RSI Signal with filters by S.Kodirov📌 English
RSI Signal with Multi-Timeframe Filters
This TradingView indicator generates RSI-based buy and sell signals on the 15-minute timeframe with additional filtering from other timeframes (5M, 30M, 1M).
🔹 Signal Types:
✅ 15/5B & 15/5S – RSI 15M filtered by 5M
✅ 15/30/1B & 15/30/1S – RSI 15M filtered by 30M & 1M
✅ 15B & 15S – RSI 15M without filters
🔹 How It Works:
Signals are displayed as colored triangles on the chart.
Labels indicate the type of signal (e.g., 15/5B, 15S).
Alerts notify users when a signal appears.
🚀 Best for short-term trading with RSI confirmation from multiple timeframes!
📌 Русский
Индикатор RSI с мульти-таймфрейм фильтрами
Этот индикатор для TradingView генерирует сигналы покупки и продажи на 15-минутном таймфрейме, используя фильтрацию с других таймфреймов (5M, 30M, 1M).
🔹 Типы сигналов:
✅ 15/5B & 15/5S – RSI 15M с фильтром 5M
✅ 15/30/1B & 15/30/1S – RSI 15M с фильтрами 30M и 1M
✅ 15B & 15S – RSI 15M без фильтров
🔹 Как это работает:
Сигналы отображаются как цветные треугольники на графике.
Подписи показывают тип сигнала (например, 15/5B, 15S).
Алерты уведомляют трейдера о появлении сигнала.
🚀 Идеально для краткосрочной торговли с подтверждением RSI на нескольких таймфреймах!
📌 O'zbekcha
Ko'p vaqt oralig‘idagi RSI signallari
Ushbu TradingView indikatori 15 daqiqalik vaqt oralig‘ida RSI asosida sotib olish va sotish signallarini yaratadi. Bundan tashqari, boshqa vaqt oralig‘idagi (5M, 30M, 1M) RSI filtrlarini ham hisobga oladi.
🔹 Signal turlari:
✅ 15/5B & 15/5S – 5M bilan filtrlangan RSI 15M
✅ 15/30/1B & 15/30/1S – 30M va 1M bilan filtrlangan RSI 15M
✅ 15B & 15S – Filtrsiz RSI 15M
🔹 Qanday ishlaydi?
Signallar rangli uchburchaklar shaklida ko‘rsatiladi.
Yozuvlar signal turini ko‘rsatadi (masalan, 15/5B, 15S).
Xabarnomalar yangi signal paydo bo‘lganda treyderni ogohlantiradi.
🚀 Ko‘p vaqt oralig‘ida RSI tasdig‘i bilan qisqa muddatli savdo uchun ideal!
Cari dalam skrip untuk "30年国债收益率"
Crypto Scanner v4This guide explains a version 6 Pine Script that scans a user-provided list of cryptocurrency tokens to identify high probability tradable opportunities using several technical indicators. The script combines trend, momentum, and volume-based analyses to generate potential buying or selling signals, and it displays the results in a neatly formatted table with alerts for trading setups. Below is a detailed walkthrough of the script’s design, how traders can interpret its outputs, and recommendations for optimizing indicator inputs across different timeframes.
## Overview and Key Components
The script is designed to help traders assess multiple tokens by calculating several indicators for each one. The key components include:
- **Input Settings:**
- A comma-separated list of symbols to scan.
- Adjustable parameters for technical indicators such as ADX, RSI, MFI, and a custom Wave Trend indicator.
- Options to enable alerts and set update frequencies.
- **Indicator Calculations:**
- **ADX (Average Directional Index):** Measures trend strength. A value above the provided threshold indicates a strong trend, which is essential for validating momentum before entering a trade.
- **RSI (Relative Strength Index):** Helps determine overbought or oversold conditions. When the RSI is below the oversold level, it may present a buying opportunity, while an overbought condition (not explicitly part of this setup) could suggest selling.
- **MFI (Money Flow Index):** Similar in concept to RSI but incorporates volume, thus assessing buying and selling pressure. Values below the designated oversold threshold indicate potential undervaluation.
- **Wave Trend:** A custom indicator that calculates two components (WT1 and WT2); a crossover where WT1 moves from below to above WT2 (particularly near oversold levels) may signal a reversal and a potential entry point.
- **Scanning and Trading Zone:**
- The script identifies a *bullish setup* when the following conditions are met for a token:
- ADX exceeds the threshold (strong trend).
- Both RSI and MFI are below their oversold levels (indicating potential buying opportunities).
- A Wave Trend crossover confirms near-term reversal dynamics.
- A *trading zone* condition is also defined by specific ranges for ADX, RSI, MFI, and a limited difference between WT1 and WT2. This zone suggests that the token might be in a consolidation phase where even small moves may be significant.
- **Alerts and Table Reporting:**
- A table is generated, with each row corresponding to a token. The table contains columns for the symbol, ADX, RSI, MFI, WT1, WT2, and the trading zone status.
- Visual cues—such as different background colors—highlight tokens with a bullish setup or that are within the trading zone.
- Alerts are issued based on the detection of a bullish setup or entry into a trading zone. These alerts are limited per bar to avoid flooding the trader with notifications.
## How to Interpret the Indicator Outputs
Traders should use the indicator values as guidance, verifying them against their own analysis before making any trading decision. Here’s how to assess each output:
- **ADX:**
- **High values (above threshold):** Indicate strong trends. If other indicators confirm an oversold condition, a trader may consider a long position for a corrective reversal.
- **Low values:** Suggest that the market is not trending strongly, and caution should be taken when considering entry.
- **RSI and MFI:**
- **Below oversold levels:** These conditions are traditionally seen as signals that an asset is undervalued, potentially triggering a bounce.
- **Above typical resistance levels (not explicitly used here):** Would normally caution a trader against entering a long position.
- **Wave Trend (WT1 and WT2):**
- A crossover where WT1 moves upward above WT2 in an oversold environment can signal the beginning of a recovery or reversal, thereby reinforcing buy signals.
- **Trading Zone:**
- Being “in zone” means that the asset’s current values for ADX, RSI, MFI, and the closeness of the Wave Trend lines indicate a period of consolidation. This scenario might be suitable for both short-term scalping or as an early exit indicator, depending on further market analysis.
## Timeframe Optimization Input Table
Traders can optimize indicator inputs depending on the timeframe they use. The following table provides a set of recommended input values for various timeframes. These values are suggestions and should be adjusted based on market conditions and individual trading styles.
Timeframe ADX RSI MFI ADX RSI MFI WT Channel WT Average
5-min 10 10 10 20 30 20 7 15
15-min 12 12 12 22 30 20 9 18
1-hour 14 14 14 25 30 20 10 21
4-hour 16 16 16 27 30 20 12 24
1-day 18 18 18 30 30 20 14 28
Adjust these parameters directly in the script’s input settings to match the selected timeframe. For shorter timeframes (e.g., 5-min or 15-min), the shorter lengths help filter high-frequency noise. For longer timeframes (e.g., 1-day), longer input values may reduce false signals and capture more significant trends.
## Best Practices and Usage Tips
- **Token Limit:**
- Limit the number of tokens scanned to 10 per query line. If you need to scan more tokens, initiate a new query line. This helps manage screen real estate and ensures the table remains legible.
- **Confirming Signals:**
- Use this script as a starting point for identifying high potential trades. Each indicator’s output should be used to confirm your trading decision. Always cross-reference with additional technical analysis tools or market context.
- **Regular Review:**
- Since the script updates the table every few bars (as defined by the update frequency), review the table and alerts regularly. Market conditions change rapidly, so timely decisions are crucial.
## Conclusion
This Pine Script provides a comprehensive approach for scanning multiple cryptocurrencies using a combination of trend strength (ADX), momentum (RSI and MFI), and reversal signals (Wave Trend). By using the provided recommendation table for different timeframes and limiting the tokens to 20 per query line (with a maximum of four query lines), traders can streamline their scanning process and more effectively identify high probability tradable tokens. Ultimately, the outputs should be critically evaluated and combined with additional market research before executing any trades.
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.
Timed Ranges [mktrader]The Timed Ranges indicator helps visualize price ranges that develop during specific time periods. It's particularly useful for analyzing market behavior in instruments like NASDAQ, S&P 500, and Dow Jones, which often show reactions to sweeps of previous ranges and form reversals.
### Key Features
- Visualizes time-based ranges with customizable lengths (30 minutes, 90 minutes, etc.)
- Tracks high/low range development within specified time periods
- Shows multiple cycles per day for pattern recognition
- Supports historical analysis across multiple days
### Parameters
#### Settings
- **First Cycle (HHMM-HHMM)**: Define the time range of your first cycle. The duration of this range determines the length of all subsequent cycles (e.g., "0930-1000" creates 30-minute cycles)
- **Number of Cycles per Day**: How many consecutive cycles to display after the first cycle (1-20)
- **Maximum Days to Display**: Number of historical days to show the ranges for (1-50)
- **Timezone**: Select the appropriate timezone for your analysis
#### Style
- **Box Transparency**: Adjust the transparency of the range boxes (0-100)
### Usage Example
To track 30-minute ranges starting at market open:
1. Set First Cycle to "0930-1000" (creates 30-minute cycles)
2. Set Number of Cycles to 5 (will show ranges until 11:30)
3. The indicator will display:
- Range development during each 30-minute period
- Visual progression of highs and lows
- Color-coded cycles for easy distinction
### Use Cases
- Identify potential reversal points after range sweeps
- Track regular time-based support and resistance levels
- Analyze market structure within specific time windows
- Monitor range expansions and contractions during key market hours
### Tips
- Use in conjunction with volume analysis for better confirmation
- Pay attention to breaks and sweeps of previous ranges
- Consider market opens and key session times when setting cycles
- Compare range sizes across different time periods for volatility analysis
Market Movement After OpenDescription:
This script provides a detailed visualization of market movements during key trading hours: the German market opening (08:00–09:00 UTC+1) and the US market opening (15:30–16:30 UTC+1). It is designed to help traders analyze price behavior in these critical trading periods by capturing and presenting movement patterns and trends directly on the chart and in an interactive table.
Key Features:
Market Movement Analysis:
Tracks the price movement during the German market's first hour (08:00–09:00 UTC+1) and the US market's opening session (15:30–16:30 UTC+1).
Analyzes whether the price moved up or down during these intervals.
Visual Representation:
Dynamically colored price lines indicate upward (green) or downward (red) movement during the respective periods.
Labels ("DE" for Germany and "US" for the United States) mark key moments in the chart.
Historical Data Table:
Displays the past 10 trading days' movement trends in an interactive table, including:
Date: Trading date.
German Market Movement: Up (▲), Down (▼), or Neutral (-) for 08:00–09:00 UTC+1.
US Market Movement: Up (▲), Down (▼), or Neutral (-) for 15:30–16:30 UTC+1.
The table uses color coding for easy interpretation: green for upward movements, red for downward, and gray for neutral.
Real-Time Updates:
Automatically updates during live trading sessions to reflect the most recent movements.
Highlights incomplete periods (e.g., ongoing sessions) to indicate their status.
Customizable:
Suitable for intraday analysis or broader studies of market trends.
Designed to overlay directly on any price chart.
Use Case:
This script is particularly useful for traders who focus on market openings, which are often characterized by high volatility and significant price movements. By providing a clear visual representation of historical and live data, it aids in understanding and capitalizing on market trends during these critical periods.
