Volatility IndicatorThe volatility indicator presented here is based on multiple volatility indices that reflect the market’s expectation of future price fluctuations across different asset classes, including equities, commodities, and currencies. These indices serve as valuable tools for traders and analysts seeking to anticipate potential market movements, as volatility is a key factor influencing asset prices and market dynamics (Bollerslev, 1986).
Volatility, defined as the magnitude of price changes, is often regarded as a measure of market uncertainty or risk. Financial markets exhibit periods of heightened volatility that may precede significant price movements, whether upward or downward (Christoffersen, 1998). The indicator presented in this script tracks several key volatility indices, including the VIX (S&P 500), GVZ (Gold), OVX (Crude Oil), and others, to help identify periods of increased uncertainty that could signal potential market turning points.
Volatility Indices and Their Relevance
Volatility indices like the VIX are considered “fear gauges” as they reflect the market’s expectation of future volatility derived from the pricing of options. A rising VIX typically signals increasing investor uncertainty and fear, which often precedes market corrections or significant price movements. In contrast, a falling VIX may suggest complacency or confidence in continued market stability (Whaley, 2000).
The other volatility indices incorporated in the indicator script, such as the GVZ (Gold Volatility Index) and OVX (Oil Volatility Index), capture the market’s perception of volatility in specific asset classes. For instance, GVZ reflects market expectations for volatility in the gold market, which can be influenced by factors such as geopolitical instability, inflation expectations, and changes in investor sentiment toward safe-haven assets. Similarly, OVX tracks the implied volatility of crude oil options, which is a crucial factor for predicting price movements in energy markets, often driven by geopolitical events, OPEC decisions, and supply-demand imbalances (Pindyck, 2004).
Using the Indicator to Identify Market Movements
The volatility indicator alerts traders when specific volatility indices exceed a defined threshold, which may signal a change in market sentiment or an upcoming price movement. These thresholds, set by the user, are typically based on historical levels of volatility that have preceded significant market changes. When a volatility index exceeds this threshold, it suggests that market participants expect greater uncertainty, which often correlates with increased price volatility and the possibility of a trend reversal.
For example, if the VIX exceeds a pre-determined level (e.g., 30), it could indicate that investors are anticipating heightened volatility in the equity markets, potentially signaling a downturn or correction in the broader market. On the other hand, if the OVX rises significantly, it could point to an upcoming sharp movement in crude oil prices, driven by changing market expectations about supply, demand, or geopolitical risks (Geman, 2005).
Practical Application
To effectively use this volatility indicator in market analysis, traders should monitor the alert signals generated when any of the volatility indices surpass their thresholds. This can be used to identify periods of market uncertainty or potential market turning points across different sectors, including equities, commodities, and currencies. The indicator can help traders prepare for increased price movements, adjust their risk management strategies, or even take advantage of anticipated price swings through options trading or volatility-based strategies (Black & Scholes, 1973).
Traders may also use this indicator in conjunction with other technical analysis tools to validate the potential for significant market movements. For example, if the VIX exceeds its threshold and the market is simultaneously approaching a critical technical support or resistance level, the trader might consider entering a position that capitalizes on the anticipated price breakout or reversal.
Conclusion
This volatility indicator is a robust tool for identifying market conditions that are conducive to significant price movements. By tracking the behavior of key volatility indices, traders can gain insights into the market’s expectations of future price fluctuations, enabling them to make more informed decisions regarding market entries and exits. Understanding and monitoring volatility can be particularly valuable during times of heightened uncertainty, as changes in volatility often precede substantial shifts in market direction (French et al., 1987).
References
• Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Christoffersen, P. F. (1998). Evaluating Interval Forecasts. International Economic Review, 39(4), 841-862.
• Whaley, R. E. (2000). Derivatives on Market Volatility. Journal of Derivatives, 7(4), 71-82.
• Pindyck, R. S. (2004). Volatility and the Pricing of Commodity Derivatives. Journal of Futures Markets, 24(11), 973-987.
• Geman, H. (2005). Commodities and Commodity Derivatives: Modeling and Pricing for Agriculturals, Metals and Energy. John Wiley & Sons.
• Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
• French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected Stock Returns and Volatility. Journal of Financial Economics, 19(1), 3-29.
Cari dalam skrip untuk "Futures"
Soul Button Scalping (1 min chart) V 1.0Indicator Description
- P Signal: The foundational buy signal. It should be confirmed by observing RSI divergence on the 1-minute chart.
- Green, Orange, and Blue Signals: Three buy signals generated through the combination of multiple oscillators. These signals should also be cross-referenced with the RSI on the 1-minute chart.
- Big White and Big Yellow Signals: These represent strong buy signals, triggered in extreme oversold conditions.
- BEST BUY Signal: The most reliable and powerful buy signal available in this indicator.
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Red Sell Signal: A straightforward sell signal indicating potential overbought conditions.
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Usage Guidance
This scalping indicator is specifically designed for use on the 1-minute chart, incorporating data from the 5-minute chart for added context. It is most effective when used in conjunction with:
• VWAP (Volume Weighted Average Price), already included in the indicator.
• RSI on the 1-minute chart, which should be opened as a separate indicator.
• Trendlines, structure breakouts, and price action analysis to confirm signals.
Intended for Crypto Scalping:
The indicator is optimized for scalping cryptocurrency markets.
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Future Enhancements:
• Integration of price action and candlestick patterns.
• A refined version tailored for trading futures contracts, specifically ES and MES in the stock market.
UM VIX status table and Roll Yield with EMA
Description :
This oscillator indicator gives you a quick snapshot of VIX, VIX futures prices, and the related VIX roll yield at a glance. When the roll yield is greater than 0, The front-month VX1 future contract is less than the next-month VX2 contract. This is called Contango and is typical for the majority of the time. If the roll yield falls below zero. This is considered backwardation where the front-month VX1 contract is higher than the value of the next-month VX2 contract. Contango is most common. When Backwardation occurs, there is usually high volatility present.
Features :
The red and green fill indicate the current roll yield with the gray line being zero.
An Exponential moving average is overlaid on the roll yield. It is red when trending down and green when trending up. If you right-click the indicator, you can set alerts for roll yield EMA color transitions green to red or red to green.
Suggested uses:
The author suggests a one hour chart using the 55 period EMA with a 60 minute setting in the indicator. This gives you a visual idea of whether the roll yield is rising or falling. The roll yield will often change directions at market turning points. For example if the roll yield EMA changes from red to green, this indicates a rising roll yield and volatility is subsiding. This could be considered bullish. If the roll yield begins falling, this indicates volatility is rising. This may be negative for stocks and indexes.
I look for short volatility positions (SVIX) when the roll yield is rising. I look for long volatility positions (VXX, UVXY, UVIX) when the roll yield begins falling. The indicator can be added to any chart. I suggest using the VX1, SPY, VIX, or other major stock index.
