides integrated trade management with stop loss calculation methods, configurable risk-reward ratios, and real-time performance tracking through dashboard displays showing trade statistics, bias direction, and active position status.
This advanced system is designed for low timeframe trading, typically performing optimally on 1 to 15-minute charts across popular instruments such as OANDA:XAUUSD , CME_MINI:MES1! , CME_MINI:ES1! , CME_MINI:MNQ1! , CBOT_MINI:YM1! , CBOT_MINI:MYM1! , BYBIT:BTCUSDT.P , BYBIT:ETHUSDT.P , or any asset and timeframe of your preference.
🟢 How It Works
The indicator operates using a dual-timeframe mathematical framework where higher timeframe exponential moving averages establish directional bias through cross-over analysis, while simultaneously scanning for specific market structure patterns on the POI timeframe. The timeframe calculation engine uses multiplication factors to determine analysis periods, ensuring the bias timeframe provides trend context while the POI timeframe captures structural formations.
The structural analysis begins with FVG detection, which systematically scans price action to identify imbalances where gaps exist between consecutive candle ranges with no overlapping wicks. When such gaps are detected, the algorithm measures their size against minimum thresholds to filter out insignificant formations. Concurrently, OB recognition analyzes three-candle sequences, examining specific open/close relationships that indicate potential institutional accumulation zones. Once these structural patterns are identified, the algorithm cross-references them against the higher timeframe bias direction, creating a validation filter that only permits entries aligned with the prevailing EMA cross-over state. When price subsequently intersects these validated POI zones, entry signals generate with the system calculating entry levels at zone midpoints, then applying the selected stop loss methodology combined with the configured risk-reward ratio to determine take profit placement.
To mirror realistic trading conditions, the indicator incorporates configurable slippage calculations that account for execution differences between intended and actual fill prices. When trades reach their take profit or stop loss levels, the algorithm applies slippage adjustments that worsen the exit prices in a conservative manner - reducing take profit fills and increasing stop loss impact. This approach ensures backtesting results reflect more realistic performance expectations by accounting for spread costs, market volatility during execution, and liquidity constraints that occur in live trading environments.
It also has a performance dashboard that continuously tracks and displays comprehensive trading metrics:
1/ Bias TF / POI TF: Displays the calculated timeframes used for bias analysis and POI detection, showing the actual periods (e.g., "15m / 5m") that result from the multiplier settings to confirm proper adaptive timeframe selection
2/ Bias Direction: Shows current market trend assessment (Bullish, Bearish, or Sideways) derived from EMA cross-over analysis to indicate which trade directions align with prevailing momentum
3/ Data Processing: Indicates how many price bars have been analyzed by the system, helping users verify if complete historical data has been processed for comprehensive strategy validation
4/ Total Trades: Displays the cumulative number of completed trades plus any active positions, providing volume assessment for statistical significance of other metrics
5/ Wins/Losses: Shows the raw count of profitable versus unprofitable trades, offering immediate insight into strategy effectiveness frequency
6/ Win Rate: Reveals the percentage of successful trades, where values above 50% generally indicate effective entry timing and values below suggest strategy refinement needs
7/ Total R-Multiple: Displays cumulative risk-reward performance across all trades, with positive values demonstrating profitable system operation and negative values indicating net losses requiring analysis
8/ Average R Win/Loss: Shows average risk-reward ratios for winning and losing trades separately, where winning averages approaching the configured take profit ratio indicate minimal slippage impact while losing averages near -1.0 suggest effective stop loss execution
9/ TP Ratio / Slippage: Displays the configured take profit ratio and slippage settings with calculated performance impact, showing how execution costs affect actual versus theoretical returns
10/ Profit Factor: Calculates the ratio of total winning amounts to total losing amounts, where values above 1.5 suggest robust profitability, values between 1.0-1.5 indicate modest success, and values below 1.0 show net losses
11/ Maximum Drawdown: Tracks the largest peak-to-trough decline in R-multiple terms, with smaller negative values indicating better capital preservation and risk control during losing streaks
🟢 How to Use
Start by applying the indicator to your chart and observe its performance across different market conditions to understand how it identifies bias direction and POI formations. Then navigate to the settings panel to configure the Bias Timeframe Multiplier for trend context sensitivity and POI Timeframe Multiplier for structural analysis frequency according to your trading preference and objectives.
Next, fine-tune the EMA periods in Bias Settings to control trend detection sensitivity and select your preferred POI types based on your analytical preference. Proceed to configure your Risk Management approach by selecting from the available stop loss calculation methods and setting the Take Profit ratio that aligns with your risk tolerance and profit objectives. Complete the setup by customizing Display Settings to control table visibility and trade visualization elements, adjusting UI positioning and colors for optimal chart readability, then activate Alert Conditions for automated notifications on trade entries, exits, and bias direction changes to support systematic trade management.
🟢 Examples
OANDA:XAUUSD
CME_MINI:MES1!
CME_MINI:ES1!
CME_MINI:MNQ1!
CBOT_MINI:YM1!
BYBIT:BTCUSDT.P
BINANCE:SOLUSD
*Disclaimer: Past performance is not indicative of future results. None of our statements, claims, or signals from our indicators are intended to be financial advice. All trading involves substantial risk of loss, not just upside potential. Users are highly recommended to carefully consider their financial situation and risk tolerance before trading.
Cerca negli script per "profit"
KST Strategy [Skyrexio]Overview
KST Strategy leverages Know Sure Thing (KST) indicator in conjunction with the Williams Alligator and Moving average to obtain the high probability setups. KST is used for for having the high probability to enter in the direction of a current trend when momentum is rising, Alligator is used as a short term trend filter, while Moving average approximates the long term trend and allows trades only in its direction. Also strategy has the additional optional filter on Choppiness Index which does not allow trades if market is choppy, above the user-specified threshold. Strategy has the user specified take profit and stop-loss numbers, but multiplied by Average True Range (ATR) value on the moment when trade is open. The strategy opens only long trades.
Unique Features
ATR based stop-loss and take profit. Instead of fixed take profit and stop-loss percentage strategy utilizes user chosen numbers multiplied by ATR for its calculation.
Configurable Trading Periods. Users can tailor the strategy to specific market windows, adapting to different market conditions.
Optional Choppiness Index filter. Strategy allows to choose if it will use the filter trades with Choppiness Index and set up its threshold.
Methodology
The strategy opens long trade when the following price met the conditions:
Close price is above the Alligator's jaw line
Close price is above the filtering Moving average
KST line of Know Sure Thing indicator shall cross over its signal line (details in justification of methodology)
If the Choppiness Index filter is enabled its value shall be less than user defined threshold
When the long trade is executed algorithm defines the stop-loss level as the low minus user defined number, multiplied by ATR at the trade open candle. Also it defines take profit with close price plus user defined number, multiplied by ATR at the trade open candle. While trade is in progress, if high price on any candle above the calculated take profit level or low price is below the calculated stop loss level, trade is closed.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.5, number of ATRs to calculate stop-loss level)
ATR Take Profit (by default = 3.5, number of ATRs to calculate take profit level)
Filter MA Type (by default = Least Squares MA, type of moving average which is used for filter MA)
Filter MA Length (by default = 200, length for filter MA calculation)
Enable Choppiness Index Filter (by default = true, setting to choose the optional filtering using Choppiness index)
Choppiness Index Threshold (by default = 50, Choppiness Index threshold, its value shall be below it to allow trades execution)
Choppiness Index Length (by default = 14, length used in Choppiness index calculation)
KST ROC Length #1 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST ROC Length #2 (by default = 15, value used in KST indicator calculation, more information in Justification of Methodology)
KST ROC Length #3 (by default = 20, value used in KST indicator calculation, more information in Justification of Methodology)
KST ROC Length #4 (by default = 30, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #1 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #2 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #3 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #4 (by default = 15, value used in KST indicator calculation, more information in Justification of Methodology)
KST Signal Line Length (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Before understanding why this particular combination of indicator has been chosen let's briefly explain what is KST, Williams Alligator, Moving Average, ATR and Choppiness Index.
The KST (Know Sure Thing) is a momentum oscillator developed by Martin Pring. It combines multiple Rate of Change (ROC) values, smoothed over different timeframes, to identify trend direction and momentum strength. First of all, what is ROC? ROC (Rate of Change) is a momentum indicator that measures the percentage change in price between the current price and the price a set number of periods ago.
ROC = 100 * (Current Price - Price N Periods Ago) / Price N Periods Ago
In our case N is the KST ROC Length inputs from settings, here we will calculate 4 different ROCs to obtain KST value:
KST = ROC1_smooth × 1 + ROC2_smooth × 2 + ROC3_smooth × 3 + ROC4_smooth × 4
ROC1 = ROC(close, KST ROC Length #1), smoothed by KST SMA Length #1,
ROC2 = ROC(close, KST ROC Length #2), smoothed by KST SMA Length #2,
ROC3 = ROC(close, KST ROC Length #3), smoothed by KST SMA Length #3,
ROC4 = ROC(close, KST ROC Length #4), smoothed by KST SMA Length #4
Also for this indicator the signal line is calculated:
Signal = SMA(KST, KST Signal Line Length)
When the KST line rises, it indicates increasing momentum and suggests that an upward trend may be developing. Conversely, when the KST line declines, it reflects weakening momentum and a potential downward trend. A crossover of the KST line above its signal line is considered a buy signal, while a crossover below the signal line is viewed as a sell signal. If the KST stays above zero, it indicates overall bullish momentum; if it remains below zero, it points to bearish momentum. The KST indicator smooths momentum across multiple timeframes, helping to reduce noise and provide clearer signals for medium- to long-term trends.
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
The next indicator is Moving Average. It has a lot of different types which can be chosen to filter trades and the Least Squares MA is used by default settings. Let's briefly explain what is it.
The Least Squares Moving Average (LSMA) — also known as Linear Regression Moving Average — is a trend-following indicator that uses the least squares method to fit a straight line to the price data over a given period, then plots the value of that line at the most recent point. It draws the best-fitting straight line through the past N prices (using linear regression), and then takes the endpoint of that line as the value of the moving average for that bar. The LSMA aims to reduce lag and highlight the current trend more accurately than traditional moving averages like SMA or EMA.
Key Features:
It reacts faster to price changes than most moving averages.
It is smoother and less noisy than short-term EMAs.
It can be used to identify trend direction, momentum, and potential reversal points.
ATR (Average True Range) is a volatility indicator that measures how much an asset typically moves during a given period. It was introduced by J. Welles Wilder and is widely used to assess market volatility, not direction.
To calculate it first of all we need to get True Range (TR), this is the greatest value among:
High - Low
abs(High - Previous Close)
abs(Low - Previous Close)
ATR = MA(TR, n) , where n is number of periods for moving average, in our case equals 14.
ATR shows how much an asset moves on average per candle/bar. A higher ATR means more volatility; a lower ATR means a calmer market.
The Choppiness Index is a technical indicator that quantifies whether the market is trending or choppy (sideways). It doesn't indicate trend direction — only the strength or weakness of a trend. Higher Choppiness Index usually approximates the sideways market, while its low value tells us that there is a high probability of a trend.
Choppiness Index = 100 × log10(ΣATR(n) / (MaxHigh(n) - MinLow(n))) / log10(n)
where:
ΣATR(n) = sum of the Average True Range over n periods
MaxHigh(n) = highest high over n periods
MinLow(n) = lowest low over n periods
log10 = base-10 logarithm
Now let's understand how these indicators work in conjunction and why they were chosen for this strategy. KST indicator approximates current momentum, when it is rising and KST line crosses over the signal line there is high probability that short term trend is reversing to the upside and strategy allows to take part in this potential move. Alligator's jaw (blue) line is used as an approximation of a short term trend, taking trades only above it we want to avoid trading against trend to increase probability that long trade is going to be winning.
Almost the same for Moving Average, but it approximates the long term trend, this is just the additional filter. If we trade in the direction of the long term trend we increase probability that higher risk to reward trade will hit the take profit. Choppiness index is the optional filter, but if it turned on it is used for approximating if now market is in sideways or in trend. On the range bounded market the potential moves are restricted. We want to decrease probability opening trades in such condition avoiding trades if this index is above threshold value.
When trade is open script sets the stop loss and take profit targets. ATR approximates the current volatility, so we can make a decision when to exit a trade based on current market condition, it can increase the probability that strategy will avoid the excessive stop loss hits, but anyway user can setup how many ATRs to use as a stop loss and take profit target. As was said in the Methodology stop loss level is obtained by subtracting number of ATRs from trade opening candle low, while take profit by adding to this candle's close.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2025.05.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 60%
Maximum Single Position Loss: -5.53%
Maximum Single Profit: +8.35%
Net Profit: +5175.20 USDT (+51.75%)
Total Trades: 120 (56.67% win rate)
Profit Factor: 1.747
Maximum Accumulated Loss: 1039.89 USDT (-9.1%)
Average Profit per Trade: 43.13 USDT (+0.6%)
Average Trade Duration: 27 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 1h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrexio commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation.