Notes:
The script works best when the chart is set to the appropriate timezone (UTC+1 for the German market or your local equivalent).
For precise trading decisions, consider combining this script with other technical indicators or trading strategies.
Feel free to share feedback or suggest additional features to enhance the script!
Overnight Effect High Volatility Crypto (AiBitcoinTrend)👽 Overview of the Strategy
This strategy leverages the overnight effect in the cryptocurrency market, specifically targeting the two-hour window from 21:00 UTC to 23:00 UTC. The strategy is designed to be applied only during periods of high volatility, which is determined using historical volatility data. This approach, inspired by research from Padyšák and Vojtko (2022), aims to capitalize on statistically significant return patterns observed during these hours.
Deep Backtesting with a High Volatility Filter
Deep Backtesting without a High Volatility Filter
👽 How the Strategy Works
Volatility Calculation:
Each day at 00:00 UTC, the strategy calculates the 30-day historical volatility of crypto returns (typically Bitcoin). The historical volatility is the standard deviation of the log returns over the past 30 days, representing the market's recent volatility level.
Median Volatility Benchmark:
The median of the 30-day historical volatility is calculated over a 365-day period (one year). This median acts as a benchmark to classify each day as either:
👾 High Volatility: When the current 30-day volatility exceeds the median volatility.
👾 Low Volatility: When the current 30-day volatility is below the median.
Trading Rule:
If the day is classified as a High Volatility Day, the strategy executes the following trades:
👾 Buy at 21:00 UTC.
👾 Sell at 23:00 UTC.
Trade Execution Details:
The strategy uses a 0.02% fee per trade.
Each trade is executed with 25% of the available capital. This allocation helps manage risk while allowing for compounding returns.
Rationale:
The returns during the 22:00 and 23:00 UTC hours have been found to be statistically significant during high volatility periods. The overnight effect is believed to drive this phenomenon due to the asynchronous closing hours of global financial markets. This creates unique trading opportunities in the cryptocurrency market, where exchanges remain open 24/7.
👽 Market Context and Global Time Zone Impact
👾 Why 21:00 to 23:00 UTC?
During this window, major traditional financial markets are closed:
NYSE (New York) closes at 21:00 UTC.
London and European markets are closed during these hours.
Asian markets (Tokyo, Hong Kong, etc.) open later, leaving this window largely unaffected by traditional trading flows.
This global market inactivity creates a period where significant moves can occur in the cryptocurrency market, particularly during high volatility.
👽 Strategy Parameters
Volatility Period: 30 days.
The lookback period for calculating historical volatility.
Median Period: 365 days.
The lookback period for calculating the median volatility benchmark.
Entry Time: 21:00 UTC.
Adjust this to your local time if necessary (e.g., 16:00 in New York, 22:00 in Stockholm).
Exit Time: 23:00 UTC.
Adjust this to your local time if necessary (e.g., 18:00 in New York, 00:00 midnight in Stockholm).
👽 Benefits of the Strategy
Seasonality Effect:
The strategy captures consistent patterns driven by the overnight effect and high volatility periods.
Risk Reduction:
Since trades are executed during a specific window and only on high volatility days, the strategy helps mitigate exposure to broader market risk.
Simplicity and Efficiency:
The strategy is moderately complex, making it accessible for traders while offering significant returns.
Global Applicability:
Suitable for traders worldwide, with clear guidelines on adjusting for local time zones.
👽 Considerations
Market Conditions: The strategy works best in a high-volatility environment.
Execution: Requires precise timing to enter and exit trades at the specified hours.
Time Zone Adjustments: Ensure you convert UTC times accurately based on your location to execute trades at the correct local times.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
David_30-Minute Boxes This indicator draws boxes for 30-minute intervals on the chart, highlighting significant price movements. The boxes begin at 00:00 and end at 22:00. Each box is color-coded according to price action:
Green Box: The closing price at the end of the 30-minute period is above the opening price.
Red Box: The closing price at the end of the 30-minute period is below the opening price.
Dark Green Box: The closing price at the end of the box is higher than the high of the previous box.
Dark Red Box: The closing price at the end of the box is lower than the low of the previous box.
The boxes dynamically adjust within each 30-minute interval to reflect the high and low of the period. The border of each box is fully transparent for a clean and uncluttered visual display.
Optional Candle Numbering
In the indicator settings, you can enable or disable the numbering of individual candles within each box. The numbering restarts at 1 for each new box, helping to track the progression of individual 30-minute intervals.
Use Cases
This indicator is particularly useful for traders who want to analyze short-term movements and the dynamics within 30-minute intervals. The color-coding of the boxes provides quick visual insights into the strength of price action within a time interval, making it easier to spot momentum shifts or important support and resistance levels.
Highest Volume* 지표 설명
이 지표는 다양한 기간 동안의 최대 거래량을 시각적으로 표시하여 거래자들이 중요한 거래량 패턴을 쉽게 식별할 수 있도록 도와줍니다. 30, 60, 90, 120 캔들 기간 동안의 최대 거래량을 감지하고, 이를 차트 상에 색상 코드로 표시합니다.
다중 기간 분석: 30, 60, 90, 120 캔들 기간에 대한 최대 거래량을 동시에 추적합니다.
기간에 따른 색상 표시: 기간이 길어질수록 표시되는 색상이 짙어집니다.
* 주요 기능
거래량 급증 감지: 갑작스러운 거래량 증가를 빠르게 포착할 수 있습니다.
* 부가 설명
초록색 배경: 최근 120 캔들 중 최대 거래량
노란색 배경: 최근 90 캔들 중 최대 거래량 (120 캔들 최대가 아닌 경우)
주황색 배경: 최근 60 캔들 중 최대 거래량 (90, 120 캔들 최대가 아닌 경우)
빨간색 배경: 최근 30 캔들 중 최대 거래량 (60, 90, 120 캔들 최대가 아닌 경우)
* Indicator Description
This indicator visually displays the maximum trading volume over various periods, helping traders easily identify important volume patterns. It detects the highest volume over 30, 60, 90, and 120 candle periods and represents this on the chart using color codes.
Multi-period analysis: Simultaneously tracks the maximum volume for 30, 60, 90, and 120 candle periods.
Color display according to period: The color becomes darker as the period gets longer.
* Key Features
Rapid volume surge detection: Quickly captures sudden increases in trading volume.
* Additional Explanation
Green background: Highest volume among the most recent 120 candles
Yellow background: Highest volume among the most recent 90 candles (when not the highest in 120 candles)
Orange background: Highest volume among the most recent 60 candles (when not the highest in 90 or 120 candles)
Red background: Highest volume among the most recent 30 candles (when not the highest in 60, 90, or 120 candles)
Moving Average Ratio [InvestorUnknown]Overview
The "Moving Average Ratio" (MAR) indicator is a versatile tool designed for valuation, mean-reversion, and long-term trend analysis. This indicator provides multiple display modes to cater to different analytical needs, allowing traders and investors to gain deeper insights into the market dynamics.
Features
1. Moving Average Ratio (MAR):
Calculates the ratio of the chosen source (close, open, ohlc4, hl2 …) to a longer-term moving average of choice (SMA, EMA, HMA, WMA, DEMA)
Useful for identifying overbought or oversold conditions, aiding in mean-reversion strategies and valuation of assets.
For some high beta asset classes, like cryptocurrencies, you might want to use logarithmic scale for the raw MAR, below you can see the visual difference of using Linear and Logarithmic scale on BTC
2. MAR Z-Score:
Computes the Z-Score of the MAR to standardize the ratio over chosen time period, making it easier to identify extreme values relative to the historical mean.
Helps in detecting significant deviations from the mean, which can indicate potential reversal points and buying/selling opportunities
3. MAR Trend Analysis:
Uses a combination of short-term (default 1, raw MAR) and long-term moving averages of the MAR to identify trend changes.
Provides a visual representation of bullish and bearish trends based on moving average crossings.
Using Logarithmic scale can improve the visuals for some asset classes.
4. MAR Momentum:
Measures the momentum of the MAR by calculating the difference over a specified period.
Useful for detecting changes in the market momentum and potential trend reversals.
5. MAR Rate of Change (ROC):
Calculates the rate of change of the MAR to assess the speed and direction of price movements.
Helps in identifying accelerating or decelerating trends.
MAR Momentum and Rate of Change are very similar, the only difference is that the Momentum is expressed in units of the MAR change and ROC is expressed as % change of MAR over chosen time period.
Customizable Settings
General Settings:
Display Mode: Select the display mode from MAR, MAR Z-Score, MAR Trend, MAR Momentum, or MAR ROC.
Color Bars: Option to color the bars based on the current display mode.
Wait for Bar Close: Toggle to wait for the bar to close before updating the MAR value.
MAR Settings:
Length: Period for the moving average calculation.
Source: Data source for the moving average calculation.
Moving Average Type: Select the type of moving average (SMA, EMA, WMA, HMA, DEMA).
Z-Score Settings:
Z-Score Length: Period for the Z-Score calculation.
Trend Analysis Settings:
Moving Average Type: Select the type of moving average for trend analysis (SMA, EMA).
Longer Moving Average: Period for the longer moving average.
Shorter Moving Average: Period for the shorter moving average.
Momentum Settings:
Momentum Length: Period for the momentum calculation.
Rate of Change Settings:
ROC Length: Period for the rate of change calculation.
Calculation and Plotting
Moving Average Ratio (MAR):
Calculates the ratio of the price to the selected moving average type and length.
Plots the MAR with a gradient color based on its Z-Score, aiding in visual identification of extreme values.
// Moving Average Ratio (MAR)
ma_main = switch ma_main_type
"SMA" => ta.sma(src, len)
"EMA" => ta.ema(src, len)
"WMA" => ta.wma(src, len)
"HMA" => ta.hma(src, len)
"DEMA" => ta.dema(src, len)
mar = (waitforclose ? src : src) / ma_main
z_col = color.from_gradient(z, -2.5, 2.5, color.green, color.red)
plot(disp_mode.mar ? mar : na, color = z_col, histbase = 1, style = plot.style_columns)
barcolor(color_bars ? (disp_mode.mar ? (z_col) : na) : na)
MAR Z-Score:
Computes the Z-Score of the MAR and plots it with a color gradient indicating the magnitude of deviation from the mean.
// MAR Z-Score
mean = ta.sma(math.log(mar), z_len)
stdev = ta.stdev(math.log(mar),z_len)
z = (math.log(mar) - mean) / stdev
plot(disp_mode.mar_z ? z : na, color = z_col, histbase = 0, style = plot.style_columns)
plot(disp_mode.mar_z ? 1 : na, color = color.new(color.red,70))
plot(disp_mode.mar_z ? 2 : na, color = color.new(color.red,50))
plot(disp_mode.mar_z ? 3 : na, color = color.new(color.red,30))
plot(disp_mode.mar_z ? -1 : na, color = color.new(color.green,70))
plot(disp_mode.mar_z ? -2 : na, color = color.new(color.green,50))
plot(disp_mode.mar_z ? -3 : na, color = color.new(color.green,30))
barcolor(color_bars ? (disp_mode.mar_z ? (z_col) : na) : na)
MAR Trend:
Plots the MAR along with its short-term and long-term moving averages.
Uses color changes to indicate bullish or bearish trends based on moving average crossings.