Set the time frame to your trading style. The default is 60 minutes. Note, the timeframe of the indicator does NOT utilize the current chart timeframe, it must be set to the desired timeframe. I manually input text on the chart indicator for understanding periods of Long and Short Volatility.
Settings and Defaults
The EMA is set to 55 by default and the table location is set to the lower right. The default time frame is 60 minutes. These features are all user configurable.
Other considerations
Sometimes the Tradingview data when a VX contract expires and another contract begins, may not transition cleanly and appear as a break on the chart. Tradingview is working on this as stated from my last request. This VX contract from one expiring contract to the next can be fixed on the price chart manually: ( Chart settings, Symbol, check the "Adjust for contract changes" box)
Observations
Pull up a one-hour chart of VX1 or SPY. Add this indicator. roll it back in time to see how the market and volatility reacts when the EMA changes from red to green and green to red. Adjust the EMA to your trading style and time frame. Use this for added confirmation of your long and short volatility trades with the Volatility ETFs SVIX, SVXY, VXX, UVXY, UVIX. or use it for long/short indexes such as SPY.
S&P 500 E-Mini TrackerThis script generates a reference price for the S&P 500 ETF - SPY based on the current price of the ES contract, which is an E-Mini Futures contract representing the S&P 500 index. The indicator plots this reference price on the chart, providing a unique view of the relationship between these two popular markets.
Advantages:
Identifies divergence between the ES and SPY prices, indicating potential trading opportunities or shifts in market sentiment.
Confirms trends by showing the correlation between the ES and SPY prices.
Eliminates the need for multiple charts, allowing traders to focus on a single screen and make more informed decisions.
Customizable Parameters:
Color Scheme: Choose from various color options to customize the appearance of the indicator.
Line Style: Select from different line styles to change the visual representation of the reference price.
Divisor: Set the dividing factor to adjust the ratio at which the reference price is calculated. (Default value: 10). It is recommended to keep it at 10 for SPY.
To use it with other Stocks/ ETFs, use simple ratio math to calculate the divisor and you can customize the indicator to scale accordingly.
By using this indicator, traders can gain a deeper understanding of the relationship between the E-Mini and SPY markets, making it easier to identify trading opportunities and confirm trends.
Temporary Help Services Jobs - Trend Allocation StrategyThis strategy is designed to capitalize on the economic trends represented by the Temporary Help Services (TEMPHELPS) index, which is published by the Federal Reserve Economic Data (FRED). Temporary Help Services Jobs are often regarded as a leading indicator of labor market conditions, as changes in temporary employment levels frequently precede broader employment trends.
Methodology:
Data Source: The strategy uses the FRED dataset TEMPHELPS for monthly data on temporary help services.
Trend Definition:
Uptrend: When the current month's value is greater than the previous month's value.
Downtrend: When the current month's value is less than the previous month's value.
Entry Condition: A long position is opened when an uptrend is detected, provided no position is currently held.
Exit Condition: The long position is closed when a downtrend is detected.
Scientific Basis:
The TEMPHELPS index serves as a leading economic indicator, as noted in studies analyzing labor market cyclicality (e.g., Katz & Krueger, 1999). Temporary employment is often considered a proxy for broader economic conditions, particularly in predicting recessions or recoveries. Incorporating this index into trading strategies allows for aligning trades with potential macroeconomic shifts, as suggested by research on employment trends and market performance (Autor, 2001; Valetta & Bengali, 2013).
Usage:
This strategy is best suited for long-term investors or macroeconomic trend followers who wish to leverage labor market signals for equity or futures trading. It operates exclusively on end-of-month data, ensuring minimal transaction costs and noise.
Brazil Real Interest RateEste script foi criado para calcular e exibir a Taxa de Juros Real, permitindo compreender o impacto da inflação sobre os juros nominais do mercado. Ele utiliza dois indicadores principais: a taxa de juros nominal, que reflete os juros antes de considerar a inflação, e a taxa de inflação anual, que mede o aumento dos preços em um ano.
O script funciona da seguinte forma: ele obtém diariamente os dados da taxa de juros nominal (representada pelo contrato futuro DI1) e da inflação anual (indicada pelo BRIRYY). Esses valores são processados para calcular a taxa de juros real, utilizando a fórmula de Fisher, que ajusta os juros nominais ao descontar o efeito da inflação. O resultado é uma medida mais precisa do retorno ou custo real, considerando o poder de compra.
Depois de realizar o cálculo, o script exibe a Taxa de Juros Real diretamente no gráfico, representada por uma linha verde. Isso permite acompanhar, de forma clara e visual, como a inflação e os juros afetam o cenário econômico ao longo do tempo.
This script was created to calculate and display the Real Interest Rate, allowing us to understand the impact of inflation on nominal market interest rates. It uses two main indicators: the nominal interest rate, which reflects interest rates before considering inflation, and the annual inflation rate, which measures the increase in prices over a year.
The script works as follows: it obtains daily data on the nominal interest rate (represented by the DI1 futures contract) and annual inflation (indicated by BRIRYY). These values are processed to calculate the real interest rate, using the Fisher formula, which adjusts nominal interest rates by discounting the effect of inflation. The result is a more accurate measure of real return or cost, considering purchasing power.
After performing the calculation, the script displays the Real Interest Rate directly on the graph, represented by a green line. This allows you to monitor, clearly and visually, how inflation and interest rates affect the economic scenario over time.
Volume-MACD-RSI Integrated StrategyDescription:
This script integrates three well-known technical analysis tools—Volume, MACD, and RSI—into a single signal meant to help traders identify potential turning points under strong market conditions.
Concept Overview:
Volume Filter: We compare the current bar’s volume to a 20-period volume average and require it to exceed a specified multiplier. This ensures that signals occur only during periods of heightened market participation. The logic is that moves on low volume are less reliable, so we wait for increased activity to confirm potential trend changes.
MACD Momentum Shift:
We incorporate MACD crossovers to determine when momentum is changing direction. MACD is a popular momentum indicator that identifies shifts in trend by comparing short-term and long-term EMAs. A bullish crossover (MACD line crossing above the signal line) may suggest upward momentum is building, while a bearish crossunder can indicate momentum turning downward.
RSI Market Condition Check:
RSI helps us identify overbought or oversold conditions. By requiring that RSI be oversold on buy signals and overbought on sell signals, we attempt to pinpoint entries where price could be at an extreme. The idea is to position entries or exits at junctures where price may be due for a reversal.
How the Script Works Together:
Volume Confirmation: No signals fire unless there’s strong volume. This reduces false positives.
MACD Momentum Check: Once volume confirms market interest, MACD crossover events serve as a trigger to initiate consideration of a trade signal.
RSI Condition: Finally, RSI determines whether the market is at an extreme. This final layer helps ensure we only act on signals that have both momentum shift and a price at an extreme level, potentially increasing the reliability of signals.
