IPO Date ScreenerThis script, the IPO Date Screener, allows traders to visually identify stocks that are relatively new, based on the number of bars (days) since their IPO. The user can set a custom threshold for the number of days (bars) after the IPO, and the script will highlight new stocks that fall below that threshold.
Key Features:
Customizable IPO Days Threshold: Set the threshold for considering a stock as "new." Since Pine screener limits number bars to 500, it will work for stocks having trading days below 500 since IPO which almost 2 years.
Column Days since IPO: Sort this column from low to high to see newest to oldest STOCK with 500 days of trading.
Since a watchlist is limited to 1000 stocks, use this pines script to screen stocks within the watch list having trading days below 500 or user can select lower number of days from settings.
This is not helpful to add on chart, this is to use on pine screener as utility.
Educational
IronCondor 10am 30TF by RMThe IronCondor 10am 30TF indicator shows Iron Condor trades win rate over a large number of days.
The default ETFs in this indicators are "QQQ", "SPY", "RUT" , "CBTX" and "SPX", other entries have not been tested.
Iron Condor quick explanation:
- Iron Condors trades have four options, generally, are based around a Midpoint price (Current Market Price Strike) and
- Two equally distances Strikes for the SELL components (called the Body of the Iron Condor)
- Further away from the two SELLs, another Two BUYs for protection (not considered in this indicator)
- Iron Condors are used for Passive Income based on small gains most of the time.
The IronCondor 10am 30TF has its logic created based on the premises that:
- Most days the market prices stay within a range.
- As example the S&P market prices would stay within 1% on about 80% of the time
- The moving markets (bullish or bearish) occur about 20% of the time
- The biggest market price volatility generally occurs before market opens and then around the first hour or so of trade in the day.
- After the first hour or so of the market the prices would be most likely to stay within a range.
The operation is simple:
- At the Trade Star time in the day (say 10:30 Hrs.) draws a vertical yellow line, then
- Creates two blue horizontal lines for the SELL limits in the Iron Condor Body, at +/- 1% price boundary (check Ticker list below for values)
- At the Trade End time (say 16:00 Hrs.) checks that none of the SELL limits have been broken by highs or lows during the trade day
(The check is done calculating at Trade End time the high/lows 10 bars back for 30 min TF - timeframe)
- There is a label at each Trade End time with Win/Loss and Body value.
- There is one final label with overall calculated past performance in Win percentage out of 'n' trades
Defaults and User Entries:
- The User can modify the Midpoint price called 'IronCondor Midpoint STRIKE' (default is the Candle Close at the selected time)
- The User can modify the Body value called 'IronCondor Body' (default is the Ticker's selected value as per list below)
"QQQ" or "SPY" Body = 5
"RUT" or "CBTX" Body = 20
"SPX" Body = 60
* Disclaimer: This is not a Financial tool, it cannot used as any kind of advice to invest or risk moneys in any market,
Markets are volatile in nature - with little or no warning - and will drain your account if you are not careful.
Use only as an academic demonstrator => * Use at your own risk *
Forward Curve Visualization ToolProvide the spot symbol and the futures product root, and the script automatically scans all relevant contracts for you—no more tedious manual searches. The result is a clean, intuitive chart showing the live forward curve in real time.
It also detects contango or backwardation conditions (based on spot < F1 < F2 < F3).
Future Features:
Plot historical snapshots of the curve (1 day, 1 week, or 1 month ago) to understand market trends over time.
Display additional metrics such as annualized basis, cost of carry (CoC), and even volume or open interest for deeper insights.
If you trade futures and watch the forward curve, this script will give you the actionable data you need and get more ideas or features you’d like to see. Let’s build them together!
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting.
This post and the script don’t provide any financial advice.
Son Model ICT [TradingFinder] HTF DOL H1 + Sweep M15 + FVG M1🔵 Introduction
The ICT Son Model setup is a precise trading strategy based on market structure and liquidity, implemented across multiple timeframes. This setup first identifies a liquidity level in the 1-hour (1H) timeframe and then confirms a Market Structure Shift (MSS) in the 5-minute (5M) timeframe to validate the trend. After confirmation, the price forms a new swing in the 5-minute timeframe, absorbing liquidity.
Once this level is broken, traders typically drop to the 30-second (30s) timeframe and enter trades based on a Fair Value Gap (FVG). However, since access to the 30-second timeframe is not available to most traders, we take the entry signal directly from the 5-minute timeframe, using the same liquidity zones and confirmed breakouts to execute trades. This approach simplifies execution and makes the strategy accessible to all traders.
This model operates in two setups :
Bullish ICT Son Model and Bearish ICT Son Model. In the bullish setup, liquidity is first accumulated at the lows of the 1-hour timeframe, and after confirming a market structure shift, a long position is initiated. Conversely, in the bearish setup, liquidity is first drawn from higher levels, and upon confirmation of a bearish trend, a short position is executed.
Bullish Setup :
Bearish Setup :
🔵 How to Use
The ICT Son Model setup is designed around liquidity analysis and market structure shifts and can be applied in both bullish and bearish market conditions. The strategy first identifies a liquidity level in the 1-hour (1H) timeframe and then confirms a Market Structure Shift (MSS) in the 5-minute (5M) timeframe.
After this shift, the price forms a new swing, absorbing liquidity. When this level is broken in the 5-minute timeframe, the trader enters based on a Fair Value Gap (FVG). While the ideal entry is in the 30-second (30s) timeframe, due to accessibility constraints, we take entry signals directly from the 5-minute timeframe.
🟣 Bullish Setup
In the Bullish ICT Son Model, the 1-hour timeframe first identifies liquidity at the market lows, where price sweeps this level to absorb liquidity. Then, in the 5-minute timeframe, an MSS confirms the bullish shift.
