Dual Timeframe Stochastic Momentum Index w/buy sell signalsThis indicator combines momentum analysis across two timeframes to identify high-probability trading opportunities. It plots the Stochastic Momentum Index (SMI) for both the chart timeframe and a higher timeframe (default 10 minutes) to help traders align with the broader market trend.
Key Features
Displays SMI and its EMA for both timeframes
Background shading indicates favorable trading conditions
Signal dots mark potential entry points
Customizable parameters for fine-tuning
Signals Explained
Bullish Signals (Green Dots)
Appear when the chart timeframe SMI crosses above its EMA
Only trigger during periods when the higher timeframe shows:
SMI is above its EMA (increasing momentum)
SMI is between -40 and +40 (not overbought/oversold)
Bearish Signals (Red Dots)
Appear when the chart timeframe SMI crosses below its EMA
Only trigger during periods when the higher timeframe shows:
SMI is below its EMA (decreasing momentum)
SMI is between -40 and +40 (not overbought/oversold)
Settings
%K Length: Lookback period for SMI calculation (default: 10)
%D Length: Smoothing period for primary calculation (default: 3)
EMA Length: Smoothing period for signal line (default: 3)
Alternative Timeframe: Higher timeframe for trend analysis (default: 10 minutes)
Best Practices
Use higher timeframe signals to determine market bias
Wait for signal dots in the chart timeframe for entry timing
Avoid trades when higher timeframe SMI is in extreme zones (above 40 or below -40)
Consider additional confirmation from price action or other indicators
Note: This indicator combines trend and momentum analysis but should be used as part of a complete trading strategy that includes proper risk management.
ค้นหาในสคริปต์สำหรับ "中海油+10年股价涨幅"
Wick Detection (1 and 0) - AYNETDetailed Scientific Explanation
1. Wick Detection Logic
Definition of a Wick:
A wick, also known as a shadow, represents the price action outside the range of a candlestick's body (the region between open and close).
Upper Wick: Occurs when the high value exceeds the greater of open and close.
Lower Wick: Occurs when the low value is lower than the smaller of open and close.
Upper Wick Detection:
pinescript
Kodu kopyala
bool has_upper_wick = high > math.max(open, close)
This checks if the high price of the candle is greater than the maximum of the open and close prices. If true, an upper wick exists.
Lower Wick Detection:
pinescript
Kodu kopyala
bool has_lower_wick = low < math.min(open, close)
This checks if the low price of the candle is less than the minimum of the open and close prices. If true, a lower wick exists.
2. Binary Representation
The presence of a wick is encoded as a binary value for simplicity and computational analysis:
Upper Wick: Represented as 1 if present, otherwise 0.
pinescript
Kodu kopyala
float upper_wick_binary = has_upper_wick ? 1 : 0
Lower Wick: Represented as 1 if present, otherwise 0. This value is inverted (-1) for visualization purposes.
pinescript
Kodu kopyala
float lower_wick_binary = has_lower_wick ? 1 : 0
3. Visualization with Histograms
The plot function is used to create histograms for visualizing the binary wick data:
Upper Wicks: Plotted as positive values with green columns:
pinescript
Kodu kopyala
plot(upper_wick_binary, title="Upper Wick", color=color.new(color.green, 0), style=plot.style_columns, linewidth=2)
Lower Wicks: Plotted as negative values with red columns:
pinescript
Kodu kopyala
plot(lower_wick_binary * -1, title="Lower Wick", color=color.new(color.red, 0), style=plot.style_columns, linewidth=2)
Features and Applications
1. Wick Visualization:
Upper wicks are displayed as positive green columns.
Lower wicks are displayed as negative red columns.
This provides a clear visual representation of wick presence in historical data.
2. Technical Analysis:
Wick formations often indicate market sentiment:
Upper Wicks: Sellers pushed the price lower after buyers drove it higher, signaling rejection at the top.
Lower Wicks: Buyers pushed the price higher after sellers drove it lower, signaling rejection at the bottom.
3. Signal Generation:
Traders can use wick detection to build strategies, such as identifying key price levels or market reversals.