Intended Use:
This script can help highlight potential reversal points or trend shifts during active market periods.
Traders can use these signals as a starting point for deeper analysis. For instance, a “BUY” arrow may prompt a trader to investigate the market context, confirm with other methods, or look for patterns that further support a long entry.
The script is best used on markets with reliable volume data, such as stocks or futures, and can be experimented with across different timeframes. Adjusting the RSI thresholds, MACD parameters, and volume multiplier can help tailor it to specific instruments or trading styles.
Chart Setup:
When adding this script to your chart, it should be the only indicator present, so you can clearly see the red “BUY” arrows and green “SELL” arrows at the candle closes where signals occur.
The chart should be kept clean and uncluttered for clarity. No other indicators are necessary since the logic is already integrated into this single script.
R-based Strategy Template [Daveatt]Have you ever wondered how to properly track your trading performance based on risk rather than just profits?
This template solves that problem by implementing R-multiple tracking directly in TradingView's strategy tester.
This script is a tool that you must update with your own trading entry logic.
Quick notes
Before we dive in, I want to be clear: this is a template focused on R-multiple calculation and visualization.
I'm using a basic RSI strategy with dummy values just to demonstrate how the R tracking works. The actual trading signals aren't important here - you should replace them with your own strategy logic.
R multiple logic
Let's talk about what R-multiple means in practice.
Think of R as your initial risk per trade.
For instance, if you have a $10,000 account and you're risking 1% per trade, your 1R would be $100.
A trade that makes twice your risk would be +2R ($200), while hitting your stop loss would be -1R (-$100).
This way of measuring makes it much easier to evaluate your strategy's performance regardless of account size.
Whenever the SL is hit, we lose -1R
Proof showing the strategy tester whenever the SL is hit: i.imgur.com
The magic happens in how we calculate position sizes.
The script automatically determines the right position size to risk exactly your specified percentage on each trade.
This is done through a simple but powerful calculation:
risk_amount = (strategy.equity * (risk_per_trade_percent / 100))
sl_distance = math.abs(entry_price - sl_price)
position_size = risk_amount / (sl_distance * syminfo.pointvalue)
Limitations with lower timeframe gaps
This ensures that if your stop loss gets hit, you'll lose exactly the amount you intended to risk. No more, no less.
Well, could be more or less actually ... let's assume you're trading futures on a 15-minute chart but in the 1-minute chart there is a gap ... then your 15 minute SL won't get filled and you'll likely to not lose exactly -1R
This is annoying but it can't be fixed - and that's how trading works anyway.
Features
The template gives you flexibility in how you set your stop losses. You can use fixed points, ATR-based stops, percentage-based stops, or even tick-based stops.
Regardless of which method you choose, the position sizing will automatically adjust to maintain your desired risk per trade.
To help you track performance, I've added a comprehensive statistics table in the top right corner of your chart.
It shows you everything you need to know about your strategy's performance in terms of R-multiples: how many R you've won or lost, your win rate, average R per trade, and even your longest winning and losing streaks.
Happy trading!
And remember, measuring your performance in R-multiples is one of the most classical ways to evaluate and improve your trading strategies.
Daveatt
ATR SL Band (No-Repaint, Multi-Timeframe) + Risk per ContractThis indicator draws a non-repainting band for ATR-based Stoploss placement.
If used on Futures, it shows the distance + risk from the previous candle close, as well as from the current price.
The risk value is automatically calculated for the following symbols:
(Micro) ES (S&P 500)
(Micro) NQ (NASDAQ 100)
(Micro) YM (Dow Jones Industrial Average / US30)
The timeframe can be set individually. It is not recommended to use a lower timeframe than the chart timeframe as values differ from the actual timeframe's ATR SL in this case.
Price Action Dynamics Oscillator (PADO)1 minute ago
Price Action Dynamics Oscillator (PADO)
Indicator Overview and Technical Deep Dive
Concept and Philosophy
The Price Action Dynamics Oscillator (PADO) is a sophisticated technical analysis tool designed to provide multi-dimensional insights into market behavior by decomposing price action into manipulation and distribution metrics. The indicator goes beyond traditional momentum or trend indicators by introducing a nuanced approach to understanding market microstructure.
Key Architectural Components
1. Timeframe and Depth Selection
Pivot Depth Options:
Short Term (Length: 12 periods)
Intermediate Term (Length: 20 periods)
Long Term (Length: 100 periods)
This flexible configuration allows traders to adapt the indicator's sensitivity to different market conditions and trading styles.
2. Core Calculation Methodology
Manipulation Metrics
Calculates manipulation differently for green (bullish) and red (bearish) candles
Normalized against Average True Range (ATR) for consistent comparison across different volatility environments
Green Candle Manipulation: (Open - Low) / ATR
Red Candle Manipulation: (High - Open) / ATR
Distribution Metrics
Measures the directional strength and potential momentum shift
Green Candle Distribution: (Close - Open)
Red Candle Distribution: (Open - Close)
3. Normalization and Smoothing
Uses Simple Moving Average (SMA) for smoothing
Dynamic length calculation based on price range distance
Ensures minimum SMA length of 2 to prevent calculation errors
Unique Features
Visualization Toggles
Traders can selectively display:
Manipulation data
Distribution data
Long-term reference lines
Valuation metrics
Strategy signals
Valuation Comparative Analysis
Compares current manipulation and distribution metrics to 1000-bar long-term averages
Color-coded visualization for quick interpretation
Blue: Manipulation above average
Purple: Manipulation below average
Orange: Distribution above average
Yellow: Distribution below average
Strategy Deployment
Generates a composite strategy signal by comparing manipulation and distribution valuations
Uses Exponential Moving Average (EMA) for smoother signal generation
Incorporates volatility bands for context-aware signal interpretation
Quadrant Analysis
Classifies market state into four quadrants based on manipulation and distribution valuations:
Q1: Low Manipulation, High Distribution
Q2: High Manipulation, High Distribution
Q3: Low Manipulation, Low Distribution
Q4: High Manipulation, Low Distribution
Each quadrant is color-coded to provide visual market state representation.
Warning Signals
Manipulation Warning: When strategy crosses below low volatility band
Distribution Warning: When strategy crosses above high volatility band
Visual Indicators
Bar coloration based on strategy momentum
Multiple color states representing different market dynamics
Recommended Use Cases
Intraday and swing trading
Multi-timeframe market analysis
Volatility and momentum assessment
Trend reversal and continuation identification
Potential Limitations
Complexity might require significant trader education
Performance can vary across different market conditions
Requires careful parameter optimization
Recommended Settings
Best used on liquid markets with clear price action
Ideal for:
Forex
Futures
Large-cap stocks
Cryptocurrency pairs
Customization and Optimization
Traders should:
Backtest across multiple assets
Adjust timeframe settings
Calibrate visualization toggles
Use in conjunction with other technical indicators
Licensing
Mozilla Public License 2.0
Open-source and modification-friendly
Conclusion
The PADO represents an advanced approach to market analysis, blending traditional technical analysis with innovative metrics for deeper market understanding.