After confirmation, the price forms a new swing, absorbing liquidity at a higher level. The price then retraces into a Fair Value Gap (FVG) created in the 5-minute timeframe, where the trader enters a long position, placing the stop-loss below the FVG.
🟣 Bearish Setup
In the Bearish ICT Son Model, liquidity at higher market levels is identified in the 1-hour timeframe, where price sweeps these levels to absorb liquidity. Then, in the 5-minute timeframe, an MSS confirms the bearish trend.
After confirmation, the price forms a new swing, absorbing liquidity at a lower level. The price then retraces into a Fair Value Gap (FVG) created in the 5-minute timeframe, where the trader enters a short position, placing the stop-loss above the FVG.
🔵 Settings
Swing period : You can set the swing detection period.
Max Swing Back Method : It is in two modes "All" and "Custom". If it is in "All" mode, it will check all swings, and if it is in "Custom" mode, it will check the swings to the extent you determine.
Max Swing Back : You can set the number of swings that will go back for checking.
FVG Length : Default is 120 Bar.
MSS Length : Default is 80 Bar.
FVG Filter : This refines the number of identified FVG areas based on a specified algorithm to focus on higher quality signals and reduce noise.
Types of FVG filters :
Very Aggressive Filter: Adds a condition where, for an upward FVG, the last candle's highest price must exceed the middle candle's highest price, and for a downward FVG, the last candle's lowest price must be lower than the middle candle's lowest price. This minimally filters out FVGs.
Aggressive Filter: Builds on the Very Aggressive mode by ensuring the middle candle is not too small, filtering out more FVGs.
Defensive Filter: Adds criteria regarding the size and structure of the middle candle, requiring it to have a substantial body and specific polarity conditions, filtering out a significant number of FVGs.
Very Defensive Filter: Further refines filtering by ensuring the first and third candles are not small-bodied doji candles, retaining only the highest quality signals.
🔵 Conclusion
The ICT Son Model setup is a structured and precise method for trade execution based on liquidity analysis and market structure shifts. This strategy first identifies a liquidity level in the 1-hour timeframe and then confirms a trend shift using the 5-minute timeframe.
Trade entries are executed based on Fair Value Gaps (FVGs), which highlight optimal entry points. By applying this model, traders can leverage existing market liquidity to enter high-probability trades. The bullish setup activates when liquidity is swept from market lows and a market structure shift confirms an upward trend, whereas the bearish setup is used when liquidity is drawn from market highs, confirming a downtrend.
This approach enables traders to identify high-probability trade setups with greater precision compared to many other strategies. Additionally, since access to the 30-second timeframe is limited, the strategy remains fully functional in the 5-minute timeframe, making it more practical and accessible for a wider range of traders.
Cluster Reversal Zones📌 Cluster Reversal Zones – Smart Market Turning Point Detector
📌 Category : Public (Restricted/Closed-Source) Indicator
📌 Designed for : Traders looking for high-accuracy reversal zones based on price clustering & liquidity shifts.
🔍 Overview
The Cluster Reversal Zones Indicator is an advanced market reversal detection tool that helps traders identify key turning points using a combination of price clustering, order flow analysis, and liquidity tracking. Instead of relying on static support and resistance levels, this tool dynamically adjusts to live market conditions, ensuring traders get the most accurate reversal signals possible.
📊 Core Features:
✅ Real-Time Reversal Zone Mapping – Detects high-probability market turning points using price clustering & order flow imbalance.
✅ Liquidity-Based Support/Resistance Detection – Identifies strong rejection zones based on real-time liquidity shifts.
✅ Order Flow Sensitivity for Smart Filtering – Filters out weak reversals by detecting real market participation behind price movements.
✅ Momentum Divergence for Confirmation – Aligns reversal zones with momentum divergences to increase accuracy.
✅ Adaptive Risk Management System – Adjusts risk parameters dynamically based on volatility and trend state.
🔒 Justification for Mashup
The Cluster Reversal Zones Indicator contains custom-built methodologies that extend beyond traditional support/resistance indicators:
✔ Smart Price Clustering Algorithm: Instead of plotting fixed support/resistance lines, this system analyzes historical price clustering to detect active reversal areas.
✔ Order Flow Delta & Liquidity Shift Sensitivity: The tool tracks real-time order flow data, identifying price zones with the highest accumulation or distribution levels.
✔ Momentum-Based Reversal Validation: Unlike traditional indicators, this tool requires a momentum shift confirmation before validating a potential reversal.
✔ Adaptive Reversal Filtering Mechanism: Uses a combination of historical confluence detection + live market validation to improve accuracy.
🛠️ How to Use:
• Works well for reversal traders, scalpers, and swing traders seeking precise turning points.
• Best combined with VWAP, Market Profile, and Delta Volume indicators for confirmation.
• Suitable for Forex, Indices, Commodities, Crypto, and Stock markets.
🚨 Important Note:
For educational & analytical purposes only.
Stochastic-Dynamic Volatility Band ModelThe Stochastic-Dynamic Volatility Band Model is a quantitative trading approach that leverages statistical principles to model market volatility and generate buy and sell signals. The strategy is grounded in the concepts of volatility estimation and dynamic market regimes, where the core idea is to capture price fluctuations through stochastic models and trade around volatility bands.
Volatility Estimation and Band Construction
The volatility bands are constructed using a combination of historical price data and statistical measures, primarily the standard deviation (σ) of price returns, which quantifies the degree of variation in price movements over a specific period. This methodology is based on the classical works of Black-Scholes (1973), which laid the foundation for using volatility as a core component in financial models. Volatility is a crucial determinant of asset pricing and risk, and it plays a pivotal role in this strategy's design.
Entry and Exit Conditions
The entry conditions are based on the price’s relationship with the volatility bands. A long entry is triggered when the price crosses above the lower volatility band, indicating that the market may have been oversold or is experiencing a reversal to the upside. Conversely, a short entry is triggered when the price crosses below the upper volatility band, suggesting overbought conditions or a potential market downturn.