S4_IBS_Mean_Rev_3candleExitOverview:
This is a rules-based, mean reversion strategy designed to trade pullbacks using the Internal Bar Strength (IBS) indicator. The system looks for oversold conditions based on IBS, then enters long trades , holding for a maximum of 3 bars or until the trade becomes profitable.
The strategy includes:
✅ Strict entry rules based on IBS
✅ Hardcoded exit conditions for risk management
✅ A clean visual table summarizing key performance metrics
How It Works:
1. Internal Bar Strength (IBS) Setup:
The IBS is calculated using the previous bar’s price range:
IBS = (Previous Close - Previous Low) / (Previous High - Previous Low)
IBS values closer to 0 indicate price is near the bottom of the previous range, suggesting oversold conditions.
2. Entry Conditions:
IBS must be ≤ 0.25, signaling an oversold setup.
Trade entries are only allowed within a user-defined backtest window (default: 2024).
Only one trade at a time is permitted (long-only strategy).
3. Exit Conditions:
If the price closes higher than the entry price, the trade exits with a profit.
If the trade has been open for 3 bars without showing profit, the trade is forcefully exited.
All trades are closed automatically at the end of the backtest window if still open.
Additional Features:
📊 A real-time performance metrics table is displayed on the chart, showing:
- Total trades
- % of profitable trades
- Total P&L
- Profit Factor
- Max Drawdown
- Best/Worst trade performance
📈 Visual markers indicate trade entries (green triangle) and exits (red triangle) for easy chart interpretation.
Who Is This For?
This strategy is designed for:
✅ Traders exploring systematic mean reversion approaches
✅ Those who prefer strict, rules-based setups with no subjective decision-making
✅ Traders who want built-in performance tracking directly on the chart
Note: This strategy is provided for educational and research purposes. It is a backtested model and past performance does not guarantee future results. Users should paper trade and validate performance before considering real capital.
Canuck Trading Trader StrategyCanuck Trading Trader Strategy
Overview
The Canuck Trading Trader Strategy is a high-performance, trend-following trading system designed for NASDAQ:TSLA on a 15-minute timeframe. Optimized for precision and profitability, this strategy leverages short-term price trends to capture consistent gains while maintaining robust risk management. Ideal for traders seeking an automated, data-driven approach to trading Tesla’s volatile market, it delivers strong returns with controlled drawdowns.
Key Features
Trend-Based Entries: Identifies short-term trends using a 2-candle lookback period and a minimum trend strength of 0.2%, ensuring responsive trade signals.
Risk Management: Includes a configurable 3.0% stop-loss to cap losses and a 2.0% take-profit to lock in gains, balancing risk and reward.
High Precision: Utilizes bar magnification for accurate backtesting, reflecting realistic trade execution with 1-tick slippage and 0.1 commission.
Clean Interface: No on-chart indicators, providing a distraction-free trading experience focused on performance.
Flexible Sizing: Allocates 10% of equity per trade with support for up to 2 simultaneous positions (pyramiding).
Performance Highlights
Backtested from March 1, 2024, to June 20, 2025, on NASDAQ:TSLA (15-minute timeframe) with $1,000,000 initial capital:
Net Profit: $2,279,888.08 (227.99%)
Win Rate: 52.94% (3,039 winning trades out of 5,741)
Profit Factor: 3.495
Max Drawdown: 2.20%
Average Winning Trade: $1,050.91 (0.55%)
Average Losing Trade: $338.20 (0.18%)
Sharpe Ratio: 2.468
Note: Past performance is not indicative of future results. Always validate with your own backtesting and forward testing.
Usage Instructions
Setup:
Apply the strategy to a NASDAQ:TSLA 15-minute chart.
Ensure your TradingView account supports bar magnification for accurate results.
Configuration:
Lookback Candles: Default is 2 (recommended).
Min Trend Strength: Set to 0.2% for optimal trade frequency.
Stop Loss: Default 3.0% to cap losses.
Take Profit: Default 2.0% to secure gains.
Order Size: 10% of equity per trade.
Pyramiding: Allows up to 2 orders.
Commission: Set to 0.1.
Slippage: Set to 1 tick.
Enable "Recalculate After Order is Filled" and "Recalculate on Every Tick" in backtest settings.
Backtesting:
Run backtests over March 1, 2024, to June 20, 2025, to verify performance.
Adjust stop-loss (e.g., 2.5%) or take-profit (e.g., 1–3%) to suit your risk tolerance.
Live Trading:
Use with a compatible broker or TradingView alerts for automated execution.
Monitor execution for slippage or latency, especially given the high trade frequency (5,741 trades).
Validate in a demo account before deploying with real capital.
Risk Disclosure
Trading involves significant risk and may result in losses exceeding your initial capital. The Canuck Trading Trader Strategy is provided for educational and informational purposes only. Users are responsible for their own trading decisions and should conduct thorough testing before using in live markets. The strategy’s high trade frequency requires reliable execution infrastructure to minimize slippage and latency.
Adaptive Momentum Deviation Oscillator | QuantMACAdaptive Momentum Deviation Oscillator | QuantMAC 📊
Overview 🎯
The Adaptive Momentum Deviation Oscillator (AMDO) is an advanced technical analysis indicator that combines the power of Bollinger Bands with adaptive momentum calculations to identify optimal entry and exit points in financial markets. This sophisticated oscillator creates dynamic bands that adapt to market volatility while providing clear visual signals for both trending and ranging market conditions.
How It Works 🔧
Core Methodology
The AMDO employs a sophisticated multi-layered approach to market analysis through four distinct phases:
Bollinger Band Foundation : The indicator begins by establishing a volatility baseline using traditional Bollinger Bands. These bands are calculated using a simple moving average as the center line, with upper and lower bands positioned at a specific number of standard deviations away from this centerline. The distance between these bands expands and contracts based on market volatility, creating a dynamic envelope around price action.
BB% Normalization Process : The raw price data is then transformed into a normalized percentage format that represents where the current price sits within the Bollinger Band envelope. When price is at the lower band, this percentage reads 0%; at the upper band, it reads 100%. This normalization allows for consistent comparison across different timeframes and price levels, creating a standardized oscillator that oscillates between extreme values.
Adaptive Momentum Band Construction : The normalized BB% values undergo a secondary volatility analysis where their own standard deviation is calculated over a specified period. This creates "bands around the bands" - upper and lower boundaries that adapt to the volatility of the normalized price position itself. These adaptive bands expand during periods of high momentum volatility and contract during consolidation phases.
Intelligent Signal Synthesis : The final layer combines the adaptive momentum bands with user-defined threshold levels to create a sophisticated trigger system. The indicator monitors when the dynamic bands cross above or below these thresholds, filtering out noise while capturing significant momentum shifts. This creates a dual-confirmation system where both volatility adaptation and threshold breaches must align for signal generation.
Key Components 🛠️
Adaptive Momentum Bands 📈
Dynamic Volatility Response : These bands automatically widen during periods of high momentum volatility and narrow during consolidation phases. Unlike fixed oscillator boundaries, they continuously recalibrate based on recent price behavior within the Bollinger Band framework.
Dual-Layer Calculation : The bands are derived from the volatility of the normalized price position itself, creating a "volatility of volatility" measurement. This provides early warning signals when momentum characteristics are changing, even before price breakouts occur.
State-Aware Visualization : The bands employ intelligent color coding that transitions between active and neutral states based on their interaction with threshold levels. Active states indicate high-probability momentum conditions, while neutral states suggest consolidation or indecision.
Momentum Persistence Tracking : The bands maintain memory of recent momentum characteristics, allowing them to distinguish between genuine momentum shifts and temporary price spikes or dips.
Threshold Levels 🎚️
Statistical Significance Boundaries : The threshold levels (default 83 for long, 40 for short) are positioned to capture statistically significant momentum events while filtering out market noise. These levels represent points where momentum probability shifts meaningfully in favor of directional moves.
Asymmetric Design Philosophy : The intentional asymmetry between long and short thresholds (83 vs 40) reflects the natural upward bias of many financial markets and the different risk/reward profiles of long versus short positions.
Contextual Sensitivity : The thresholds work in conjunction with the adaptive bands to create context-sensitive triggers. A threshold breach is only meaningful when it occurs in the proper sequence with band interactions.
Risk-Adjusted Positioning : The threshold levels are calibrated to provide favorable risk-adjusted entry points, considering both the probability of success and the potential magnitude of subsequent moves.
Bollinger Bands Overlay 📊
Multi-Timeframe Context : The price chart overlay provides essential context by showing traditional Bollinger Bands alongside the oscillator. This dual perspective allows traders to see both the absolute price position and the momentum characteristics simultaneously.
Support/Resistance Identification : The filled band area creates a visual representation of dynamic support and resistance levels. Price interaction with these bands provides additional confirmation for oscillator signals.
Volatility Environment Assessment : The width and slope of the bands offer immediate visual feedback about the current volatility environment, helping traders adjust their expectations and risk management accordingly.
Confluence Analysis : The overlay enables traders to identify confluence between price action at Bollinger Band levels and oscillator signals, creating higher-probability trade setups.
Signal Generation ⚡
The AMDO generates signals through precise mathematical crossover events:
Long Signals 🟢
Momentum Accumulation Detection : Long signals are generated when the lower adaptive momentum band crosses above the 83 threshold, indicating that downside momentum has exhausted and bullish momentum is beginning to accumulate. This represents a shift from defensive to offensive market posture.
Statistical Edge Confirmation : The crossing event occurs only when momentum characteristics have shifted sufficiently to provide a statistical edge for long positions. The adaptive nature ensures the signal quality remains consistent across different market volatility regimes.
Visual State Synchronization : Upon signal generation, the entire indicator ecosystem shifts to a bullish state - bar colors change, band states update, and the visual hierarchy emphasizes the long bias until conditions change.
Momentum Persistence Validation : The signal incorporates momentum persistence analysis to distinguish between genuine trend starts and false breakouts, reducing whipsaw trades in choppy market conditions.
Short Signals 🔴
Momentum Exhaustion Recognition : Short signals trigger when the upper adaptive momentum band crosses below the 40 threshold, signaling that bullish momentum has peaked and bearish momentum is emerging. This asymmetric threshold reflects the different dynamics of bullish versus bearish market phases.
Volatility-Adjusted Timing : The adaptive band system ensures that short signals are generated with appropriate timing regardless of the underlying volatility environment, maintaining signal quality in both high and low volatility conditions.
Regime-Aware Activation : Short signals are only active in Long/Short trading mode, recognizing that not all trading strategies benefit from short positions. The indicator adapts its behavior based on the selected trading approach.
Risk-Calibrated Thresholds : The 40 threshold is specifically calibrated to capture meaningful bearish momentum shifts while accounting for the higher risk typically associated with short positions.
Cash Signals 💰
Defensive Positioning Logic : In Long/Cash mode, cash signals are generated when short conditions are met, allowing traders to move to a defensive cash position rather than taking on short exposure. This preserves capital during unfavorable market conditions.
Risk Mitigation Strategy : Cash signals represent a risk-off approach that removes market exposure when momentum conditions favor the short side, protecting long-biased portfolios from adverse market movements.
Opportunity Cost Optimization : The cash position allows traders to avoid negative returns while maintaining flexibility to re-enter long positions when momentum conditions improve, optimizing the risk-adjusted return profile.
Features & Customization ⚙️
Color Schemes 🎨
9 pre-built color schemes (Classic through Classic9)
Custom color override option
Dynamic color changes based on signal states
Trading Modes 📈
Long/Short : Full bidirectional trading capability
Long/Cash : Long-only strategy with cash positions
Performance Metrics 📊
The indicator includes a comprehensive suite of advanced performance analytics that provide deep insights into strategy effectiveness:
Risk-Adjusted Return Metrics
Sortino Ratio : Measures returns relative to downside deviation only, providing a more accurate assessment of risk-adjusted performance by focusing on harmful volatility rather than total volatility. This metric is particularly valuable for asymmetric return distributions.
Sharpe Ratio : Calculates excess return per unit of total risk, offering a standardized measure of risk-adjusted performance that allows for comparison across different strategies and timeframes.
Omega Ratio : Employs probability-weighted analysis to compare the likelihood and magnitude of gains versus losses, providing insights into the overall shape of the return distribution and tail risk characteristics.
Drawdown and Risk Analysis
Maximum Drawdown : Tracks the largest peak-to-trough equity decline, providing crucial information about the worst-case scenario and helping traders understand the emotional and financial stress they might encounter.
