Previous Day High/Low (8AM–4PM)A simple indicator for NQ and ES futures that marks the previous day high and low on the current trading day excluding premarket.
Cerca negli script per "Futures"
Session Status Table📌 Session Status Table
Session Status Table is an indicator that displays the real-time status of the four major trading sessions:
* 🇯🇵 Asia (Tokyo)
* 🇬🇧 London
* 🇺🇸 New York AM
* 🇺🇸 New York PM
It shows which sessions are currently open, how much time remains until they open or close, and optionally sends alerts in advance.
🧩 Features:
* Real-time session table — shows the status of each session on the chart.
* Color-coded statuses:
* 🟢 Green – Session is open
* 🔴 Red – Session is closed
* ⚪ Gray – Weekend
* Countdown timers until session open or close.
* User alerts — receive a notification a custom number of minutes before a session starts.
⚙️ Customization:
* Table position — fully configurable.
* Session colors — customizable for open, closed, and weekend states.
* Session labels — customizable with icons.
* Notifications:
* Enabled through TradingView's Alerts panel.
* User-defined lead time before session opens.
🕒 Time Zones:
All times are calculated in UTC to ensure consistency across different markets and regions, avoiding discrepancies from time zones and daylight saving time.
🚨 How to enable alerts:
1. Open the "Alerts" panel in TradingView.
2. Click "Create Alert".
3. In the condition dropdown, choose "Session Status Table".
4. Set to any alert() trigger.
5. Save — you'll be notified a set number of minutes before each session begins.
ℹ️ Technical Notes:
* Built with Pine Script version 6.
* Logically divided into clear sections: inputs, session calculations, table rendering, and alerts.
* Optimized for performance and reliability on all timeframes.
Ideal for traders who use session activity in their strategies — especially in Forex, crypto, and futures markets.
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment
## 1. Introduction
Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985).
The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014).
## 2. Theoretical Foundation
### 2.1 Momentum Theory and Market Anomalies
The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015).
### 2.2 Drawdown Analysis and Risk Management
Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators.
### 2.3 Adaptive Smoothing and Market Regimes
The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets.
## 3. Methodology
### 3.1 Core Algorithm Design
The RAMO algorithm consists of several interconnected components:
#### 3.1.1 Risk-Adjusted Momentum Calculation
The fundamental innovation of RAMO lies in its risk adjustment mechanism:
Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor)
Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05)
This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952).
#### 3.1.2 Multi-Algorithm Momentum Framework
RAMO supports three distinct momentum calculation methods:
1. Rate of Change: Traditional percentage-based momentum (Pring, 2002)
2. Price Momentum: Absolute price differences
3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997)
This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013).
### 3.2 Leading Indicator Components
#### 3.2.1 Momentum Acceleration Analysis
The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes:
Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n)
This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969).
#### 3.2.2 Linear Regression Prediction
RAMO incorporates linear regression-based prediction to project momentum values forward:
Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset)
This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes.
#### 3.2.3 Volume-Based Exhaustion Detection
The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns:
Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20)
This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989).
### 3.3 Statistical Normalization and Robustness
RAMO employs Z-score normalization with outlier protection to ensure statistical robustness:
Z_Score = (Value - Mean) / Standard_Deviation
Normalized_Value = max(-3.5, min(3.5, Z_Score))
This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions.
### 3.4 Adaptive Threshold Calculation
Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992):
Upper_Threshold = Mean + (Multiplier × Standard_Deviation)
Lower_Threshold = Mean - (Multiplier × Standard_Deviation)
This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992).
## 4. Implementation Details
### 4.1 Adaptive Smoothing Algorithm
The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility:
Volatility_Percentile = Percentrank(Volatility, 100)
Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100)
This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995).
### 4.2 Risk Environment Classification
RAMO classifies market conditions into three risk environments:
- Low Risk: Current_DD < 30% × Max_DD
- Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD
- High Risk: Current_DD ≥ 70% × Max_DD
This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014).
## 5. Signal Generation and Interpretation
### 5.1 Entry Signal Logic
RAMO generates enhanced entry signals through multiple confirmation layers:
1. Primary Signal: Crossover between indicator and signal line
2. Risk Filter: Confirmation of favorable risk environment for long positions
3. Leading Component: Early warning signals via acceleration analysis
4. Exhaustion Filter: Volume-based reversal detection
This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992).
### 5.2 Divergence Analysis
RAMO incorporates both traditional and leading divergence detection:
- Traditional Divergence: Price and indicator divergence over 3-5 periods
- Slope Divergence: Momentum slope versus price direction
- Acceleration Divergence: Changes in momentum acceleration
This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999).
## 6. Empirical Advantages and Applications
### 6.1 Risk-Adjusted Performance
The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980).
### 6.2 Regime Awareness
RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments.
### 6.3 Institutional Applications
The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013).
## 7. Limitations and Future Research
### 7.1 Parameter Sensitivity
Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions.
### 7.2 Market Microstructure Considerations
RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance.
### 7.3 Transaction Cost Integration
Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004).
## References
Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons.
Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press.
Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin.
Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985.
Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764.
Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press.
Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58.
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247.
Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84.
Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall.
Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291.
Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill.
Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082.
Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91.
Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance.
Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library.
Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill.
Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press.
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790.
Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314.
Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239.
Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Henry Hub Verfallstermine 2025This Pine Script plots vertical lines on your TradingView chart at each 2025 Henry Hub Natural Gas futures expiry date. Each line:
Appears from top to bottom of the chart.
Is labeled with the exact expiry date (e.g., 2025-06-26).
Has a customizable color via the script settings.
This helps you visually track key contract rollover points directly on your chart.
Demand Index (Hybrid Sibbet) by TradeQUODemand Index (Hybrid Sibbet) by TradeQUO \
\Overview\
The Demand Index (DI) was introduced by James Sibbet in the early 1990s to gauge “real” buying versus selling pressure by combining price‐change information with volume intensity. Unlike pure price‐based oscillators (e.g. RSI or MACD), the DI highlights moves backed by above‐average volume—helping traders distinguish genuine demand/supply from false breakouts or low‐liquidity noise.
\Calculation\
\
\ \Step 1: Weighted Price (P)\
For each bar t, compute a weighted price:
```
Pₜ = Hₜ + Lₜ + 2·Cₜ
```
where Hₜ=High, Lₜ=Low, Cₜ=Close of bar t.
Also compute Pₜ₋₁ for the prior bar.
\ \Step 2: Raw Range (R)\
Calculate the two‐bar range:
```
Rₜ = max(Hₜ, Hₜ₋₁) – min(Lₜ, Lₜ₋₁)
```
This Rₜ is used indirectly in the exponential dampener below.
\ \Step 3: Normalize Volume (VolNorm)\
Compute an EMA of volume over n₁ bars (e.g. n₁=13):
```
EMA_Volₜ = EMA(Volume, n₁)ₜ
```
Then
```
VolNormₜ = Volumeₜ / EMA_Volₜ
```
If EMA\_Volₜ ≈ 0, set VolNormₜ to a small default (e.g. 0.0001) to avoid division‐by‐zero.
\ \Step 4: BuyPower vs. SellPower\
Calculate “raw” BuyPowerₜ and SellPowerₜ depending on whether Pₜ > Pₜ₋₁ (bullish) or Pₜ < Pₜ₋₁ (bearish). Use an exponential dampener factor Dₜ to moderate extreme moves when true range is small. Specifically:
• If Pₜ > Pₜ₋₁,
```
BuyPowerₜ = (VolNormₜ) / exp
```
otherwise
```
BuyPowerₜ = VolNormₜ.
```
• If Pₜ < Pₜ₋₁,
```
SellPowerₜ = (VolNormₜ) / exp
```
otherwise
```
SellPowerₜ = VolNormₜ.
```
Here, H₀ and L₀ are the very first bar’s High/Low—used to calibrate the scale of the dampening. If the denominator of the exponential is near zero, substitute a small epsilon (e.g. 1e-10).
\ \Step 5: Smooth Buy/Sell Power\
Apply a short EMA (n₂ bars, typically n₂=2) to each:
```
EMA_Buyₜ = EMA(BuyPower, n₂)ₜ
EMA_Sellₜ = EMA(SellPower, n₂)ₜ
```
\ \Step 6: Raw Demand Index (DI\_raw)\
```
DI_rawₜ = EMA_Buyₜ – EMA_Sellₜ
```
A positive DI\_raw indicates that buying force (normalized by volume) exceeds selling force; a negative value indicates the opposite.