These entry signals are consistent with the mean reversion theory, which asserts that asset prices tend to revert to their long-term average after deviating from it. According to Poterba and Summers (1988), mean reversion occurs due to overreaction to news or temporary disturbances, leading to price corrections.
The exit condition is based on the number of bars that have elapsed since the entry signal. Specifically, positions are closed after a predefined number of bars, typically set to seven bars, reflecting a short-term trading horizon. This exit mechanism is in line with short-term momentum trading strategies discussed in literature, where traders capitalize on price movements within specific timeframes (Jegadeesh & Titman, 1993).
Market Adaptability
One of the key features of this strategy is its dynamic nature, as it adapts to the changing volatility environment. The volatility bands automatically adjust to market conditions, expanding in periods of high volatility and contracting when volatility decreases. This dynamic adjustment helps the strategy remain robust across different market regimes, as it is capable of identifying both trend-following and mean-reverting opportunities.
This dynamic adaptability is supported by the adaptive market hypothesis (Lo, 2004), which posits that market participants evolve their strategies in response to changing market conditions, akin to the adaptive nature of biological systems.
References:
Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
Bollinger, J. (1980). Bollinger on Bollinger Bands. Wiley.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Moneyball EMA-MACD indicator [VinnieTheFish]Summary of the Moneyball EMA-MACD Indicator Script
Author: VinnieTheFish
Purpose:
This indicator helps traders identify trend direction, momentum shifts, and potential trade signals based on EMA and MACD crossovers.
This Pine Script is a custom indicator that combines Exponential Moving Averages (EMAs) and MACD (Moving Average Convergence Divergence) to analyze price trends and momentum. The script uses a custom 9/50 MACD with a 16 smoothing period. The script is written in a way that you can create your own custom MACD settings and create alerts based on those parameters. The chart bars are color coded based on the relative position of the MACD and Signal line primarily for bullish long trade setups.
Bar color coding helps the trader spot potential reversals based on where the price currently resides in relation to the custom 9/50 EMA based MACD and the 16 period smoothing period for the signal line. Indicator also has custom alerts to notify the trader when a potential trade setup exists that correspond with the bar color change.
Question: So why is this called the Moneywell EMA-MACD Indicator?
Answer: In the movie Moneyball the Oakland A's broke down how to win a championship based on data. To make the playoffs you needed so many wins, then broken down by runs and then broken down to base hits. A base hit was good as a walk. With trading often times we look too often for home runs and ignore the importance of getting on base with small wins. This indicator was designed on shorter timeframes to identify those base hits, but can also be adapted to higher timeframes for swing trading.
Key Features:
User Inputs:
Configurable fast and slow lengths for MACD calculation.
Choice between SMA and EMA for oscillator and signal line smoothing.
Customizable signal smoothing length.
EMA Calculation:
Computes 3 EMA, 9 EMA, 20 EMA, and 50 EMA to track short-term and long-term trends.
MACD Calculation:
Computes MACD using either SMA or EMA based on user selection.
Generates the MACD signal line for comparison.
Crossover Conditions:
Detects MACD and Signal line crossovers above and below the zero line.
Identifies price momentum shifts.
Bar Coloring Logic:
Green: MACD is above 0 and above the signal line.
White: MACD is below the signal line.
Orange: MACD is below 0 but above the signal line.
Fuchsia: Bullish EMA 3/9 cross but price is still below the 20/50 EMA.
Alerts for Key Trading Signals:
MACD crossing above/below the zero line.
Signal line crossing above/below the zero line.
MACD reaching new highs/lows.
Alerts for colored bar conditions.
Candlestick Color Change AlertIt is an alert for change of candlestick color.
Identifies Candle Type
A candle is bullish if the closing price is higher than the opening price.
A candle is bearish if the closing price is lower than the opening price.
Detects a Color Change
The script checks if the current candle is bullish while the previous candle was bearish, or vice versa.
If a change is detected, an alert is triggered.
Triggers an Alert
Users receive an alert notification whenever a candlestick color change occurs.
Alerts can be set for popup, email, mobile push, or webhook notifications.
Visual Highlighting (Optional)
The script can also apply a background color (blue) on the chart to visually mark color changes.
Blackflag FTS (1H Trailing) + MSB-OB FibThis indicator combines a 1-hour trailing stop system with multi-timeframe Fibonacci retracement levels and ZigZag structure detection to assist traders in identifying trend direction and potential reversal zones.
Features:
✅ 1-Hour Trailing Stop: Uses an ATR-based trailing stop mechanism to track trend direction and dynamic support/resistance.
✅ Multi-Timeframe Approach: The trailing stop is calculated on the 1-hour timeframe, while the ZigZag and Fibonacci retracement levels are based on the 15-minute chart.
✅ ZigZag Structure Detection: Helps filter market swings and trend reversals dynamically.
✅ Fibonacci Levels (0.5 & 0.786): Key retracement levels to watch for price reactions.
✅ Alerts for Key Levels: Get notified when the price crosses important levels (1H trailing stop, Fib 0.5, Fib 0.786).
How It Works:
The trailing stop adapts dynamically based on ATR values and determines trend direction.
ZigZag detection filters out minor price movements to highlight major swing points.
Fibonacci levels are calculated based on ZigZag swings, helping traders spot potential reversal zones.
This tool is useful for trend-following traders, breakout traders, and Fibonacci-based strategies.
Let me know if you'd like any modifications! 🚀
Candle Momentum ExhaustionCandle Momentum Exhaustion
The Candle Momentum Exhaustion indicator is designed to help traders spot potential turning points in a trend by identifying when the prevailing momentum may be “running on empty.” The indicator works by comparing the size of each candle’s body (the absolute difference between the open and close) to the average body size over a recent period. When a candle’s body exceeds a user‐defined multiple of this average, it is flagged as an “exhaustion” candle.