Dynamic Drawdown Monitoring : Continuously updates drawdown calculations in real-time, allowing traders to monitor current drawdown levels relative to historical maximums.
Trade Statistics and Profitability
Profit Factor Analysis : Compares gross profits to gross losses, revealing the efficiency of the trading approach and the relationship between winning and losing trades.
Win Rate Calculation : Provides the percentage of profitable trades, which must be interpreted in conjunction with profit factor and average trade size for meaningful analysis.
Trade Frequency Tracking : Monitors total trade count to assess strategy turnover and transaction cost implications.
Position Sizing Guidance
Half Kelly Percentage : Calculates optimal position sizing based on Kelly Criterion methodology, then applies a conservative 50% reduction to account for parameter uncertainty and reduce volatility. This provides mathematically-based position sizing guidance that balances growth with risk management.
Parameters & Settings 🔧
BMD Settings
- Base Length : Period for Bollinger Band calculation (default: 10)
- Source : Price data source (default: close)
- Standard Deviation Length : Period for volatility calculation (default: 35)
- SD Multiplier : Bollinger Band width multiplier (default: 1.0)
- BB% Multiplier : Scaling factor for BB% calculation (default: 100)
BMD Settings
Base Length : Period for Bollinger Band calculation (default: 10)
Source : Price data source (default: close)
Standard Deviation Length : Period for volatility calculation (default: 35)
SD Multiplier : Bollinger Band width multiplier (default: 1.0)
BB% Multiplier : Scaling factor for BB% calculation (default: 100)
Signal Thresholds 🎯
Long Threshold : Trigger level for long signals (default: 83)
Short Threshold : Trigger level for short signals (default: 40)
Display Options 🖥️
Toggleable metrics table with 6 position options
Customizable date range limiter
Multiple visual elements for comprehensive analysis
Use Cases & Applications 💡
Trend Following
Identifies momentum shifts in trending markets
Provides early entry signals during trend continuations
Adaptive bands adjust to changing volatility conditions
Mean Reversion
Detects oversold/overbought conditions
Signals potential reversal points
Works effectively in ranging markets
Risk Management
Built-in performance metrics for strategy evaluation
Half Kelly percentage for position sizing guidance
Maximum drawdown monitoring
Advantages ✅
Adaptive Nature : Automatically adjusts to market volatility
Dual Display : Oscillator and price chart components work together
Comprehensive Metrics : Built-in performance analysis
Flexible Trading Modes : Supports different trading strategies
Visual Clarity : Color-coded signals and states
Customizable : Extensive parameter adjustment options
Important Considerations ⚠️
This indicator is designed for educational and analysis purposes
Should be used in conjunction with other technical analysis tools
Proper risk management is essential when trading
Backtest thoroughly before implementing in live trading
Market conditions can change rapidly, affecting indicator performance
Disclaimer ⚠️
Past performance is not indicative of future results. Trading involves substantial risk of loss and is not suitable for all investors. The information provided by this indicator should not be considered as financial advice. Always conduct your own research.
No indicator guarantees profitable trades - Always use proper risk management! 🛡️
Kijun Shifting Band Oscillator | QuantMAC🎯 Kijun Shifting Band Oscillator | QuantMAC
📊 **Revolutionary Technical Analysis Tool Combining Ancient Ichimoku Wisdom with Cutting-Edge Statistical Methods**
🌟 Overview
The Kijun Shifting Band Oscillator represents a sophisticated fusion of traditional Japanese technical analysis and modern statistical theory. Built upon the foundational concepts of the Ichimoku Kinko Hyo system, this indicator transforms the classic Kijun-sen (base line) into a dynamic, multi-dimensional analysis tool that provides traders with unprecedented market insights.
This advanced oscillator doesn't just show you where price has been – it reveals the underlying momentum dynamics and volatility patterns that drive market movements, giving you a statistical edge in your trading decisions.
🔥 Key Features & Innovations
Dual Trading Modes for Maximum Flexibility: 🚀
Long/Short Mode: Full bidirectional trading capability for aggressive traders seeking to capitalize on both bullish and bearish market conditions
Long/Cash Mode: Conservative approach perfect for risk-averse traders, taking long positions during uptrends and moving to cash during downtrends (avoiding short exposure)
Advanced Visual Intelligence: 🎨
9 Professional Color Schemes: From classic blue/navy to vibrant orange/purple combinations, each optimized for different chart backgrounds and personal preferences
Dynamic Gradient Histogram: Color intensity reflects oscillator strength, providing instant visual feedback on momentum magnitude
Intelligent Overlay Bands: Semi-transparent fills create clear visual boundaries without cluttering your chart
Smart Candle Coloring: Real-time color changes reflect current market state and trend direction
Customizable Threshold Lines: Clearly marked entry and exit levels with contrasting colors
Professional-Grade Analytics: 📊
Real-Time Performance Metrics: Live calculation of 9 key performance indicators
Risk-Adjusted Returns: Sharpe, Sortino, and Omega ratios for comprehensive performance evaluation
Position Sizing Guidance: Half-Kelly percentage for optimal risk management
Drawdown Analysis: Maximum drawdown tracking for risk assessment
📈 Deep Technical Foundation
Kijun-Based Mathematical Framework: 🧮
The indicator begins with the traditional Kijun-sen calculation but extends it significantly:
Statistical Enhancements: 📉
Adaptive Volatility: Bands expand and contract based on market volatility
Momentum Filtering: EMA smoothing of oscillator for trend confirmation
State Management: Intelligent signal filtering prevents whipsaws and false signals
Multi-Timeframe Compatibility: Optimized algorithms work across all timeframes
⚙️ Comprehensive Parameter Control
Kijun Core Settings: 🎛️
Kijun Length (Default: 30): Controls the lookback period for the base calculation. Shorter periods = more responsive, longer periods = smoother signals
Source Selection: Choose from Close, Open, High, Low, or HL2. Close price recommended for most applications
Calculation Method: Uses traditional Ichimoku methodology ensuring compatibility with classic analysis
Advanced Oscillator Configuration: 📊
Standard Deviation Length (Default: 36): Determines volatility measurement period. Affects band width and sensitivity
SD Multiplier (Default: 2.1): Fine-tune band distance from basis line. Higher values = wider bands, lower values = tighter bands
Oscillator Multiplier (Default: 100): Scales the final oscillator output. Useful for matching other indicators or personal preference
Smoothing Algorithm: Built-in EMA smoothing prevents noise while maintaining responsiveness
Signal Threshold Optimization: 🎯
Long Threshold (Default: 83): Oscillator level that triggers long entries. Higher values = fewer but stronger signals
Short Threshold (Default: 42): Oscillator level that triggers short entries. Lower values = fewer but stronger signals
Threshold Logic: Crossover-based system with state management prevents signal overlap
Customization Range: Fully adjustable to match your trading style and risk tolerance
Precision Date Control: 📅
Start Date/Month/Year: Precise backtesting control down to the day
Historical Analysis: Test strategies on specific market periods or events
Strategy Validation: Isolate performance during different market conditions
📊 Professional Metrics Dashboard
Risk Assessment Metrics: 💼
Maximum Drawdown %: Largest peak-to-trough decline in portfolio value. Critical for understanding worst-case scenarios and position sizing
Sortino Ratio: Risk-adjusted return measure focusing only on downside volatility. Superior to Sharpe ratio for asymmetric return distributions
Sharpe Ratio: Classic risk-adjusted performance metric. Values above 1.0 considered good, above 2.0 excellent
Omega Ratio: Probability-weighted ratio capturing all moments of return distribution. More comprehensive than Sharpe or Sortino
Performance Analytics: 📈
Profit Factor: Gross Profit ÷ Gross Loss. Values above 1.0 indicate profitability, above 2.0 considered excellent
Win Rate %: Percentage of profitable trades. Consider alongside average win/loss size for complete picture
Net Profit %: Total return on initial capital. Accounts for compounding effects
Total Trades: Sample size for statistical significance assessment
Advanced Position Sizing: 🎯
Half Kelly %: Optimal position size based on Kelly Criterion, reduced by 50% for safety margin
Risk Management: Helps determine appropriate position size relative to account equity
Mathematical Foundation: Based on win probability and profit factor calculations
Practical Application: Directly usable percentage for position sizing decisions
🎨 Advanced Display Options
Flexible Interface Design: 🖥️
6 Positioning Options: Top/Bottom/Middle × Left/Right combinations for optimal chart organization
Toggle Functionality: Show/hide metrics table for clean chart presentation during analysis
Color Coordination: Metrics table colors match selected oscillator color scheme
Professional Styling: Clean, readable format with proper spacing and alignment
Visual Hierarchy: 🎭
Oscillator Histogram: Primary focus with gradient intensity showing momentum strength
Threshold Lines: Clear horizontal references for entry/exit levels
Zero Line: Neutral reference point for trend bias determination
Background Bands: Subtle overlay context without chart clutter
🚀 Advanced Signal Generation System
Multi-Layer Signal Logic: ⚡
Primary Signal Generation: Oscillator crossover above Long Threshold (default 83) triggers long entries
Exit Signal Processing: Oscillator crossunder below Short Threshold (default 42) triggers position exits
State Management System: Prevents duplicate signals and ensures clean position transitions
Mode-Specific Logic: Different behavior for Long/Short vs Long/Cash modes
Date Range Filtering: Signals only generated within specified backtesting period
Confirmation Requirements: Bar confirmation prevents false signals from intrabar price spikes
Intelligent Position Management: 🧠
Entry Tracking: Precise entry price recording for accurate P&L calculations
Position State Monitoring: Continuous tracking of long/short/cash positions
Automatic Exit Logic: Seamless position closure and new position initiation
Performance Calculation: Real-time P&L tracking with compounding effects
📉📈 Comprehensive Band Interpretation Guide
Dynamic Band Analysis: 🔍
Upper Band Function: Represents dynamic resistance based on recent volatility. Price approaching upper band suggests potential reversal or breakout
Lower Band Function: Represents dynamic support with volatility adjustment. Price near lower band indicates oversold conditions or support testing
Middle Line (Basis): Trend direction indicator. Price above = bullish bias, price below = bearish bias
Band Width Interpretation: Wide bands = high volatility, narrow bands = low volatility/potential breakout setup
Band Slope Analysis: Rising bands = strengthening trend, falling bands = weakening trend
Oscillator Interpretation: 📊
Values Above 50: Price in upper half of recent range, bullish momentum
Values Below 50: Price in lower half of recent range, bearish momentum
Extreme Values (>80 or <20): Overbought/oversold conditions, potential reversal zones
Momentum Divergence: Oscillator direction vs price direction for early reversal signals
Trend Confirmation: Oscillator direction confirming or contradicting price trends
💡 Strategic Trading Applications
Primary Trading Strategies: 🎯
Trend Following: Use threshold crossovers to capture major directional moves. Best in trending markets with clear directional bias
Mean Reversion: Identify extreme oscillator readings for counter-trend opportunities. Effective in range-bound markets
Breakout Trading: Monitor band compressions followed by expansions for breakout signals
Swing Trading: Combine oscillator signals with band interactions for swing position entries/exits
Risk Management: Use metrics dashboard for position sizing and risk assessment
Market Condition Optimization: 🌊
Trending Markets: Increase threshold separation for fewer, stronger signals
Choppy Markets: Decrease threshold separation for more responsive signals
High Volatility: Increase SD multiplier for wider bands
Low Volatility: Decrease SD multiplier for tighter bands and earlier signals
⚙️ Advanced Configuration Tips
Parameter Optimization Guidelines: 🔧
Kijun Length Adjustment: Shorter periods (10-20) for faster signals, longer periods (50-100) for smoother trends
SD Length Tuning: Match to your trading timeframe - shorter for responsive, longer for stability
Threshold Calibration: Backtest different levels to find optimal entry/exit points for your market
Color Scheme Selection: Choose schemes that provide best contrast with your chart background and other indicators
Integration with Other Indicators: 🔗
Volume Indicators: Confirm oscillator signals with volume spikes
Support/Resistance: Use key levels to filter oscillator signals
Momentum Indicators: RSI, MACD confirmation for signal strength
Trend Indicators: Moving averages for overall trend bias confirmation
⚠️ Important Usage Notes & Limitations
Indicator Characteristics: ⚡
Lagging Nature: Based on historical price data - signals occur after moves have begun
Best Practice: Combine with leading indicators and price action analysis
Market Dependency: Performance varies across different market conditions and instruments
Backtesting Essential: Always validate parameters on historical data before live implementation
Optimization Recommendations: 🎯
Parameter Testing: Systematically test different combinations on your preferred instruments
Walk-Forward Analysis: Regularly re-optimize parameters to maintain effectiveness
Market Regime Awareness: Adjust parameters for different market conditions (trending vs ranging)
Risk Controls: Implement maximum drawdown limits and position size controls
🔧 Technical Specifications
Performance Optimization: ⚡
Efficient Algorithms: Optimized calculations for smooth real-time operation
Memory Management: Smart array handling for metrics calculations
Visual Optimization: Balanced detail vs performance for responsive charts
Multi-Symbol Ready: Consistent performance across different assets
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The Kijun Shifting Band Oscillator represents the evolution of technical analysis, bridging the gap between traditional methods and modern quantitative approaches. This indicator provides traders with a comprehensive toolkit for market analysis, combining the intuitive wisdom of Japanese candlestick analysis with the precision of statistical mathematics.