\ \Step 7: Optional EMA Smoothing on DI (DI)\
To reduce choppiness, compute an EMA over DI\_raw (n₃ bars, e.g. n₃ = 1–5):
```
DIₜ = EMA(DI_raw, n₃)ₜ.
```
If n₃ = 1, DI = DI\_raw (no further smoothing).
\
\Interpretation\
\
\ \Crossing Zero Line\
• DI\_raw (or DI) crossing from below to above zero signals that cumulative buying pressure (over the chosen smoothing window) has overcome selling pressure—potential Long signal.
• Crossing from above to below zero signals dominant selling pressure—potential Short signal.
\ \DI\_raw vs. DI (EMA)\
• When DI\_raw > DI (the EMA of DI\_raw), bullish momentum is accelerating.
• When DI\_raw < DI, bullish momentum is weakening (or bearish acceleration).
\ \Divergences\
• If price makes new highs while DI fails to make higher highs (DI\_raw or DI declining), this hints at weakening buying power (“bearish divergence”), possibly preceding a reversal.
• If price makes new lows while DI fails to make lower lows (“bullish divergence”), this may signal waning selling pressure and a potential bounce.
\ \Volume Confirmation\
• A strong price move without a corresponding rise in DI often indicates low‐volume “fake” moves.
• Conversely, a modest price move with a large DI spike suggests true institutional participation—often a more reliable breakout.
\
\Usage Notes & Warnings\
\
\ \Never Use DI in Isolation\
It is a \filter\ and \confirmation\ tool—combine with price‐action (trendlines, support/resistance, candlestick patterns) and risk management (stop‐losses) before executing trades.
\ \Parameter Selection\
• \Vol EMA length (n₁)\: Commonly 13–20 bars. Shorter → more responsive to volume spikes, but noisier.
• \Buy/Sell EMA length (n₂)\: Typically 2 bars for fast smoothing.
• \DI smoothing (n₃)\: Usually 1 (no smoothing) or 3–5 for moderate smoothing. Long DI\_EMA (e.g. 20–50) gives a slower signal.
\ \Market Adaptation\
Works well in liquid futures, indices, and heavily traded stocks. In thinly traded or highly erratic markets, adjust n₁ upward (e.g., 20–30) to reduce noise.
---
\In Summary\
The Demand Index (James Sibbet) uses a three‐stage smoothing (volume → Buy/Sell Power → DI) to reveal true demand/supply imbalance. By combining normalized volume with price change, Sibbet’s DI helps traders identify momentum backed by real participation—filtering out “empty” moves and spotting early divergences. Always confirm DI signals with price action and sound risk controls before trading.
COT-Index-NocTradingCOT Index Indicator
The COT Index Indicator is a powerful tool designed to visualize the Commitment of Traders (COT) data and offer insights into market sentiment. The COT Index is a measurement of the relative positioning of commercial traders versus non-commercial and retail traders in the futures market. It is widely used to identify potential market reversals by observing the extremes in trader positioning.
Customizable Timeframe: The indicator allows you to choose a custom time interval (in months) to visualize the COT data, making it flexible to fit different trading styles and strategies.
How to Use:
Visualize Market Sentiment: A COT Index near extremes (close to 0 or 100) can indicate potential turning points in the market, as it reflects extreme positioning of different market participant groups.
Adjust the Time Interval: The ability to adjust the time interval (in months) gives traders the flexibility to analyze the market over different periods, which can be useful in detecting longer-term trends or short-term shifts in sentiment.
Combine with Other Indicators: To enhance your analysis, combine the COT Index with your technical analysis.
This tool can serve as an invaluable addition to your trading strategy, providing a deeper understanding of the market dynamics and the positioning of major market participants.
atr stop loss for double SMA v6Strategy Name
atr stop loss for double SMA v6
Credit: This v6 update is based on Daveatt’s “BEST ATR Stop Multiple Strategy.”
Core Logic
Entry: Go long when the 15-period SMA crosses above the 45-period SMA; go short on the inverse cross.
Stop-Loss: On entry, compute ATR(14)×2.0 and set a fixed stop at entry ± that amount. Stop remains static until hit.
Trend Tracking: Uses barssince() to ensure only one active long or short position; stop is only active while that trend persists.
Visualization
Plots fast/slow SMA lines in teal/orange.
On each entry bar, displays a label showing “ATR value” and “ATR×multiple” positioned at the 30-bar low (long) or high (short).
Draws an “×” at the stop-price level in green (long) or red (short) while the position is open.
Execution Settings
Initial Capital: $100 000, Size = 100 shares per trade.
Commission: 0.075% per trade.
Pyramiding: 1.
Calculations: Only on bar close (no intra-bar ticks).
Usage Notes
Static ATR stop adapts to volatility but does not trail.
Ideal for trending, liquid markets (stocks, futures, FX).
Adjust SMA lengths or ATR multiple for faster/slower signals.
NSE/BSE Derivative - Next Expiry Date With HolidaysNSE & BSE Expiry Tracker with Holiday Adjustments
This Pine Script is a TradingView indicator that helps traders monitor upcoming expiry dates for major Indian derivative contracts. It dynamically adjusts these expiry dates based on weekends and holidays, and highlights any expiry that falls on the current day.
⸻
Key Features
1. Tracks Expiry Dates for Major Contracts
The script calculates and displays the next expiry dates for the following instruments:
• NIFTY (weekly expiry every Thursday)
• BANKNIFTY, FINNIFTY, MIDCPNIFTY, NIFTYNXT50 (monthly expiry on the last Thursday of the month)
• SENSEX (weekly expiry every Tuesday)
• BANKEX and SENSEX 50 (monthly expiry on the last Tuesday of the month)
• Stocks in the F&O segment (monthly expiry on the last Thursday)
2. Holiday Awareness
Users can input a list of holiday dates in the format YYYY-MM-DD,YYYY-MM-DD,.... If any calculated expiry falls on one of these holidays or a weekend, the script automatically adjusts the expiry to the previous working day (Monday to Friday).
3. Customization Options
The user can:
• Choose the position of the expiry table on the chart (e.g. top right, bottom left).
• Select the font size for the expiry table.
• Enable or disable the table entirely (if implemented as an input toggle).
4. Visual Expiry Highlighting
If today is an expiry day for any instrument, the script highlights that instrument in the display. This makes it easy to spot significant expiry days, which are often associated with increased volatility and trading volume.
⸻
How It Works
• The script calculates the next expiry for each index using built-in date/time functions.
• For weekly expiries, it finds the next occurrence of the designated weekday.
• For monthly expiries, it finds the last Thursday or Tuesday of the month.
• Each expiry date is passed through a check to adjust for holidays or weekends.
• If today matches the adjusted expiry date, that row is visually emphasized.
⸻
Use Case
This script is ideal for traders who want a quick glance at which instruments are expiring soon — especially those managing options, futures, or expiry-based strategies.
Volume pressure by GSK-VIZAG-AP-INDIA🔍 Volume Pressure by GSK-VIZAG-AP-INDIA
🧠 Overview
“Volume Pressure” is a multi-timeframe, real-time table-based volume analysis tool designed to give traders a clear and immediate view of buying and selling pressure across custom-selected timeframes. By breaking down buy volume, sell volume, total volume, and their percentages, this indicator helps traders identify demand/supply imbalances and volume momentum in the market.
🎯 Purpose / Trading Use Case
This indicator is ideal for intraday and short-term traders who want to:
Spot aggressive buying or selling activity
Track volume dynamics across multiple timeframes *1 min time frame will give best results*
Use volume pressure as a confirming tool alongside price action or trend-based systems
It helps determine when large buying/selling activity is occurring and whether such behavior is consistent across timeframes—a strong signal of institutional interest or volume-driven trend shifts.
🧩 Key Features & Logic
Real-Time Table Display: A clean, dynamic table showing:
Buy Volume
Sell Volume
Total Volume
Buy % of total volume
Sell % of total volume
Multi-Time frame Analysis: Supports 8 user-selectable custom time frames from 1 to 240 minutes, giving flexibility to analyze volume pressure at various granularities.