• A bullish exhaustion (shown with a red down–facing triangle above the bar) occurs when a very large bullish candle (close > open) is detected, suggesting that buyers may have pushed the price too far and the rally could be near its end.
• A bearish exhaustion (shown with a green up–facing triangle below the bar) occurs when a very large bearish candle (close < open) is detected, implying that selling pressure might be overdone.
These signals can alert you to a potential reversal or consolidation point. The script also includes alert conditions so that you can set up notifications whenever an exhaustion signal is generated.
How It Works
1. Average Candle Body:
The script computes a simple moving average (SMA) of the absolute candle bodies over a user-defined period (default is 14 bars).
2. Exhaustion Candidate:
A candle is flagged as an exhaustion candidate if its body size exceeds the average by more than the set multiplier (default is 2.0).
3. Signal Identification:
• If the exhaustion candle is bullish (close > open), it is marked with a red down–facing triangle above the bar.
• If it is bearish (close < open), it is marked with a green up–facing triangle below the bar.
4. Alerts:
The built-in alertcondition() calls allow you to set alerts (via TradingView’s alert system) so that you can be notified when an exhaustion event occurs.
Risk Disclaimer:
This indicator is provided for educational and informational purposes only and does not constitute financial, investment, or trading advice. Trading and investing involve significant risk, and you should not rely solely on this indicator when making any trading decisions. Past performance is not indicative of future results. Always perform your own due diligence and consult with a qualified financial advisor before making any financial decisions. The creator of this indicator shall not be held responsible for any losses incurred through its use.
US vs EU Interest Rate SpreadThis script plots the difference (Spread) between the US-Interest Rate (Symbol USINTR) and the EU Interest Rate (Symbol: EUINTR) and plots it in a seperate pane. Areas where the background is green are times were the spread was positive (US interest rate higher than EU interest rate), a red background indicates a higher EU interest rate than US interest rate.
OAT Multiple Alert ConditionsOverview:
The OAT Multiple Alert Conditions indicator is designed to enhance TradingView’s alert functionality by allowing users to set multiple conditions for webhook-based alerts. This script enables traders to define up to four independent conditions using different event types (e.g., crossing, greater than, rising, etc.), making it ideal for automated trading strategies and webhook integrations.
Features:
✅ Supports up to 4 independent conditions.
✅ Multiple event types: Crossing, Crossing Up, Crossing Down, Greater Than, Less Than, Rising, Falling.
✅ Choose between value-based or source-based conditions.
✅ Custom timeframes for each condition.
✅ Optional session filtering and expiration settings.
✅ Visual markers for triggered conditions.
✅ Alerts for individual conditions or all conditions being met.
How It Works:
Configure each condition by selecting the event type and input values.
Define whether the alert should trigger on bar close or real-time.
Enable session filtering to limit alerts to specific trading hours.
Set an expiration date for alerts if needed.
Alerts can be sent via TradingView’s webhook feature for automated execution.
Intended Use:
This script is a utility tool for traders using automated strategies with the Options Auto Trader. It does not generate trading signals or provide financial advice. It is designed to enhance alert flexibility and efficiency for trading through webhooks.
License & Compliance:
This script is published under the Mozilla Public License 2.0 and follows TradingView’s guidelines. It does not execute trades but simply provides an enhanced alerting mechanism.
Live Economic CalendarLive Economic Calendar
This TradingView indicator provides real-time economic news events directly on your charts, helping traders stay informed about key market-moving data. Built on the original Forex Factory utility by toodegrees, this version enhances functionality with customizable alerts and improved visualizations.
Key Features:
Real-Time Economic News: Displays upcoming economic events from Forex Factory, categorized by impact level (High, Medium, Low, Holiday).
Custom Alerts: Set alerts before and after news events to stay prepared for market volatility.
Timezone Adjustments: Adjust news event times to match your local timezone for accurate scheduling.
Currency-Specific News: Automatically filters news based on the currency pair you’re viewing, with manual options for specific currencies.
Flexible Display Options: Choose to display news for today, this week, or a custom period. Customize labels, lines, and tables directly on the chart.
Impact Visualization: Visual cues (lines, labels, background shading) for different impact levels to highlight significant market events.
Credits:
• Original Forex Factory Utility by toodegrees
• Alerts and enhancements by Nachodog
This Pine Script™ code is licensed under the Mozilla Public License 2.0: mozilla.org
Dynamic SL - 1 Pip (Up and Down)The Dynamic SL - 1 Pip Up and Down indicator creates two dynamic lines that follow the price at a distance of 1 pip above and below the closing price. This feature can be particularly useful for traders who want to visualize small stop-loss (SL) levels or track price movement in a highly responsive manner.
Unlike traditional stop-loss indicators, this script ensures that the lines only last for 5 seconds, keeping the chart clean and focusing only on the most relevant price movement.
Key Features
✔ Dynamic Stop-Loss Visualization:
The script draws a green line above the price (+1 pip).
A red line below the price (-1 pip) is also drawn.
✔ Auto-Clearing for a Clean Chart:
Each line lasts for 5 seconds only before automatically disappearing.
This prevents unnecessary clutter on the chart and ensures only the latest price movements are visualized.
✔ Adaptable to Multiple Assets:
Automatically calculates the pip size based on the instrument type:
Forex → Uses 0.0001 per pip.
Futures & Stocks → Uses the minimum tick size.
✔ Ideal for High-Frequency Traders & Scalpers:
Designed for 1-minute (M1) or lower timeframes where traders need to monitor price action closely.
Helps visualize ultra-tight stop-loss levels in scalping strategies.
BTC Future Gamma-Weighted Momentum Model (BGMM)The BTC Future Gamma-Weighted Momentum Model (BGMM) is a quantitative trading strategy that utilizes the Gamma-weighted average price (GWAP) in conjunction with a momentum-based approach to predict price movements in the Bitcoin futures market. The model combines the concept of weighted price movements with trend identification, where the Gamma factor amplifies the weight assigned to recent prices. It leverages the idea that historical price trends and weighting mechanisms can be utilized to forecast future price behavior.