🎯 Designed for serious traders who demand professional-grade analysis tools with institutional-quality metrics and risk management capabilities. Whether you're a discretionary trader seeking visual confirmation or a systematic trader building quantitative strategies, this indicator provides the foundation for informed trading decisions.
⚠️ IMPORTANT DISCLAIMER
Past Performance Warning: 📉⚠️
PAST PERFORMANCE IS NOT INDICATIVE OF FUTURE RESULTS. Historical backtesting results, while useful for strategy development and parameter optimization, do not guarantee similar performance in live trading conditions. Market conditions change continuously, and what worked in the past may not work in the future.
Remember: Successful trading requires discipline, continuous learning, and adaptation to changing market conditions. No indicator or strategy guarantees profits, and all trading involves substantial risk of loss.
LotSize CalculatorLotSize Calculator Documentation
Overview
The LotSize Calculator is a powerful TradingView indicator designed to help traders calculate optimal position sizes based on risk management principles. It provides a visual representation of trade setups, including entry points, stop losses, and take profits, while calculating the appropriate lot size based on your risk preferences.
Key Features
Automatic lot size calculation based on risk amount
Support for multiple asset classes (forex, commodities, indices, etc.)
Visual R-multiple levels (1R to 5R)
Real-time position tracking with drawdown and run-up statistics
Customizable visual elements and display options
Input Parameters
Risk Management Settings
Risk Amount Type: Choose between risking a fixed amount in dollars ($) or a specific lot size.
Risk Amount: The amount you want to risk on the trade (in dollars if Risk Amount Type is set to $, or in lots if set to Lots).
Overwrite TP: Optional setting to automatically set take profit at a specific R-multiple (1R, 2R, 3R, 4R, or 5R).
Table Comments: Optional field to add personal notes to the position table.
Trade Setup Levels
Trigger Price: The price at which your trade will be entered.
Stop Loss: Your predetermined exit price to limit losses.
Take Profit: Your target price to secure profits.
Time Of Setup Start Bar: The starting time for your trade setup window.
Display Settings
Plot Position Labels: Toggle to show/hide position information labels on the chart.
Plot Position Table: Toggle to show/hide the position information table.
Show Money: Toggle to display monetary values ($) in the labels and table.
Show Points: Toggle to display point values in the labels and table.
Show Ticks: Toggle to display tick values in the labels and table.
Visual Appearance
Entry Color: Color for entry level line and labels.
Take Profit Color: Color for take profit level line and labels.
Stop Loss Color: Color for stop loss level line and labels.
Label Text Color: Color for text in the position labels.
Table Background: Background color for the position information table.
Table Text: Text color for the position information table.
R Labels: Color for the R-multiple level labels.
Table Position: Position of the information table on the chart (options: Bottom Right, Bottom Left, Bottom Middle, Top Right, Top Middle).
How to Use
Basic Setup
Set your entry price in the "Trigger Price" field.
Set your stop loss level in the "Stop Loss" field.
Set your take profit level in the "Take Profit" field.
Choose your risk amount type ($ or Lots) and enter the risk amount.
Optionally, select an R-multiple for automatic take profit calculation.
Understanding the Display
The indicator will show:
Horizontal lines for entry, stop loss, and take profit levels
Colored zones between entry and take profit (potential profit zone) and between entry and stop loss (potential loss zone)
R-multiple levels based on your risk (1R, 2R, 3R, 4R, 5R)
A table displaying:
Position type (long/short) and size
Original risk and reward figures
Maximum run-up and drawdown during the trade
Trade Monitoring
Once a trade is triggered (either by price crossing a stop entry or reaching a limit entry), the indicator tracks:
Current position value
Maximum run-up (highest profit seen)
Maximum drawdown (largest loss seen)
Trade outcome when take profit or stop loss is hit
Advanced Features
Asset Type Detection
The LotSize Calculator automatically detects the type of asset being traded (forex, commodity, index, etc.) and adjusts calculations accordingly to ensure accurate position sizing.
R-Multiple Visualization
R-multiples help visualize potential reward relative to risk. For example, 2R means the potential reward is twice the amount risked. The indicator displays these levels directly on your chart for easy reference.
Adaptive Position Labels
Position labels adjust their display based on trade direction (long or short) and include relevant information about risk, reward, and current position status.
Best Practices
Always confirm your risk is appropriate for your account size (typically 1-2% of account per trade).
Use the R-multiple visualization to ensure your trades offer favorable risk-to-reward ratios.
The indicator works best when used alongside your existing strategy for entry and exit signals.
Customize the visual appearance to match your chart theme for better visibility.
Troubleshooting
If position calculations seem incorrect, verify that the indicator is detecting the correct instrument type.
For forex pairs, ensure your broker's lot size conventions match those used by the indicator.
The indicator may need adjustment for certain exotic instruments or markets with unusual tick sizes.
Stochastic RSI with MTF TableShort Description of the Script
The provided Pine Script indicator, titled "Stochastic RSI with MTF Table," calculates and displays the Stochastic RSI for the current timeframe and multiple other timeframes (5m, 15m, 30m, 60m, 240m, and daily). The Stochastic RSI is a momentum indicator that blends the Relative Strength Index (RSI) and Stochastic Oscillator to identify overbought and oversold conditions, as well as potential trend reversals via K and D line crossovers.
Key features of the script include:
Inputs: Customizable parameters such as K smoothing (default 3), D smoothing (default 3), RSI length (default 14), Stochastic length (default 14), source price (default close), and overbought/oversold levels (default 80/20).
MTF Table: A table displays the Stochastic RSI status for each timeframe:
"OB" (overbought) if K > 80, "OS" (oversold) if K < 20, or "N" (neutral) otherwise.
Crossovers: "K↑D" for bullish (K crosses above D) and "K↓D" for bearish (K crosses below D).
Visualization: Plots the K and D lines for the current timeframe, with horizontal lines at 80 (overbought), 50 (middle), and 20 (oversold), plus a background fill for clarity.
Table Position: Configurable to appear in one of four chart corners (default: top-right).
This indicator helps traders assess momentum across multiple timeframes simultaneously, aiding in the identification of trend strength and potential entry/exit points.
Trading Strategy with 50EMA and 200EMA for Highest Winning Rate
To create a strategy with the best probability of a high winning rate using the Stochastic RSI MTF indicator alongside the 50-period Exponential Moving Average (50EMA) and 200-period Exponential Moving Average (200EMA), we can combine trend identification with momentum-based entry timing. The 50EMA and 200EMA are widely used to determine medium- and long-term trends, while the Stochastic RSI MTF table provides multi-timeframe momentum signals. Here’s the strategy:
1. Determine the Overall Trend
Bullish Trend: The 50EMA is above the 200EMA on the current timeframe (e.g., daily or 60m chart). This suggests an uptrend, often associated with a "Golden Cross."
Bearish Trend: The 50EMA is below the 200EMA on the current timeframe. This indicates a downtrend, often linked to a "Death Cross."
Implementation: Plot the 50EMA and 200EMA on your chart and visually confirm their relative positions.
2. Identify Entry Signals Using the Stochastic RSI MTF Table
In a Bullish Trend (50EMA > 200EMA):
Look for timeframes in the MTF table showing:
Oversold (OS): K < 20, indicating a potential pullback in the uptrend where price may rebound.
Bullish Crossover (K↑D): K crosses above D, signaling rising momentum and a potential entry point.
Example: If the 60m and 240m timeframes show "OS" or "K↑D," this could be a buy signal.
In a Bearish Trend (50EMA < 200EMA):
Look for timeframes in the MTF table showing:
Overbought (OB): K > 80, suggesting a rally in the downtrend where price may reverse downward.
Bearish Crossover (K↓D): K crosses below D, indicating declining momentum and a potential short entry.
Example: If the 30m and daily timeframes show "OB" or "K↓D," this could be a sell/short signal.
Current Timeframe Check: Use the plotted K and D lines on your trading timeframe for precise entry timing (e.g., confirm a K↑D crossover on a 60m chart for a long trade).
3. Confirm Signals Across Multiple Timeframes
Strengthen the Signal: A higher winning rate is more likely when multiple timeframes align with the trend and signal. For instance:
Bullish trend + "OS" or "K↑D" on 60m, 240m, and daily = strong buy signal.
Bearish trend + "OB" or "K↓D" on 15m, 60m, and 240m = strong sell signal.
Prioritize Higher Timeframes: Signals from the 240m or daily timeframe carry more weight due to their indication of broader trends, increasing reliability.
4. Set Stop-Loss and Take-Profit Levels
Long Trades (Bullish):
Stop-Loss: Place below the most recent swing low or below the 50EMA, whichever is closer, to protect against trend reversals.
Take-Profit: Target a key resistance level or use a risk-reward ratio (e.g., 2:1 or 3:1) based on the stop-loss distance.
Short Trades (Bearish):
Stop-Loss: Place above the most recent swing high or above the 50EMA, whichever is closer.
Take-Profit: Target a key support level or apply a similar risk-reward ratio.
Trailing Stop Option: As the trend progresses, trail the stop below the 50EMA (for longs) or above it (for shorts) to lock in profits.
5. Risk Management
Position Sizing: Risk no more than 1-2% of your trading capital per trade to minimize losses from false signals.
Volatility Consideration: Adjust stop-loss distances and position sizes based on the asset’s volatility (e.g., wider stops for volatile stocks or crypto).
Avoid Overtrading: Wait for clear alignment between the EMA trend and MTF signals to avoid low-probability setups.
Example Scenario
Chart: 60-minute timeframe.
Trend: 50EMA > 200EMA (bullish).
MTF Table: 60m shows "OS," 240m shows "K↑D," and daily is "N."
Action: Enter a long position when the 60m K line crosses above D, confirming the table signal.
Stop-Loss: Below the recent 60m swing low (e.g., 2% below entry).
Take-Profit: At the next resistance level or a 3:1 reward-to-risk ratio.
Outcome: High probability of success due to trend alignment and multi-timeframe confirmation.
Why This Strategy Works
Trend Following: Trading in the direction of the 50EMA/200EMA trend reduces the risk of fighting the market’s momentum.
Momentum Timing: The Stochastic RSI MTF table pinpoints pullbacks or reversals within the trend, improving entry timing.
Multi-Timeframe Confirmation: Alignment across timeframes filters out noise, increasing the win rate.
Risk Control: Defined stop-loss and position sizing protect against inevitable losses.
Caveats
No strategy guarantees a 100% win rate; false signals can occur, especially in choppy markets.
Test this strategy on historical data or a demo account to verify its effectiveness for your asset and timeframe.
This approach leverages the strengths of both trend-following (EMA) and momentum (Stochastic RSI) tools, aiming for a high-probability, disciplined trading system.
Step-Based Trailing Stop-Loss IndicatorThis indicator is built for momentum traders who want to maximize winning trades and minimize losses through a smart, step-based trailing stop-loss system. Instead of using a fixed Take Profit, this tool dynamically protects profits once the trade reaches a favorable RR (Risk-to-Reward) level.
How It Works:
Manual Entry Input
You enter your Entry Price and select Buy/Sell in the settings.
This flexibility allows backtesting or live trade tracking.
Initial Setup
Default SL: 50 ticks(Tested on us30,but works on any pair you just need to adjust SL)
TP for reference: 4R — can be used for benchmarking, but we don't limit profits with a hard TP.
Trailing Logic
Once price reaches 3R in profit:
The SL begins trailing.
It starts at 2R, keeping a 1R cushion behind the max profit.
For every 0.5R gain, SL also moves up by 0.5R:
Example: At 3.5R → SL is at 2.5R
At 5.0R → SL is at 4.0R
This trailing continues until the SL is hit or the trend exhausts.
Chart Features
🟧 Entry Line
🔴 Initial SL
🟢 Reference TP (4R, optional)
🟣 Dynamic Trailing SL
🏷️ Labels for Entry & SL levels
EMA 34 Crossover with Break Even Stop LossEMA 34 Crossover with Break Even Stop Loss Strategy
This trading strategy is based on the 34-period Exponential Moving Average (EMA) and aims to enter long positions when the price crosses above the EMA 34. The strategy is designed to manage risk effectively with a dynamic stop loss and take-profit mechanism.