Enhancements and Future Improvements
1. Wick Length Measurement
Instead of binary detection, measure the actual length of the wick:
pinescript
Kodu kopyala
float upper_wick_length = high - math.max(open, close)
float lower_wick_length = math.min(open, close) - low
This approach allows for thresholds to identify significant wicks:
pinescript
Kodu kopyala
bool significant_upper_wick = upper_wick_length > 10 // For wicks longer than 10 units.
bool significant_lower_wick = lower_wick_length > 10
2. Alerts for Long Wicks
Trigger alerts when significant wicks are detected:
pinescript
Kodu kopyala
alertcondition(significant_upper_wick, title="Long Upper Wick", message="A significant upper wick has been detected.")
alertcondition(significant_lower_wick, title="Long Lower Wick", message="A significant lower wick has been detected.")
3. Combined Wick Analysis
Analyze both upper and lower wicks to assess volatility:
pinescript
Kodu kopyala
float total_wick_length = upper_wick_length + lower_wick_length
bool high_volatility = total_wick_length > 20 // Combined wick length exceeds 20 units.
Conclusion
This script provides a compact and computationally efficient way to detect candlestick wicks and represent them as binary data. By visualizing the data with histograms, traders can easily identify wick formations and use them for technical analysis, signal generation, and volatility assessment. The approach can be extended further to measure wick length, detect significant wicks, and integrate these insights into automated trading systems.
FTMO Rules MonitorFTMO Rules Monitor: Stay on Track with Your FTMO Challenge Goals
TLDR; You can test with this template whether your strategy for one asset would pass the FTMO challenges step 1 then step 2, then with real money conditions.
Passing a prop firm challenge is ... challenging.
I believe a toolkit allowing to test in minutes whether a strategy would have passed a prop firm challenge in the past could be very powerful.
The FTMO Rules Monitor is designed to help you stay within FTMO’s strict risk management guidelines directly on your chart. Whether you’re aiming for the $10,000 or the $200,000 account challenge, this tool provides real-time tracking of your performance against FTMO’s rules to ensure you don’t accidentally breach any limits.
NOTES
The connected indicator for this post doesn't matter.
It's just a dummy double supertrends (see below)
The strategy results for this script post does not matter as I'm posting a FTMO rules template on which you can connect any indicator/strategy.
//@version=5
indicator("Supertrends", overlay=true)
// Supertrend 1 Parameters
var string ST1 = "Supertrend 1 Settings"
st1_atrPeriod = input.int(10, "ATR Period", minval=1, maxval=50, group=ST1)
st1_factor = input.float(2, "Factor", minval=0.5, maxval=10, step=0.5, group=ST1)
// Supertrend 2 Parameters
var string ST2 = "Supertrend 2 Settings"
st2_atrPeriod = input.int(14, "ATR Period", minval=1, maxval=50, group=ST2)
st2_factor = input.float(3, "Factor", minval=0.5, maxval=10, step=0.5, group=ST2)
// Calculate Supertrends
= ta.supertrend(st1_factor, st1_atrPeriod)
= ta.supertrend(st2_factor, st2_atrPeriod)
// Entry conditions
longCondition = direction1 == -1 and direction2 == -1 and direction1 == 1
shortCondition = direction1 == 1 and direction2 == 1 and direction1 == -1
// Optional: Plot Supertrends
plot(supertrend1, "Supertrend 1", color = direction1 == -1 ? color.green : color.red, linewidth=3)
plot(supertrend2, "Supertrend 2", color = direction2 == -1 ? color.lime : color.maroon, linewidth=3)
plotshape(series=longCondition, location=location.belowbar, color=color.green, style=shape.triangleup, title="Long")
plotshape(series=shortCondition, location=location.abovebar, color=color.red, style=shape.triangledown, title="Short")
signal = longCondition ? 1 : shortCondition ? -1 : na
plot(signal, "Signal", display = display.data_window)
To connect your indicator to this FTMO rules monitor template, please update it as follow
Create a signal variable to store 1 for the long/buy signal or -1 for the short/sell signal
Plot it in the display.data_window panel so that it doesn't clutter your chart
signal = longCondition ? 1 : shortCondition ? -1 : na
plot(signal, "Signal", display = display.data_window)
In the FTMO Rules Monitor template, I'm capturing this external signal with this input.source variable
entry_connector = input.source(close, "Entry Connector", group="Entry Connector")
longCondition = entry_connector == 1
shortCondition = entry_connector == -1
🔶 USAGE
This indicator displays essential FTMO Challenge rules and tracks your progress toward meeting each one. Here’s what’s monitored:
Max Daily Loss
• 10k Account: $500
• 25k Account: $1,250
• 50k Account: $2,500
• 100k Account: $5,000
• 200k Account: $10,000
Max Total Loss
• 10k Account: $1,000
• 25k Account: $2,500
• 50k Account: $5,000
• 100k Account: $10,000
• 200k Account: $20,000
Profit Target
• 10k Account: $1,000
• 25k Account: $2,500
• 50k Account: $5,000
• 100k Account: $10,000
• 200k Account: $20,000
Minimum Trading Days: 4 consecutive days for all account sizes
🔹 Key Features
1. Real-Time Compliance Check
The FTMO Rules Monitor keeps track of your daily and total losses, profit targets, and trading days. Each metric updates in real-time, giving you peace of mind that you’re within FTMO’s rules.