Theoretical Background:
1. Momentum in Financial Markets:
Momentum is a well-established concept in financial market theory, referring to the tendency of assets to continue moving in the same direction after initiating a trend. Any observed market return over a given time period is likely to continue in the same direction, a phenomenon known as the “momentum effect.” Deviations from a mean or trend provide potential trading opportunities, particularly in highly volatile assets like Bitcoin.
Numerous empirical studies have demonstrated that momentum strategies, based on price movements, especially those correlating long-term and short-term trends, can yield significant returns (Jegadeesh & Titman, 1993). Given Bitcoin’s volatile nature, it is an ideal candidate for momentum-based strategies.
2. Gamma-Weighted Price Strategies:
Gamma weighting is an advanced method of applying weights to price data, where past price movements are weighted by a Gamma factor. This weighting allows for the reinforcement or reduction of the influence of historical prices based on an exponential function. The Gamma factor (ranging from 0.5 to 1.5) controls how much emphasis is placed on recent data: a value closer to 1 applies an even weighting across periods, while a value closer to 0 diminishes the influence of past prices.
Gamma-based models are used in financial analysis and modeling to enhance a model’s adaptability to changing market dynamics. This weighting mechanism is particularly advantageous in volatile markets such as Bitcoin futures, as it facilitates quick adaptation to changing market conditions (Black-Scholes, 1973).
Strategy Mechanism:
The BTC Future Gamma-Weighted Momentum Model (BGMM) utilizes an adaptive weighting strategy, where the Bitcoin futures prices are weighted according to the Gamma factor to calculate the Gamma-Weighted Average Price (GWAP). The GWAP is derived as a weighted average of prices over a specific number of periods, with more weight assigned to recent periods. The calculated GWAP serves as a reference value, and trading decisions are based on whether the current market price is above or below this level.
1. Long Position Conditions:
A long position is initiated when the Bitcoin price is above the GWAP and a positive price movement is observed over the last three periods. This indicates that an upward trend is in place, and the market is likely to continue in the direction of the momentum.
2. Short Position Conditions:
A short position is initiated when the Bitcoin price is below the GWAP and a negative price movement is observed over the last three periods. This suggests that a downtrend is occurring, and a continuation of the negative price movement is expected.
Backtesting and Application to Bitcoin Futures:
The model has been tested exclusively on the Bitcoin futures market due to Bitcoin’s high volatility and strong trend behavior. These characteristics make the market particularly suitable for momentum strategies, as strong upward or downward movements are often followed by persistent trends that can be captured by a momentum-based approach.
Backtests of the BGMM on the Bitcoin futures market indicate that the model achieves above-average returns during periods of strong momentum, especially when the Gamma factor is optimized to suit the specific dynamics of the Bitcoin market. The high volatility of Bitcoin, combined with adaptive weighting, allows the model to respond quickly to price changes and maximize trading opportunities.
Scientific Citations and Sources:
• Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65–91.
• Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637–654.
• Fama, E. F., & French, K. R. (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, 47(2), 427–465.
GWAP (Gamma Weighted Average Price)Gamma Weighted Average Price (GWAP) Indicator
The Gamma Weighted Average Price (GWAP) is a dynamic financial indicator that applies exponentially decaying weights to historical prices to calculate a weighted average. The method leverages the exponential decay function, controlled by a gamma factor, to prioritize recent price data while gradually diminishing the influence of older observations. This approach builds upon techniques commonly found in time-series analysis, including Exponentially Weighted Moving Averages (EWMA), which are extensively used in financial modeling (Campbell, Lo & MacKinlay, 1997).
Theoretical Context and Justification
The gamma-weighted approach follows principles similar to those in Exponentially Weighted Moving Averages (EWMA), often used in volatility modeling, where weights decay exponentially over time. The exponential decay model can improve signal responsiveness compared to simple moving averages (Hyndman & Athanasopoulos, 2018). This design helps capture recent market dynamics without ignoring past trends, a common requirement in high-frequency trading systems (Bandi & Russell, 2006).
Practical Applications
1. Trend Detection:
The GWAP can help identify bullish and bearish trends:
• When the price is above GWAP, the market exhibits bullish momentum.
• Conversely, when the price is below GWAP, bearish momentum prevails.
2. Volatility Filtering:
Because of the gamma weighting mechanism, GWAP reduces the noise commonly seen in volatile markets, making it a useful tool for traders looking to smooth price fluctuations while retaining actionable signals.
3. Crossovers for Trade Signals:
Similar to moving average strategies, traders can use price crossovers with the GWAP as trade signals:
• Buy Signal: When the price crosses above the GWAP.
• Sell Signal: When the price crosses below the GWAP.
4. Adaptive Gamma Weighting:
The gamma factor allows for further customization.
• Higher gamma values (>1) place greater emphasis on older data, suitable for long-term trend analysis.
• Lower gamma values (<1) heavily weight recent price movements, ideal for fast-moving markets.
Example Use Case
A trader analyzing the S&P 500 may use a gamma factor of 0.92 with a 14-period GWAP to detect shifts in market sentiment during periods of heightened volatility. When the index price crosses above the GWAP, this could signal a potential recovery, prompting a buy entry. Conversely, when the price moves below the GWAP during a correction, it may suggest a short-selling opportunity.
Scientific References
• Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton University Press.
• Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
• Bandi, F. M., & Russell, J. R. (2006). Microstructure Noise, Realized Variance, and Optimal Sampling. Econometrica.
Multiple Candlestick Patterns - AlgomaxxA comprehensive candlestick pattern detection indicator that identifies seven major Japanese candlestick patterns in real-time. This indicator helps traders identify potential reversal and continuation patterns with customizable visual alerts and labels.