Color-Coded Volume Bias:
Green for dominant Buy pressure
Red for dominant Sell pressure
Yellow for Neutral
Intensity-based blinking for extreme values (over 70%)
Dynamic Data Calculation:
Uses volume * (close > open) logic to estimate buy vs sell volumes bar-by-bar, then aggregates by timeframe.
⚙️ User Inputs & Settings
Timeframe Selectors (TF1 to TF8): Choose any 8 timeframes you want to monitor volume pressure across.
Text & Color Settings:
Customize text colors for Buy, Sell, Total volumes
Choose Buy/Sell bias colors
Enable/disable blinking for visual emphasis on extremes
Table Appearance:
Set header color, metric background, and text size
Table positioning: top-right, bottom-right, etc.
Blinking Highlight Toggle: Enable this to visually highlight when Buy/Sell % exceeds 70%—a sign of strong pressure.
📊 Visual Elements Explained
The table has 6 rows and 10 columns:
Row 0: Headers for Today and TF1 to TF8
Rows 1–3: Absolute values (Buy Vol, Sell Vol, Total Vol)
Rows 4–5: Relative percentages (Buy %, Sell %), with dynamic background color
First column shows the metric names (e.g., “Buy Vol”)
Cells blink using alternate background colors if volume pressure crosses thresholds
💡 How to Use It Effectively
Use Buy/Sell % rows to confirm potential breakout trades or identify volume exhaustion zones
Look for multi-timeframe confluence: If 5 or more TFs show >70% Buy pressure, buyers are in control
Combine with price action (e.g., breakouts, reversals) to increase conviction
Suitable for equities, indices, futures, crypto, especially on lower timeframes (1m to 15m)
🏆 What Makes It Unique
Table-based MTF Volume Pressure Display: Most indicators only show volume as bars or histograms; this script summarizes and color-codes volume bias across timeframes in a tabular format.
Customization-friendly: Full control over colors, themes, and timeframes
Blinking Alerts: Rare visual feature to capture user attention during extreme pressure
Designed with performance and readability in mind—even for fast-paced scalping environments.
🚨 Alerts / Extras
While this script doesn’t include TradingView alert functions directly, the visual blinking serves as a strong real-time alert mechanism.
Future versions may include built-in alert conditions for buy/sell bias thresholds.
🔬 Technical Concepts Used
Volume Dissection using close > open logic (to estimate buyer vs seller pressure)
Simple aggregation of volume over custom timeframes
Table plotting using Pine Script table.new, table.cell
Dynamic color logic for bias identification
Custom blinking logic using na(bar_index % 2 == 0 ? colorA : colorB)
⚠️ Disclaimer
This indicator is a tool for analysis, not financial advice. Always backtest and validate strategies before using any indicator for live trading. Past performance is not indicative of future results. Use at your own risk and apply proper risk management.
✍️ Author & Signature
Indicator Name: Volume Pressure
Author: GSK-VIZAG-AP-INDIA
TradingView Username: prowelltraders
AMD Setup - Full (Long + Short) ICT ModelICTSNIPERKILLS!
Accumulation, Manipulation, Distribution (AMD) Script!
1. Clarifies Structure: Accumulation, Manipulation, Distribution (AMD)
The script visualizes the AMD framework:
Accumulation → Price ranges inside Initial Balance (IB).
Manipulation → Liquidity sweep above IB High or below IB Low.
Distribution → Market Structure Shift (MSS) confirms a directional move.
This gives you a narrative structure for each session, helping you avoid random trades.
🧠 2. Filters Out Noise with MSS Confirmation
It waits for:
A liquidity sweep (manipulation),
Followed by a market structure shift (MSS),
And then confirms an entry only after a candle closes beyond structure.
This structure:
Reduces false signals,
Improves trade timing,
Helps you align with smart money delivery.
🕘 3. Focuses on the Right Time Window (Initial Balance)
You only engage after the 10:30 AM EST close, once the Initial Balance is formed.This aligns with ICT's focus on:
Killzones (like 9:30–11:00),
Avoiding early overtrading,
Letting the market tip its hand first (through sweeps + MSS).
This timing logic supports discipline and consistency.
🟢🔴 4. Marks Entries with Risk/Reward Guidance
It plots:
AMD SHORT / LONG entries after MSS + candle confirmation,
Basic TP and SL visual markers using a static risk-reward (2:1),
Optional Fair Value Gaps (FVGs) for refinement zones.
While static, these help plan trades visually and frame targets quickly, especially if you're scalping or trading micro futures like MNQ.
📈 5. Alerts You in Real Time
Instead of manually watching:
You'll get alerts when sweeps or MSS setups appear.
You can stay focused during the killzone or walk away and return when signals trigger.
This supports patience and alert-based discipline.
💡
You already:
Use 15M/1M execution,
Wait for ERL or HOD/LOD sweeps,
Look for MSS + CISD,
Trade in killzones only,
Target 50–62–70% Fibs with SMT/FVG confluence.
This script:✅ Automates sweep + MSS detection✅ Plots AMD-based entries visually✅ Simplifies your killzone execution✅ Helps avoid FOMO by filtering setups✅ Keeps your journal entries clean with structure
Delta Volume Color CoderDelta Volume Color Coder - Smart Money Footprint Visualizer
OVERVIEW
The Delta Volume Color Coder is a clean, minimalist indicator that highlights candles with exceptional delta volume, helping you instantly identify where smart money is actively trading. Unlike complex volume indicators that clutter your chart, this tool simply colors candles when institutional-level volume appears, leaving your normal price action untouched.
WHAT IS DELTA VOLUME?
Delta volume represents the difference between buying and selling pressure within each candle. Positive delta indicates more aggressive buying, while negative delta shows stronger selling. When delta reaches extreme levels, it often signals institutional activity or significant market events.
KEY FEATURES
- Clean Chart Design - Only colors candles with significant delta volume
- No Chart Compression - Overlay indicator that doesn't distort price scales
- Smart Detection - Automatically calculates dynamic thresholds based on recent activity
- Customizable Thresholds - Adjust sensitivity to match your trading style
- Multiple Calculation Methods - Classic or Range-Based delta calculations
COLOR CODING (Default)
- White Candles - Extreme positive delta (massive institutional buying)
- Green Candles - High positive delta (strong buying pressure)
- Red Candles - High negative delta (strong selling pressure)
- Violet Candles - Extreme negative delta (massive institutional selling)
- Normal Candles - Unchanged (standard TradingView red/green)
HOW TO USE
1. Add to any chart - Works on all timeframes and instruments
2. Look for colored candles - These mark significant volume events
3. White/Violet candles often mark reversals or breakouts
4. Multiple colored candles in sequence indicate strong trends
5. Colored candles at support/resistance levels are especially significant
SETTINGS EXPLAINED
- Lookback Period (20) - Bars used to calculate average delta
- High Delta Threshold (1.5x) - Triggers green/red coloring
- Extreme Delta Threshold (2.5x) - Triggers white/violet coloring
- Delta Calculation - Classic (open/close) or Range Based (close position)
- Color Wicks - Option to color entire candle or just the body
- All colors fully customizable
TRADING APPLICATIONS
- Reversal Detection - White/violet candles often mark exhaustion points
- Breakout Confirmation - Colored candles on breakouts show conviction
- Support/Resistance - High delta at key levels indicates significance
- Trend Strength - Frequency of colored candles shows trend momentum
- Institutional Tracking - Extreme delta reveals where big players are active
BEST PRACTICES
- Lower timeframes (1-15m) - Use for scalping and day trading entries
- Higher timeframes (1H+) - Identify major accumulation/distribution
- Combine with price action - Most effective at key technical levels
- Watch for clusters - Multiple extreme candles = major event
- Volume confirmation - Extreme delta + high volume = highest significance
TIPS FOR SUCCESS
1. White candles after downtrends often mark bottoms
2. Violet candles after uptrends often mark tops
3. Consecutive colored candles confirm trend direction
4. Lack of colored candles = low volatility, potential breakout ahead
5. Extreme delta at round numbers indicates institutional interest
WHY THIS INDICATOR?