2. Color-Coded Visual Feedback
Each rule’s status is shown clearly with a ✓ for compliance or ✗ if the limit is breached. When a rule is broken, the indicator highlights it in red, so there’s no confusion.
3. Completion Notification
Once all FTMO requirements are met, the indicator closes all open positions and displays a celebratory message on your chart, letting you know you’ve successfully completed the challenge.
4. Easy-to-Read Table
A table on your chart provides an overview of each rule, your target, current performance, and whether you’re meeting each goal. The table adjusts its color scheme based on your chart settings for optimal visibility.
5. Dynamic Position Sizing
Integrated ATR-based position sizing helps you manage risk and avoid large drawdowns, ensuring each trade aligns with FTMO’s risk management principles.
Daveatt
Globex Trap ZoneGlobex Trap Indicator
A powerful tool designed to identify potential trading opportunities by analyzing the relationship between Globex session ranges and Supply & Demand zones during regular trading hours.
Key Features
Tracks and visualizes Globex session price ranges
Identifies key Supply & Demand zones during regular trading hours
Highlights potential trap areas where price might experience significant reactions
Fully customizable time ranges and visual settings
Clear labeling of Globex highs and lows
How It Works
The indicator tracks two key periods:
Globex Session (Default: 6:00 PM - 9:30 AM)
Monitors overnight price action
Marks session high and low
Helps identify potential range breakouts
Supply & Demand Zone (Default: 8:00 AM - 11:00 AM)
Tracks price action during key market hours
Identifies potential reaction zones
Helps spot institutional trading areas
Best Practices for Using This Indicator
Use on 1-hour timeframe or lower for optimal visualization
Best suited for futures and other instruments traded during Globex sessions
Pay attention to areas where Globex range and Supply/Demand zones overlap
Use in conjunction with your existing trading strategy for confirmation
Recommended minimum of 10 days of historical data for context
Settings Explanation
Globex Session: Customizable time range for overnight trading session
Supply & Demand Zone: Adjustable time range for regular trading hours
Days to Look Back: Number of historical days to display (default: 10)
Visual Settings: Customizable colors and transparency for both zones
Important Notes
All times are based on exchange timezone
The indicator respects overnight sessions and properly handles timezone transitions
Historical data requirements: Minimum 10 days recommended
Performance impact: Optimized for smooth operation with minimal resource usage
Disclaimer
Past performance is not indicative of future results. This indicator is designed to be used as part of a comprehensive trading strategy and should not be relied upon as the sole basis for trading decisions.
Updates and Support
I actively maintain this indicator and welcome feedback from the trading community. Please feel free to leave comments or suggestions for improvements.
Momentum Entry & Trend Strategy M5Momentum Entry & Trend Strategy M5
Description:
The Momentum Entry & Trend Strategy M5 is an indicator script designed to assist traders in determining optimal buy and sell moments based on momentum and trend analysis. This script operates using two different momentum levels—Momentum Length for Entry (5) and Momentum Length for Trend (10)—along with the HMA (Hull Moving Average) indicator for trend confirmation.