Key Features:
EMA 34 Crossover:
The strategy generates a long entry signal when the closing price of the current bar crosses above the 34-period EMA, with the condition that the previous closing price was below the EMA. This crossover indicates a potential upward trend.
Risk Management:
Upon entering a trade, the strategy sets a stop loss at the low of the previous bar. This helps in controlling the downside risk.
A take profit level is set at a 10:1 risk-to-reward ratio, meaning the potential profit is ten times the amount risked on the trade.
Break-even Stop Loss:
As the price moves in favor of the trade and reaches a 3:1 risk-to-reward ratio, the strategy moves the stop loss to the entry price (break-even). This ensures that no loss will be incurred if the market reverses, effectively protecting profits.
Exit Conditions:
The strategy exits the trade when either the stop loss is hit (if the price drops below the stop loss level) or the take profit target is reached (if the price rises to the take profit level).
If the price reaches the break-even level (entry price), the stop loss is adjusted to lock in profits and prevent any loss.
Visualization:
The stop loss and take profit levels are plotted on the chart for easy visualization, helping traders track the status of their trade.
Trade Management Summary:
Long Entry: When price crosses above the 34-period EMA.
Stop Loss: Set to the low of the previous candle.
Take Profit: Set to a 10:1 risk-to-reward ratio.
Break-even: Stop loss is moved to entry price when a 3:1 risk-to-reward ratio is reached.
Exit: The trade is closed either when the stop loss or take profit levels are hit.
This strategy is designed to minimize losses by employing a dynamic stop loss and to maximize gains by setting a favorable risk-to-reward ratio, making it suitable for traders who prefer a structured, automated approach to risk management and trend-following.
Guntavnook Katta - Fair Value PROOverview:
This script is designed to help long-term investors estimate the fair value of a stock using a combination of fundamental financial metrics and a proprietary multi-factor scoring model. It is especially useful for those who wish to assess whether a stock is undervalued or overvalued based on key fundamentals and recent price behavior.
This script is suitable for stocks, and is best applied on the Daily timeframe.
Purpose:
Many investors rely on Price-to-Earnings (PE) ratios, but not all businesses deserve the same PE due to differences in quality, growth visibility, brand strength, and financial health. This tool attempts to automate the estimation of a fair PE ratio for each company, based on key qualitative and quantitative metrics.
Core Logic:
The script takes the EPS (Earnings Per Share) for the recent financial year from TradingView’s built-in fundamental database and multiplies it by a calculated ideal PE ratio, derived from scoring logic applied to the following parameters:
Financial Parameters Considered:
ROCE (Return on Capital Employed): Indicates how efficiently a company is using its capital to generate profits. Higher ROCE generally reflects strong capital allocation.
ROE (Return on Equity): Shows how effectively the company uses shareholders’ equity. A high ROE may imply strong profitability.
Dividend Yield: Companies that share profits with shareholders via dividends are generally viewed favorably, especially if the yield is sustainable.
Promoter Holding: Higher promoter holding reflects confidence of the founders or promoters in the business. Companies with very low promoter holding might raise governance concerns.
Debt to Equity Ratio: Measures financial risk. Companies with low debt are generally safer, except for banks and NBFCs where high debt is normal.
Sales Growth (5 Years): Reflects business expansion. Consistent growth signals strong demand and operational scalability.
Profit Growth (5 Years): Indicates the company’s ability to grow net earnings over time. High profit growth with low sales growth can sometimes indicate improved margins.
Brand Value: Users can assign qualitative ratings to the company's brand strength, which significantly affects valuation.
Professional Management: If promoter holding is 0%, the company may be professionally or institutionally managed, which adds value in many sectors.
Special Edge: A user-defined optional scoring input for businesses with a strong moat, monopoly, or hard-to-replicate model.
Each of these parameters contributes positively or negatively to the Ideal PE score, which is then used to compute the Fair Value = EPS × Ideal PE.
Why This Scoring Approach?
In fast-moving and diverse market environments, the concept of fair value cannot be treated as a one-size-fits-all number. Traditional valuation models often apply a static PE ratio across stocks, overlooking the individual nuances that define each business. However, real-world investing calls for a more contextual approach—one that acknowledges the dynamic nature of companies, sectors, and economic cycles.
This script attempts to address that gap by offering a systematic way to estimate the fair price of a stock, based on both qualitative and quantitative parameters. The scoring logic is derived from concepts and patterns observed in popular books on fundamental investing and valuation. It encapsulates capital efficiency, ownership structure, growth performance, and brand power—all of which influence a company’s ability to command a premium valuation. The goal is not to suggest decisions but to enable custom, data-supported valuation assessments.
User Instructions:
Apply the script to a stock chart using Daily timeframe.
Open the indicator Settings Panel.
Choose either:
Auto-calculated PE: Let the script determine Ideal PE from scoring inputs.
Manual PE: If you're confident in the fair PE value, input it directly.
Hover over (i) icons in settings for explanation of each input.
Most inputs like ROE, ROCE, D/E ratio, etc., can be found from official filings, annual reports, or financial platforms.
Overbought & Oversold Signals:
This script also provides technical signals based on price deviation from fair value:
Uses RSI-based crossover logic in combination with user-defined price deviation thresholds.
Users can enable/disable signals independently.
Thresholds define how far above/below fair value the stock should move before a signal is triggered.
For example:
If the price moves above the fair value by a percentage equal to or greater than the Overbought threshold set by the user and the RSI crosses below 70, a red Overbought label appears.
If the price drops below the fair value by a percentage equal to or greater than the Oversold threshold set by the user and the RSI crosses above 30, a green Oversold label appears.
You can use the average deviation values displayed in the info table to determine suitable threshold levels based on historical price behavior.
Why RSI?
The Relative Strength Index (RSI) is a widely accepted momentum indicator used to assess whether a stock is overbought or oversold based on recent price performance. In this script, RSI serves as a reliable trigger mechanism when combined with fair value deviations. While the fair value estimation captures long-term fundamentals, RSI helps identify short-term extremes in price action. By using RSI crossovers, the script ensures signals are technically validated and not triggered solely by deviation, thus improving accuracy.
Visual Aids:
The green line shows the calculated Fair Value.
Candle colors:
Red: RSI ≥ 70
Green: RSI ≤ 30
Yellow: Neutral zone
An info table at the top-right displays:
Ideal PE
Current PE (based on FY EPS)
Calculated Fair Value
Avg Upper and Lower Price Deviation % from Fair Value
Note:
This tool is primarily optimized for evaluating Indian stocks, especially those listed on NSE/BSE, where metrics like promoter holding and ROCE are commonly used.
Disclaimer:
This script is intended for educational and research purposes only. It is not investment advice. The logic is based on publicly available data and scoring heuristics designed for learning and valuation awareness.
Momentum Volume Divergence (MVD) EnhancedMomentum Volume Divergence (MVD) Enhanced is a powerful indicator that detects price-momentum divergences and momentum suppression for reversal trading. Optimized for XRP on 1D charts, it features dynamic lookbacks, ATR-adjusted thresholds, and SMA confirmation. Signals include strong divergences (triangles) and suppression warnings (crosses). Includes a detailed user guide—try it out and share your feedback!
Setup: Add to XRP 1D chart with defaults (mom_length_base=8, vol_length_base=10). Signals: Red triangle (sell), Green triangle (buy), Orange cross (bear warning), Yellow cross (bull warning). Confirm with 5-day SMA crossovers. See full guide for details!
Disclaimer: This indicator is for educational purposes only, not financial advice. Trading involves risk—use at your discretion.
Momentum Volume Divergence (MVD) Enhanced Indicator User Guide
Version: Pine Script v6
Designed for: TradingView
Recommended Use: XRP on 1-day (1D) chart
Date: March 18, 2025
Author: Herschel with assistance from Grok 3 (xAI)
Overview
The Momentum Volume Divergence (MVD) Enhanced indicator is a powerful tool for identifying price-momentum divergences and momentum suppression patterns on XRP’s 1-day (1D) chart. Plotted below the price chart, it provides clear visual signals to help traders spot potential reversals and trend shifts.
Purpose
Detect divergences between price and momentum for buy/sell opportunities.
Highlight momentum suppression as warnings of fading trends.
Offer actionable trading signals with intuitive markers.
Indicator Components
Main Plot
Volume-Weighted Momentum (vw_mom): Blue line showing momentum adjusted by volume.
Above 0 = bullish momentum.
Below 0 = bearish momentum.
Zero Line: Gray dashed line at 0, separating bullish/bearish zones.
Key Signals
Strong Bearish Divergence:
Marker: Red triangle at the top.
Meaning: Price makes a higher high, but momentum weakens, confirmed by a drop below the 5-day SMA.
Action: Potential sell/short signal.
Strong Bullish Divergence:
Marker: Green triangle at the bottom.
Meaning: Price makes a lower low, but momentum strengthens, confirmed by a rise above the 5-day SMA.
Action: Potential buy/long signal.
Bearish Suppression:
Marker: Orange cross at the top + red background.
Meaning: Strong bullish momentum with low volume in a volume downtrend, suggesting fading strength.
Action: Warning to avoid longs or exit early.
Bullish Suppression:
Marker: Yellow cross at the bottom + green background.
Meaning: Strong bearish momentum with low volume in a volume uptrend, suggesting fading weakness.
Action: Warning to avoid shorts or exit early.
Debug Plots (Optional)
Volume Ratio: Gray line (volume vs. its MA) vs. yellow line (threshold).
Momentum Threshold: Purple lines (positive/negative momentum cutoffs).
Smoothed Momentum: Orange line (raw momentum).
Confirmation SMA: Purple line (price trend confirmation).
Labels
Text labels (e.g., "Bear Div," "Bull Supp") mark detected patterns.
How to Use the Indicator
Step-by-Step Trading Process
1. Monitor the Chart
Load your XRP 1D chart with the indicator applied.
Observe the blue vw_mom line and signal markers.
2. Spot a Signal
Primary Signals: Look for red triangles (strong_bear) or green triangles (strong_bull).
Warnings: Note orange crosses (suppression_bear) or yellow crosses (suppression_bull).
3. Confirm the Signal
For Strong Bullish Divergence (Buy):
Green triangle appears.
Price closes above the 5-day SMA (purple line) and a recent swing high.
Optional: Volume ratio (gray line) exceeds the threshold (yellow line).
For Strong Bearish Divergence (Sell):
Red triangle appears.
Price closes below the 5-day SMA and a recent swing low.
Optional: Volume ratio (gray line) falls below the threshold (yellow line).
4. Enter the Trade
Long:
Buy at the close of the signal bar.
Stop loss: Below the recent swing low or 2 × ATR(14) below entry.
Short:
Sell/short at the close of the signal bar.
Stop loss: Above the recent swing high or 2 × ATR(14) above entry.
5. Manage the Trade
Take Profit:
Aim for a 2:1 or 3:1 risk-reward ratio (e.g., risk $0.05, target $0.10-$0.15).
Or exit when an opposite suppression signal appears (e.g., orange cross for longs).
Trailing Stop:
Move stop to breakeven after a 1:1 RR move.
Trail using the 5-day SMA or 2 × ATR(14).
Early Exit:
Exit if a suppression signal appears against your position (e.g., suppression_bull while short).
6. Filter Out Noise
Avoid trades if a suppression signal precedes a divergence within 2-3 days.
Optional: Add a 50-day SMA on the price chart:
Longs only if price > 50-SMA.
Shorts only if price < 50-SMA.
Example Trades (XRP 1D)
Bullish Trade
Signal: Green triangle (strong_bull) at $0.55.
Confirmation: Price closes above 5-SMA and $0.57 high.
Entry: Buy at $0.58.
Stop Loss: $0.53 (recent low).
Take Profit: $0.63 (2:1 RR) or exit on suppression_bear.
Outcome: Price hits $0.64, exit at $0.63 for profit.
Bearish Trade
Signal: Red triangle (strong_bear) at $0.70.
Confirmation: Price closes below 5-SMA and $0.68 low.
Entry: Short at $0.67.
Stop Loss: $0.71 (recent high).
Take Profit: $0.62 (2:1 RR) or exit on suppression_bull.
Outcome: Price drops to $0.61, exit at $0.62 for profit.
Tips for Success
Combine with Price Levels:
Use support/resistance zones (e.g., weekly pivots) to confirm entries.
Monitor Volume:
Rising volume (gray line above yellow) strengthens signals.
Adjust Sensitivity:
Too many signals? Increase div_strength_threshold to 0.7.