- Simple Yet Powerful - No complex analysis needed
- Instant Visual Feedback - See institutional activity at a glance
- Clean Charts - No overlays, lines, or clutter
- Real-Time Detection - Updates with each new candle
- Universal Application - Works on stocks, forex, crypto, futures
UNIQUE ADVANTAGES
Unlike traditional volume indicators that require separate panes or compress your chart, the Delta Volume Color Coder seamlessly integrates with your existing setup. It answers one simple question: "Where is the smart money trading RIGHT NOW?"
Perfect for traders who want institutional-level insights without the complexity. Just add to your chart and let the colors guide you to where the real action is happening.
ScalpZone NQ 1M - Volume Signals with Highlight Box📊 ScalpZone NQ 1M - Volume Signals with Highlight Box
ScalpZone is a professional-grade indicator designed specifically for 1-minute scalping on Nasdaq Futures (NQ), focusing on high-volume price action zones. It automatically detects aggressive buying/selling activity based on volume spikes and visualizes potential entry zones with dynamic horizontal lines and price boxes.
🔍 Key Features:
Volume Spike Detection: Identifies high-volume candles using an adjustable EMA-based volume threshold.
Directional Volume Signals: Highlights candles with directional momentum (bullish or bearish) based on real-time volume dominance.
Scalp Zone Visualization:
Draws horizontal support/resistance lines at volume signal prices.
Renders price boxes around those levels to highlight actionable zones.
Zones automatically extend when respected by price, and disappear when invalidated.
Visual Candle Enhancement: Dynamically colors candles to reflect normalized volume intensity and direction.
Customizable Parameters:
Volume EMA & threshold multiplier
Line and box dimensions
Toggle zone visibility
🛠️ Use Case:
Perfect for scalpers and short-term traders looking to exploit volume-based reversals or breakout traps on the NQ 1-minute chart. Traders can use the visual cues to time entries, manage stops, or validate confluence with other tools (e.g., order flow, delta spikes, or footprint charts).
TICK Extreme Levels & AlertsAutomatically draws horizontal lines at +1000 and -1000 TICK levels
Sends alerts when TICK crosses those levels (for potential scalping/reversal setups)
Strategy: How to Use TICK in Real-Time Trading
1. Confirm Market Breadth
Use TICK to confirm broad participation in the move:
• Long S&P futures or SPY? Only buy breakouts if TICK is above +600 to +1000
• Shorting? Confirm with TICK below –600 to –1000
2. Fade Extremes for Scalps
Look for reversals at extreme levels:
• Fade +1200+: market likely overbought short term → scalp short
• Fade –1200–: market likely oversold → scalp long
Use in combo with other signals (like price exhaustion, candlestick reversal, or VWAP touches)
3. Avoid Trading in the Choppy Zone
If TICK remains between –400 and +400, institutions are not committed. This is where fakeouts are common.
4. Time Entries with TICK Swings
For example:
• TICK moves from –800 to +600 = momentum shift → look for long entries
• TICK stalling around +1000 = momentum climax → partial profit or fade play
Topological Market Stress (TMS) - Quantum FabricTopological Market Stress (TMS) - Quantum Fabric
What Stresses The Market?
Topological Market Stress (TMS) represents a revolutionary fusion of algebraic topology and quantum field theory applied to financial markets. Unlike traditional indicators that analyze price movements linearly, TMS examines the underlying topological structure of market data—detecting when the very fabric of market relationships begins to tear, warp, or collapse.
Drawing inspiration from the ethereal beauty of quantum field visualizations and the mathematical elegance of topological spaces, this indicator transforms complex mathematical concepts into an intuitive, visually stunning interface that reveals hidden market dynamics invisible to conventional analysis.
Theoretical Foundation: Topology Meets Markets
Topological Holes in Market Structure
In algebraic topology, a "hole" represents a fundamental structural break—a place where the normal connectivity of space fails. In markets, these topological holes manifest as:
Correlation Breakdown: When traditional price-volume relationships collapse
Volatility Clustering Failure: When volatility patterns lose their predictive power
Microstructure Stress: When market efficiency mechanisms begin to fail
The Mathematics of Market Topology
TMS constructs a topological space from market data using three key components:
1. Correlation Topology
ρ(P,V) = correlation(price, volume, period)
Hole Formation = 1 - |ρ(P,V)|
When price and volume decorrelate, topological holes begin forming.
2. Volatility Clustering Topology
σ(t) = volatility at time t
Clustering = correlation(σ(t), σ(t-1), period)
Breakdown = 1 - |Clustering|
Volatility clustering breakdown indicates structural instability.
3. Market Efficiency Topology
Efficiency = |price - EMA(price)| / ATR
Measures how far price deviates from its efficient trajectory.
Multi-Scale Topological Analysis
Markets exist across multiple temporal scales simultaneously. TMS analyzes topology at three distinct scales:
Micro Scale (3-15 periods): Immediate structural changes, market microstructure stress
Meso Scale (10-50 periods): Trend-level topology, medium-term structural shifts
Macro Scale (50-200 periods): Long-term structural topology, regime-level changes
The final stress metric combines all scales:
Combined Stress = 0.3×Micro + 0.4×Meso + 0.3×Macro
How TMS Works
1. Topological Space Construction
Each market moment is embedded in a multi-dimensional topological space where:
- Price efficiency forms one dimension
- Correlation breakdown forms another
- Volatility clustering breakdown forms the third
2. Hole Detection Algorithm
The indicator continuously scans this topological space for:
Hole Formation: When stress exceeds the formation threshold
Hole Persistence: How long structural breaks maintain
Hole Collapse: Sudden topology restoration (regime shifts)
3. Quantum Visualization Engine
The visualization system translates topological mathematics into intuitive quantum field representations:
Stress Waves: Main line showing topological stress intensity
Quantum Glow: Surrounding field indicating stress energy
Fabric Integrity: Background showing structural health
Multi-Scale Rings: Orbital representations of different timeframes
4. Signal Generation
Stable Topology (✨): Normal market structure, standard trading conditions
Stressed Topology (⚡): Increased structural tension, heightened volatility expected
Topological Collapse (🕳️): Major structural break, regime shift in progress
Critical Stress (🌋): Extreme conditions, maximum caution required
Inputs & Parameters
🕳️ Topological Parameters
Analysis Window (20-200, default: 50)
Primary period for topological analysis
20-30: High-frequency scalping, rapid structure detection
50: Balanced approach, recommended for most markets
100-200: Long-term position trading, major structural shifts only
Hole Formation Threshold (0.1-0.9, default: 0.3)
Sensitivity for detecting topological holes
0.1-0.2: Very sensitive, detects minor structural stress
0.3: Balanced, optimal for most market conditions
0.5-0.9: Conservative, only major structural breaks
Density Calculation Radius (0.1-2.0, default: 0.5)
Radius for local density estimation in topological space
0.1-0.3: Fine-grained analysis, sensitive to local changes
0.5: Standard approach, balanced sensitivity
1.0-2.0: Broad analysis, focuses on major structural features
Collapse Detection (0.5-0.95, default: 0.7)
Threshold for detecting sudden topology restoration
0.5-0.6: Very sensitive to regime changes
0.7: Balanced, reliable collapse detection
0.8-0.95: Conservative, only major regime shifts
📊 Multi-Scale Analysis
Enable Multi-Scale (default: true)
- Analyzes topology across multiple timeframes simultaneously
- Provides deeper insight into market structure at different scales
- Essential for understanding cross-timeframe topology interactions
Micro Scale Period (3-15, default: 5)
Fast scale for immediate topology changes
3-5: Ultra-fast, tick/minute data analysis
5-8: Fast, 5m-15m chart optimization
10-15: Medium-fast, 30m-1H chart focus
Meso Scale Period (10-50, default: 20)
Medium scale for trend topology analysis
10-15: Short trend structures
20-25: Medium trend structures (recommended)
30-50: Long trend structures
Macro Scale Period (50-200, default: 100)
Slow scale for structural topology
50-75: Medium-term structural analysis
100: Long-term structure (recommended)
150-200: Very long-term structural patterns
⚙️ Signal Processing
Smoothing Method (SMA/EMA/RMA/WMA, default: EMA) Method for smoothing stress signals
SMA: Simple average, stable but slower
EMA: Exponential, responsive and recommended
RMA: Running average, very smooth
WMA: Weighted average, balanced approach
Smoothing Period (1-10, default: 3)