Key Features:
Momentum Entry: Calculates momentum using the difference between the current price and the price from previous periods to determine the strength and direction of price movements.
Trend Identification: Utilizes two momentum levels (5 and 10) to identify bullish and bearish trend conditions.
HMA for Trend Confirmation: The HMA indicator is used to provide trend confirmation signals. When HMA indicates bullish, a buy signal is displayed; conversely, a bearish HMA results in a sell signal.
Signal Display: Displays buy (BUY) and sell (SELL) signals on the chart when the conditions for market entry are met, providing clear visualization for traders.
Background Color: Offers a green background for uptrends and a red background for downtrends, allowing traders to easily identify the overall market condition.
ATR (Average True Range): Calculates and plots a smoothed ATR to help traders measure market volatility.
Settings:
Momentum Length for Entry: 5 (to determine entry signals)
Momentum Length for Trend: 10 (to determine trend conditions)
HMA Length: 300 (period length for HMA to confirm trends)
ATR Length: 14 (period length for ATR to measure volatility)
Benefits:
This script is designed to provide visual and data-driven guidance for better trading decision-making. By combining momentum and trend analysis, traders can enhance the accuracy of their signals and reduce the risk of errors when identifying entry and exit points in the market.
Note:
This script is intended for use on the M5 time frame but can be adjusted for other time frames as needed. It is always recommended to conduct thorough testing before applying trading strategies on a live account.
Indicator SELL UBScript Name: UB Sell Indicator based on 10Y Volume and Trend
Description: This indicator uses the 10-year interest rate (10Y1!) volume and price data to generate sell signals on the UB contract. When the 10Y1! volume exceeds a fixed threshold and the 10Y1! price is rising, a sell signal is issued to help traders anticipate bearish moves on the UB.
Features:
10Y1! Volume: Identifies periods of high volume.
10Y1! Price: Detects bullish trends in the 10Y1!.
Sell Signals: Displays red arrows to indicate selling opportunities on UB when conditions are met.
Visual Indicators: Colors and arrows for easy signal interpretation.
Parameters:
Fixed Volume Threshold: 114 (modifiable as needed).
Moving Average Period: 10 (to calculate the 10Y1! price trend).
Usage:
Watch for red arrows to identify selling opportunities on UB.
Combine with other analyses and indicators for a complete trading strategy.
Author: Jm Smeers
Publication Date: 26/10/2024
FuTech V-Spike & V-HighlighterFuTech V-Spike & V-Highlighter
In the context of trading and technical analysis, this volume spike & volume highlighter indicator is the perfect indicator used to assess market activity and make informed trading decisions.
Let's Understand in brief as below :
1) Volume Spike :
A volume spike refers to a sudden, significant increase in trading volume compared to the average volume over a specific period.
This spike can indicate heightened interest in a particular security, often preceding price movements.
Traders may look for volume spikes as signals for potential buy or sell opportunities because they can suggest that a stock is experiencing increased activity, possibly due to news, earnings releases, or other catalysts.
Characteristics of a Volume Spike:
Assess the sudden increase in trading volume.
It Can occur in both upward and downward price movements.
Often compared to the average volume over a defined period (e.g., the last 20 days).
2) Volume Highlighter:
A volume highlighter emphasizes significant changes in trading volume on a price chart. This indicator typically uses color coding to highlight periods of high volume, making it easier for traders to identify volume spikes at a glance.
How this indicator works:
a) Volume Spike will calculate the highest volume spike as per the user defined threshold multiplier. (Multiplier = when volume exceeds a certain threshold)
b) The threshold Multiplier can be set based on a fixed number or as a multiple of the Exponential Moving Average volume. Volume Spike Multiplier default is 1.5, means the EMA volume should cross 1.5 times the user defined lookback period
c) The result is derived from the user defined lookback candles by using its EMA instead of SMA, which will give us more precise results.
d) By default, last 10 candles EMA average is used to calculate the Volume Spike but you can choose your lookback period as many days, weeks, months, years of your choice !
e) In Volume Spike -
- Green candle will show Yellow color Bar as Bullish sentiments, whereas
- Red candle will show Black Bar as Bearish sentiments.