Features
Detects 7 major candlestick patterns:
Doji
Hammer
Shooting Star
Bullish Engulfing
Bearish Engulfing
Morning Star
Evening Star
Color-coded candlesticks for easy pattern identification
Customizable pattern indicators above/below candles
Optional pattern labels with adjustable position
Alert conditions for each pattern
Grouped settings for easy customization
Settings
General Settings
Lookback Period: Number of candles to analyze (default: 20)
Body Size Threshold: Minimum relative size for candle body (default: 0.6)
Pattern Settings
Toggle visibility for each pattern type:
Doji Pattern
Hammer Pattern
Shooting Star Pattern
Bullish Engulfing Pattern
Bearish Engulfing Pattern
Morning Star Pattern
Evening Star Pattern
Label Settings
Show Labels: Toggle pattern labels on/off
Label Text Color: Customize label color
Label Position: Choose between Left/Center/Right alignment
Label Offset: Adjust distance of labels from candles
Pattern Descriptions
Doji: Shows indecision when open and close prices are very close
Yellow color
Cross symbol below candle
Hammer: Potential bullish reversal with long lower shadow
Green color
Triangle up symbol below candle
Shooting Star: Potential bearish reversal with long upper shadow
Red color
Triangle down symbol above candle
Bullish Engulfing: Bullish reversal pattern where current green candle completely engulfs previous red candle
Light green color
Triangle up symbol below candle
Bearish Engulfing: Bearish reversal pattern where current red candle completely engulfs previous green candle
Light red color
Triangle down symbol above candle
Morning Star: Three-candle bullish reversal pattern
Seafoam green color
Triangle up symbol below candle
Evening Star: Three-candle bearish reversal pattern
Pink red color
Triangle down symbol above candle
How to Use
Add the indicator to your chart
Customize the settings based on your preferences:
Enable/disable specific patterns you want to monitor
Adjust label settings for better visibility
Set up alerts for patterns you want to be notified about
Pattern Recognition:
Watch for color changes in candlesticks indicating pattern formation
Look for shape indicators above/below candles
Read pattern labels for quick pattern identification
Trading Suggestions:
Use in conjunction with other technical indicators
Consider overall trend and support/resistance levels
Confirm patterns with volume and price action
Wait for pattern completion before making trading decisions
Tips
Patterns work best when used with multiple timeframes
Combine with trend lines and support/resistance levels
Use volume to confirm pattern strength
Consider market context and overall trend
Larger timeframes typically produce more reliable signals
Use alerts to avoid missing important pattern formations
Disclaimer
This indicator is for informational and educational purposes only. No guarantee is made regarding the accuracy of pattern detection or potential future price movements. Always use proper risk management and consider multiple factors before making trading decisions.
Ultimate Volatility Scanner by NHBprod - Requested by Client!Hey Everyone!
I created another script to add to my growing library of strategies and indicators that I use for automated crypto and stock trading! This strategy is for BITCOIN but can be used on any stock or crypto. This was requested by a client so I thought I should create it and hopefully build off of it and build variants!
This script gets and compares the 14-day volatility using the ATR percentage for a list of cryptocurrencies and stocks. Cryptocurrencies are preloaded into the script, and the script will show you the TOP 5 coins in terms of volatility, and then compares it to the Bitcoin volatility as a reference. It updates these values once per day using daily timeframe data from TradingView. The coins are then sorted in descending order by their volatility.
If you don't want to use the preloaded set of coins, you have the option of inputting your own coins AND/OR stocks!
Let me know your thoughts.
Volatility Arbitrage Spread Oscillator Model (VASOM)The Volatility Arbitrage Spread Oscillator Model (VASOM) is a systematic approach to capitalizing on price inefficiencies in the VIX futures term structure. By analyzing the differential between front-month and second-month VIX futures contracts, we employ a momentum-based oscillator (Relative Strength Index, RSI) to signal potential market reversion opportunities. Our research builds upon existing financial literature on volatility risk premia and contango/backwardation dynamics in the volatility markets (Zhang & Zhu, 2006; Alexander & Korovilas, 2012).
Volatility derivatives have become essential tools for managing risk and engaging in speculative trades (Whaley, 2009). The Chicago Board Options Exchange (CBOE) Volatility Index (VIX) measures the market’s expectation of 30-day forward-looking volatility derived from S&P 500 option prices (CBOE, 2018). Term structures in VIX futures often exhibit contango or backwardation, depending on macroeconomic and market conditions (Alexander & Korovilas, 2012).
This strategy seeks to exploit the spread between the front-month and second-month VIX futures as a proxy for term structure dynamics. The spread’s momentum, quantified by the RSI, serves as a signal for entry and exit points, aligning with empirical findings on mean reversion in volatility markets (Zhang & Zhu, 2006).
• Entry Signal: When RSI_t falls below the user-defined threshold (e.g., 30), indicating a potential undervaluation in the spread.
• Exit Signal: When RSI_t exceeds a threshold (e.g., 70), suggesting mean reversion has occurred.
Empirical Justification
The strategy aligns with findings that suggest predictable patterns in volatility futures spreads (Alexander & Korovilas, 2012). Furthermore, the use of RSI leverages insights from momentum-based trading models, which have demonstrated efficacy in various asset classes, including commodities and derivatives (Jegadeesh & Titman, 1993).
References
• Alexander, C., & Korovilas, D. (2012). The Hazards of Volatility Investing. Journal of Alternative Investments, 15(2), 92-104.
• CBOE. (2018). The VIX White Paper. Chicago Board Options Exchange.
• Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
• Zhang, C., & Zhu, Y. (2006). Exploiting Predictability in Volatility Futures Spreads. Financial Analysts Journal, 62(6), 62-72.
• Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105.