Period for signal smoothing
1-2: Minimal smoothing, noisy but fast
3-5: Balanced, recommended for most applications
6-10: Heavy smoothing, slow but very stable
Normalization (Fixed/Adaptive/Rolling, default: Adaptive)
Method for normalizing stress values
Fixed: Static 0-1 range normalization
Adaptive: Dynamic range adjustment (recommended)
Rolling: Rolling window normalization
🎨 Quantum Visualization
Fabric Style Options:
Quantum Field: Flowing energy visualization with smooth gradients
Topological Mesh: Mathematical topology with stepped lines
Phase Space: Dynamical systems view with circular markers
Minimal: Clean, simple display with reduced visual elements
Color Scheme Options:
Quantum Gradient: Deep space blue → Quantum red progression
Thermal: Black → Hot orange thermal imaging style
Spectral: Purple → Gold full spectrum colors
Monochrome: Dark gray → Light gray elegant simplicity
Multi-Scale Rings (default: true)
- Display orbital rings for different time scales
- Visualizes how topology changes across timeframes
- Provides immediate visual feedback on cross-scale dynamics
Glow Intensity (0.0-1.0, default: 0.6)
Controls the quantum glow effect intensity
0.0: No glow, pure line display
0.6: Balanced, recommended setting
1.0: Maximum glow, full quantum field effect
📋 Dashboard & Alerts
Show Dashboard (default: true)
Real-time topology status display
Current market state and trading recommendations
Stress level visualization and fabric integrity status
Show Theory Guide (default: true)
Educational panel explaining topological concepts
Dashboard interpretation guide
Trading strategy recommendations
Enable Alerts (default: true)
Extreme stress detection alerts
Topological collapse notifications
Hole formation and recovery signals
Visual Logic & Interpretation
Main Visualization Elements
Quantum Stress Line
Primary indicator showing topological stress intensity
Color intensity reflects current market state
Line style varies based on selected fabric style
Glow effect indicates stress energy field
Equilibrium Line
Silver line showing average stress level
Reference point for normal market conditions
Helps identify when stress is elevated or suppressed
Upper/Lower Bounds
Red upper bound: High stress threshold
Green lower bound: Low stress threshold
Quantum fabric fill between bounds shows stress field
Multi-Scale Rings
Aqua circles : Micro-scale topology (immediate changes)
Orange circles: Meso-scale topology (trend-level changes)
Provides cross-timeframe topology visualization
Dashboard Information
Topology State Icons:
✨ STABLE: Normal market structure, standard trading conditions
⚡ STRESSED: Increased structural tension, monitor closely
🕳️ COLLAPSE: Major structural break, regime shift occurring
🌋 CRITICAL: Extreme conditions, reduce risk exposure
Stress Bar Visualization:
Visual representation of current stress level (0-100%)
Color-coded based on current topology state
Real-time percentage display
Fabric Integrity Dots:
●●●●● Intact: Strong market structure (0-30% stress)
●●●○○ Stressed: Weakening structure (30-70% stress)
●○○○○ Fractured: Breaking down structure (70-100% stress)
Action Recommendations:
✅ TRADE: Normal conditions, standard strategies apply
⚠️ WATCH: Monitor closely, increased vigilance required
🔄 ADAPT: Change strategy, regime shift in progress
🛑 REDUCE: Lower risk exposure, extreme conditions
Trading Strategies
In Stable Topology (✨ STABLE)
- Normal trading conditions apply
- Use standard technical analysis
- Regular position sizing appropriate
- Both trend-following and mean-reversion strategies viable
In Stressed Topology (⚡ STRESSED)
- Increased volatility expected
- Widen stop losses to account for higher volatility
- Reduce position sizes slightly
- Focus on high-probability setups
- Monitor for potential regime change
During Topological Collapse (🕳️ COLLAPSE)
- Major regime shift in progress
- Adapt strategy immediately to new market character
- Consider closing positions that rely on previous regime
- Wait for new topology to stabilize before major trades
- Opportunity for contrarian plays if collapse is extreme
In Critical Stress (🌋 CRITICAL)
- Extreme market conditions
- Significantly reduce risk exposure
- Avoid new positions until stress subsides
- Focus on capital preservation
- Consider hedging existing positions
Advanced Techniques
Multi-Timeframe Topology Analysis
- Use higher timeframe TMS for regime context
- Use lower timeframe TMS for precise entry timing
- Alignment across timeframes = highest probability trades
Topology Divergence Trading
- Most powerful at regime boundaries
- Price makes new high/low but topology stress decreases
- Early warning of potential reversals
- Combine with key support/resistance levels
Stress Persistence Analysis
- Long periods of stable topology often precede major moves
- Extended stress periods often resolve in regime changes
- Use persistence tracking for position sizing decisions
Originality & Innovation
TMS represents a genuine breakthrough in applying advanced mathematics to market analysis:
True Topological Analysis: Not a simplified proxy but actual topological space construction and hole detection using correlation breakdown, volatility clustering analysis, and market efficiency measurement.
Quantum Aesthetic: Transforms complex topology mathematics into an intuitive, visually stunning interface inspired by quantum field theory visualizations.
Multi-Scale Architecture: Simultaneous analysis across micro, meso, and macro timeframes provides unprecedented insight into market structure dynamics.
Regime Detection: Identifies fundamental market character changes before they become obvious in price action, providing early warning of structural shifts.
Practical Application: Clear, actionable signals derived from advanced mathematical concepts, making theoretical topology accessible to practical traders.
This is not a combination of existing indicators or a cosmetic enhancement of standard tools. It represents a fundamental reimagining of how we measure, visualize, and interpret market dynamics through the lens of algebraic topology and quantum field theory.
Best Practices
Start with defaults: Parameters are optimized for broad market applicability
Match timeframe: Adjust scales based on your trading timeframe
Confirm with price action: TMS shows market character, not direction
Respect topology changes: Reduce risk during regime transitions
Use appropriate strategies: Adapt approach based on current topology state
Monitor persistence: Track how long topology states maintain
Cross-timeframe analysis: Align multiple timeframes for highest probability trades
Alerts Available
Extreme Topological Stress: Market fabric under severe deformation
Topological Collapse Detected: Regime shift in progress
Topological Hole Forming: Market structure breakdown detected
Topology Stabilizing: Market structure recovering to normal
Chart Requirements
Recommended Markets: All liquid markets (forex, stocks, crypto, futures)
Optimal Timeframes: 5m to Daily (adaptable to any timeframe)
Minimum History: 200 bars for proper topology construction
Best Performance: Markets with clear regime characteristics
Academic Foundation
This indicator draws from cutting-edge research in:
- Algebraic topology and persistent homology
- Quantum field theory visualization techniques
- Market microstructure analysis
- Multi-scale dynamical systems theory
- Correlation topology and network analysis
Disclaimer
This indicator is for educational and research purposes only. It does not constitute financial advice or provide direct buy/sell signals. Topological analysis reveals market structure characteristics, not future price direction. Always use proper risk management and combine with your own analysis. Past performance does not guarantee future results.
See markets through the lens of topology. Trade the structure, not the noise.
Bringing advanced mathematics to practical trading through quantum-inspired visualization.
Trade with insight. Trade with structure.
— Dskyz , for DAFE Trading Systems
Spectral Order Flow Resonance (SOFR) Spectral Order Flow Resonance (SOFR)
See the Market’s Hidden Rhythms—Trade the Resonance, Not the Noise!
The Spectral Order Flow Resonance (SOFR) is a next-generation tool for traders who want to go beyond price and volume, tapping into the underlying “frequency signature” of order flow itself. Instead of chasing lagging signals or reacting to surface-level volatility, SOFR lets you visualize and quantify the real-time resonance of market activity—helping you spot when the crowd is in sync, and when the regime is about to shift.
What Makes SOFR Unique?
Not Just Another Oscillator:
SOFR doesn’t just measure momentum or volume. It applies spectral analysis (using Fast Fourier Transform) to normalized order flow, extracting the dominant cycles and their resonance strength. This reveals when the market is harmonizing around key frequencies—often the precursor to major moves.