Too few signals? Decrease to 0.3.
Backtest:
Review 20-30 past signals on XRP 1D to assess performance.
Avoid Choppy Markets:
Skip signals during low volatility (tight price ranges).
Troubleshooting
No Signals:
Lower div_strength_threshold to 0.3 or mom_threshold_base to 0.2.
Check if XRP’s volatility is unusually low.
False Signals:
Increase sma_confirm_length to 7 or add a 50-SMA filter.
Indicator Not Loading:
Ensure the script compiles without errors.
Customization (Optional)
Change Colors: Edit color.* values (e.g., color.red to color.purple).
Add Alerts: Use TradingView’s alert menu for "Strong Bearish Divergence Confirmed," etc.
Test Other Assets: Experiment with BTC or ETH, adjusting inputs as needed.
Disclaimer
This indicator is for educational purposes only and not financial advice. Trading involves risk, and past performance does not guarantee future results. Use at your own discretion.
Setup: Use on XRP 1D with defaults (mom_length_base=8, vol_length_base=10). Signals: Red triangle (sell), Green triangle (buy), Orange cross (bear warning), Yellow cross (bull warning). Confirm with 5-day SMA cross. Stop: 2x ATR(14). Profit: 2:1 RR or suppression exit. Full guide available separately!
AO Smart Scalper – 5M Dynamic SL Edition📈 AO Signals with Fixed and Dynamic SL – Optimized for 5-Minute Charts 📉
This indicator is built for 5-minute timeframe trading, combining powerful momentum signals from the Awesome Oscillator (AO) with both Fixed and Dynamic Stop Loss (SL) levels to enhance trade management and risk control.
✅ Buy/Sell Signals:
The indicator generates clear BUY and SELL signals based on the AO crossing above or below the zero line, helping traders capture momentum shifts early.
🛑 Fixed Stop Loss:
Each trade signal comes with a Fixed SL, calculated based on the high (for shorts) or low (for longs) of the previous candle, with a customizable percentage offset. This SL is plotted with a red line, providing a clear initial risk level.
⚡ Dynamic Stop Loss: Continuous Presence, Strategic Use:
A secondary Dynamic SL line is plotted, which is continuously present on the chart. This dynamic level responds to market conditions and can serve as a trailing stop or key decision point.
💡 Recommended Use: It is recommended to actively start using the Dynamic SL once the trade has moved into profit. This allows protecting obtained profits and minimizing the risk of losses in case of a market reversal.
🛡️ Enhanced Dynamic Stop-Loss Strategy:
🔒 Initial Protection: Utilize the Fixed SL as the initial stop-loss, placed below relevant lows (for longs) or above relevant highs (for shorts), or as provided by the fixed SL indicator.
🛤️ Dynamic Tracking:
🟢 Long Trades: Once in profit, the Dynamic SL will dynamically adjust, moving upwards as higher lows are formed, effectively trailing the price and securing profits.
🔴 Short Trades: Conversely, in short trades, once in profit, the Dynamic SL will move downwards as lower highs are formed, protecting gains.
🔄 Alternatively the dynamic stop loss will follow the dynamic SL line provided by the indicator.
🚪 Exiting Trades: When the price crosses below the Dynamic SL line in a LONG trade, or above it in a SHORT trade, the recommended action is to exit the trade.
↩️ Re-entry Consideration: You may consider re-entering only if the price clearly returns above the Dynamic SL (for longs) or below it (for shorts).
⚠️ IMPORTANT - 5-Minute Strategy Guidance ⏱️
This tool is specifically optimized for the 5-minute timeframe. This approach helps filter out weak setups and maintain discipline in volatile market conditions.
✨ Additional Features:
👁️ Visual and editable SL levels
📊 200-period SMA for trend context
💻 Simple and effective interface for intraday trading setups
🎯 Ideal for traders seeking a clean, rule-based system that combines momentum entry signals with layered stop loss protection.
🔑 Key Changes:
It was emphasized that the Dynamic SL is always present, but its active use is recommended once the trade is in profit.
It was clarified the use of the Fixed SL, giving the option to use the one provided by the indicator, or to place it according to the price action.
Mile Runner - Swing Trade LONGMile Runner - Swing Trade LONG Indicator - By @jerolourenco
Overview
The Mile Runner - Swing Trade LONG indicator is designed for swing traders who focus on LONG positions in stocks, BDRs (Brazilian Depositary Receipts), and ETFs. It provides clear entry signals, stop loss, and take profit levels, helping traders identify optimal buying opportunities with a robust set of technical filters. The indicator is optimized for daily candlestick charts and combines multiple technical analysis tools to ensure high-probability trades.
Key Features
Entry Signals: Visualized as green triangles below the price bars, indicating a potential LONG entry.
Stop Loss and Take Profit Levels: Automatically plotted on the chart for easy reference.
Stop Loss: Based on the most recent pivot low (support level).
Take Profit: Calculated using a Fibonacci-based projection from the entry price to the stop loss.
Trend and Momentum Filters: Ensures trades align with the prevailing trend and have sufficient momentum.
Volume and Volatility Confirmation: Verifies market interest and price movement potential.
How It Works
The indicator uses a combination of technical tools to filter and confirm trade setups:
Exponential Moving Averages (EMAs):
A short EMA (default: 9 periods) and a long EMA (default: 21 periods) identify the trend.
A bullish crossover (EMA9 crosses above EMA21) signals a potential upward trend.
Money Flow Index (MFI):
Confirms buying pressure when MFI > 50.
Average True Range (ATR):
Ensures sufficient volatility by checking if ATR exceeds its 20-period moving average.
Volume:
Confirms market interest when volume exceeds its 20-period moving average.
Pivot Lows:
Identifies recent support levels (pivot lows) to set the stop loss.
Ensures the pivot low is recent (within the last 10 bars by default).
Additional Trend Filter:
Confirms the long EMA is rising, reinforcing the bullish trend.
Inputs and Customization
The indicator is highly customizable, allowing traders to tailor it to their strategies:
EMA Periods: Adjust the short and long EMA lengths.
ATR and MFI Periods: Modify lookback periods for volatility and momentum.
Pivot Lookback: Control the sensitivity of pivot low detection.
Fibonacci Level: Adjust the Fibonacci retracement level for take profit.
Take Profit Multiplier: Fine-tune the aggressiveness of the take profit target.
Max Pivot Age: Set the maximum bars since the last pivot low for relevance.
Usage Instructions
Apply the Indicator:
Add the "Mile Runner - Swing Trade LONG" indicator to your TradingView chart.
Best used on daily charts for swing trading.
Look for Entry Signals:
A green triangle below the price bar signals a potential LONG entry.
Set Stop Loss and Take Profit:
Stop Loss: Red dashed line indicating the stop loss level.
Take Profit: Purple dashed line showing the take profit level.
Monitor the Trade:
The entry price is marked with a green dashed line for reference.
Adjust trade management based on the plotted levels.
Set Alerts:
Use the built-in alert condition to get notified of new LONG entry signals.
Important Notes
For LONG Positions Only : Designed exclusively for swing trading LONG positions.
Timeframe: Optimized for daily charts but can be tested on other timeframes.
Asset Types: Works best with stocks, BDRs, and ETFs.
Risk Management: Always align stop loss and take profit levels with your risk tolerance.
Why Use Mile Runner?
The Mile Runner indicator simplifies swing trading by integrating trend, momentum, volume, and volatility filters into one user-friendly tool. It helps traders:
Identify high-probability entry points.
Establish clear stop loss and take profit levels.
Avoid low-volatility or low-volume markets.
Focus on assets with strong buying pressure and recent support.
By following its signals and levels, traders can make informed decisions and enhance their swing trading performance. Customize the inputs and test it on your favorite assets—happy trading!
Cash And Carry Arbitrage BTC Compare Month 6 by SeoNo1Detailed Explanation of the BTC Cash and Carry Arbitrage Script
Script Title: BTC Cash And Carry Arbitrage Month 6 by SeoNo1
Short Title: BTC C&C ABT Month 6
Version: Pine Script v5
Overlay: True (The indicators are plotted directly on the price chart)
Purpose of the Script
This script is designed to help traders analyze and track arbitrage opportunities between the spot market and futures market for Bitcoin (BTC). Specifically, it calculates the spread and Annual Percentage Yield (APY) from a cash-and-carry arbitrage strategy until a specific expiry date (in this case, June 27, 2025).
The strategy helps identify profitable opportunities when the futures price of BTC is higher than the spot price. Traders can then buy BTC in the spot market and short BTC futures contracts to lock in a risk-free profit.
1. Input Settings
Spot Symbol: The real-time BTC spot price from Binance (BTCUSDT).
Futures Symbol: The BTC futures contract that expires in June 2025 (BTCUSDM2025).
Expiry Date: The expiration date of the futures contract, set to June 27, 2025.
These inputs allow users to adjust the symbols or expiry date according to their trading needs.
2. Price Data Retrieval
Spot Price: Fetches the latest closing price of BTC from the spot market.
Futures Price: Fetches the latest closing price of BTC futures.
Spread: The difference between the futures price and the spot price (futures_price - spot_price).
The spread indicates how much higher (or lower) the futures price is compared to the spot market.
3. Time to Maturity (TTM) and Annual Percentage Yield (APY) Calculation
Current Date: Gets the current timestamp.
Time to Maturity (TTM): The number of days left until the futures contract expires.
APY Calculation:
Formula:
APY = ( Spread / Spot Price ) x ( 365 / TTM Days ) x 100
This represents the annualized return from holding a cash-and-carry arbitrage position if the trader buys BTC at the spot price and sells BTC futures.
4. Display Information Table on the Chart
A table is created on the chart's top-right corner showing the following data:
Metric: Labels such as Spread and APY
Value: Displays the calculated spread and APY
The table automatically updates at the latest bar to display the most recent data.
5. Alert Condition
This sets an alert condition that triggers every time the script runs.
In practice, users can modify this alert to trigger based on specific conditions (e.g., APY exceeds a threshold).
6. Plotting the APY and Spread
APY Plot: Displays the annualized yield as a blue line on the chart.
Spread Plot: Visualizes the futures-spot spread as a red line.
This helps traders quickly identify arbitrage opportunities when the spread or APY reaches desirable levels.
How to Use the Script
Monitor Arbitrage Opportunities:
A positive spread indicates a potential cash-and-carry arbitrage opportunity.
The larger the APY, the more profitable the arbitrage opportunity could be.
Timing Trades:
Execute a buy on the BTC spot market and simultaneously sell BTC futures when the APY is attractive.
Close both positions upon futures contract expiry to realize profits.
Risk Management:
Ensure you have sufficient margin to hold both positions until expiry.
Monitor funding rates and volatility, which could affect returns.
Conclusion
This script is an essential tool for traders looking to exploit price discrepancies between the BTC spot market and futures market through a cash-and-carry arbitrage strategy. It provides real-time data on spreads, annualized returns (APY), and visual alerts, helping traders make informed decisions and maximize their profit potential.
Forex Hammer and Hanging Man StrategyThe strategy is based on two key candlestick chart patterns: Hammer and Hanging Man. These chart patterns are widely used in technical analysis to identify potential reversal points in the market. Their relevance in the Forex market, known for its high liquidity and volatile price movements, is particularly pronounced. Both patterns provide insights into market sentiment and trader psychology, which are critical in currency trading, where short-term volatility plays a significant role.
1. Hammer:
• Typically occurs after a downtrend.
• Signals a potential trend reversal to the upside.
• A Hammer has:
• A small body (close and open are close to each other).
• A long lower shadow, at least twice as long as the body.
• No or a very short upper shadow.
2. Hanging Man:
• Typically occurs after an uptrend.
• Signals a potential reversal to the downside.
• A Hanging Man has:
• A small body, similar to the Hammer.
• A long lower shadow, at least twice as long as the body.
• A small or no upper shadow.
These patterns are a manifestation of market psychology, specifically the tug-of-war between buyers and sellers. The Hammer reflects a situation where sellers tried to push the price down but were overpowered by buyers, while the Hanging Man shows that buyers failed to maintain the upward movement, and sellers could take control.
Relevance of Chart Patterns in Forex
In the Forex market, chart patterns are vital tools because they offer insights into price action and market sentiment. Since Forex trading often involves large volumes of trades, chart patterns like the Hammer and Hanging Man are important for recognizing potential shifts in market momentum. These patterns are a part of technical analysis, which aims to forecast future price movements based on historical data, relying on the psychology of market participants.
Scientific Literature on the Relevance of Candlestick Patterns
1. Behavioral Finance and Candlestick Patterns:
Research on behavioral finance supports the idea that candlestick patterns, such as the Hammer and Hanging Man, are relevant because they reflect shifts in trader psychology and sentiment. According to Lo, Mamaysky, and Wang (2000), patterns like these could be seen as representations of collective investor behavior, influenced by overreaction, optimism, or pessimism, and can often signal reversals in market trends.