Buyable Gap Ups (BGU) ScreenerBuyable Gap Ups (BGU) Screener
This custom indicator detects Buyable Gap Ups (BGU), designed to identify stocks with significant price movements driven by gap-ups, often signaling strong bullish momentum. It helps traders spot potential opportunities where a stock has gapped up above the previous day's high with increased volume, suggesting the possibility of continued price strength.
Key Features:
Gap Percentage Threshold: Set a minimum gap percentage required for a valid buyable gap-up.
Volume Change Threshold: Identifies gap-ups accompanied by a significant increase in volume compared to the 50-day average.
ATR-Based Gap Detection (Optional): Use Average True Range (ATR) to determine whether the gap is large enough, factoring in recent volatility.
Customizable Lookback Period: Adjust the number of recent bars to track the frequency of BGU occurrences.
Volume Confirmation: Only signals buyable gaps when volume surpasses a defined threshold above the 50-day average.
Input Parameters:
Gap Percentage Threshold: Adjusts the minimum percentage gap for a valid signal.
Volume Change Threshold: Determines the minimum percentage increase in volume compared to the 50-day moving average.
Use ATR Gap: Option to use ATR to determine the minimum gap size instead of the percentage gap.
ATR Multiplier for Gap: Customizes the gap size based on the ATR.
ATR Length: Adjusts the lookback period for calculating ATR.
Lookback Period for BGU: Set the number of bars over which to calculate the BGU count.
Alerts & Signals:
The script will plot signals below bars where a valid BGU condition is met.
Alerts can be set for when a BGU is detected, giving real-time notifications for potential trading opportunities.
This indicator is designed to help traders find stocks showing strong bullish momentum, especially after earnings or other market-moving events, with the potential for continued uptrend. It is ideal for those looking to incorporate gap-based strategies in their trading.
Walk Forward PatternsINTRO
In Euclidean geometry, every mathematical output has a planar projection. 'Walk Forward Patterns' can be considered a practical example of this concept. On the other hand, this indicator might also be viewed as an experiment in 'how playing with Lego as a child contributes to time series analysis' :)
OVERVIEW
This script dynamically generates the necessary optimization and testing ranges for Walk Forward Analysis based on user-defined bar count and length inputs. It performs automatic calculations for each step, offers 8 different window options depending on the inputs, and visualizes the results dynamically. I should also note that most of the window models consist of original patterns I have created.
ADDITIONAL INFO : WHAT IS WALK FORWARD ANALYSIS?
Although it is not the main focus of this indicator, providing a brief definition of Walk Forward Analysis can be helpful in correctly interpreting the results it generates. Walk Forward Analysis (WFA) is a systematic method for optimizing parameters and validating trading strategies. It involves dividing historical data into variable segments, where a strategy is first optimized on an in-sample period and then tested on an out-of-sample period. This process repeats by shifting the windows forward, ensuring that each test evaluates the strategy on unseen data, helping to assess its robustness and adaptability in real market conditions.
ORIGINALITY
There are very few studies on Walk Forward Analysis in TradingView. Even worse, there are no any open-source studies available. Someone has to start somewhere, I suppose. And in my personal opinion, determining the optimization and backtest intervals is the most challenging part of WFA. These intervals serve as a prerequisite for automated parameter optimization. I felt the need to publish this pattern module, which I use in my own WFA models, partly due to this gap on community scripts.
INDICATOR MECHANICS
To use the indicator effectively, you only need to perform four simple tasks:
Specify the total number of bars in your chart in the 'Bar Index' parameter.
Define the optimization (In-Sample Test) length.
Define the testing (Out-Of-Sample Test) length.
Finally, select the window type.
The indicator automatically models everything else (including the number of steps) based on your inputs. And the result; you now have a clear idea of which bars to use for your Walk Forward tests!
A COMMONLY USED WINDOW SELECTION METHOD: ROLLING
A more concrete definition of Walk Forward Analysis, specifically for the widely used Rolling method, can be described as follows:
Parameters that have performed well over a certain period are identified (Optimization: In-Sample).
These parameters are then tested on a shorter, subsequent period (Backtest: Out-of-Sample).
The process is repeated forward in time (At each step, the optimization and backtest periods are shifted by the backtest length).
If the cumulative percentage profit obtained from the backtest results is greater than half of the historical optimization profit, the strategy is considered "successful."
If the strategy is successful, the most recent (untested) optimization values are used for live trading.
OTHER WINDOW OPTIONS
ANCHORED: That's a pattern based on progressively expanding optimization ranges at each step. Backtest ranges move forward in a staircase-like manner.
STATIC: Optimization ranges remain fixed, while backtest ranges are shifted forward.
BLOCKED: Optimization ranges are shifted forward in groups of three blocks. Backtest ranges are also shifted in a staircase manner, even at the cost of creating gaps from the optimization end bars.
TRIANGULAR: Optimization ranges are shifted forward in triangular regions, while backtest ranges move in a staircase pattern.
RATIO: The optimization length increases by 25% of the initial step’s fixed length at each step. In other words, the length grows by 25% of the first step's length incrementally. Backtest ranges always start from the bar where the optimization ends.
FIBONACCI: A variation of the Ratio method, where the optimization shift factor is set to 0.618
RANDOM WALK
Unlike the window models explained above, we can also generate optimization and backtest ranges completely randomly—offering almost unlimited variations! When you select the "Random" option in the "Window" parameter on the indicator interface, random intervals are generated based on various trigonometric calculations. By changing the numerical value in the '🐒' parameter, you can create entirely unique patterns.
WHY THE 🐒 EMOJI?
Two reasons.
First, I think that as humanity, we are a species of tailless primates who become happy when we understand things :). At least evolutionarily. The entire history of civilization is built on the effort to express the universe in a scale we can comprehend. 'Knowledge' is an invention born from this effort, which is why we feel happiness when we 'understand'. Second, I can't think of a better metaphor for randomness than a monkey sitting at a keyboard. See: Monkey Test.