f) Volume Highlighter will highlight the candles background if the highest volume is crossing as per the the user defined lookback period
g) Default Lookback period is 20, Color coding (e.g., Blue for bullish spikes, Red for bearish spikes) helps traders quickly assess the volume context.
h) Volume Highlighter plotting shapes and positions can be modified
Illustration:
From the Above pic,
If last Volume Spike is 10 lookback candles, Spike multiplier is 1.5 and Highlighter lookback candles is 20,
Then yellow candle means the spike in volume which is 1.5 times higher than the last 10 candles
Highlighting color defines the highest volumes trading from the last 20 candles which is either Blue (Bullish) or Red (Bearish) sentiments
Conclusion :
Using FuTech V-Spike & V-Highlighter indicator - will help traders identify potential trading opportunities and better understand market dynamics.
By analyzing volume in conjunction with price movements, traders can make more informed decisions based on market sentiment.
Thank you !
Jai Swaminarayan Dasna Das !
He Hari ! Bas Ek Tu Raji Tha !
Financial Crisis Predictor - Doomsday ClockThe **Financial Crisis Predictor - Doomsday Clock** is a composite indicator that evaluates multiple market conditions to determine financial risk levels. It combines four key metrics: market volatility (via VIX), yield curve spread, stock market momentum, and credit risk (via high-yield spread). Each metric contributes to a weighted "risk score," scaled between 0 and 100, which helps gauge the probability of a financial crisis. Here's a breakdown of how it works:
### 1. **Market Volatility (VIX)**
- **How it's measured:**
- Uses the VIX index, which represents expected market volatility.
- Applies two exponential moving averages (EMAs) to smooth out the data—one fast and one slow.
- Triggers a signal if the fast EMA crosses above the slow EMA and VIX exceeds a defined threshold (default is 30).
- **Weighting:**
- Contributes up to 35% of the total risk score when active.
### 2. **Yield Curve Spread**
- **How it's measured:**
- Takes the difference between the yields of 10-year and 2-year U.S. Treasury bonds (inversion indicates recession risk).
- If the spread drops below a certain threshold (default is 0.2), it signals a potential recession.
- **Weighting:**
- Contributes up to 25% of the risk score.
### 3. **Stock Market Momentum**
- **How it's measured:**
- Analyzes the S&P 500 (SPY) using a 20-day EMA for price momentum.
- Checks for a cross under the 20-day EMA and if the 5-day rate of change (ROC) is less than -2.
- This combination signals bearish market momentum.
- **Weighting:**
- Contributes up to 20% of the risk score.
### 4. **Credit Risk (High Yield Spread)**
- **How it's measured:**
- Assesses high-yield corporate bond spreads using EMAs, similar to the VIX logic.
- A crossover of the fast EMA above the slow EMA combined with spreads exceeding a defined threshold (default is 5.0) indicates increased credit risk.
- **Weighting:**
- Contributes up to 20% of the total risk score.
### 5. **Risk Score Calculation**
- The final **risk score** ranges from 0 to 100 and is calculated using the weighted sum of the four indicators.
- The score is smoothed to minimize false signals and maintain stability.
### 6. **Risk Zones**
- **Extreme Risk:** If the risk score is ≥ 75, indicating a severe crisis warning.
- **High Risk:** If the risk score is between 15 and 75, signaling heightened risk.
- **Moderate Risk:** If the risk score is between 10 and 15, representing potential concerns.
- **Low Risk:** If the risk score is < 10, suggesting stable conditions.
### 7. **Visual & Alerts**
- The indicator plots the risk score on a chart with color-coded backgrounds to indicate risk levels: green (low), yellow (moderate), orange (high), and red (extreme).
- Alert conditions are set for each risk zone, notifying users when the risk level transitions into a higher zone.
This indicator aims to quickly detect potential financial crises by aggregating signals from key market factors, making it a versatile tool for traders, analysts, and risk managers.
Macro Timeframes with Opening PriceDescription: Macro Timeframe Horizontal Line Indicator
This indicator highlights macro periods on the chart by drawing a horizontal line at the opening price of each macro period. The macro timeframe is defined as the last 10 minutes of an hour (from :50 to :00) and the first 10 minutes of the following hour (from :00 to :10).