2. Statistical Validation of Chart Patterns:
Studies by Brock, Lakonishok, and LeBaron (1992) explored the profitability of technical analysis strategies, including candlestick patterns, and found evidence that certain patterns, such as the Hammer, can have predictive value in financial markets. While their study primarily focused on stock markets, their findings are generally applicable to the Forex market as well.
3. Market Efficiency and Candlestick Patterns:
The efficient market hypothesis (EMH) posits that all available information is reflected in asset prices, but some studies suggest that markets may not always be perfectly efficient, allowing for profitable exploitation of certain chart patterns. For instance, Jegadeesh and Titman (1993) found that momentum strategies, which often rely on price patterns and trends, could generate significant returns, suggesting that patterns like the Hammer or Hanging Man may provide a slight edge, particularly in short-term Forex trading.
Testing the Strategy in Forex Using the Provided Script
The provided script allows traders to test and evaluate the Hammer and Hanging Man patterns in Forex trading by entering positions when these patterns appear and holding the position for a specified number of periods. This strategy can be tested to assess its performance across different currency pairs and timeframes.
1. Testing on Different Timeframes:
• The effectiveness of candlestick patterns can vary across different timeframes, as market dynamics change with the level of detail in each timeframe. Shorter timeframes may provide more frequent signals, but with higher noise, while longer timeframes may produce more reliable signals, but with fewer opportunities. This multi-timeframe analysis could be an area to explore to enhance the strategy’s robustness.
2. Exit Strategies:
• The script incorporates an exit strategy where positions are closed after holding them for a specified number of periods. This is useful for testing how long the reversal patterns typically take to play out and when the optimal exit occurs for maximum profitability. It can also help to adjust the exit logic based on real-time market behavior.
Conclusion
The Hammer and Hanging Man patterns are widely recognized in technical analysis as potential reversal signals, and their application in Forex trading is valuable due to the market’s high volatility and liquidity. This strategy leverages these candlestick patterns to enter and exit trades based on shifts in market sentiment and psychology. Testing and optimization, as offered by the script, can help refine the strategy and improve its effectiveness.
For further refinement, it could be valuable to consider combining candlestick patterns with other technical indicators or using multi-timeframe analysis to confirm patterns and increase the probability of successful trades.
References:
• Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance, 55(4), 1705-1770.
• Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance, 47(5), 1731-1764.
• Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
This provides a theoretical basis for the use of candlestick patterns in trading, supported by academic literature and research on market psychology and efficiency.
Dynamic Support and Resistance Pivot Strategy The Dynamic Support and Resistance Pivot Strategy is a flexible and adaptive tool designed to identify short-term support and resistance levels using the concept of price pivots.
### Key Elements of the Strategy
1. Pivot points as support and resistance levels
Pivots are significant turning points on the price chart, often marking local highs and lows where the price has reversed direction. A pivot high occurs when the price forms a local peak, while a pivot low occurs when the price forms a local trough. When a new pivot high is formed, it creates a resistance level. Conversely, when a new pivot low is formed, it creates a support level.
The strategy continuously updates these levels as new pivots are detected, ensuring they remain relevant to the current market conditions. By identifying these price levels, the strategy dynamically adjusts to market conditions, allowing it to adapt to both trending and ranging markets, since it has a long target and can perform reversal operations.
2. Entry Criteria
- Buy (Long): A long position is triggered when the price is near the support level and then crosses it from below to above. This suggests that the price has found support and may start moving upwards.
- Sell (Short): A short position is triggered when the price is near the resistance level and then crosses it from above to below. This indicates that the price may be reversing and moving downward.
3. Support/Resistance distance (%)
- This parameter establishes a percentage range around the identified support and resistance level. For example, if the Support Resistance Distance is 0.4% (default), the closing price must be within a range of 0.4% above support or below the resistance to be considered "close" and trigger a trade.
4. Exit criteria
- Take profit = 27 %
- Stop loss = 10 %
- Reversal if a new entry point is identified in the opposite direction
5. No Repainting
- The Dynamic Support and Resistance Pivot Strategy is not subject to repainting.
6. Position Sizing by Equity and risk management
- This strategy has a default configuration to operate with 35% of the equity. The stop loss is set to 10% from the entry price. This way, the strategy is putting at risk about 10% of 35% of equity, that is, around 3.5% of equity for each trade. The percentage of equity and stop loss can be adjusted by the user according to their risk management.
7. Backtest results
- This strategy was subjected to backtest and operations in replay mode on **1000000MOGUSDT.P**, with the inclusion of transaction fees at 0.12% and slipagge of 5 ticks, and the past results have shown consistent profitability. Past results are no guarantee of future results. The strategy's backtest results may even be due to overfitting with past data.
8. Chart Visualization
- Support and resistance levels are displayed as green (support) and red (resistance) lines.
- Pivot prices are displayed as green (pivot low) and red (pivot high) labels.
In this image above, the Support/Resistance distance (%) parameter was set to 0.8.
9. Default Configuration
Chart Timeframe: 1h
Pivot Lengh: 2
Support/Resistance distance (%): 0.4*
Stop Loss: 10 %
Take Profit: 27 %
* This parameter can alternatively be set to 0.8.
10. Alternative Configuration
Chart Timeframe: 20 min
Pivot Lengh: 4
Support/Resistance distance (%): 0.1
Stop Loss: 10 %
Take Profit: 25 %
BYBIT:1000000MOGUSDT.P
BullBear with Volume-Percentile TP - Strategy [presentTrading] Happy New Year, everyone! I hope we have a fantastic year ahead.
It's been a while since I published an open script, but it's time to return.
This strategy introduces an indicator called Bull Bear Power, combined with an advanced take-profit system, which is the main innovative and educational aspect of this script. I hope all of you find some useful insights here. Welcome to engage in meaningful exchanges. This is a versatile tool suitable for both novice and experienced traders.
█ Introduction and How it is Different
Unlike traditional strategies that rely solely on price or volume indicators, this approach combines Bull Bear Power (BBP) with volume percentile analysis to identify optimal entry and exit points. It features a dynamic take-profit mechanism based on ATR (Average True Range) multipliers adjusted by volume and percentile factors, ensuring adaptability to diverse market conditions. This multifaceted strategy not only improves signal accuracy but also optimizes risk management, distinguishing it from conventional trading methods.
BTCUSD 6hr performance
Disable the visualization of Bull Bear Power (BBP) to clearly view the Z-Score.
█ Strategy, How it Works: Detailed Explanation
The BBP Strategy with Volume-Percentile TP utilizes several interconnected components to analyze market data and generate trading signals. Here's an overview with essential equations:
🔶 Core Indicators and Calculations
1. Exponential Moving Average (EMA):
- **Purpose:** Smoothens price data to identify trends.
- **Formula:**
EMA_t = (Close_t * (2 / (lengthInput + 1))) + (EMA_(t-1) * (1 - (2 / (lengthInput + 1))))
- Usage: Baseline for Bull and Bear Power.
2. Bull and Bear Power:
- Bull Power: `BullPower = High_t - EMA_t`
- Bear Power: `BearPower = Low_t - EMA_t`
- BBP:** `BBP = BullPower + BearPower`
- Interpretation: Positive BBP indicates bullish strength, negative indicates bearish.
3. Z-Score Calculation:
- Purpose: Normalizes BBP to assess deviation from the mean.
- Formula:
Z-Score = (BBP_t - bbp_mean) / bbp_std
- Components:
- `bbp_mean` = SMA of BBP over `zLength` periods.
- `bbp_std` = Standard deviation of BBP over `zLength` periods.
- Usage: Identifies overbought or oversold conditions based on thresholds.
🔶 Volume Analysis
1. Volume Moving Average (`vol_sma`):
vol_sma = (Volume_1 + Volume_2 + ... + Volume_vol_period) / vol_period
2. Volume Multiplier (`vol_mult`):
vol_mult = Current Volume / vol_sma
- Thresholds:
- High Volume: `vol_mult > 2.0`
- Medium Volume: `1.5 < vol_mult ≤ 2.0`
- Low Volume: `1.0 < vol_mult ≤ 1.5`
🔶 Percentile Analysis
1. Percentile Calculation (`calcPercentile`):
Percentile = (Number of values ≤ Current Value / perc_period) * 100
2. Thresholds:
- High Percentile: >90%
- Medium Percentile: >80%
- Low Percentile: >70%
🔶 Dynamic Take-Profit Mechanism
1. ATR-Based Targets:
TP1 Price = Entry Price ± (ATR * atrMult1 * TP_Factor)
TP2 Price = Entry Price ± (ATR * atrMult2 * TP_Factor)
TP3 Price = Entry Price ± (ATR * atrMult3 * TP_Factor)
- ATR Calculation:
ATR_t = (True Range_1 + True Range_2 + ... + True Range_baseAtrLength) / baseAtrLength
2. Adjustment Factors:
TP_Factor = (vol_score + price_score) / 2
- **vol_score** and **price_score** are based on current volume and price percentiles.
Local performance
🔶 Entry and Exit Logic
1. Long Entry: If Z-Score crosses above 1.618, then Enter Long.
2. Short Entry: If Z-Score crosses below -1.618, then Enter Short.
3. Exiting Positions:
If Long and Z-Score crosses below 0:
Exit Long
If Short and Z-Score crosses above 0:
Exit Short
4. Take-Profit Execution:
- Set multiple exit orders at dynamically calculated TP levels based on ATR and adjusted by `TP_Factor`.
█ Trade Direction
The strategy determines trade direction using the Z-Score from the BBP indicator:
- Long Positions:
- Condition: Z-Score crosses above 1.618.
- Short Positions:
- Condition: Z-Score crosses below -1.618.
- Exiting Trades:
- Long Exit: Z-Score drops below 0.
- Short Exit: Z-Score rises above 0.
This approach aligns trades with prevailing market trends, increasing the likelihood of successful outcomes.
█ Usage
Implementing the BBP Strategy with Volume-Percentile TP in TradingView involves:
1. Adding the Strategy:
- Copy the Pine Script code.
- Paste it into TradingView's Pine Editor.
- Save and apply the strategy to your chart.
2. Configuring Settings:
- Adjust parameters like EMA length, Z-Score thresholds, ATR multipliers, volume periods, and percentile settings to match your trading preferences and asset behavior.
3. Backtesting:
- Use TradingView’s backtesting tools to evaluate historical performance.
- Analyze metrics such as profit factor, drawdown, and win rate.
4. Optimization:
- Fine-tune parameters based on backtesting results.
- Test across different assets and timeframes to enhance adaptability.
5. Deployment:
- Apply the strategy in a live trading environment.
- Continuously monitor and adjust settings as market conditions change.
█ Default Settings
The BBP Strategy with Volume-Percentile TP includes default parameters designed for balanced performance across various markets. Understanding these settings and their impact is essential for optimizing strategy performance:
Bull Bear Power Settings:
- EMA Length (`lengthInput`): 21
- **Effect:** Balances sensitivity and trend identification; shorter lengths respond quicker but may generate false signals.
- Z-Score Length (`zLength`): 252
- **Effect:** Long period for stable mean and standard deviation, reducing false signals but less responsive to recent changes.
- Z-Score Threshold (`zThreshold`): 1.618
- **Effect:** Higher threshold filters out weaker signals, focusing on significant market moves.
Take Profit Settings:
- Use Take Profit (`useTP`): Enabled (`true`)
- **Effect:** Activates dynamic profit-taking, enhancing profitability and risk management.
- ATR Period (`baseAtrLength`): 20
- **Effect:** Shorter period for sensitive volatility measurement, allowing tighter profit targets.
- ATR Multipliers:
- **Effect:** Define conservative to aggressive profit targets based on volatility.
- Position Sizes:
- **Effect:** Diversifies profit-taking across multiple levels, balancing risk and reward.
Volume Analysis Settings:
- Volume MA Period (`vol_period`): 100
- **Effect:** Longer period for stable volume average, reducing the impact of short-term spikes.
- Volume Multipliers:
- **Effect:** Determines volume conditions affecting take-profit adjustments.
- Volume Factors:
- **Effect:** Adjusts ATR multipliers based on volume strength.
Percentile Analysis Settings:
- Percentile Period (`perc_period`): 100
- **Effect:** Balances historical context with responsiveness to recent data.
- Percentile Thresholds:
- **Effect:** Defines price and volume percentile levels influencing take-profit adjustments.
- Percentile Factors:
- **Effect:** Modulates ATR multipliers based on price percentile strength.
Impact on Performance:
- EMA Length: Shorter EMAs increase sensitivity but may cause more false signals; longer EMAs provide stability but react slower to market changes.