// MAR Trend - Moving Average Crossing
mar_ma_long = switch ma_trend_type
"SMA" => ta.sma(mar, len_trend_long)
"EMA" => ta.ema(mar, len_trend_long)
mar_ma_short = switch ma_trend_type
"SMA" => ta.sma(mar, len_trend_short)
"EMA" => ta.ema(mar, len_trend_short)
plot(disp_mode.mar_t ? mar : na, color = mar_ma_long < mar_ma_short ? color.new(color.green,50) : color.new(color.red,50), histbase = 1, style = plot.style_columns)
plot(disp_mode.mar_t ? mar_ma_long : na, color = mar_ma_long < mar_ma_short ? color.green : color.red, linewidth = 4)
plot(disp_mode.mar_t ? mar_ma_short : na, color = mar_ma_long < mar_ma_short ? color.green : color.red, linewidth = 2)
barcolor(color_bars ? (disp_mode.mar_t ? (mar_ma_long < mar_ma_short ? color.green : color.red) : na) : na)
MAR Momentum:
Plots the momentum of the MAR, coloring the bars to indicate increasing or decreasing momentum.
// MAR Momentum
mar_mom = mar - mar
// MAR Momentum
mom_col = mar_mom > 0 ? (mar_mom > mar_mom ? color.new(color.green,0): color.new(color.green,30)) : (mar_mom < mar_mom ? color.new(color.red,0): color.new(color.red,30))
plot(disp_mode.mar_m ? mar_mom : na, color = mom_col, histbase = 0, style = plot.style_columns)
MAR Rate of Change (ROC):
Plots the ROC of the MAR, using color changes to show the direction and strength of the rate of change.
// MAR Rate of Change
mar_roc = ta.roc(mar,len_roc)
// MAR ROC
roc_col = mar_roc > 0 ? (mar_roc > mar_roc ? color.new(color.green,0): color.new(color.green,30)) : (mar_roc < mar_roc ? color.new(color.red,0): color.new(color.red,30))
plot(disp_mode.mar_r ? mar_roc : na, color = roc_col, histbase = 0, style = plot.style_columns)
Summary:
This multi-purpose indicator provides a comprehensive toolset for various trading strategies, including valuation, mean-reversion, and trend analysis. By offering multiple display modes and customizable settings, it allows users to tailor the indicator to their specific analytical needs and market conditions.
PubLibTrendLibrary "PubLibTrend"
trend, multi-part trend, double trend and multi-part double trend conditions for indicator and strategy development
rlut()
return line uptrend condition
Returns: bool
dt()
downtrend condition
Returns: bool
ut()
uptrend condition
Returns: bool
rldt()
return line downtrend condition
Returns: bool
dtop()
double top condition
Returns: bool
dbot()
double bottom condition
Returns: bool
rlut_1p()
1-part return line uptrend condition
Returns: bool
rlut_2p()
2-part return line uptrend condition
Returns: bool
rlut_3p()
3-part return line uptrend condition
Returns: bool
rlut_4p()
4-part return line uptrend condition
Returns: bool
rlut_5p()
5-part return line uptrend condition
Returns: bool
rlut_6p()
6-part return line uptrend condition
Returns: bool
rlut_7p()
7-part return line uptrend condition
Returns: bool
rlut_8p()
8-part return line uptrend condition
Returns: bool
rlut_9p()
9-part return line uptrend condition
Returns: bool
rlut_10p()
10-part return line uptrend condition
Returns: bool
rlut_11p()
11-part return line uptrend condition
Returns: bool
rlut_12p()
12-part return line uptrend condition
Returns: bool
rlut_13p()
13-part return line uptrend condition
Returns: bool
rlut_14p()
14-part return line uptrend condition
Returns: bool
rlut_15p()
15-part return line uptrend condition
Returns: bool
rlut_16p()
16-part return line uptrend condition
Returns: bool
rlut_17p()
17-part return line uptrend condition
Returns: bool
rlut_18p()
18-part return line uptrend condition
Returns: bool
rlut_19p()
19-part return line uptrend condition
Returns: bool
rlut_20p()
20-part return line uptrend condition
Returns: bool
rlut_21p()
21-part return line uptrend condition
Returns: bool
rlut_22p()
22-part return line uptrend condition
Returns: bool
rlut_23p()
23-part return line uptrend condition
Returns: bool
rlut_24p()
24-part return line uptrend condition
Returns: bool
rlut_25p()
25-part return line uptrend condition
Returns: bool
rlut_26p()
26-part return line uptrend condition
Returns: bool
rlut_27p()
27-part return line uptrend condition
Returns: bool
rlut_28p()
28-part return line uptrend condition
Returns: bool
rlut_29p()
29-part return line uptrend condition
Returns: bool
rlut_30p()
30-part return line uptrend condition
Returns: bool
dt_1p()
1-part downtrend condition
Returns: bool
dt_2p()
2-part downtrend condition
Returns: bool
dt_3p()
3-part downtrend condition
Returns: bool
dt_4p()
4-part downtrend condition
Returns: bool
dt_5p()
5-part downtrend condition
Returns: bool
dt_6p()
6-part downtrend condition
Returns: bool
dt_7p()
7-part downtrend condition
Returns: bool
dt_8p()
8-part downtrend condition
Returns: bool
dt_9p()
9-part downtrend condition
Returns: bool
dt_10p()
10-part downtrend condition
Returns: bool
dt_11p()
11-part downtrend condition
Returns: bool
dt_12p()
12-part downtrend condition
Returns: bool
dt_13p()
13-part downtrend condition
Returns: bool
dt_14p()
14-part downtrend condition
Returns: bool
dt_15p()
15-part downtrend condition
Returns: bool
dt_16p()
16-part downtrend condition
Returns: bool
dt_17p()
17-part downtrend condition
Returns: bool
dt_18p()
18-part downtrend condition
Returns: bool
dt_19p()
19-part downtrend condition
Returns: bool
dt_20p()
20-part downtrend condition
Returns: bool
dt_21p()
21-part downtrend condition
Returns: bool
dt_22p()
22-part downtrend condition
Returns: bool
dt_23p()
23-part downtrend condition
Returns: bool
dt_24p()
24-part downtrend condition
Returns: bool
dt_25p()
25-part downtrend condition
Returns: bool
dt_26p()
26-part downtrend condition
Returns: bool
dt_27p()
27-part downtrend condition
Returns: bool
dt_28p()
28-part downtrend condition
Returns: bool
dt_29p()
29-part downtrend condition
Returns: bool
dt_30p()
30-part downtrend condition
Returns: bool
ut_1p()
1-part uptrend condition
Returns: bool
ut_2p()
2-part uptrend condition
Returns: bool
ut_3p()
3-part uptrend condition
Returns: bool
ut_4p()
4-part uptrend condition
Returns: bool
ut_5p()
5-part uptrend condition
Returns: bool
ut_6p()
6-part uptrend condition
Returns: bool
ut_7p()
7-part uptrend condition
Returns: bool
ut_8p()
8-part uptrend condition
Returns: bool
ut_9p()
9-part uptrend condition
Returns: bool
ut_10p()
10-part uptrend condition
Returns: bool
ut_11p()
11-part uptrend condition
Returns: bool
ut_12p()
12-part uptrend condition
Returns: bool
ut_13p()
13-part uptrend condition
Returns: bool
ut_14p()
14-part uptrend condition
Returns: bool
ut_15p()
15-part uptrend condition
Returns: bool
ut_16p()
16-part uptrend condition
Returns: bool
ut_17p()
17-part uptrend condition
Returns: bool
ut_18p()
18-part uptrend condition
Returns: bool
ut_19p()
19-part uptrend condition
Returns: bool
ut_20p()
20-part uptrend condition
Returns: bool
ut_21p()
21-part uptrend condition
Returns: bool
ut_22p()
22-part uptrend condition
Returns: bool
ut_23p()
23-part uptrend condition
Returns: bool
ut_24p()
24-part uptrend condition
Returns: bool
ut_25p()
25-part uptrend condition
Returns: bool
ut_26p()
26-part uptrend condition
Returns: bool
ut_27p()
27-part uptrend condition
Returns: bool
ut_28p()
28-part uptrend condition
Returns: bool
ut_29p()
29-part uptrend condition
Returns: bool
ut_30p()
30-part uptrend condition
Returns: bool
rldt_1p()
1-part return line downtrend condition
Returns: bool
rldt_2p()
2-part return line downtrend condition
Returns: bool
rldt_3p()
3-part return line downtrend condition
Returns: bool
rldt_4p()
4-part return line downtrend condition
Returns: bool
rldt_5p()
5-part return line downtrend condition
Returns: bool
rldt_6p()
6-part return line downtrend condition
Returns: bool
rldt_7p()
7-part return line downtrend condition
Returns: bool
rldt_8p()
8-part return line downtrend condition
Returns: bool
rldt_9p()
9-part return line downtrend condition
Returns: bool
rldt_10p()
10-part return line downtrend condition
Returns: bool
rldt_11p()
11-part return line downtrend condition
Returns: bool
rldt_12p()
12-part return line downtrend condition
Returns: bool
rldt_13p()
13-part return line downtrend condition
Returns: bool
rldt_14p()
14-part return line downtrend condition
Returns: bool
rldt_15p()
15-part return line downtrend condition
Returns: bool
rldt_16p()
16-part return line downtrend condition
Returns: bool
rldt_17p()
17-part return line downtrend condition
Returns: bool
rldt_18p()
18-part return line downtrend condition
Returns: bool
rldt_19p()
19-part return line downtrend condition
Returns: bool
rldt_20p()
20-part return line downtrend condition
Returns: bool
rldt_21p()
21-part return line downtrend condition
Returns: bool
rldt_22p()
22-part return line downtrend condition
Returns: bool
rldt_23p()
23-part return line downtrend condition
Returns: bool
rldt_24p()
24-part return line downtrend condition
Returns: bool
rldt_25p()
25-part return line downtrend condition
Returns: bool
rldt_26p()
26-part return line downtrend condition
Returns: bool
rldt_27p()
27-part return line downtrend condition
Returns: bool
rldt_28p()
28-part return line downtrend condition
Returns: bool
rldt_29p()
29-part return line downtrend condition
Returns: bool
rldt_30p()
30-part return line downtrend condition
Returns: bool
dut()
double uptrend condition
Returns: bool
ddt()
double downtrend condition
Returns: bool
dut_1p()
1-part double uptrend condition
Returns: bool
dut_2p()
2-part double uptrend condition
Returns: bool
dut_3p()
3-part double uptrend condition
Returns: bool
dut_4p()
4-part double uptrend condition
Returns: bool
dut_5p()
5-part double uptrend condition
Returns: bool
dut_6p()
6-part double uptrend condition
Returns: bool
dut_7p()
7-part double uptrend condition
Returns: bool
dut_8p()
8-part double uptrend condition
Returns: bool
dut_9p()
9-part double uptrend condition
Returns: bool
dut_10p()
10-part double uptrend condition
Returns: bool
dut_11p()
11-part double uptrend condition
Returns: bool
dut_12p()
12-part double uptrend condition
Returns: bool
dut_13p()
13-part double uptrend condition
Returns: bool
dut_14p()
14-part double uptrend condition
Returns: bool
dut_15p()
15-part double uptrend condition
Returns: bool
dut_16p()
16-part double uptrend condition
Returns: bool
dut_17p()
17-part double uptrend condition
Returns: bool
dut_18p()
18-part double uptrend condition
Returns: bool
dut_19p()
19-part double uptrend condition
Returns: bool
dut_20p()
20-part double uptrend condition
Returns: bool
dut_21p()
21-part double uptrend condition
Returns: bool
dut_22p()
22-part double uptrend condition
Returns: bool
dut_23p()
23-part double uptrend condition
Returns: bool
dut_24p()
24-part double uptrend condition
Returns: bool
dut_25p()
25-part double uptrend condition
Returns: bool
dut_26p()
26-part double uptrend condition
Returns: bool
dut_27p()
27-part double uptrend condition
Returns: bool
dut_28p()
28-part double uptrend condition
Returns: bool
dut_29p()
29-part double uptrend condition
Returns: bool
dut_30p()
30-part double uptrend condition
Returns: bool
ddt_1p()
1-part double downtrend condition
Returns: bool
ddt_2p()
2-part double downtrend condition
Returns: bool
ddt_3p()
3-part double downtrend condition
Returns: bool
ddt_4p()
4-part double downtrend condition
Returns: bool
ddt_5p()
5-part double downtrend condition
Returns: bool
ddt_6p()
6-part double downtrend condition
Returns: bool
ddt_7p()
7-part double downtrend condition
Returns: bool
ddt_8p()
8-part double downtrend condition
Returns: bool
ddt_9p()
9-part double downtrend condition
Returns: bool
ddt_10p()
10-part double downtrend condition
Returns: bool
ddt_11p()
11-part double downtrend condition
Returns: bool
ddt_12p()
12-part double downtrend condition
Returns: bool
ddt_13p()
13-part double downtrend condition
Returns: bool
ddt_14p()
14-part double downtrend condition
Returns: bool
ddt_15p()
15-part double downtrend condition
Returns: bool
ddt_16p()
16-part double downtrend condition
Returns: bool
ddt_17p()
17-part double downtrend condition
Returns: bool
ddt_18p()
18-part double downtrend condition
Returns: bool
ddt_19p()
19-part double downtrend condition
Returns: bool
ddt_20p()
20-part double downtrend condition
Returns: bool
ddt_21p()
21-part double downtrend condition
Returns: bool
ddt_22p()
22-part double downtrend condition
Returns: bool
ddt_23p()
23-part double downtrend condition
Returns: bool
ddt_24p()
24-part double downtrend condition
Returns: bool
ddt_25p()
25-part double downtrend condition
Returns: bool
ddt_26p()
26-part double downtrend condition
Returns: bool
ddt_27p()
27-part double downtrend condition
Returns: bool
ddt_28p()
28-part double downtrend condition
Returns: bool
ddt_29p()
29-part double downtrend condition
Returns: bool
ddt_30p()
30-part double downtrend condition
Returns: bool
ICT Killzones and Sessions W/ Silver Bullet + MacrosForex and Equity Session Tracker with Killzones, Silver Bullet, and Macro Times
This Pine Script indicator is a comprehensive timekeeping tool designed specifically for ICT traders using any time-based strategy. It helps you visualize and keep track of forex and equity session times, kill zones, macro times, and silver bullet hours.