PADO Quadrant Color Analysis: Deep Dive
Quadrant Color Scheme Breakdown
Quadrant 1: Lime Green Background (RGB: 0, 255, 21, 90)
Condition: val_manip < 1 AND val_distr > 1
Market Interpretation:
Low Manipulation Pressure
High Distribution Activity
Potential Scenario:
Smart money might be gradually distributing positions
Trading Implications:
Caution for current trend followers
Potential preparation for trend change
Increased probability of consolidation or reversal
Quadrant 2: Bright Blue Background (RGB: 0, 191, 255, 90)
Condition: val_manip > 1 AND val_distr > 1
Market Interpretation:
High Manipulation Pressure
High Distribution Activity
Potential Scenario:
Strong institutional involvement
Potential market transition phase
Significant volume and momentum
Trading Implications:
High volatility expected
Increased market uncertainty
Potential for sharp price movements
Requires careful risk management
Quadrant 3: Light Gray Background (RGB: 252, 252, 252, 90)
Condition: val_manip < 1 AND val_distr < 1
Market Interpretation:
Low Manipulation Pressure
Low Distribution Activity
Potential Scenario:
Market consolidation
Reduced institutional activity
Potential low-volatility period
Trading Implications:
Range-bound market
Reduced trading opportunities
Potential setup for future breakout
Ideal for mean reversion strategies
Quadrant 4: Light Yellow Background (Hex: #f6ff0019)
Condition: val_manip > 1 AND val_distr < 1
Market Interpretation:
High Manipulation Pressure
Low Distribution Activity
Potential Scenario:
Accumulation of positions
Trading Implications:
Increased probability of directional move soon
Color Psychology and Technical Significance
Color Selection Rationale
Lime Green (Q1): Represents potential growth and transition
Bright Blue (Q2): Signifies high energy and institutional activity
Light Gray (Q3): Indicates neutrality and consolidation
Transparent Green (Q4): Suggests emerging trend potential
Advanced Interpretation Guidelines
Color Transition Analysis
Observe how the quadrant colors change
Rapid color shifts might indicate:
Market regime changes
Shifts in institutional sentiment
Potential trend acceleration or reversal
Technical Implementation Notes
Calculation Snippet
pinescriptCopyq1 = (val_manip < 1) and (val_distr > 1)
q2 = (val_manip > 1) and (val_distr > 1)
q3 = (val_manip < 1) and (val_distr < 1)
q4 = (val_manip > 1) and (val_distr < 1)
bgcolor(q1 ? color.rgb(0, 255, 21, 90):
q2 ? color.rgb(0, 191, 255, 90):
q3 ? color.rgb(252, 252, 252, 90):
q4 ? #f6ff0019:na)
Alpha Channel (Transparency)
90 and 0x19 values ensure background color doesn't overwhelm chart
Allows underlying price action to remain visible
Subtle visual cue without significant chart obstruction
Practical Trading Recommendations
Never Trade Solely on Quadrant Colors
Use as a complementary analysis tool
Combine with other technical and fundamental indicators
Timeframe Considerations
Validate quadrant signals across multiple timeframes
Longer timeframes provide more reliable signals
Risk Management
Set appropriate stop-loss levels
Use position sizing strategies
Be prepared for false signals
Recommended Workflow
Identify current quadrant
Assess overall market context
Confirm with other indicators
Execute with proper risk management
Roman's Ranges(GOLD FUTURES)This indicator provides the user with Gold Future's previous day’s range and how long it took for the price to reach its first extreme for the day. This information is used to predict the most probable daily direction trend and estimate how long you should expect to hold your winning trade. The distance and time are based on the market open candle (6:30 am). It measures from the retracement wick of the candle to the last 5m close of the day’s first extreme low or high point. It also includes that distance in pts.
Previous market data does not guarantee future results, however, you can leverage the knowledge of the previous day’s ranges to set reasonable take profit levels and when your target is not met automatically, you know how long it took on the previous day to reach the day’s first low/high. If you are nearing that amount of time and your trade is not as profitable as expected, it is easier to get out with less profits using this estimated time rather than hoping the market closes in your favor.
Markets go through cycles and it can be difficult to trade them all if you have a fault expectation how how far the price is expected to move. Price tends to deviate slowly from the average ranges slightly day after day, but you can expect an average range to prevail throughout the week +/- 3 points. It can be very easy to be stuck on 5-point take-profit levels that you don’t pay attention to the average range being twice or three times that distance. The same can be said for the opposite scenario with having higher profit expectations than reasonably possible.
This indicator and my statements are not financial advice. This is meant for educational purposes only.
Spreads between contractsA simple indicator that automatically calculates and charts the difference between the nearby futures contract (1!) and the next contract (2!), enabling contango and backwardation analysis. If needed, any two contracts can also be manually entered.
New Day [UkutaLabs]█ OVERVIEW
The New Day indicator is a useful trading tool that automatically identifies the first bar of each trading day for the user’s convenience.
█ USAGE
At the beginning of each trading day, this indicator will automatically create a line that will display the first bar of the trading day. This is a useful way to visualize where each day begins and ends.
When this indicator is used on a stock or futures chart, the first bar of the session will be identified as the first bar of the trading day. If this indicator is used on crypto or forex charts, which are tradable for 24 hours, the indicator will identify the bar closest to midnight as the first bar of the trading day.
█ SETTINGS
Configuration
• Line Color: This setting allows the user to determine the color of the New Day line.
• Line Width: This setting allows the user to determine the width of the New Day line.
• Line Style: This setting allows the user to determine the style of the New Day line.
Vexly_ML_levelsProvide a number into each box (start), (middle), (end)
this is for a buy zone, mid zone, sell zone.
This is mainly geared towards futures and is just a box drawing script.
There is no inherent alpha in this.
We use this to draw our own levels.
Watchlist & Symbols Distribution [Daveatt]TLDR;
I got bored so I just coded the TradingView watchlist interface in Pinescript :)
TLDR 2:
Sharing it open-source what took me 1 full day to code - haven't coded in Pinescript in a long time, so I'm a bit slow for now :)
█ OVERVIEW
This script offers a comprehensive market analysis tool inspired by TradingView's native watchlist interface features.
It combines an interactive watchlist with powerful distribution visualization capabilities and a performance comparison panel.
The script was developed with a focus on providing multiple visualization methods while working within PineScript's limitations.
█ DEVELOPMENT BACKGROUND
The pie chart implementation was greatly inspired by the ( "Crypto Map Dashboard" script / )
adapting its circular visualization technique to create dynamic distribution charts. However, due to PineScript's 500-line limitation per script, I had to optimize the code to allow users to switch between pie chart analysis and performance comparison modes rather than displaying both simultaneously.