Regime Detection, Not Guesswork:
By tracking harmonic alignment and phase coherence across multiple Fibonacci-based frequencies, SOFR identifies when the market is entering a bullish, bearish, or neutral resonance regime. This is visualized with a dynamic dashboard and info line, so you always know the current state at a glance.
Dynamic Dashboard:
The on-chart dashboard color-codes each key metric—regime, dominant frequency, harmonic alignment, phase coherence, and energy concentration—so you can instantly gauge the strength and direction of the current resonance. No more guesswork or clutter.
Universal Application:
Works on any asset, any timeframe, and in any market—futures, stocks, crypto, forex. If there’s order flow, SOFR can reveal its hidden structure.
How Does It Work?
Order Flow Normalization:
SOFR calculates the net buying/selling pressure and normalizes it using a rolling mean and standard deviation, making the signal robust across assets and timeframes.
Spectral Analysis:
The script applies FFT to the normalized order flow, extracting the magnitude and phase of several key frequencies (typically Fibonacci numbers). This allows you to see which cycles are currently dominating the market.
Resonance & Regime Logic:
When multiple frequencies align and exceed a dynamic resonance threshold, and phase coherence is high, SOFR detects a “resonance regime”—bullish, bearish, or neutral. This is when the market is most likely to experience a strong, sustained move.
Visual Clarity:
The indicator plots each frequency’s magnitude, highlights the dominant one, and provides a real-time dashboard with color-coded metrics for instant decision-making.
SOFR Dashboard Metrics Explained
Regime:
What it means: The current “state” of the market as detected by SOFR—Bullish, Bearish, or Neutral.
Why it matters: The regime tells you whether the market’s order flow is resonating in a way that favors upward moves (Bullish), downward moves (Bearish), or is out of sync (Neutral). This helps you align your trades with the prevailing market force, or stand aside when there’s no clear edge.
Dominant Freq:
What it means: The most powerful frequency (cycle length, in bars) currently detected in the order flow.
Why it matters: Markets often move in cycles. The dominant frequency shows which cycle is currently driving price action, helping you time entries and exits with the market’s “heartbeat.”
Harmonic Align:
What it means: The number of key frequencies (out of 3) that are currently in resonance (above threshold).
Why it matters: When multiple frequencies align, it signals that different groups of traders (with different time horizons) are acting in concert. This increases the probability of a strong, sustained move.
Phase Coh.:
What it means: A measure (0–100%) of how “in sync” the phases of the key frequencies are.
Why it matters: High phase coherence means the market’s cycles are reinforcing each other, not cancelling out. This is a classic signature of trending or explosive moves.
Energy Conc.:
What it means: The concentration of spectral energy in the dominant frequency, relative to the average.
Why it matters: High energy concentration means the market’s activity is focused in one cycle, increasing the odds of a decisive move. Low concentration means the market is scattered and less predictable.
How to Use
Bullish Regime:
When the dashboard shows a green regime and high harmonic alignment, the market is in a bullish resonance—look for long opportunities or trend continuations.
Bearish Regime:
When the regime is red and alignment is high, the market is in a bearish resonance—look for short opportunities or trend continuations.
Neutral Regime:
When the regime is gray or alignment is low, the market is out of sync—consider waiting for clearer signals or using other tools.
Combine with Your Strategy:
Use SOFR as a confirmation tool, a filter for trend/range conditions, or as a standalone regime detector. The dashboard’s color-coded metrics help you instantly spot when the market is entering or exiting resonance.
Inputs Explained
FFT Window Length :
Controls the number of bars used for spectral analysis. Higher values smooth the signal, lower values make it more sensitive.
Order Flow Period:
Sets the lookback for normalizing order flow. Shorter periods react faster, longer periods are smoother.
Fibonacci Frequencies:
Choose which cycles to analyze. Default values (5, 8, 13) capture common market rhythms.
Resonance Threshold:
Sets how strong a frequency’s signal must be to count as “in resonance.” Lower for more signals, higher for stricter filtering.
Signal Smoothing & Amplify:
Fine-tune the display for your chart and asset.
Dashboard & Info Line Toggles:
Show or hide the on-chart dashboard and info line as needed.
Why This Matters
Most indicators show you what just happened. SOFR shows you when the market is entering a state of resonance—when crowd behavior is most likely to produce powerful, sustained moves. By visualizing the hidden structure of order flow, you gain a tactical edge over traders who only see the surface.
For educational purposes only. Not financial advice. Always use proper risk management.
Use with discipline. Trade your edge.
— Dskyz, for DAFE Trading Systems
Multi-Session ORBThe Multi-Session ORB Indicator is a customizable Pine Script (version 6) tool designed for TradingView to plot Opening Range Breakout (ORB) levels across four major trading sessions: Sydney, Tokyo, London, and New York. It allows traders to define specific ORB durations and session times in Central Daylight Time (CDT), making it adaptable to various trading strategies.
Key Features:
1. Customizable ORB Duration: Users can set the ORB duration (default: 15 minutes) via the inputMax parameter, determining the time window for calculating the high and low of each session’s opening range.
2. Flexible Session Times: The indicator supports user-defined session and ORB times for:
◦ Sydney: Default ORB (17:00–17:15 CDT), Session (17:00–01:00 CDT)
◦ Tokyo: Default ORB (19:00–19:15 CDT), Session (19:00–04:00 CDT)
◦ London: Default ORB (02:00–02:15 CDT), Session (02:00–11:00 CDT)
◦ New York: Default ORB (08:30–08:45 CDT), Session (08:30–16:00 CDT)
3. Session-Specific ORB Levels: For each session, the indicator calculates and tracks the high and low prices during the specified ORB period. These levels are updated dynamically if new highs or lows occur within the ORB timeframe.
4. Visual Representation:
◦ ORB high and low lines are plotted only during their respective session times, ensuring clarity.
◦ Each session’s lines are color-coded for easy identification:
▪ Sydney: Light Yellow (high), Dark Yellow (low)
▪ Tokyo: Light Pink (high), Dark Pink (low)
▪ London: Light Blue (high), Dark Blue (low)
▪ New York: Light Purple (high), Dark Purple (low)
◦ Lines are drawn with a linewidth of 2 and disappear when the session ends or if the timeframe is not intraday (or exceeds the ORB duration).
5. Intraday Compatibility: The indicator is optimized for intraday timeframes (e.g., 1-minute to 15-minute charts) and only displays when the chart’s timeframe multiplier is less than or equal to the ORB duration.
How It Works:
• Session Detection: The script uses the time() function to check if the current bar falls within the user-defined ORB or session time windows, accounting for all days of the week.
• ORB Logic: At the start of each session’s ORB period, the script initializes the high and low based on the first bar’s prices. It then updates these levels if subsequent bars within the ORB period exceed the current high or fall below the current low.
• Plotting: ORB levels are plotted as horizontal lines during the respective session, with visibility controlled to avoid clutter outside session times or on incompatible timeframes.
Use Case:
Traders can use this indicator to identify key breakout levels for each trading session, facilitating strategies based on price action around the opening range. The flexibility to adjust ORB and session times makes it suitable for various markets (e.g., forex, stocks, or futures) and time zones.
Limitations:
• The indicator is designed for intraday timeframes and may not display on higher timeframes (e.g., daily or weekly) or if the timeframe multiplier exceeds the ORB duration.
• Time inputs are in CDT, requiring users to adjust for their local timezone or market requirements.
• If you need to use this for GC/CL/SPY/QQQ you have to adjust the times by one hour.
This indicator is ideal for traders focusing on session-based breakout strategies, offering clear visualization and customization for global market sessions.
Bitcoin Open Interest [SAKANE]Bitcoin Open Interest
— Unveiling the True Flow of Capital
PurposeVisualize and compare Bitcoin open interest (OI) from CME and Binance, the leading derivatives exchanges, in a single intuitive chart, providing traders with clear insights into crypto market capital dynamics.
Background & MotivationIn the 24/7 crypto market, price movements alone reveal only part of the story. Open interest (OI)—the total outstanding futures contracts—offers critical clues to the market’s next move. Yet, accessing and interpreting OI data is challenging:
CME Constraints: Commitment of Traders (COT) reports are weekly, and standalone BTC1! or BTC2! OI is noisy due to contract rollovers, obscuring true OI changes.