Features:
Session and Killzone Lines:
Green: London Open (LO)
White: New York (NY)
Orange: Australian (AU)
Purple: Asian (AS)
Includes AM and PM session markers.
Dotted/Striped Lines indicate overlapping kill zones within the session timeline.
Customization Options:
Display sessions and killzones in collapsed or full view.
Hide specific sessions or killzones based on your preferences.
Customize colors, texts, and sizes.
Option to hide drawings older than the current day.
Automatic Updates:
The indicator draws all lines and boxes at the start of a new day.
Automatically adjusts time-based boxes according to the New York timezone.
Killzone Time Windows (for indices):
London KZ: 02:00 - 05:00
New York AM KZ: 07:00 - 10:00
New York PM KZ: 13:30 - 16:00
Silver Bullet Times:
03:00 - 04:00
10:00 - 11:00
14:00 - 15:00
Macro Times:
02:33 - 03:00
04:03 - 04:30
08:50 - 09:10
09:50 - 10:10
10:50 - 11:10
11:50 - 12:50
Latest Update:
January 15:
Added option to automatically change text coloring based on the chart.
Included additional optional macro times per user request:
12:50 - 13:10
13:50 - 14:15
14:50 - 15:10
15:50 - 16:15
Usage:
To maximize your experience, minimize the pane where the script is drawn. This minimizes distractions while keeping the essential time markers visible. The script is designed to help traders by clearly annotating key trading periods without overwhelming their charts.
Originality and Justification:
This indicator uniquely integrates various time-based strategies essential for ICT traders. Unlike other indicators, it consolidates session times, kill zones, macro times, and silver bullet hours into one comprehensive tool. This allows traders to have a clear and organized view of critical trading periods, facilitating better decision-making.
Credits:
This script incorporates open-source elements with significant improvements to enhance functionality and user experience.
Forex and Equity Session Tracker with Killzones, Silver Bullet, and Macro Times
This Pine Script indicator is a comprehensive timekeeping tool designed specifically for ICT traders using any time-based strategy. It helps you visualize and keep track of forex and equity session times, kill zones, macro times, and silver bullet hours.
Features:
Session and Killzone Lines:
Green: London Open (LO)
White: New York (NY)
Orange: Australian (AU)
Purple: Asian (AS)
Includes AM and PM session markers.
Dotted/Striped Lines indicate overlapping kill zones within the session timeline.
Customization Options:
Display sessions and killzones in collapsed or full view.
Hide specific sessions or killzones based on your preferences.
Customize colors, texts, and sizes.
Option to hide drawings older than the current day.
Automatic Updates:
The indicator draws all lines and boxes at the start of a new day.
Automatically adjusts time-based boxes according to the New York timezone.
Killzone Time Windows (for indices):
London KZ: 02:00 - 05:00
New York AM KZ: 07:00 - 10:00
New York PM KZ: 13:30 - 16:00
Silver Bullet Times:
03:00 - 04:00
10:00 - 11:00
14:00 - 15:00
Macro Times:
02:33 - 03:00
04:03 - 04:30
08:50 - 09:10
09:50 - 10:10
10:50 - 11:10
11:50 - 12:50
Latest Update:
January 15:
Added option to automatically change text coloring based on the chart.
Included additional optional macro times per user request:
12:50 - 13:10
13:50 - 14:15
14:50 - 15:10
15:50 - 16:15
ICT Sessions and Kill Zones
What They Are:
ICT Sessions: These are specific times during the trading day when market activity is expected to be higher, such as the London Open, New York Open, and the Asian session.
Kill Zones: These are specific time windows within these sessions where the probability of significant price movements is higher. For example, the New York AM Kill Zone is typically from 8:30 AM to 11:00 AM EST.
How to Use Them:
Identify the Session: Determine which trading session you are in (London, New York, or Asian).
Focus on Kill Zones: Within that session, focus on the kill zones for potential trade setups. For instance, during the New York session, look for setups between 8:30 AM and 11:00 AM EST.
Silver Bullets
What They Are:
Silver Bullets: These are specific, high-probability trade setups that occur within the kill zones. They are designed to be "one shot, one kill" trades, meaning they aim for precise and effective entries and exits.
How to Use Them:
Time-Based Setup: Look for these setups within the designated kill zones. For example, between 10:00 AM and 11:00 AM for the New York AM session .
Chart Analysis: Start with higher time frames like the 15-minute chart and then refine down to 5-minute and 1-minute charts to identify imbalances or specific patterns .
Macros
What They Are:
Macros: These are broader market conditions and trends that influence your trading decisions. They include understanding the overall market direction, seasonal tendencies, and the Commitment of Traders (COT) reports.
How to Use Them:
Understand Market Conditions: Be aware of the macroeconomic factors and market conditions that could affect price movements.
Seasonal Tendencies: Know the seasonal patterns that might influence the market direction.
COT Reports: Use the Commitment of Traders reports to understand the positioning of large traders and commercial hedgers .
Putting It All Together
Preparation: Understand the macro conditions and review the COT reports.
Session and Kill Zone: Identify the trading session and focus on the kill zones.
Silver Bullet Setup: Look for high-probability setups within the kill zones using refined chart analysis.
Execution: Execute the trade with precision, aiming for a "one shot, one kill" outcome.
By following these steps, you can effectively use ICT sessions, kill zones, silver bullets, and macros to enhance your trading strategy.
Usage:
To maximize your experience, shrink the pane where the script is drawn. This minimizes distractions while keeping the essential time markers visible. The script is designed to help traders by clearly annotating key trading periods without overwhelming their charts.
Originality and Justification:
This indicator uniquely integrates various time-based strategies essential for ICT traders. Unlike other indicators, it consolidates session times, kill zones, macro times, and silver bullet hours into one comprehensive tool. This allows traders to have a clear and organized view of critical trading periods, facilitating better decision-making.
Credits:
This script incorporates open-source elements with significant improvements to enhance functionality and user experience. All credit goes to itradesize for the SB + Macro boxes
[imba]lance algo🟩 INTRODUCTION
Hello, everyone!
Please take the time to review this description and source code to utilize this script to its fullest potential.
🟩 CONCEPTS
This is a trend indicator. The trend is the 0.5 fibonacci level for a certain period of time.
A trend change occurs when at least one candle closes above the level of 0.236 (for long) or below 0.786 (for short). Also it has massive amout of settings and features more about this below.
With good settings, the indicator works great on any market and any time frame!
A distinctive feature of this indicator is its backtest panel. With which you can dynamically view the results of setting up a strategy such as profit, what the deposit size is, etc.
Please note that the profit is indicated as a percentage of the initial deposit. It is also worth considering that all profit calculations are based on the risk % setting.
🟩 FEATURES
First, I want to show you what you see on the chart. And I’ll show you everything closer and in more detail.
1. Position
2. Statistic panel
3. Backtest panel
Indicator settings:
Let's go in order:
1. Strategies
This setting is responsible for loading saved strategies. There are only two preset settings, MANUAL and UNIVERSAL. If you choose any strategy other than MANUAL, then changing the settings for take profits, stop loss, sensitivity will not bring any results.
You can also save your customized strategies, this is discussed in a separate paragraph “🟩HOW TO SAVE A STRATEGY”
2. Sensitive
Responsible for the time period in bars to create Fibonacci levels
3. Start calculating date
This is the time to start backtesting strategies
4. Position group
Show checkbox - is responsible for displaying positions
Fill checkbox - is responsible for filling positions with background
Risk % - is responsible for what percentage of the deposit you are willing to lose if there is a stop loss
BE target - here you can choose when you reach which take profit you need to move your stop loss to breakeven
Initial deposit- starting deposit for profit calculation
5. Stoploss group
Fixed stoploss % checkbox - If choosed: stoploss will be calculated manually depending on the setting below( formula: entry_price * (1 - stoploss percent)) If NOT choosed: stoploss will be ( formula: fibonacci level(0.786/0.236) * (1 + stoploss percent))
6. Take profit group
This group of settings is responsible for how far from the entry point take profits will be and what % of the position to fix
7. RSI
Responsible for configuring the built-in RSI. Suitable bars will be highlighted with crosses above or below, depending on overbought/oversold
8. Infopanels group
Here I think everything is clear, you can hide or show information panels
9. Developer mode
If enabled, all events that occur will be shown, for example, reaching a take profit or stop loss with detailed information about the unfixed balance of the position
🟩 HOW TO USE
Very simple. All you need is to wait for the trend to change to long or short, you will immediately see a stop loss and four take profits, and you will also see prices. Like in this picture:
🟩 ALERTS
There are 3 types of alerts:
1. Long signal
2. Short signal
3. Any alert() function call - will be send to you json with these fields
{
"side": "LONG",
"entry": "64.454",
"tp1": "65.099",
"tp2": "65.743",
"tp3": "66.388",
"tp4": "67.032",
"winrate": "35.42%",
"strategy": "MANUAL",
"beTargetTrigger": "1",
"stop": "64.44"
}
🟩 HOW TO SAVE A STRATEGY
First, you need to make sure that the “MANUAL” strategy is selected in the strategy settings.