█ SETUP AND DISPLAY
For optimal visualization, users need to adjust the chart's display settings manually.
This involves:
Expanding the indicator window vertically to accommodate both the watchlist and graphical elements
Adjusting the Y-axis scale by dragging it to ensure proper spacing for the comparison panel grid
Modifying the X-axis scale to achieve the desired time window display
Fine-tuning these adjustments whenever switching between pie chart and comparison panel modes
These manual adjustments are necessary due to PineScript's limitations in controlling chart scaling programmatically. While this requires some initial setup, it allows users to customize the display to their preferred viewing proportions.
█ MAIN FEATURES
Distribution Analysis
The script provides three distinct distribution visualization modes through a pie chart.
Users can analyze their symbols by exchanges, asset types (such as Crypto, Forex, Futures), or market sectors.
If you can't see it well at first, adjust your chart scaling until it's displayed nicely.
Asset Exchanges
www.tradingview.com
Asset Types
Asset Sectors
The pie charts feature an optional 3D effect with adjustable depth and angle parameters. To enhance visual customization, four different color schemes are available: Default, Pastel, Dark, and Neon.
Each segment of the pie chart includes interactive tooltips that can be configured to show different levels of detail. Importantly, the pie chart only visualizes the distribution of selected assets (those marked with a checkmark in the watchlist), providing a focused view of the user's current interests.
Interactive Watchlist
The watchlist component displays real-time data for up to 10 user-defined symbols. Each entry shows current price, price changes (both absolute and percentage), volume metrics, and a comparison toggle.
The table is dynamically updated and features color-coded entries that correspond to their respective performance lines in the comparison chart. The watchlist serves as both an information display and a control panel for the comparison feature.
Performance Comparison
One of the script's most innovative features is its performance comparison panel.
Using polylines for smooth visualization, it tracks the 30-day performance of selected symbols relative to a 0% baseline.
The comparison chart includes a sophisticated grid system with 5% intervals and a dynamic legend showing current performance values.
The polyline implementation allows for fluid, continuous lines that accurately represent price movements, providing a more refined visual experience than traditional line plots. Like the pie charts, the comparison panel only displays performance lines for symbols that have been selected in the watchlist, allowing users to focus on their specific assets of interest.
█ TECHNICAL IMPLEMENTATION
The script utilizes several advanced PineScript features:
Dynamic array management for symbol tracking
Polyline-based charting for smooth performance visualization
Real-time data processing with security calls
Interactive tooltips and labels
Optimized drawing routines to maintain performance
Selective visualization based on user choices
█ CUSTOMIZATION
Users can personalize almost every aspect of the script:
Symbol selection and comparison preferences
Visual theme selection with four distinct color schemes
Pie chart dimensions and positioning
Tooltip information density
Component visibility toggles
█ LIMITATIONS
The primary limitation stems from PineScript's 500-line restriction per script.
This constraint necessitated the implementation of a mode-switching system between pie charts and the comparison panel, as displaying both simultaneously would exceed the line limit. Additionally, the script relies on manual chart scale adjustments, as PineScript doesn't provide direct control over chart scaling when overlay=false is enabled.
However, these limitations led to a more focused and efficient design approach that gives users control over their viewing experience.
█ CONCLUSION
All those tools exist in the native TradingView watchlist interface and they're better than what I just did.
However, now it exists in Pinescript... so I believe it's a win lol :)
Globex Trap ZoneGlobex Trap Indicator
A powerful tool designed to identify potential trading opportunities by analyzing the relationship between Globex session ranges and Supply & Demand zones during regular trading hours.
Key Features
Tracks and visualizes Globex session price ranges
Identifies key Supply & Demand zones during regular trading hours
Highlights potential trap areas where price might experience significant reactions
Fully customizable time ranges and visual settings
Clear labeling of Globex highs and lows
How It Works
The indicator tracks two key periods:
Globex Session (Default: 6:00 PM - 9:30 AM)
Monitors overnight price action
Marks session high and low
Helps identify potential range breakouts
Supply & Demand Zone (Default: 8:00 AM - 11:00 AM)
Tracks price action during key market hours
Identifies potential reaction zones
Helps spot institutional trading areas
Best Practices for Using This Indicator
Use on 1-hour timeframe or lower for optimal visualization
Best suited for futures and other instruments traded during Globex sessions
Pay attention to areas where Globex range and Supply/Demand zones overlap
Use in conjunction with your existing trading strategy for confirmation
Recommended minimum of 10 days of historical data for context
Settings Explanation
Globex Session: Customizable time range for overnight trading session
Supply & Demand Zone: Adjustable time range for regular trading hours
Days to Look Back: Number of historical days to display (default: 10)
Visual Settings: Customizable colors and transparency for both zones
Important Notes
All times are based on exchange timezone
The indicator respects overnight sessions and properly handles timezone transitions
Historical data requirements: Minimum 10 days recommended
Performance impact: Optimized for smooth operation with minimal resource usage
Disclaimer
Past performance is not indicative of future results. This indicator is designed to be used as part of a comprehensive trading strategy and should not be relied upon as the sole basis for trading decisions.
Updates and Support
I actively maintain this indicator and welcome feedback from the trading community. Please feel free to leave comments or suggestions for improvements.
The Pattern-Synced Moving Average System (PSMA)Description:
The Pattern-Synced Moving Average System (PSMA) is a comprehensive trading indicator that combines the reliability of moving averages with automated candlestick pattern detection, real-time alerts, and dynamic risk management to enhance both trend-following and reversal strategies. The PSMA system integrates key elements of trend analysis and pattern recognition to provide users with configurable entry, stop-loss, and take-profit levels. It is designed for all levels of traders who seek to trade in alignment with market context, using signals from trend direction and established candlestick patterns.
Key Functional Components:
Multi-Type Moving Average:
Provides flexibility with multiple moving average options: SMA, EMA, WMA, and SMMA.
The selected moving average helps users determine market trend direction, with price positions relative to the MA acting as a trend confirmation.
Automatic Candlestick Pattern Detection:
Identifies pivotal patterns, including bullish/bearish engulfing and reversal signals.
Helps traders spot potential market turning points and adjust their strategies accordingly.
Configurable Entry, Stop-Loss, and Take-Profit:
Risk management is customizable through risk/reward ratios and risk tolerance settings.
Entry, stop-loss, and take-profit levels are automatically plotted when patterns appear, facilitating rapid trade decision-making with predefined exit points.
Higher Timeframe Trend Confirmation:
Optional feature to verify trend alignment on a higher timeframe (e.g., checking a daily trend on an intraday chart).
This added filter improves signal reliability by focusing on patterns aligned with the broader market trend.
Real-Time Alerts:
Alerts can be set for key pattern detections, allowing traders to respond promptly without constant chart monitoring.