Existing Tool Limitations: Most OI indicators are fixed to either USD or BTC, limiting flexible analysis.
This indicator overcomes these hurdles, enabling seamless comparison of CME and Binance OI to track the market’s “capital center of gravity” in real time.
Key Features
Synthetic CME OI: Combines BTC1! and BTC2! to deliver high-accuracy OI, eliminating rollover noise.
Multi-Timeframe Analysis: Displays daily CME OI as pseudo-candlestick (OHLC) on any timeframe (e.g., 4H), allowing intuitive capital flow tracking across timeframes.
CME/Binance One-Click Toggle: Instantly compare institutional-driven CME and retail-driven Binance OI.
USD/BTC Flexibility: Switch between BTC (real demand) and USD (margin) perspectives for OI analysis.
Robust Design: Concise, global-scope code ensures stability and adaptability to TradingView updates.
Insights & Use Cases
Holistic Market Sentiment: Analyze capital flows by region and exchange for a multidimensional view.
Signal Detection: E.g., a sharp drop in CME OI during a sell-off may signal institutional withdrawal.
Retail Trends: A surge in Binance OI suggests retail-driven inflows.
Event-Driven Insights: E.g., during a hypothetical April 2025 “Trump Tariff Shock,” instantly identify which exchange drives capital shifts.
Unique ValueUnlike price-centric indicators, this tool focuses on capital flow (OI). It’s the only indicator offering one-click multi-timeframe and multi-exchange OI comparison, empowering traders to uncover the market’s “true intent” and gain a strategic edge.
ConclusionBitcoin Open Interest makes the market’s hidden capital movements accessible to all. By capturing market dynamics and pinpointing the “leading forces” during events, it sets a new standard for traders seeking a revolutionary perspective.
H2-25 cuts (bp)This custom TradingView indicator tracks and visualizes the implied pricing of Federal Reserve rate cuts in the market, specifically for the second half of 2025. It does so by comparing the price differences between two specific Fed funds futures contracts: one for June 2025 and one for December 2025. These contracts are traded on the Chicago Board of Trade (CBOT) and are a widely-used market gauge of the expected path of U.S. interest rates.
The indicator calculates the difference between the implied rates for June and December 2025, and then multiplies the result by 100 to express it in basis points (bps). Each 0.01 change in the spread corresponds to a 1-basis point change in expectations for future rate cuts. A positive value indicates that the market is pricing in a higher likelihood of one or more rate cuts in 2025, while a negative value suggests that the market expects the Fed to hold rates steady or even raise them.
The plot represents the difference in implied rate cuts (in basis points) between the two contracts:
June 2025 (ZQM2025): A contract representing the implied Fed funds rate for June 2025.
December 2025 (ZQZ2025): A contract representing the implied Fed funds rate for December 2025.
Key Open LevelsThis Pine Script indicator (Key Open Levels) allows users to highlight up to six specific open prices from different times of the trading day as horizontal lines on the chart.
Each line can be customized with user-defined style, width, and color settings.
Users also have the option to display price labels directly on the lines for added clarity.
The indicator is designed to work seamlessly across all intraday timeframes, including seconds, minutes, and hourly intervals, making it versatile for various trading strategies that rely on key intraday price levels.
This indicator has proved to be a key indicator especially for people studying Futures market reaction around Key Open Levels.
FeraTrading Sessions High/LowThe FeraTradiang Sessions High/Low Indicator plots precise high and low levels for the New York, London, and Asian trading sessions — without any clutter.
We designed this tool for simplicity, clarity and accuracy, automatically adjusting to any timeframe and time zone — no manual setup required.
🔍 Key Features:
Clean horizontal lines marking session highs and lows
Lines start at the actual high/low
Session times:
New York: 09:30 – 17:00
London: 03:00 – 08:00
Asian: 18:00 – 03:00
Real-time updates that trail live candles
Only shows the most relevant sessions:
Yesterday’s NY
Last night’s Asia + morning continuation
Today’s London
Fully customizable:
Session colors
Session toggles
Label toggles
Line extension settings
Enable extended trading hours on your chart for best results.
Whether you're trading futures, forex, or crypto, this indicator provides clean session context without the mess. Open-source for extra customization and designed for real-time usability.
Polarity-VoVix Fusion Index (PVFI) Polarity-VoVix Fusion Index (PVFI) - Order Flow and Volatility Regime Detector
The PVFI is a next-generation indicator that fuses the Order Flow Polarity Index (OFPI) with a proprietary VoVix Volume Delta (VVD) engine. This tool is designed for traders who want to see not just how much volume is trading, but who is in control and how volatility is shifting beneath the surface.
What Makes PVFI Standout from the rest?
- Dual Engine: PVFI combines two advanced signals:
* OFPI: Measures real-time buy/sell pressure using candle body position and volume, then smooths it with a T3 moving average for clarity and responsiveness.
* VVD: Captures the "volatility of volume delta" - a normalized, memory-boosted measure of aggressive buying/selling, with a custom non-linear clamp for organic, non-pegged signals.
- Visual Clarity: Neon-glow OFPI line and shadowed, color-gradient VVD area make regime shifts and momentum instantly visible.
- Adaptive Dashboard: Toggle between a full-featured dashboard (desktop) and a compact info line (mobile) for seamless use on any device.
- Universal: Works on any asset - crypto, stocks, futures, forex - and any timeframe.
- No Chart Clutter: Clean, modern visuals and toggles for a pro look.
Inputs:
OFPI Lookback Length (ofpi_len): Sets the window for order flow pressure calculation. Shorter = more sensitive, longer = smoother. For scalping, try 5-10. For swing trading, 15-30. Crypto often benefits from shorter windows due to volatility.
OFPI T3 Smoothing Length (t3_len): Controls the smoothness of the OFPI line. Lower = more responsive, higher = smoother. Use 3-7 for fast markets, 8-15 for slow or higher timeframes.
OFPI T3 Volume Factor (t3_vf): Adjusts the T3’s sensitivity. Higher = more responsive, lower = more stable. 0.6-0.8 is typical. Raise for more “snappy” signals, lower for less noise.
VVD Delta Lookback (delta_len): Sets the window for VVD’s volume delta calculation. 10-20 for most assets. Shorter for high-volatility, longer for slow markets.
VVD Volatility Normalization Length (vol_norm_len): Normalizes VVD by recent volume. 15-30 is typical. Use higher for assets with wild volume swings.
VVD Momentum Memory (momentum_mem): Adds a “memory” boost to VVD, amplifying persistent buying/selling. 2-5 is common. Lower for choppy markets, higher for trending.
Show Dashboard (showDash): Toggles the full dashboard table (best for desktop). Turn off for a minimalist or mobile setup.
Show Compact Info Line (showInfoLabel): Toggles a single-line info label (best for mobile). Turn on for mobile or minimalist setups.
How PVFI Works:
- OFPI Calculation: Splits each candle’s volume into buy/sell pressure based on where the close is within the range. Aggregates over your chosen lookback, then smooths with a T3 moving average for a neon, lag-minimized signal.
- VVD Calculation: Measures the “aggression” of volume (body-weighted), normalizes by recent volume, and applies a memory boost for persistent trends. Uses a custom tanh clamp for a natural, non-pegged range.
- Visuals: OFPI is plotted as a neon line (with glow). VVD is a color-gradient area with a soft shadow, instantly showing regime shifts.
- Dashboard/Info Line: Desktop: Full dashboard with all key stats, color-coded and branded. Mobile: Compact info line with arrows for quick reads.
How you'll use PVFI:
- Bullish OFPI (Teal Neon, Up Arrow): Buyers are dominating. Look for breakouts, trend continuations, or confirmation with your own system.
- Bearish OFPI (Green Neon, Down Arrow): Sellers are in control. Watch for breakdowns or short setups.
- VVD Positive (Teal Area): Aggressive buying is increasing. Confirm with price action.
- VVD Negative (Purple Area): Aggressive selling is increasing. Use for risk management or short bias.
- Neutral/Flat: Market is balanced or indecisive. Consider waiting for a clear regime shift.
- Dashboard/Info Line: Use the dashboard for full context, or the info line for a quick glance on mobile.