After this, you can start selecting parameters that will show the largest profit in the statistics panel.
I have highlighted what you need to pay attention to when choosing a strategy
Let's assume you have set up a strategy. The main question is how to preserve it?
Let’s say the strategy turned out with the following parameters:
Next we need to find this section of code:
// STRATS
selector(string strategy_name) =>
strategy_settings = Strategy_settings.new()
switch strategy_name
"MANUAL" =>
strategy_settings.sensitivity := 18
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"UNIVERSAL" =>
strategy_settings.sensitivity := 20
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
// "NEW STRATEGY" =>
// strategy_settings.sensitivity := 20
// strategy_settings.risk_percent := 1
// strategy_settings.break_even_target := "1"
// strategy_settings.tp1_percent := 1
// strategy_settings.tp1_percent_fix := 40
// strategy_settings.tp2_percent := 2
// strategy_settings.tp2_percent_fix := 30
// strategy_settings.tp3_percent := 3
// strategy_settings.tp3_percent_fix := 20
// strategy_settings.tp4_percent := 4
// strategy_settings.tp4_percent_fix := 10
// strategy_settings.fixed_stop := false
// strategy_settings.sl_percent := 0.0
strategy_settings
// STRATS
Let's uncomment on the latest strategy called "NEW STRATEGY" rename it to "SOL 5m" and change the sensitivity:
// STRATS
selector(string strategy_name) =>
strategy_settings = Strategy_settings.new()
switch strategy_name
"MANUAL" =>
strategy_settings.sensitivity := 18
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"UNIVERSAL" =>
strategy_settings.sensitivity := 20
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"SOL 5m" =>
strategy_settings.sensitivity := 15
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
strategy_settings
// STRATS
Now let's find this code:
strategy_input = input.string(title = "STRATEGY", options = , defval = "MANUAL", tooltip = "EN:\nTo manually configure the strategy, select MANUAL otherwise, changing the settings won't have any effect\nRU:\nЧтобы настроить стратегию вручную, выберите MANUAL в противном случае изменение настроек не будет иметь никакого эффекта")
And let's add our new strategy there, it turned out like this:
strategy_input = input.string(title = "STRATEGY", options = , defval = "MANUAL", tooltip = "EN:\nTo manually configure the strategy, select MANUAL otherwise, changing the settings won't have any effect\nRU:\nЧтобы настроить стратегию вручную, выберите MANUAL в противном случае изменение настроек не будет иметь никакого эффекта")
That's all. Our new strategy is now saved! It's simple! Now we can select it in the list of strategies:
HighLowBox+220MAs[libHTF]HighLowBox+220MAs
This is a sample script of libHTF to use HTF values without request.security().
import nazomobile/libHTFwoRS/1
HTF candles are calculated internally using 'GMT+3' from current TF candles by libHTF .
To calcurate Higher TF candles, please display many past bars at first.
The advantage and disadvantage is that the data can be generated at the current TF granularity.
Although the signal can be displayed more sensitively, plots such as MAs are not smooth.
In this script, assigned ➊,➋,➌,➍ for htf1,htf2,htf3,htf4.
HTF candles
Draw candles for HTF1-4 on the right edge of the chart. 2 candles for each HTF.
They are updated with every current TF bar update.
Left edge of HTF candles is located at the x-postion latest bar_index + offset.
DMI HTF
ADX/+DI/DI arrows(8lines) are shown each timeframes range.
Current TF's is located at left side of the HighLowBox.
HTF's are located at HighLowBox of HTF candles.
The top of HighLowBox is 100, The bottom of HighLowBox is 0.
HighLowBox HTF
Enclose in a square high and low range in each timeframe.
Shows price range and duration of each box.
In current timeframe, shows Fibonacci Scale inside(23.6%, 38.2%, 50.0%, 61.8%, 76.4%)/outside of each box.
Outside(161.8%,261.8,361.8%) would be shown as next target, if break top/bottom of each box.
In HTF, shows Fibonacci Level of the current price at latest box only.
Boxes:
1 for current timeframe.
4 for higher timeframes.(Steps of timeframe: 5, 15, 60, 240, D, W, M, 3M, 6M, Y)
HighLowBox TrendLine
Draw TrendLine for each HighLow Range. TrendLine is drawn between high and return high(or low and return low) of each HighLowBox.
Style of TrendLine is same as each HighLowBox.
HighLowBox RSI
RSI Signals are shown at the bottom(RSI<=30) or the top(RSI>=70) of HighLowBox in each timeframe.
RSI Signal is color coded by RSI9 and RSI14 in each timeframe.(current TF: ●, HTF1-4: ➊➋➌➍)
In case of RSI<=30, Location: bottom of the HighLowBox
white: only RSI9 is <=30
aqua: RSI9&RSI14; <=30 and RSI9RSI14
green: only RSI14 <=30
In case of RSI>=70, Location: top of the HighLowBox
white: only RSI9 is >=70
yellow: RSI9&RSI14; >=70 and RSI9>RSI14
orange: RSI9&RSI14; >=70 and RSI9=70
blue/green and orange/red could be a oversold/overbought sign.
20/200 MAs
Shows 20 and 200 MAs in each TFs(tfChart and 4 Higher).
TFs:
current TF
HTF1-4
MAs:
20SMA
20EMA
200SMA
200EMA
Physics CandlesPhysics Candles embed volume and motion physics directly onto price candles or market internals according to the cyclic pattern of financial securities. The indicator works on both real-time “ticks” and historical data using statistical modeling to highlight when these values, like volume or momentum, is unusual or relatively high for some periodic window in time. Each candle is made out of one or more sub-candles that each contain their own information of motion, which converts to the color and transparency, or brightness, of that particular candle segment. The segments extend throughout the entire candle, both body and wicks, and Thick Wicks can be implemented to see the color coding better. This candle segmentation allows you to see if all the volume or energy is evenly distributed throughout the candle or highly contained in one small portion of it, and how intense these values are compared to similar time periods without going to lower time frames. Candle segmentation can also change a trader’s perspective on how valuable the information is. A “low” volume candle, for instance, could signify high value short-term stopping volume if the volume is all concentrated in one segment.
The Candles are flexible. The physics information embedded on the candles need not be from the same price security or market internal as the chart when using the Physics Source option, and multiple Candles can be overlayed together. You could embed stock price Candles with market volume, market price Candles with stock momentum, market structure with internal acceleration, stock price with stock force, etc. My particular use case is scalping the SPX futures market (ES), whose price action is also dictated by the volume action in the associated cash market, or SPY, as well as a host of other securities. Physics allows you to embed the ES volume on the SPY price action, or the SPY volume on the ES price action, or you can combine them both by overlaying two Candle streams and increasing the Number of Overlays option to two. That option decreases the transparency levels of your coloring scheme so that overlaying multiple Candles converges toward the same visual color intensity as if you had one. The Candle and Physics Sources allows for both Symbols and Spreads to visualize Candle physics from a single ticker or some mathematical transformation of tickers.
Due to certain TradingView programming restrictions, each Candle can only be made out of a maximum of 8 candle segments, or an “8-bit” resolution. Since limits are just an opportunity to go beyond, the user has the option to stack multiple Candle indicators together to further increase the candle resolution. If you don’t want to see the Candles for some particular period of the day, you can hide them, or use the hiding feature to have multiple Candles calibrated to show multiple parts of the trading day. Securities tend to have low volume after hours with sharp spikes at the open or close. Multiple Candles can be used for multiple parts of the trading day to accommodate these different cycles in volume.
The Candles do not need be associated with the nominal security listed on the TV chart. The Candle Source allows the user to look at AAPL Candles, for instance, while on a TSLA or SPY chart, each with their respective volume actions integrated into the candles, for instance, to allow the user to see multiple security price and volume correlation on a single chart.
The physics information currently embeddable on Candles are volume or time, velocity, momentum, acceleration, force, and kinetic energy. In order to apply equations of motion containing a mass variable to financial securities, some analogous value for mass must be assumed. Traders often regard volume or time as inextricable variables to a securities price that can indicate the direction and strength of a move. Since mass is the inextricable variable to calculating the momentum, force, or kinetic energy of motion, the user has the option to assume either time or volume is analogous to mass. Volume may be a better option for mass as it is not strictly dependent on the speed of a security, whereas time is.
Data transformations and outlier statistics are used to color code the intensity of the physics for each candle segment relative to past periodic behavior. A million shares during pre-market or a million shares during noontime may be more intense signals than a typical million shares traded at the open, and should have more intense color signals. To account for a specific cyclic behavior in the market, the user can specify the Window and Cycle Time Frames. The Window Time Frame splits up a Cycle into windows, samples and aggregates the statistics for each window, then compares the current physics values against past values in the same window. Intraday traders may benefit from using a Daily Cycle with a 30-minute Window Time Frame and 1-minute Sample Time Frame. These settings sample and compare the physics of 1-minute candles within the current 30-minute window to the same 30-minute window statistics for all past trading days, up until the data limit imposed by TradingView, or until the Data Collection Start Date specified in the settings. Longer-term traders may benefit from using a Monthly Cycle with a Weekly Time Frame, or a Yearly Cycle with a Quarterly Time Frame.
Multiple statistics and data transformation methods are available to convey relative intensity in different ways for different trading signals. Physics Candles allows for both Normal and Log-Normal assumptions in the physics distribution. The data can then be transformed by Linear, Logarithmic, Z-Score, or Power-Law scoring, where scoring simply assigns an intensity to the relative physics value of each candle segment based on some mathematical transformation. Z-scoring often renders adequate detection by scoring the segment value, such as volume or momentum, according to the mean and standard deviation of the data set in each window of the cycle. Logarithmic or power-law transformation with a gamma below 1 decreases the disparity between intensities so more less-important signals will show up, whereas the power-law transformation with gamma values above 1 increases the disparity between intensities, so less more-important signals will show up. These scores are then converted to color and transparency between the Min Score and the Max Score Cutoffs. The Auto-Normalization feature can automatically pick these cutoffs specific to each window based on the mean and standard deviation of the data set, or the user can manually set them. Physics was developed with novices in mind so that most users could calibrate their own settings by plotting the candle segment distributions directly on the chart and fiddling with the settings to see how different cutoffs capture different portions of the distribution and affect the relative color intensities differently. Security distributions are often skewed with fat-tails, known as kurtosis, where high-volume segments for example, have a higher-probabilities than expected for a normal distribution. These distribution are really log-normal, so that taking the logarithm leads to a standard bell-shaped distribution. Taking the Z-score of the Log-Normal distribution could make the most statistical sense, but color sensitivity is a discretionary preference.