How to Use PSMA:
Set Moving Average Preferences:
Choose the preferred moving average type and length based on your trading strategy. The MA acts as a foundational trend indicator, with price positions indicating potential uptrends (price above MA) or downtrends (price below MA).
Adjust Risk Management Settings:
Set a Risk/Reward Ratio for defining take-profit levels relative to the entry and stop-loss levels.
Modify the Risk Tolerance Percentage to adjust stop-loss placement, adding flexibility in managing trades based on market volatility.
Activate Higher Timeframe Confirmation (Optional):
Enable higher timeframe trend confirmation to filter out counter-trend trades, ensuring that detected patterns are in sync with the larger market trend.
Review Alerts and Trade Levels:
With PSMA’s real-time alerts, traders receive notifications for detected patterns without having to continuously monitor charts.
Visualized entry, stop-loss, and take-profit lines simplify trade execution by highlighting levels directly on the chart.
Execute Based on Entry and Exit Levels:
The entry line suggests the potential entry price once a bullish or bearish pattern is detected.
The stop-loss line is based on your set risk tolerance, establishing a predefined risk level.
The take-profit line is calculated according to your preferred risk/reward ratio, providing a clear profit target.
Example Strategy:
Ensure price is above or below the selected moving average to confirm trend direction.
Await a PSMA signal for a bullish or bearish pattern.
Review the plotted entry, stop-loss, and take-profit lines, and enter the trade if the setup aligns with your risk/reward criteria.
Activate alerts for continuous monitoring, allowing PSMA to notify you of emerging trade opportunities.
Release Notes:
Line Color and Style Customization: Customizable colors and line styles for entry, stop-loss, and take-profit levels.
Dynamic Trade Tracking: Tracks trade statistics, including total trades, win rate, and average P/L, displayed in the data window for comprehensive trade performance analysis.
Summary: The PSMA indicator is a powerful, user-friendly tool that combines trend detection, pattern recognition, and risk management into a cohesive system for improved trade decision-making. Suitable for stocks, forex, and futures, PSMA offers a unique blend of adaptability and precision, making it valuable for day traders and long-term investors alike. Enjoy this tool as it enhances your ability to execute timely, well-informed trades on TradingView.
Daily Volatility Limit Channel
Hello, this is the simplest yet most powerful tool I have discovered regarding volatility. Using the ATR17 value based on a 4-hour timeframe, this tool displays the most significant volatility thresholds for the day, clearly showing when strong trends occur as these boundaries are breached. Once a boundary is crossed, the price of Bitcoin (as well as other actively traded asset classes like stocks and futures) tends to continue moving in the direction of the breakout. If the price reaches a boundary but fails to break through, this point often becomes the lowest point of pullback or correction, effectively serving as a pivot point and the optimal entry for buying.
The indicator features color and arrow options, enhancing your trading experience. The arrows appear below the candles when the trend changes to an upward impulse and above the candles when it shifts to a downward impulse. This visual aid allows traders to quickly identify trend reversals and make informed decisions.
In summary, this tool effectively highlights volatility limits and trend reversals, making it a valuable asset for any trader looking to navigate the market efficiently.
This indicator is recommended for use on 2-hour or 4-hour candlestick charts. These timeframes allow for clearer visualization of volatility and help effectively identify strong trends and volatility boundaries.
안녕하세요. 이것은 변동성에 관해 제가 발견한 것 중 가장 심플하고도 강력한 툴입니다. 4시간 기준의 ATR17값을 사용한 이 툴은 당일의 가장 강력한 변동성 한계점을 보여주며, 이 변동성 경계가 돌파될 때 강한 추세가 일어나는 것을 명확히 보여줍니다. 한 번 경계가 돌파되면 비트코인 가격(그리고 주식, 선물 등 다른 대부분의 모든 가격을 가지고 활발하게 거래되는 자산군)은 해당 돌파 쪽의 트렌드로 계속 움직이는 경향이 있습니다. 만약 가격이 경계에 도달한 채로 이 경계를 돌파하지 못할 때는 이 자리가 눌림과 조정의 최저점, 즉 피봇 포인트가 되어 매수의 최적 지점이 되는 것을 보실 수 있습니다.
지표에는 컬러 옵션과 화살표 옵션이 있어 거래 경험을 향상시킵니다. 트렌드가 상승 임펄스로 변경될 때 화살표가 캔들 아래에 나타나고, 하락 임펄스로 변경될 때는 캔들 위에 나타납니다. 이 시각적 도구는 트렌드 반전을 빠르게 식별할 수 있도록 도와주어, 거래자들이 정보에 기반한 결정을 내리는 데 유용합니다.
요약하자면, 이 툴은 변동성 한계와 트렌드 반전을 효과적으로 강조하여, 시장을 효율적으로 탐색하려는 모든 거래자에게 가치 있는 자산이 될 것입니다.
이 지표는 2시간 또는 4시간 캔들 차트에서 사용하는 것이 권장됩니다. 이러한 시간대는 지표의 변동성을 보다 명확하게 시각화하며, 강한 추세와 변동성 한계점을 효과적으로 식별하는 데 도움을 줍니다.
ICT Open Range Gap & 1st FVG (fadi)In his 2024 mentorship program, ICT detailed how price action interacts with Open Range Gaps and the initial 1-minute Fair Value Gap following the market open at 9:30 AM.
What is an Open Range Gap?
An Open Range Gap occurs when the market opens at 9:30 AM at a higher or lower level compared to the previous day's close at 4:14 PM, primarily relevant in futures trading. According to ICT, there is a statistical probability of 70% that the price action will close 50% or more of the Open Range Gap within the first 30 minutes of trading (9:30 AM to 10:00 AM).
What is the First 1-Minute Fair Value Gap?
ICT places significant emphasis on the first 1-minute Fair Value Gap (FVG) that forms after the market opens at 9:30 AM. The FVG must occur at 9:31 AM or later to be considered valid. This gap often presents key opportunities for traders, as it represents a temporary imbalance between supply and demand that the market seeks to correct.
Understanding and leveraging these patterns can enhance trading strategies by offering insights into potential price movements shortly after market open.
ICT Open Range Gap & 1st FVG
This indicator is engineered to identify and highlight the Open Range Gaps and the first 1-minute Fair Value Gap. Furthermore, it functions across multiple timeframes, from seconds to hours, catering to various trading preferences. This flexibility is particularly beneficial for traders who favor higher timeframes or wish to observe these patterns' application at broader intervals.
Settings
The Open Range Gap indicator offers flexible display settings. It identifies the quadrants and provides optional color coding to distinguish them. Additionally, it tracks the "fill" level to visualize how far the price action has progressed into the gap, enhancing traders' ability to monitor and analyze price movements effectively. By default, the Open Range Gap will stop extending at 10:00 AM; however, there is an option to continue extending until the end of the trading day.