Anyway, I’m rambling :)
NOTES
The indicator generates results for up to 100 steps. As the number of steps increases, the table may extend beyond the screen—don’t forget to zoom out!
FINAL WORDS
I haven’t published a Walk Forward script yet . However, there seem to be examples that can perform parameter optimization in the true sense of the word, producing more realistic results without falling into overfitting in my library. Hopefully, I’ll have the chance to publish one in the coming weeks. Sincerely thanks to Kıvanç Özbilgiç, Robert Pardo, Kevin Davey, Ernest P. Chan for their inspiring publishments.
DISCLAIMER
That's just a script, nothing more. I hope it helps everyone. Do not forget to manage your risk. And trade as safely as possible. Best of luck!
© dg_factor
USDT.D + USDT.C ALL TIMEFRAMESThis indicator combines the dominance of USDT (USDT.D) and USDC (USDC.D) to track total stablecoin market share across all timeframes. It displays the combined dominance as candlesticks, providing a clearer view of market liquidity shifts and investor sentiment.
📌 How to Use:
Green candles indicate rising stablecoin dominance (potential risk-off sentiment).
Red candles indicate declining stablecoin dominance (potential risk-on sentiment).
Works on all timeframes, from intraday scalping to macro trend analysis.
This tool is essential for traders looking to analyze stablecoin liquidity flow, identify market turning points, and refine trading strategies based on stablecoin dominance behavior. 🚀
Optimized Dynamic SupertrendDetailed Explanation of the Optimized Dynamic Supertrend Script
This Supertrend script is designed to dynamically adapt to different market conditions using ATR expansion, volume confirmation, and trend filtering. Below is a step-by-step breakdown of how it works and its functions.
1 ATR-Based Supertrend Calculation
📌 Key Purpose:
The script calculates an adaptive ATR-based Supertrend line, which acts as a dynamic support or resistance level for trend direction.
📌 How it Works:
ATR (Average True Range) is used to measure market volatility.
A dynamic ATR multiplier is applied based on price standard deviation (instead of a fixed value).
The Supertrend is calculated as:
Upper Band: SMA(close, ATR length) + (ATR Multiplier * ATR Value)
Lower Band: SMA(close, ATR length) - (ATR Multiplier * ATR Value)
The Supertrend flips when price crosses and holds beyond the Supertrend line.
🔹 Dynamic Adjustment:
Instead of using a fixed ATR multiplier, the script adjusts it using:
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dynamicFactor = ta.stdev(close, atrLength) / ta.sma(close, atrLength)
atrMultiplier = input(1.5, title="Base ATR Multiplier") * dynamicFactor
High volatility → Wider Supertrend bands (to avoid false signals).
Low volatility → Tighter Supertrend bands (for faster detection).
2 Trend Detection Logic
📌 Key Purpose:
Determines if the market is in a bullish or bearish trend based on price action.
Uses volume sensitivity and ATR expansion to reduce false signals.
📌 How it Works:
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var float supertrend = na
supertrend := close > nz(supertrend , lowerBand) ? lowerBand : upperBand
The Supertrend value updates dynamically.
If price is above the Supertrend line, the trend is bullish (green).
If price is below the Supertrend line, the trend is bearish (red).
3 Volume Sensitivity Confirmation
📌 Key Purpose:
Avoid false trend flips by confirming with volume (approximated using a CVD proxy).
📌 How it Works:
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priceChange = close - close
volumeWeightedTrend = priceChange * volume // Approximate CVD Behavior
trendConfirmed = volumeWeightedTrend > 0 ? close > supertrend : close < supertrend
Positive price change + High volume → Confirms bullish momentum.
Negative price change + High volume → Confirms bearish momentum.
If there’s low volume, the trend change is ignored to avoid false breakouts.
4 Noise Reduction (Final Trend Confirmation)
📌 Key Purpose:
Filter out weak or choppy price movements using ATR expansion.
📌 How it Works:
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trendUp = trendConfirmed and ta.atr(atrLength) > ta.atr(atrLength)
trendDown = not trendUp
Trend only flips when confirmed by volume + ATR expansion.
If ATR is not expanding, the script ignores weak price movements.
This ensures Supertrend signals align with strong market moves.
5 Can This Be Used on All Timeframes?
✅ YES! This Supertrend is adaptive, meaning it adjusts dynamically based on:
Volatility: Uses ATR expansion to adjust for different market conditions.
Timeframe Sensitivity: Works on any timeframe (1M, 5M, 15M, 1H, 4H, 1D, 1W).
Market Structure: Confirms trend flips using volume & price movement strength.
🚀 Best Timeframes for Trading:
For Scalping (1M - 15M) → Quick execution, best with order flow confirmation.
For Swing Trading (1H - 4H - 1D) → Stronger trend signals, reduced noise.
For High Timeframes (3D - 1W) → Identifies major market shifts.
🔥 Advantages & Disadvantages in Your Trading Setup
✅ Advantages:
✔ Fully Dynamic & Adaptive → Adjusts to different timeframes & volatility.
✔ Reduces False Signals → Uses ATR expansion & volume confirmation.
✔ Precise Trend Reversals → Labels LONG & SHORT entries clearly.
✔ Works on Any Market → Crypto, Forex, Stocks, Commodities.
✔ No Extra Indicators → Pure Supertrend-based (fits your setup).
❌ Disadvantages:
⚠ Lagging Indicator → ATR & volume confirmation add slight delay.
⚠ Needs High Volume to Confirm → Weak volume → no trend flip.
⚠ Choppy Market = Late Entries → Sideways movement can cause delays.
🚀 Final Thoughts:
It’s fully dynamic & adaptive (unlike traditional static Supertrends).
No extra indicators → Uses only Supertrend logic
Refines entry points using volume & ATR confirmation (removes noise).
This ensures you get high-probability trend signals while filtering out weak breakouts! 🎯