Background Philosophy
This indicator was developed to study and trade the physics of motion in financial securities from a visually intuitive perspective. Newton’s laws of motion are loosely applied to financial motion:
“A body remains at rest, or in motion at a constant speed in a straight line, unless acted upon by a force”.
Financial securities remain at rest, or in motion at constant speed up or down, unless acted upon by the force of traders exchanging securities.
“When a body is acted upon by a force, the time rate of change of its momentum equals the force”.
Momentum is the product of mass and velocity, and force is the product of mass and acceleration. Traders render force on the security through the mass of their trading activity and the acceleration of price movement.
“If two bodies exert forces on each other, these forces have the same magnitude but opposite directions.”
Force arises from the interaction of traders, buyers and sellers. One body of motion, traders’ capitalization, exerts an equal and opposite force on another body of motion, the financial security. A securities movement arises at the expense of a buyer or seller’s capitalization.
Volume
The premise of this indicator assumes that volume, v, is an analogous means of measuring physical mass, m. This premise allows the application of the equations of motion to the movement of financial securities. We know from E=mc^2 that mass has energy. Energy can be used to create motion as kinetic energy. Taking a simple hypothetical example, the interaction of one short seller looking to cover lower and one buyer looking to sell higher exchange shares in a security at an agreed upon price to create volume or mass, and therefore, potential energy. Eventually the short seller will actively cover and buy the security from the previous buyer, moving the security higher, or the buyer will actively sell to the short seller, moving the security lower. The potential energy inherent in the initial consolidation or trading activity between buy and seller is now converted to kinetic energy on the subsequent trading activity that moves the securities price. The more potential energy that is created in the consolidation, the more kinetic energy there is to move price. This is why point and figure traders are said to give price targets based on the level of volatility or size of a consolidation range, or why Gann traders square price and time, as time is roughly proportional to mass and trading activity. The build-up of potential energy between short sellers and buyers in GME or TSLA led to their explosive moves beyond their standard fundamental valuations.
Position
Position, p, is simply the price or value of a financial security or market internal.
Time
Time, t, is another means of measuring mass to discover price behavior beyond the time snapshots that simple candle charts provide. We know from E=mc^2 that time is related to rest mass and energy given the speed of light, c, where time ≈ distance * sqrt(mass/E). This relation can also be derived from F=ma. The more mass there is, the longer it takes to compute the physics of a system. The more energy there is, the shorter it takes to compute the physics of a system. Similarly, more time is required to build a “resting” low-volatility trading consolidation with more mass. More energy added to that trading consolidation by competing buyers and sellers decreases the time it takes to build that same mass. Time is also related to price through velocity.
Velocity = (p(t1) – p(t0)) / p(t0)
Velocity, v, is the relative percent change of a securities price, p, over a period of time, t0 to t1. The period of time is between subsequent candles, and since time is constant between candles within the same timeframe, it is not used to calculate velocity or acceleration. Price moves faster with higher velocity, and slower with slower velocity, over the same fixed period of time. The product of velocity and mass gives momentum.
Momentum = mv
This indicator uses physics definition of momentum, not finance’s. In finance, momentum is defined as the amount of change in a securities price, either relative or absolute. This is definition is unfortunate, pun intended, since a one dollar move in a security from a thousand shares traded between a few traders has the exact same “momentum” as a one dollar move from millions of shares traded between hundreds of traders with everything else equal. If momentum is related to the energy of the move, momentum should consider both the level of activity in a price move, and the amount of that price move. If we equate mass to volume to account for the level of trading activity and use physics definition of momentum as the product of mass and velocity, this revised definition now gives a thousand-times more momentum to a one-dollar price move that has a thousand-times more volume behind it. If you want to use finance’s volume-less definition of momentum, use velocity in this indicator.
Acceleration = v(t1) – v(t0)
Acceleration, a, is the difference between velocities over some period of time, t0 to t1. Positive acceleration is necessary to increase a securities speed in the positive direction, while negative acceleration is necessary to decrease it. Acceleration is related to force by mass.
Force = ma
Force is required to change the speed of a securities valuation. Price movements with considerable force have considerably more impact on future direction. A change in direction requires force.
Kinetic Energy = 0.5mv^2
Kinetic energy is the energy that a financial security gains from the change in its velocity by force. The built-up of potential energy in trading consolidations can be converted to kinetic energy on a breakout from the consolidation.
Cycle Theory and Relativity
Just as the physics of motion is relative to a point of reference, so too should the physics of financial securities be relative to a point of reference. An object moving at a 100 mph towards another object moving in the same direction at 100 mph will not appear to be moving relative to each other, nor will they collide, but from an outsider observer, the objects are going 100 mph and will collide with significant impact if they run into a stationary object relative to the observer. Similarly, trading with a hundred thousand shares at the open when the average volume is a couple million may have a much smaller impact on the price compared to trading a hundred thousand shares pre-market when the average volume is ten thousand shares. The point of reference used in this indicator is the average statistics collected for a given Window Time Frame for every Cycle Time Frame. The physics values are normalized relative to these statistics.
Examples
The main chart of this publication shows the Force Candles for the SPY. An intense force candle is observed pre-market that implicates the directional overtone of the day. The assumption that direction should follow force arises from physical observation. If a large object is accelerating intensely in a particular direction, it may be fair to assume that the object continues its direction for the time being unless acted upon by another force.
The second example shows a similar Force Candle for the SPY that counters the assumption made in the first example and emphasizes the importance of both motion and context. While it’s fair to assume that a heavy highly accelerating object should continue its course, if that object runs into an obstacle, say a brick wall, it’s course may deviate. This example shows SPY running into the 50% retracement wall from the low of Mar 2020, a significant support level noted in literature. The example also conveys Gann’s idea of “lost motion”, where the SPY penetrated the 50% price but did not break through it. A brick wall is not one atom thick and price support is not one tick thick. An object can penetrate only one layer of a wall and not go through it.
The third example shows how Volume Candles can be used to identify scalping opportunities on the SPY and conveys why price behavior is as important as motion and context. It doesn’t take a brick wall to impede direction if you know that the person driving the car tends to forget to feed the cats before they leave. In the chart below, the SPY breaks down to a confluence of the 5-day SMA, 20-day SMA, and an important daily trendline (not shown) after the bullish bounce from the 50% retracement days earlier. High volume candles on the SMA signify stopping volume that reverse price direction. The character of the day changes. Bulls become more aggressive than bears with higher volume on upswings and resistance, whiles bears take on a defensive position with lower volume on downswings and support. High volume stopping candles are seen after rallies, and can tell you when to take profit, get out of a position, or go short. The character change can indicate that its relatively safe to re-enter bullish positions on many major supports, especially given the overarching bullish theme from the large reaction off the 50% retracement level.
The last example emphasizes the importance of relativity. The Volume Candles in the chart below are brightest pre-market even though the open has much higher volume since the pre-market activity is much higher compared to past pre-markets than the open is compared to past opens. Pre-market behavior is a good indicator for the character of the day. These bullish Volume Candles are some of the brightest seen since the bounce off the 50% retracement and indicates that bulls are making a relatively greater attempt to bring the SPY higher at the start of the day.
Infrequently Asked Questions
Where do I start?
The default settings are what I use to scalp the SPY throughout most of the extended trading day, on a one-minute chart using SPY volume. I also overlay another Candle set containing ES future volume on the SPY price structure by setting the Physics Source to ES1! and the Number of Overlays setting to 2 for each Candle stream in order to account for pre- and post-market trading activity better. Since the closing volume is exponential-like up until the end of the regular trading day, adding additional Candle streams with a tighter Window Time Frame (e.g., 2-5 minute) in the last 15 minutes of trading can be beneficial. The Hide feature can allow you to set certain intraday timeframes to hide one Candle set in order to show another Candle set during that time.
How crazy can you get with this indicator?
I hope you can answer this question better. One interesting use case is embedding the velocity of market volume onto an internal market structure. The PCTABOVEVWAP.US is a market statistic that indicates the percent of securities above their VWAP among US stocks and is helpful for determining short term trends in the US market. When securities are rising above their VWAP, the average long is up on the day and a rising PCTABOVEVWAP.US can be viewed as more bullish. When securities are falling below their VWAP, the average short is up on the day and a falling PCTABOVEVWAP.US can be viewed as more bearish. (UPVOL.US - DNVOL.US) / TVOL.US is a “spread” symbol, in TV parlance, that indicates the decimal percent difference between advancing volume and declining volume in the US market, showing the relative flow of volume between stocks that are up on the day, and stocks that are down on the day. Setting PCTABOVEVWAP.US in the Candle Source, (UPVOL.US - DNVOL.US) / TVOL.US in the Physics Source, and selecting the Physics to Velocity will embed the relative velocity of the spread symbol onto the PCTABOVEVWAP.US candles. This can be helpful in seeing short term trends in the US market that have an increasing amount of volume behind them compared to other trends. The chart below shows Volume Candles (top) and these Spread Candles (bottom). The first top at 9:30 and second top at 10:30, the high of the day, break down when the spread candles light up, showing a high velocity volume transfer from up stocks to down stocks.
How do I plot the indicator distribution and why should I even care?
The distribution is visually helpful in seeing how different normalization settings effect the distribution of candle segments. It is also helpful in seeing what physics intensities you want to ignore or show by segmenting part of the distribution within the Min and Max Cutoff values. The intensity of color is proportional to the physics value between the Min and Max Cutoff values, which correspond to the Min and Max Colors in your color scheme. Any physics value outside these Min and Max Cutoffs will be the same as the Min and Max Colors.
Select the Print Windows feature to show the window numbers according to the Cycle Time Frame and Window Time Frame settings. The window numbers are labeled at the start of each window and are candle width in size, so you may need to zoom into to see them. Selecting the Plot Window feature and input the window number of interest to shows the distribution of physics values for that particular window along with some statistics.
A log-normal volume distribution of segmented z-scores is shown below for 30-minute opening of the SPY. The Min and Max Cutoff at the top of the graph contain the part of the distribution whose intensities will be linearly color-coded between the Min and Max Colors of the color scheme. The part of the distribution below the Min Cutoff will be treated as lowest quality signals and set to the Min Color, while the few segments above the Max Cutoff will be treated as the highest quality signals and set to the Max Color.
What do I do if I don’t see anything?
Troubleshooting issues with this indicator can involve checking for error messages shown near the indicator name on the chart or using the Data Validation section to evaluate the statistics and normalization cutoffs. For example, if the Plot Window number is set to a window number that doesn’t exist, an error message will tell you and you won’t see any candles. You can use the Print Windows option to show windows that do exist for you current settings. The auto-normalization cutoff values may be inappropriate for your particular use case and literally cut the candles out of the chart. Try changing the chart time frame to see if they are appropriate for your cycle, sample and window time frames. If you get a “Timeframe passed to the request.security_lower_tf() function must be lower than the timeframe of the main chart” error, this means that the chart timeframe should be increased above the sample time frame. If you get a “Symbol resolve error”, ensure that you have correct symbol or spread in the Candle or Physics Source.
How do I see a relative physics values without cycles?
Set the Window Time Frame to be equal to the Cycle Time Frame. This will aggregate all the statistics into one bucket and show the physics values, such as volume, relative to all the past volumes that TV will allow.
How do I see candles without segmentation?