Tips:
- For scalping, use lower lookbacks and smoothing.
- For swing trading, increase lookbacks and smoothing for stability.
- Works on all assets and timeframes - tune to your style.
Why PVFI is Unique:
- Fusion of Order Flow and Volatility: No other indicator combines body-based order flow with a volatility-of-volume delta, both visualized with modern, pro-grade graphics.
- Adaptive, Not Static: PVFI adapts to market regime, not just price movement.
- Mobile-Ready: Dashboard and info line toggles for any device.
- No Chart Clutter: Clean, color-coded, and easy to read.
For Educational Use Only
PVFI is a research and educational tool, not financial advice. Always use proper risk management and combine with your own strategy.
Trade with clarity. Trade with edge.
— Dskyz , for DAFE Trading Systems
JPMorgan G7 Volatility IndexThe JPMorgan G7 Volatility Index: Scientific Analysis and Professional Applications
Introduction
The JPMorgan G7 Volatility Index (G7VOL) represents a sophisticated metric for monitoring currency market volatility across major developed economies. This indicator functions as an approximation of JPMorgan's proprietary volatility indices, providing traders and investors with a normalized measurement of cross-currency volatility conditions (Clark, 2019).
Theoretical Foundation
Currency volatility is fundamentally defined as "the statistical measure of the dispersion of returns for a given security or market index" (Hull, 2018, p.127). In the context of G7 currencies, this volatility measurement becomes particularly significant due to the economic importance of these nations, which collectively represent more than 50% of global nominal GDP (IMF, 2022).
According to Menkhoff et al. (2012, p.685), "currency volatility serves as a global risk factor that affects expected returns across different asset classes." This finding underscores the importance of monitoring G7 currency volatility as a proxy for global financial conditions.
Methodology
The G7VOL indicator employs a multi-step calculation process:
Individual volatility calculation for seven major currency pairs using standard deviation normalized by price (Lo, 2002)
- Weighted-average combination of these volatilities to form a composite index
- Normalization against historical bands to create a standardized scale
- Visual representation through dynamic coloring that reflects current market conditions
The mathematical foundation follows the volatility calculation methodology proposed by Bollerslev et al. (2018):
Volatility = σ(returns) / price × 100
Where σ represents standard deviation calculated over a specified timeframe, typically 20 periods as recommended by the Bank for International Settlements (BIS, 2020).
Professional Applications
Professional traders and institutional investors employ the G7VOL indicator in several key ways:
1. Risk Management Signaling
According to research by Adrian and Brunnermeier (2016), elevated currency volatility often precedes broader market stress. When the G7VOL breaches its high volatility threshold (typically 1.5 times the 100-period average), portfolio managers frequently reduce risk exposure across asset classes. As noted by Borio (2019, p.17), "currency volatility spikes have historically preceded equity market corrections by 2-7 trading days."
2. Counter-Cyclical Investment Strategy
Low G7 volatility periods (readings below the lower band) tend to coincide with what Shin (2017) describes as "risk-on" environments. Professional investors often use these signals to increase allocations to higher-beta assets and emerging markets. Campbell et al. (2021) found that G7 volatility in the lowest quintile historically preceded emerging market outperformance by an average of 3.7% over subsequent quarters.
3. Regime Identification
The normalized volatility framework enables identification of distinct market regimes:
- Readings above 1.0: Crisis/high volatility regime
- Readings between -0.5 and 0.5: Normal volatility regime
- Readings below -1.0: Unusually calm markets
According to Rey (2015), these regimes have significant implications for global monetary policy transmission mechanisms and cross-border capital flows.
Interpretation and Trading Applications
G7 currency volatility serves as a barometer for global financial conditions due to these currencies' centrality in international trade and reserve status. As noted by Gagnon and Ihrig (2021, p.423), "G7 currency volatility captures both trade-related uncertainty and broader financial market risk appetites."
Professional traders apply this indicator in multiple contexts:
- Leading indicator: Research from the Federal Reserve Board (Powell, 2020) suggests G7 volatility often leads VIX movements by 1-3 days, providing advance warning of broader market volatility.
- Correlation shifts: During periods of elevated G7 volatility, cross-asset correlations typically increase what Brunnermeier and Pedersen (2009) term "correlation breakdown during stress periods." This phenomenon informs portfolio diversification strategies.
- Carry trade timing: Currency carry strategies perform best during low volatility regimes as documented by Lustig et al. (2011). The G7VOL indicator provides objective thresholds for initiating or exiting such positions.
References
Adrian, T. and Brunnermeier, M.K. (2016) 'CoVaR', American Economic Review, 106(7), pp.1705-1741.
Bank for International Settlements (2020) Monitoring Volatility in Foreign Exchange Markets. BIS Quarterly Review, December 2020.
Bollerslev, T., Patton, A.J. and Quaedvlieg, R. (2018) 'Modeling and forecasting (un)reliable realized volatilities', Journal of Econometrics, 204(1), pp.112-130.
Borio, C. (2019) 'Monetary policy in the grip of a pincer movement', BIS Working Papers, No. 706.
Brunnermeier, M.K. and Pedersen, L.H. (2009) 'Market liquidity and funding liquidity', Review of Financial Studies, 22(6), pp.2201-2238.
Campbell, J.Y., Sunderam, A. and Viceira, L.M. (2021) 'Inflation Bets or Deflation Hedges? The Changing Risks of Nominal Bonds', Critical Finance Review, 10(2), pp.303-336.
Clark, J. (2019) 'Currency Volatility and Macro Fundamentals', JPMorgan Global FX Research Quarterly, Fall 2019.
Gagnon, J.E. and Ihrig, J. (2021) 'What drives foreign exchange markets?', International Finance, 24(3), pp.414-428.
Hull, J.C. (2018) Options, Futures, and Other Derivatives. 10th edn. London: Pearson.
International Monetary Fund (2022) World Economic Outlook Database. Washington, DC: IMF.
Lo, A.W. (2002) 'The statistics of Sharpe ratios', Financial Analysts Journal, 58(4), pp.36-52.
Lustig, H., Roussanov, N. and Verdelhan, A. (2011) 'Common risk factors in currency markets', Review of Financial Studies, 24(11), pp.3731-3777.
Menkhoff, L., Sarno, L., Schmeling, M. and Schrimpf, A. (2012) 'Carry trades and global foreign exchange volatility', Journal of Finance, 67(2), pp.681-718.
Powell, J. (2020) Monetary Policy and Price Stability. Speech at Jackson Hole Economic Symposium, August 27, 2020.
Rey, H. (2015) 'Dilemma not trilemma: The global financial cycle and monetary policy independence', NBER Working Paper No. 21162.
Shin, H.S. (2017) 'The bank/capital markets nexus goes global', Bank for International Settlements Speech, January 15, 2017.
Gamma Blast Detector (Nifty)The Gamma Blast Detector (Nifty) is a custom TradingView indicator designed to help intraday traders identify sudden and explosive price movements—commonly referred to as "gamma blasts"—in the Nifty index during the final minutes of the trading session, particularly on expiry days. These movements are typically caused by rapid delta changes in ATM options, resulting in aggressive short-covering or option unwinding.
This indicator specifically monitors price action between 3:10 PM and 3:20 PM IST, which translates to 09:40 AM to 09:50 AM UTC on TradingView. It is optimized for use on 5-minute charts of the Nifty spot or futures index, where gamma-driven volatility is most likely to occur during this time window.
The core logic behind the indicator involves identifying unusually large candles within this time frame. It compares the size of the current candle to the average size of the previous five candles. If the current candle is at least twice as large and shows clear direction (bullish or bearish), the script flags it as a potential gamma blast. A bullish candle suggests a Call Option (CE) is likely to blast upward, while a bearish candle points to a Put Option (PE) gaining sharply.
When such a condition is detected, the indicator visually marks the candle on the chart: a "CE 🚀" label is shown below the candle for a bullish move, and a "PE 🔻" label appears above for a bearish move. It also includes alert conditions, allowing users to set real-time alerts for potential blasts and act quickly.
This tool is especially useful for expiry day scalpers, option traders, and anyone looking to ride momentum generated by gamma effects in the final minutes of the market. It provides a visual and alert-based edge to anticipate short-term, high-impact moves often missed in normal technical analysis.