The 1st Fair Value Gap (FVG) can be viewed on any timeframe the indicator is active on, offering various styling options to match each trader's preferences. While the 1st FVG is particularly relevant to the day it is created, previous 1st FVGs within the same week may provide additional value. This indicator allows traders to extend Monday's 1st FVG, marking the first FVG of the week, or to extend all 1st FVGs throughout the week.
Advanced Multi-Seasonality StrategyThe Multi-Seasonality Strategy is a trading system based on seasonal market patterns. Seasonality refers to recurring market trends driven by predictable calendar-based events. These patterns emerge due to economic cycles, corporate activities (e.g., earnings reports), and investor behavior around specific times of the year. Studies have shown that such effects can influence asset prices over defined periods, leading to opportunities for traders who exploit these patterns (Hirshleifer, 2001; Bouman & Jacobsen, 2002).
How the Strategy Works:
The strategy allows the user to define four distinct periods within a calendar year. For each period, the trader selects:
Entry Date (Month and Day): The date to enter the trade.
Holding Period: The number of trading days to remain in the trade after the entry.
Trade Direction: Whether to take a long or short position during that period.
The system is designed with flexibility, enabling the user to activate or deactivate each of the four periods. The idea is to take advantage of seasonal patterns, such as buying during historically strong periods and selling during weaker ones. A well-known example is the "Sell in May and Go Away" phenomenon, which suggests that stock returns are higher from November to April and weaker from May to October (Bouman & Jacobsen, 2002).
Seasonality in Financial Markets:
Seasonal effects have been documented across different asset classes and markets:
Equities: Stock markets tend to exhibit higher returns during certain months, such as the "January effect," where prices rise after year-end tax-loss selling (Haugen & Lakonishok, 1987).
Commodities: Agricultural commodities often follow seasonal planting and harvesting cycles, which impact supply and demand patterns (Fama & French, 1987).
Forex: Currency pairs may show strength or weakness during specific quarters based on macroeconomic factors, such as fiscal year-end flows or central bank policy decisions.
Scientific Basis:
Research shows that market anomalies like seasonality are linked to behavioral biases and institutional practices. For example, investors may respond to tax incentives at the end of the year, and companies may engage in window dressing (Haugen & Lakonishok, 1987). Additionally, macroeconomic factors, such as monetary policy shifts and holiday trading volumes, can also contribute to predictable seasonal trends (Bouman & Jacobsen, 2002).
Risks of Seasonal Trading:
While the strategy seeks to exploit predictable patterns, there are inherent risks:
Market Changes: Seasonal effects observed in the past may weaken or disappear as market conditions evolve. Increased algorithmic trading, globalization, and policy changes can reduce the reliability of historical patterns (Lo, 2004).
Overfitting: One of the risks in seasonal trading is overfitting the strategy to historical data. A pattern that worked in the past may not necessarily work in the future, especially if it was based on random chance or external factors that no longer apply (Sullivan, Timmermann, & White, 1999).
Liquidity and Volatility: Trading during specific periods may expose the trader to low liquidity, especially around holidays or earnings seasons, leading to slippage and larger-than-expected price swings.
Economic and Geopolitical Shocks: External events such as pandemics, wars, or political instability can disrupt seasonal patterns, leading to unexpected market behavior.
Conclusion:
The Multi-Seasonality Strategy capitalizes on the predictable nature of certain calendar-based patterns in financial markets. By entering and exiting trades based on well-established seasonal effects, traders can potentially capture short-term profits. However, caution is necessary, as market dynamics can change, and seasonal patterns are not guaranteed to persist. Rigorous backtesting, combined with risk management practices, is essential to successfully implementing this strategy.
References:
Bouman, S., & Jacobsen, B. (2002). The Halloween Indicator, "Sell in May and Go Away": Another Puzzle. American Economic Review, 92(5), 1618-1635.
Fama, E. F., & French, K. R. (1987). Commodity Futures Prices: Some Evidence on Forecast Power, Premiums, and the Theory of Storage. Journal of Business, 60(1), 55-73.
Haugen, R. A., & Lakonishok, J. (1987). The Incredible January Effect: The Stock Market's Unsolved Mystery. Dow Jones-Irwin.
Hirshleifer, D. (2001). Investor Psychology and Asset Pricing. Journal of Finance, 56(4), 1533-1597.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Sullivan, R., Timmermann, A., & White, H. (1999). Data-Snooping, Technical Trading Rule Performance, and the Bootstrap. Journal of Finance, 54(5), 1647-1691.
This strategy harnesses the power of seasonality but requires careful consideration of the risks and potential changes in market behavior over time.
Statistical ArbitrageThe Statistical Arbitrage Strategy, also known as pairs trading, is a quantitative trading method that capitalizes on price discrepancies between two correlated assets. The strategy assumes that over time, the prices of these two assets will revert to their historical relationship. The core idea is to take advantage of mean reversion, a principle suggesting that asset prices will revert to their long-term average after deviating significantly.
Strategy Mechanics:
1. Selection of Correlated Assets:
• The strategy focuses on two historically correlated assets (e.g., equity index futures like Dow Jones Mini and S&P 500 Mini). These assets tend to move in the same direction due to similar underlying fundamentals, such as overall market conditions. By tracking their relative prices, the strategy seeks to exploit temporary mispricings.
2. Spread Calculation:
• The spread is the difference between the prices of the two assets. This spread represents the relationship between the assets and serves as the basis for determining when to enter or exit trades.
3. Mean and Standard Deviation:
• The historical average (mean) of the spread is calculated using a Simple Moving Average (SMA) over a chosen period. The strategy also computes the standard deviation (volatility) of the spread, which measures how far the spread has deviated from the mean over time. This allows the strategy to define statistically significant price deviations.
4. Entry Signal (Mean Reversion):
• A buy signal is triggered when the spread falls below the mean by a multiple (e.g., two) of the standard deviation. This indicates that one asset is temporarily undervalued relative to the other, and the strategy expects the spread to revert to its mean, generating profits as the prices converge.
5. Exit Signal:
• The strategy exits the trade when the spread reverts to the mean. At this point, the mispricing has been corrected, and the profit from the mean reversion is realized.
Academic Support:
Statistical arbitrage has been widely studied in finance and economics. Gatev, Goetzmann, and Rouwenhorst’s (2006) landmark study on pairs trading demonstrated that this strategy could generate excess returns in equity markets. Their research found that by focusing on historically correlated stocks, traders could identify pricing anomalies and profit from their eventual correction.
Additionally, Avellaneda and Lee (2010) explored statistical arbitrage in different asset classes and found that exploiting deviations in price relationships can offer a robust, market-neutral trading strategy. In these studies, the strategy’s success hinges on the stability of the relationship between the assets and the timely execution of trades when deviations occur.
Risks of Statistical Arbitrage:
1. Correlation Breakdown:
• One of the primary risks is the breakdown of correlation between the two assets. Statistical arbitrage assumes that the historical relationship between the assets will hold in the future. However, market conditions, company fundamentals, or external shocks (e.g., macroeconomic changes) can cause these assets to deviate permanently, leading to potential losses.