Segmentation can be very helpful in one context or annoying in another. Segmentation can be removed by setting the candle resolution value to 1.
Notes
I have yet to find a trading platform that consistently provides accurate real-time volume and pricing information, lacking adequate end-user data validation or quality control. I can provide plenty of examples of real-time volume counts or prices provided by TradingView and other platforms that were significantly off from what they should have been when comparing against the exchanges own data, and later retroactively corrected or not corrected at all. Since no indicator can work accurately with inaccurate data, please use at your own discretion.
The first version is a beta version. Debugging and validating code in Pine script is difficult without proper unit testing. Please report any bugs with enough information to reproduce them and indicate why they are important. I also encourage you to export the data from TradingView and verify the calculations for your particular use case.
The indicator works on real-time updates that occur at a higher frequency than the candle time frame, which TV incorrectly refers to as ticks. They use this terminology inaccurately as updates are really aggregated tick data that can take place at different prices and may not accurately reflect the real tick price action. Consequently, this inaccuracy also impacts the real-time segmentation accuracy to some degree. TV does not provide a means of retaining “tick” information, so the higher granularity of information seen real-time will be lost on a disconnect.
TV does not provide time and sales information. The volume and price information collected using the Sample Time Frame is intraday, which provides only part of the picture. Intraday volume is generally 50 to 80% of the end of day volume. Consequently, the daily+ OHLC prices are intraday, and may differ significantly from exchanged settled OHLC prices.
The Cycle and Window Time Frames refer to calendar days and time, not trading days or time. For example, the first window week of a monthly cycle is the first seven days of the month, not the first Monday through Friday of trading for the month.
Chart Time Frames that are higher than the Window Time Frames average the normalized physics for price action that occurred within a given Candle segment. It does not average price action that did not occur.
One of the main performance bottleneck in TradingView’s Pine Script is client-side drawing and plotting. The performance of this indicator can be increased by lowering the resolution (the number of sub-candles this indicator plots), getting a faster computer, or increasing the performance of your computer like plugging your laptop in and eliminating unnecessary processes.
The statistical integrity of this indicator relies on the number of samples collected per sample window in a given cycle. Higher sample counts can be obtained by increasing the chart time frame or upgrading the TradingView plan for a higher bar count. While increasing the chart time frame doesn’t increase the visual number of bars plotted on the chart, it does increase the number of bars that can be pulled at a lower time frame, up to 100,000.
Due to a limitation in Pine Scripts request_lower_tf() function, using a spread symbol will only work for regular trading hours, not extended trading hours.
Ideally, velocity or momentum should be calculated between candle closes. To eliminate the need to deal with price gaps that would lead to an incorrect statistical distributions, momentum is calculated between candle open and closes as a percent change of the price or value, which should not be an issue for most liquid securities.
Relative Volume (rVol), Better Volume, Average Volume ComparisonThis is the best version of relative volume you can find a claim which is based on the logical soundness of its calculation.
I have amalgamated various volume analysis into one synergistic script. I wasn't going to opensource it. But, as one of the lucky few winners of TradingClue 2. I felt obligated to give something back to the community.
Relative volume traditionally compares current volume to prior bar volume or SMA of volume. This has drawbacks. The question of relative volume is "Volume relative to what?" In the traditional scripts you'll find it displays current volume relative to the last number of bars. But, is that the best way to compare volume. On a daily chart, possibly. On a daily chart this can work because your units of time are uniform. Each day represents a full cycle of volume. However, on an intraday chart? Not so much.
Example: If you have a lookback of 9 on an hourly chart in a 24 hour market, you are then comparing the average volume from Midnight - 9 AM to the 9 AM volume. What do you think you'll find? Well at 9:30 when NY exchanges open the volume should be consistently and predictably higher. But though rVol is high relative to the lookback period, its actually just average or maybe even below average compared to prior NY session opens. But prior NY session opens are not included in the lookback and thus ignored.
This problem is the most visibly noticed when looking at the volume on a CME futures chart or some equivalent. In a 24 hour market, such as crypto, there are website's like skew can show you the volume disparity from time of day. This led me to believe that the traditional rVol calculation was insufficient. A better way to calculate it would be to compare the 9:30 am 30m bar today to the last week's worth of 9:30 am 30m bars. Then I could know whether today's volume at 9:30 am today is high or low based on prior 9:30 am bars. This seems to be a superior method on an intraday basis and is clearly superior in markets with irregular volume
This led me to other problems, such as markets that are open for less than 24 hours and holiday hours on traditional market exchanges. How can I know that the script is accurately looking at the correct prior relevant bars. I've created and/or adapted solutions to all those problems and these calculations and code snippets thus have value that extend beyond this rVol script for other pinecoders.
The Script
This rVol script looks back at the bars of the same time period on the viewing timeframe. So, as we said, the last 9:30 bars. Averages those, then divides the: . The result is a percentage expressed as x.xxx. Thus 1.0 mean current volume is equal to average volume. Below 1.0 is below the average and above 1.0 is above the average.
This information can be viewed on its own. But there are more levels of analysis added to it.
Above the bars are signals that correlate to the "Better Volume Indicator" developed by, I believe, the folks at emini-watch and originally adapted to pinescript by LazyBear. The interpretation of these symbols are in a table on the right of the indicator.
The volume bars can also be colored. The color is defined by the relationship between the average of the rVol outputs and the current volume. The "Average rVol" so to speak. The color coding is also defined by a legend in the table on the right.
These can be researched by you to determine how to best interpret these signals. I originally got these ideas and solid details on how to use the analysis from a fellow out there, PlanTheTrade.
I hope you find some value in the code and in the information that the indicator presents. And I'd like to thank the TradingView team for producing the most innovative and user friendly charting package on the market.
(p.s. Better Volume is provides better information with a longer lookback value than the default imo)
Credit for certain code sections and ideas is due to:
LazyBear - Better Volume
Grimmolf (From GitHub) - Logic for Loop rVol
R4Rocket - The idea for my rVol 1 calculation
And I can't find the guy who had the idea for the multiples of volume to the average. Tag him if you know him
Final Note: I'd like to leave a couple of clues of my own for fellow seekers of trading infamy.
Indicators: indicators are like anemometers (The things that measure windspeed). People talk bad about them all the time because they're "lagging." Well, you can't tell what the windspeed is unless the wind is blowing. anemometers are lagging indicators of wind. But forecasters still rely on them. You would use an indicator, which I would define as a instrument of measure, to tell you the windspeed of the markets. Conversely, when people talk positively about indicators they say "This one is great and this one is terrible." This is like a farmer saying "Shovels are great, but rakes are horrible." There are certain tools that have certain functions and every good tool has a purpose for a specific job. So the next time someone shares their opinion with you about indicators. Just smile and nod, realizing one day they'll learn... hopefully before they go broke.
How to forecast: Prediction is accomplished by analyzing the behavior of instruments of measure to aggregate data (using your anemometer). The data is then assembled into a predictive model based on the measurements observed (a trading system). That predictive model is tested against reality for it's veracity (backtesting). If the model is predictive, you can optimize your decision making by creating parameter sets around the prediction that are synergistic with the implications of the prediction (risk, stop loss, target, scaling, pyramiding etc).
<3
How to use Leverage and Margin in PineScriptEn route to being absolutely the best and most complete trading platform out there, TradingView has just closed 2 gaps in their PineScript language.
It is now possible to create and backtest a strategy for trading with leverage.
Backtester now produces Margin Calls - so recognizes mid-trade drawdown and if it is too big for the broker to maintain your trade, some part of if will be instantly closed.
New additions were announced in official blogpost , but it lacked code examples, so I have decided to publish this script. Having said that - this is purely educational stuff.
█ LEVERAGE
Let's start with the Leverage. I will discuss this assuming we are always entering trades with some percentage of our equity balance (default_qty_type = strategy.percent_of_equity), not fixed order quantity.
If you want to trade with 1:1 leverage (so no leverage) and enter a trade with all money in your trading account, then first line of your strategy script must include this parameter:
default_qty_value = 100 // which stands for 100%
Now, if you want to trade with 30:1 leverage, you need to multipy the quantity by 30x, so you'd get 30 x 100 = 3000:
default_qty_value = 3000 // which stands for 3000%
And you can play around with this value as you wish, so if you want to enter each trade with 10% equity on 15:1 leverage you'd get default_qty_value = 150.
That's easy. Of course you can modify this quantity value not only in the script, but also afterwards in Script Settings popup, "Properties" tab.
█ MARGIN
Second newly released feature is Margin calculation together with Margin Calls. If the market goes against your trades and your trading account cannot maintain mid-trade drawdown - those trades will be closed in full or partly. Also, if your trading account cannot afford to open more trades (pyramiding those trades), Margin mechanism will prevent them from being entered.
I will not go into details about how Margin calculation works, it was all explainged in above mentioned blogpost and documentation .
All you need to do is to add two parameters to the opening line of your script:
margin_long = 1./30*50, margin_short = 1./30*50
Whereas "30" is a leverage scale as in 30:1, and "50" stands for 50% of Margin required by your broker. Personally the Required Margin number I've met most often is 50%, so I'm using value 50 here, but there are literally 1000+ brokers in this world and this is individual decision by each of them, so you'd better ask yourself.
--------------------
Please note, that if you ever encounter a strategy which triggers Margin Call at least once, then it is probably a very bad strategy. Margin Call is a last resort, last security measure - all the risks should be calculated by the strategy algorithm before it is ever hit. So if you see a Margin Call being triggred, then something is wrong with risk management of the strategy. Therefore - don't use it!
Max GainThis indicator is meant to be used for coming up with price targets based on past performances of rallies/selloffs.
It shows how much a trade could have made over a 30-day period (or other length of time) in terms of percentage gain.
It also show how much could have been lost in terms of percentage loss
The green plot shows percentage gain from current high to the low of the previous 30 days.
The red plot shows adjusted percentage loss from current low to the high of the previous 30 days.
The 30 can be adjusted and the chart can be used on any time interval.
Note on max loss adjustment:
Max loss percentage is adjusted to be higher to account for the fact that a percentage loss corresponds to a percentage
gain of a greater amount. For instance, a loss of 25% can only be recovered with a percentage gain of 33%.
A 25% loss looking at the chart from left to right would be a 33% gain looking at the same price
action from right to left. In order to compare apples to apples visually and performance wise, max loss percent needs to be adjusted.
The actual max loss percent is calculated and plottable but is not plotted by default because it is less useful and adds clutter.
There is not a great difference between actual max loss and adjusted max loss under everyday market conditions, but
major selloffs (SPY 2020), short squeezes (GME 2021), or other unusually directional moves will display percentage losses
that, in absolute terms, should be considered to be fairly incorrect. The adjusted percentages are good indicators of
relative performance when comparing the magnitudes to the magnitudes of the max gain percentages and
are more visually meaningful than the actual max loss percentages in every situation, so they are plotted despite having incorrect values.
Note on bear markets:
This indicator was designed for bull markets but should it be used in bear markets the indicators that are and aren't
plotted should be swapped using the plot check boxes in the settings dialogue if there is interest in using the loss percentages
for actual loss amount calculations while maintaining visual/performance adjustment
As can been seen in the example chart a gain of 16.3% to 17.1% appears to be a resistance level. This level was recently broken through and the next resistance is 24.5%.
The target is a 24.5% gain from the anticipated 30-day low at the time when the price can be expected to reach a 25.4% gain at the gain rate observed in recent rallies.