- Z-Score Parameters:*Longer Z-Score periods create more stable signals, while higher thresholds reduce trade frequency but increase signal reliability.
- ATR Multipliers and Position Sizes: Higher multipliers allow for larger profit targets with increased risk, while diversified position sizes help in securing profits at multiple levels.
- Volume and Percentile Settings: These adjustments ensure that take-profit targets adapt to current market conditions, enhancing flexibility and performance across different volatility environments.
- Commission and Slippage: Accurate settings prevent overestimation of profitability and ensure the strategy remains viable after accounting for trading costs.
Conclusion
The BBP Strategy with Volume-Percentile TP offers a robust framework by combining BBP indicators with volume and percentile analyses. Its dynamic take-profit mechanism, tailored through ATR adjustments, ensures that traders can effectively capture profits while managing risks in varying market conditions.
Liquitive Buy/Sell Dollar AveragerLiquitive Buy/Sell Dollar Averager Indicator
The "Liquitive Buy/Sell Dollar Averager" is a versatile trading tool designed for intraday and multi-timeframe analysis, combining advanced range-bound calculations, RSI normalization, volume spikes, and candle pattern recognition to identify optimal buy and sell conditions. This indicator is particularly suitable for traders employing strategies that focus on dollar-cost averaging, position scaling, and systematic buy/sell decision-making.
Key Features:
Adaptive RSI-Based Levels:
Dynamically calculates inner bounds (IB) and outer bounds (OB) using RSI and price ranges, helping to identify overbought and oversold conditions relative to the price action.
Normalizes RSI values to the price range for seamless visualization overlaid on the chart.
Volume and Candle Analysis:
Detects significant volume spikes relative to a moving average, signaling increased market activity.
Identifies spiking green/red candles to capture momentum-driven price movements.
Dynamic Support and Resistance:
Calculates and plots support and resistance levels based on recent swing highs and lows.
Median and boundary lines help visualize key price levels for decision-making.
Profitability Check:
Buy and Sell Signals:
Checks profitability thresholds based on percentage gains/losses.
Incorporates logic for "time to buy" and "time to sell" using target profit margins.
Implements average move percentage to define realistic thresholds for buy/sell actions.
Time-Based Trading Restrictions:
Configures trading logic to disallow trades after a specific time (e.g., 3:40 PM for intraday sessions).
Ensures logical entry and exit decisions are only made within active trading hours.
Color-Coded Visualization:
Background colors dynamically shift between green (bullish), red (bearish), and neutral, depending on RSI and price position relative to the inner bounds.
Opacity of the background adjusts based on normalized RSI differences to provide a visual cue of market strength.
Customizable Parameters:
Allows user input for key settings like lookback periods, RSI length, percent ranges, volume thresholds, and transparency levels, enabling flexible configuration tailored to individual strategies.
Actionable Alerts and Signals:
Plots "Open Position", "Add to Position", and "Close Position" markers directly on the chart, making it easy to follow systematic trading rules.
How It Works:
Buy Signals:
Triggered when price conditions, volume spikes, and RSI-based thresholds align with profitability metrics.
Designed for dollar-cost averaging, identifying opportunities to add to long positions or open new positions.
Sell Signals:
Evaluates profitability conditions to identify when to close or scale out of positions.
Incorporates real-time evaluation of market momentum and profitability.
Puell Multiple BTC | JeffreyTimmermansThe Puell Multiple is a metric that assesses the relationship between mining profitability and market cycles. It is calculated by comparing the daily value of newly issued coins (USD) to the 365-day moving average of daily coin issuance (USD).
This indicator works best on the 1D BTC Chart. When interpreting the Puell Multiple, it can generally be understood as follows:
High values indicate that miner profitability is significantly higher than the yearly average. This may lead to an increased incentive for miners to sell off their holdings, putting additional selling pressure on the market.
Low values suggest that miner profitability is lower than the yearly average. In this case, miners might experience financial strain, causing some to reduce their hash power by shutting down mining rigs. This, in turn, can reduce the number of coins being sold into the market, as remaining miners need to liquidate fewer coins to maintain operations, thereby decreasing the impact on the liquid supply.
The Puell Multiple is a metric used primarily in the cryptocurrency space, specifically for Bitcoin, to assess whether Bitcoin is overvalued or undervalued in relation to its mining rewards. It helps to gauge the profitability of miners and, by extension, to assess market conditions.
Use:
This Puell Multiple is invented for Long-Term, Trend Following Systems.
The Puell Multiple trend can be visualized through the color of the bars, which represents the direction of the trend, while the background indicates the strength of that trend.
Bar Color: The color of the bars typically changes to reflect whether the trend is bullish or bearish. For example, green bars may indicate a strong bullish trend, while red bars signal a bearish or declining trend. The color coding helps to quickly interpret the market's overall movement in relation to mining profitability.
Background Color: The background of the chart is used to reflect the strength of the trend. A darker or more intense background may signify a stronger trend, indicating that the market conditions are more pronounced, while a lighter background can suggest a weaker or more uncertain trend, showing less certainty in the market’s direction.
Together, the combination of bar color and background provides a clearer picture of both the trend's direction and its strength, making it easier to assess potential market behavior based on miner profitability and market cycles.
Puell Multiple and Moving Average: They can be used as an extra tool to confirm the bullish or bearish trend. When the Puell Multiple is above the Moving Average, this will suggest and confirm that the trend is bullish.
How you score this for your own systems is up to you.
-Jeffrey
Puell Multiple BTC | JeffreyTimmermansThe Puell Multiple is a metric that assesses the relationship between mining profitability and market cycles. It is calculated by comparing the daily value of newly issued coins (USD) to the 365-day moving average of daily coin issuance (USD).
This indicator works best on the 1D BTC Chart. When interpreting the Puell Multiple, it can generally be understood as follows:
High values indicate that miner profitability is significantly higher than the yearly average. This may lead to an increased incentive for miners to sell off their holdings, putting additional selling pressure on the market.
Low values suggest that miner profitability is lower than the yearly average. In this case, miners might experience financial strain, causing some to reduce their hash power by shutting down mining rigs. This, in turn, can reduce the number of coins being sold into the market, as remaining miners need to liquidate fewer coins to maintain operations, thereby decreasing the impact on the liquid supply.
The Puell Multiple is a metric used primarily in the cryptocurrency space, specifically for Bitcoin, to assess whether Bitcoin is overvalued or undervalued in relation to its mining rewards. It helps to gauge the profitability of miners and, by extension, to assess market conditions.
Use:
This Puell Multiple is invented for Long-Term, Trend Following Systems.
The Puell Multiple trend can be visualized through the color of the bars, which represents the direction of the trend, while the background indicates the strength of that trend.
Bar Color: The color of the bars typically changes to reflect whether the trend is bullish or bearish. For example, green bars may indicate a strong bullish trend, while red bars signal a bearish or declining trend. The color coding helps to quickly interpret the market's overall movement in relation to mining profitability.
Background Color: The background of the chart is used to reflect the strength of the trend. A darker or more intense background may signify a stronger trend, indicating that the market conditions are more pronounced, while a lighter background can suggest a weaker or more uncertain trend, showing less certainty in the market’s direction.
Together, the combination of bar color and background provides a clearer picture of both the trend's direction and its strength, making it easier to assess potential market behavior based on miner profitability and market cycles.
Puell Multiple and Moving Average: They can be used as an extra tool to confirm the bullish or bearish trend. When the Puell Multiple is above the Moving Average, this will suggest and confirm that the trend is bullish.
How you score this for your own systems is up to you.
-Jeffrey
Puell Multiple BTC | JeffreyTimmermansThe Puell Multiple is a metric that assesses the relationship between mining profitability and market cycles. It is calculated by comparing the daily value of newly issued coins (USD) to the 365-day moving average of daily coin issuance (USD).
This indicator works best on the 1D BTC Chart. When interpreting the Puell Multiple, it can generally be understood as follows:
High values indicate that miner profitability is significantly higher than the yearly average. This may lead to an increased incentive for miners to sell off their holdings, putting additional selling pressure on the market.
Low values suggest that miner profitability is lower than the yearly average. In this case, miners might experience financial strain, causing some to reduce their hash power by shutting down mining rigs. This, in turn, can reduce the number of coins being sold into the market, as remaining miners need to liquidate fewer coins to maintain operations, thereby decreasing the impact on the liquid supply.
The Puell Multiple is a metric used primarily in the cryptocurrency space, specifically for Bitcoin, to assess whether Bitcoin is overvalued or undervalued in relation to its mining rewards. It helps to gauge the profitability of miners and, by extension, to assess market conditions.
Use:
This Puell Multiple is invented for Long-Term, Trend Following Systems.
The Puell Multiple trend can be visualized through the color of the bars, which represents the direction of the trend, while the background indicates the strength of that trend.
Bar Color: The color of the bars typically changes to reflect whether the trend is bullish or bearish. For example, green bars may indicate a strong bullish trend, while red bars signal a bearish or declining trend. The color coding helps to quickly interpret the market's overall movement in relation to mining profitability.
Background Color: The background of the chart is used to reflect the strength of the trend. A darker or more intense background may signify a stronger trend, indicating that the market conditions are more pronounced, while a lighter background can suggest a weaker or more uncertain trend, showing less certainty in the market’s direction.
Together, the combination of bar color and background provides a clearer picture of both the trend's direction and its strength, making it easier to assess potential market behavior based on miner profitability and market cycles.
Puell Multiple and Moving Average: They can be used as an extra tool to confirm the bullish or bearish trend. When the Puell Multiple is above the Moving Average, this will suggest and confirm that the trend is bullish.
How you score this for your own systems is up to you.
-Jeffrey
Custom Dual EMA Crossover Strategy with Configurable LogicThis strategy is designed to assist traders in identifying and capitalizing on bullish market trends through a systematic and data-driven approach. It incorporates detailed trend analysis, volatility filtering, and percentage-based thresholds to provide actionable insights and high-confidence trade setups. It leverages the Exponential Moving Average and combines it with custom logic to detect volatility, maximum allowed price movements over last bars and trend confirmation.
Key Features:
- Buy orders follow several conditions, including but not limited to:
a. EMA Crossover: specifically designed to capture immediate market shifts rather than medium- or long-term trends, ensuring responsiveness to rapidly changing conditions but requiring additional confirmations to avoid false signals (see below).
b. Thresholds in Price Changes: Ensures recent price fluctuations remain within specific thresholds, allowing trades to be entered at optimal times and avoiding delayed or unsustainable short-term bullish trends.
c. Adequate Market Volatility: Requires sufficient market activity to avoid false signals stemming from low volatility conditions.
d. Bullish Medium-Term Trend: Validates a bullish medium-term trend using an EMA crossover to avoid trading during bearish market conditions and minimize risk.
- Leverages Take profit and Stop loss levels
- Implements an optional mechanism to automatically close trades after a predefined number of bars, supporting disciplined trade management.
The script does not rely on any public scripts or indicators. Apart the EMA, all the underlying logic, including the volatility thresholds and filtering mechanisms, has been custom developed to ensure originality and precision. The strategy's conditions are all configurable by the user in the TradingView pop-up, allowing it to adapt to different assets and timeframes. For example, users can set the EMA lengths to align with long-term trends for cryptocurrencies or adjust volatility thresholds to account for the specific price movement behavior of stocks or forex pairs.
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Recommendations:
- Identify a crypto asset with potential
- Before live trading, rigorously backtest your strategy on the chosen asset and interval over a period of at least one year*, analyzing results, refining parameters' value and eventually changing timeframe and / or asset.
- Refine your approach until you achieve consistent profitability with a high win rate. Balance the two — a high win rate is great, but only if your profits outweigh your losses in the long term.
- Once successful, remain disciplined and adhere to the parameters that yield the best results. Set up TradingView alerts to trigger real-time actions via your preferred trading bot. Alerts can be set up on the Indicator, which mirrors the strategy's logic and enables users to execute real-time actions effectively. I will provide you access to the Indicator, as well as the Strategy.
* Alternatively, you can apply the strategy to a shorter period for tactical use. While this approach may increase short-term opportunities (e.g. strong bullish short term movements), it also comes with heightened risks.
Use Cases:
- Suitable for traders focusing on bullish or range-bound markets.
- Ideal for short to medium-term trading horizons.
Access and Configuration Support:
This is an invite-only script. For access, please reach out directly for subscription details. I also provide guidance on configuring the strategy with real-world examples to optimize its use for various assets, intervals and timeframes.
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Disclaimer:
This script is a tool to support trading decisions and does not guarantee profitability. Past performance does not indicate future results. Trading carries inherent risks; always trade responsibly and manage risk accordingly.