• For instance, if two equity indices historically move together but experience divergent economic conditions or policy changes, their prices may no longer revert to the expected mean.
2. Execution Risk:
• This strategy relies on efficient execution and tight spreads. In volatile or illiquid markets, the actual price at which trades are executed may differ significantly from expected prices, leading to slippage and reduced profits.
3. Market Risk:
• Although statistical arbitrage is designed to be market-neutral (i.e., not dependent on the overall market direction), it is not entirely risk-free. Systematic market shocks, such as financial crises or sudden shifts in market sentiment, can affect both assets simultaneously, causing the spread to widen rather than revert to the mean.
4. Model Risk:
• The assumptions underlying the strategy, particularly regarding mean reversion, may not always hold true. The model assumes that asset prices will return to their historical averages within a certain timeframe, but the timing and magnitude of mean reversion can be uncertain. Misestimating this timeframe can lead to extended drawdowns or unrealized losses.
5. Overfitting:
• Over-reliance on historical data to fine-tune the strategy parameters (e.g., the lookback period or standard deviation thresholds) may result in overfitting. This means that the strategy works well on past data but fails to perform in live markets due to changing conditions.
Conclusion:
The Statistical Arbitrage Strategy offers a systematic and quantitative approach to trading that capitalizes on temporary price inefficiencies between correlated assets. It has been proven to generate returns in academic studies and is widely used by hedge funds and institutional traders for its market-neutral characteristics. However, traders must be aware of the inherent risks, including correlation breakdown, execution risks, and the potential for prolonged deviations from the mean. Effective risk management, diversification, and constant monitoring are essential for successfully implementing this strategy in live markets.
SMT Divergences [OutOfOptions]Smart Money Technique (SMT) Divergence is designed to identify discrepancies between correlated assets within the same timeframe. It occurs when two related assets exhibit opposing signals, such as one forming a higher low while the other forms a lower low. This technique is particularly useful for anticipating market shifts or reversals before they become evident through other Premium Discount (PD) Arrays.
This indicator works by identifying the highs and lows that have formed for an asset on the current chart and the correlated symbol defined in the settings. Once a pivot on either asset is formed, it checks if the pivot has taken liquidity as identified by the previous pivot in the same direction (i.e., a new high taking out a previous high). If this is the case and the corresponding asset has not taken a similar pivot, the condition is determined to be a potential valid divergence. The indicator will then filter out SMTs formed by adjacent candles, requiring at least one candle difference between the candles forming the SMT.
If the “Candle Direction Validation” setting is enabled, the indicator will further check both assets to ensure that for bullish SMTs, the last high on both assets was formed by down candle, and for bearish SMTs, the low was formed by an up candle. This check can often eliminate low-probability SMTs that are frequently broken.
The referenced chart shows divergence between Nasdaq (NQ) and S&P 500 (ES) futures, which are normally closely correlated assets that move in the same direction. The lines shown represent bullish and bearish divergences between the two when they are formed. As you can see from the chart, SMT Divergences may not always indicate a reversal, or a reversal might be just a short-term retrace. Therefore, SMT Divergences should not be used independently. However, in conjunction with other PD arrays, they can provide strong confirmation of a change in market direction.
Configurability:
Pivot strength - Indicates how many bars to the left/right of a high for pivot to be considered, recommended to keep at 1 for maximum detection speed
Candle Direction Validation - Additional SMT validation to filter out weak/low-probability SMTs be examining candle direction
Line Styling for Bullish/Bearish SMTs - Ability to customize line style, color & width for bullish/bearish SMTs
Label Control - Whether or not to show SMT label and if shown what font size & color should be used
What makes this indicator different:
Unlike other SMT indicators, this indicators has additional built-in controls to remove low-probability SMTs
RSI/MFI Divergence Finder [idahodev]Monitoring RSI (Relative Strength Index) and MFI (Money Flow Index) divergences on a stock or index chart offers several benefits to traders and analysts. Let's break down the advantages:
Comprehensive Market View: Combining both indicators provides a more complete picture of market conditions, as they measure different aspects of price movement. RSI focuses on recent gains/losses relative to price change, while MFI incorporates volume data to assess money flow in and out of a security.
Enhanced Signal Accuracy: When divergences occur simultaneously in both RSI and MFI, it may be considered a stronger signal than if only one indicator showed divergence. This can potentially lead to more reliable trading decisions.
Identification of False Breakouts: Divergences between these indicators and price action can help identify false breakouts or misleading price movements that are not supported by underlying market strength or volume.
More Nuanced Market Understanding: By examining divergent behavior between money flow (MFI) and momentum (RSI), traders gain a more detailed comprehension of the interplay between these factors in shaping market trends.
Early Warning Signs: These divergences can act as early warning signs for potential trend reversals or changes in market sentiment, allowing traders to adjust their strategies proactively.
It's important to note that RSI/MFI divergences should be used as part of a broader trading strategy rather than solely relying on them for buy/sell signals. They can serve as valuable tools for confirming trends, identifying potential turning points, or warning against overbought/oversold conditions.
When using these indicators together, traders must be cautious of false signals, especially in choppy markets or during periods of high volatility. It's crucial to combine this analysis with other technical and fundamental factors before making trading decisions.
In summary, monitoring RSI/MFI divergences may offer a way to gain insights into the underlying strengths and weaknesses of market movements.
This utility differs from other in that it allows for a choke/threshold/sensitivity setting to help weed out noisy signals. This needs to be carefully adjusted per chart.
It also allows for tuning of the MFI smoothing length (number of bars on the current chart) as well as how many previous bars it will take into consideration when calculating RSI and MFI divergences. It will signal when it sees alignment forming between RSI and MFI divergences in a direction. You will likely need to tune this script's settings every few days or at least anytime there is a change in overall market behavior or sustained volatility.
Ultimately, the goal with this script is to provide an additional level of confirmation of weakness or strength. It should be combined with other indicators such as exhaustion, pivots, supply/demand, trendline breaks or tests, and structure changes, to name a few complementary tools or strategies. It's not meant to be a standalone buy/sell signal indicator!
Here are some settings for futures that may help you get started:
ES (4m chart)
RSI Length: 26
MFI Length: 8
MFI Smoothing Length: 32
Divergence Sensitivity: 124
Left Bars for Pivot: 10
Right Bars for Pivot: 1
NQ (4m chart)
RSI Length: 14
MFI Length: 14
MFI Smoothing Length: 21
Divergence Sensitivity: 400
Left Bars for Pivot: 21
Right Bars for Pivot: 1
YM (4m chart)
RSI Length: 14
MFI Length: 14
MFI Smoothing Length: 21
Divergence Sensitivity: 810
Left Bars for Pivot: 33
Right Bars for Pivot: 1