Previous rallies are shown for reference with their 30-day periods and corresponding gain percentages which are plotted below.
A selloff is shown in red for reference as well. It was drawn backward to trick the tool into thinking it was a gain, so as to demonstrate logic behind the adjustment.
In reality, this was closer to a 9.5% loss, not 10.55%.
I am still experimenting with this indicator to see how to best use it. Ultimately, it helps me do what I was already doing with the percentage gain tools
but now I can do those analyses in a more systematic manner and with charting. Please feel free to ask questions.
Put/Call-Ratio-Buschi
English:
This script shows the Put/Call-Ratio as seen on the Cboe-Website: www.cboe.com
A higher Put/Call-Ratio means a higher trading volume of puts compared to calls, which is a sign of a higher need for protection in the market.
For best reflection of the Cboe's data, which is shown in 30 minutes intervals, a 30 min-chart is recommended.
30 min-data as well as end-of-day data are shown.
Deutsch:
Dieses Skript zeigt das Put/Call Ratio, wie es auf der Cboe-Website angegeben ist: www.cboe.com
Ein höheres Put/Call Ratio bedeutet ein höheres Handelsvolumen von Puts gegenüber Calls, was ein Zeichen für Absicherungsbedarf im Markt darstellt.
Um die Cboe-Daten bestmöglich wiederzugeben, die in 30 Minuten-Intervallen herausgegeben werden, wird ein 30 min-Chart empfohlen.
Es werden sowohl die 30-Minuten-Daten als auch die Tagesenddaten angezeigt.
RSI Based Automatic Supply and DemandA script that draws supply and demand zones based on the RSI indicator. For example if RSI is under 30 a supply zone is drawn on the chart and extended for as long as there isn't a new crossunder 30. Same goes for above 70. The threshold which by default is set to 30, which means 30 is added to 0 and subtracted from 100 to give us the classic 30/70 threshold on RSI, can be set in the indicator settings.
By only plotting the Demand Below Supply Above indicator you get automatic SD level that is updated every time RSI reaches either 30 or 70. If you plot the Resistance Zone / Support Zone you get an indicator that extends the zone instead of overwrite the earlier zone. Due to the zone being extended the chart can get a bit messy if there isn't a clear range going on.
There is also a "confirmation bars" setting where you can tell the script how many bars under over 30 / 70 you want before a zone is drawn.
Here is an image of only using the "Demand Below / Supply Above" plot.
As you can see, this could be useful "Price Flow" indicator, where we would only short if a zone appears below another zone, or long if two zones in a row are going up, like stairs.
Session TimeZonesThis indicators show background colours to identify world timezones
New York, London, Tokio, China and Sydney sessions
You can also setup timeframe intervals to show or hide.
Time Values based on UTC: ** YOU HAVE TO SETUP YOUR CHARTS TO 0-UTC TIMEZONE **
Values from: en.wikipedia.org
New York: UTC-5
Market Session: 09:30 - 16:00 (Local Time)
Market Session: 14:30 - 21:00 (UTC Based Time)
London: UTC
Market Session: 08:00 - 16:30 (Local Time)
Market Session: 08:00 - 16:30 (UTC Based Time)
Tokyo: UTC+9
Market Session: 09:00 - 15:00 (Local Time)
Market Session: 00:00 - 06:00 (UTC Based Time)
China: UTC+8
Market Session: 09:30 - 16:00 (Local Time)
Market Session: 01:30 - 08:00 (UTC Based Time)
Sydney: UTC+10
Market Session: 10:00 - 16:00 (Local Time)
Market Session: 00:00 - 06:00 (UTC Based Time)
Can be used to know from what time of the world they are traders awake or
to search correlations between big moves and timezones hours.
Thanks to:
www.tradingcode.net
01/06/2018
BTC/USD Breakout Hours – IST (Hyderabad)This indicator highlights the most volatile BTC/USD trading hours based on Hyderabad (IST) time.
It marks three key breakout windows:
London–US Overlap (17:30–20:30 IST) – Highest liquidity & volatility
US Market Open Momentum (19:00–23:30 IST) – Strong trend moves
Early London Session (12:30–15:30 IST) – Pre-US setup moves
The script automatically converts chart time to IST, shades each breakout window, and includes optional alerts for:
Window start
15 minutes before start
Ideal for traders who want to align entries with high-probability market moves while avoiding low-volume hours.
Candle Channel█ OVERVIEW
The "Candle Channel" indicator is a versatile technical analysis tool that plots a price channel based on the Simple Moving Average (SMA) of candlestick midpoints. The channel bands, calculated based on candlestick volatility, form dynamic support and resistance levels that adapt to price movements. The script generates signals for reversals from the bands and SMA breakouts, making it useful for both short-term and long-term traders. By adjusting the SMA length, the channel can vary in nature—from a wide channel encapsulating price movement to narrower support/resistance or trend-following bands. The channel width can be further customized using a scaling parameter, allowing adaptation to different trading styles and markets.
█ MECHANISM
Band Calculation
The indicator is based on the following calculations:
Candlestick Midpoint: Calculated as the arithmetic average of the candle’s high and low prices: (high + low) / 2.
Simple Moving Average (SMA): The average of candlestick midpoints over a specified length (default: 20 candles), forming the channel’s centerline.
Average Candle Height: Calculated as the average difference between the high and low prices (high - low) over the same SMA length, serving as a measure of market volatility.
Band Scaling: The user specifies a percentage of the average candle height (default: 200%), which is multiplied by the average height to create an offset. The upper band is SMA + offset, and the lower band is SMA - offset.Example: For an average candle height of 10 points and 200% scaling, the offset is 20 points, meaning the bands are ±20 points from the SMA.
Channel Characteristics: The SMA length determines the channel’s dynamics. Shorter SMA values (10–30) create a wide channel that contains price movement, ideal for scalping or short-term trading. Longer SMA values (above 30, e.g., 50–100) transform the channel into narrower support/resistance or trend-following bands, suitable for longer-term analysis. Band scaling further adjusts the channel width to match market volatility.
Signals
Reversal from Bands: Signals are generated when the price closes outside the band (above the upper or below the lower) and then returns to the channel, indicating a potential trend reversal.
SMA Breakout: Signals are generated when the price crosses the SMA upward (bullish signal) or downward (bearish signal), suggesting potential trend changes.
Visualization
Centerline: The SMA of candlestick midpoints, displayed as a thin line.
Channel Bands: Upper and lower channel boundaries, with customizable colors.
Fill: Options include a gradient (smooth color transition between bands) or solid color. The fill can also be disabled for greater clarity.
█ FEATURES AND SETTINGS
SMA Length: Determines the moving average period (default: 20). Values of 10–30 are suitable for a wide channel containing price movement, ideal for short-term timeframes. Longer values (e.g., 50–100) create narrower support/resistance or trend-following bands, better suited for higher timeframes.
Band Scaling: Percentage of the average candle height (default: 200%). Adjusts the channel width to match market volatility—smaller values (e.g., 50–100%) for narrower bands, larger values (e.g., 200–300%) for wider channels.
Fill Type: Gradient, solid, or no fill, allowing customization to user preferences.
Colors: Options to change the colors of bands, fill, and signals for better readability.
Signals: Options to enable/disable reversal signals from bands and SMA breakout signals.
█ HOW TO USE
Add the script to your chart in TradingView by clicking "Add to Chart" in the Pine Editor.
Adjust input parameters in the script settings:
SMA Length: Set to 10–30 for a wide channel containing price movement, suitable for scalping or short-term trading. Set above 30 (e.g., 50–100) for narrower support/resistance or trend-following bands.
Band Scaling: Adjust the channel width to market volatility. Smaller values (50–100%) for tighter support/resistance bands, larger values (200–300%) for wider channels containing price movement.
Fill Type and Colors: Choose a gradient for aesthetics or a solid fill for clarity.
Analyze signals:
Reversal Signals: Triangles above (bearish) or below (bullish) candles indicate potential reversal points.
SMA Breakout Signals: Circles above (bearish) or below (bullish) candles indicate trend changes.
Test the indicator on different instruments and timeframes to find optimal settings for your trading style.
█ LIMITATIONS
The indicator may generate false signals in highly volatile or consolidating markets.
On low-liquidity charts (e.g., exotic currency pairs), the bands may be less reliable.
Effectiveness depends on properly matching parameters to the market and timeframe.
Midnight 30min High/LowMidnight 30min High/Low — Overnight Liquidity Range Tracker
Capture the Overnight Session: A Strategic Level Identification Tool from Professional Trading Methodology
This indicator captures the high and low prices during the critical 30-minute midnight session (12:00-12:30 AM EST) and projects these levels forward as key support and resistance zones. These overnight ranges often contain significant liquidity and serve as crucial reference points for intraday price action, representing areas where institutional activity may have established important levels.
🔍 What This Script Does:
Identifies Critical Overnight Session Levels
- Automatically detects the 12:00-12:30 AM EST session window
- Captures the highest and lowest prices during this 30-minute period
- Projects these levels forward for multiple trading days
Creates Dynamic Support/Resistance Zones
- Extends midnight high/low levels as horizontal lines with customizable projection periods
- Fills the area between high and low to create a visual trading range
- Updates automatically each trading day with new overnight levels
Provides Clear Visual Reference Points
- Optional session start markers (●) highlight when the midnight session begins
- Color-coded lines distinguish between high and low levels
- Transparent fill area creates an easy-to-identify trading zone
Real-Time Level Tracking
- Updates levels in real-time during the active midnight session
- Maintains historical levels for reference and backtesting
- Compatible with data window for precise level values
⚙️ Customization Options:
Extend Days (1-30):** Control how many days forward the levels are projected (default: 5 days)
High Line Color:** Customize the midnight high line color (default: blue)
Low Line Color:** Customize the midnight low line color (default: orange)
Fill Color:** Adjust the transparency and color of the range area (default: light aqua, 80% transparency)
Show Session Markers:** Toggle yellow session start indicators on/off (default: enabled)
💡 How to Use:
Deploy on lower timeframes (1m-15m) for precise level identification and reaction monitoring**
Watch for key price interactions:
- Rejection at midnight high levels (potential resistance)
- Bounce from midnight low levels (potential support)
- Range-bound trading between the high and low levels
Combine with liquidity concepts:
- Monitor for stop hunts above/below these levels
- Look for false breakouts that snap back into the range
- Use as confluence with other ICT concepts like FVGs and Order Blocks
Strategic Applications:
- Range trading between midnight levels
- Breakout confirmation when price closes decisively outside the range
- Support/resistance validation for entry and exit planning
🔗 Combine With These Tools for Complete Market Structure Analysis:
✅ First FVG — Opening Range Fair Value Gap Detector.
✅ ICT Turtle Soup (Liquidity Reversal)— Spot stop hunts and false breakout scenarios
✅ ICT Macro Zones (Grey Box Version)- It tracks real-time highs and lows for each Silver Bullet session
✅ ICT SMC Liquidity Grabs and OBs- Liquidity Grabs, Order Block Zones, and Fibonacci OTE Levels, allowing traders to identify institutional entry models with clean, rule-based visual signals.
Together, these tools create a comprehensive Smart Money Concepts (SMC) framework — helping traders identify, anticipate, and capitalize on institutional-level price movements with precision and confidence during critical overnight sessions.