A horizontal black line is plotted at the opening price of the macro period, starting at :50 and extending through the duration of the macro window. However, you can customize it however you see fit.
The background of the macro period is highlighted with a customizable color to visually distinguish the timeframe.
The horizontal line updates at each macro period, ensuring that the opening price for every macro session is accurately reflected on the chart.
This tool is useful for traders who want to track the behavior of price within key macro intervals and visually assess price movement and volatility during these periods.
Business Cycle Indicators (Normalized)This script aggregates and normalizes several key economic indicators to provide a comprehensive view of the business cycle and overall market conditions. By combining these indicators into a single, normalized average line, the script helps identify overarching trends and shifts in the economy, aiding in more informed trading and investment decisions.
Included Indicators:
Inverted National Financial Conditions Index (NFCI):
Symbol: FRED:NFCI
Measures financial stress in the markets. An inverted NFCI aligns higher values with positive financial conditions.
Inverted Net Percentage of Banks Tightening Lending Standards (DRTSCIS):
Symbol: FRED:DRTSCIS
Reflects changes in bank lending practices. Inverting this indicator means higher values indicate easing lending standards, which is generally positive for economic growth.
HYG Close Price (iShares High Yield Corporate Bond ETF):
Symbol: AMEX:HYG
Represents the performance of high-yield corporate bonds, providing insight into credit market conditions.
Inverted High-Yield Credit Spread (BAMLH0A0HYM2):
Symbol: FRED:BAMLH0A0HYM2
Measures the spread between high-yield bonds and risk-free securities. A narrower (inverted) spread indicates better market conditions.
Manufacturing/Non-Manufacturing New Orders Ratio:
Symbols: ECONOMICS:USMNO (Manufacturing), ECONOMICS:USNMNO (Non-Manufacturing)
Compares manufacturing to non-manufacturing new orders to gauge shifts in economic activity.
US PMI (Purchasing Managers' Index):
Symbol: ECONOMICS:USBCOI
An indicator of the economic health of the manufacturing sector.
10-Year Inflation Breakeven (T10YIE):
Symbol: FRED:T10YIE
Represents market expectations of inflation over the next ten years.
Inverted 10-Year Real Yield (DFII10):
Symbol: FRED:DFII10
Reflects the real yield on 10-year Treasury Inflation-Protected Securities (TIPS). Inverted to align higher values with positive economic sentiment.
Copper/Gold Ratio:
Symbols: CAPITALCOM:COPPER (Copper), TVC:GOLD (Gold)
Compares the prices of copper and gold, often used as a barometer for global economic activity.
Features:
Normalized Indicators: Each indicator is normalized to a 0-100 scale to facilitate direct comparison, regardless of their original units or scales.
Normalized Average Line: Calculates and plots the average of all available normalized indicators, providing a single line that represents the combined economic signals.
Customizable Display:
Show Individual Indicators: Option to display individual normalized indicators for detailed analysis.
Show Normalized Average Line: Option to display the normalized average line for a consolidated view.
Dynamic Labeling: Displays the latest value of the normalized average directly on the chart for quick reference.
How to Use:
Adding the Script:
Apply the script to a chart in TradingView using a timeframe that aligns with the frequency of the economic data (daily or weekly recommended).
Customization:
Show Normalized Average Line: Enabled by default to display the combined indicator.
Show Individual Indicators: Enable this option in the script settings to display all individual normalized indicators.
Interpretation:
Normalized Scale (0-100): Higher values generally indicate stronger economic conditions, while lower values may suggest weakening conditions.
Trend Analysis: Use the normalized average line to identify trends and potential turning points in the business cycle.
Notes:
Data Availability: Ensure you have access to all the data sources used in the script. Some data feeds may require specific TradingView subscriptions.
Indicator Limitations: Economic indicators are subject to revisions and may not reflect real-time market conditions.
No Investment Advice: This script is a tool for analysis and should not be considered as financial advice. Always conduct your own research before making investment decisions.
Universal Ratio Trend Matrix [InvestorUnknown]The Universal Ratio Trend Matrix is designed for trend analysis on asset/asset ratios, supporting up to 40 different assets. Its primary purpose is to help identify which assets are outperforming others within a selection, providing a broad overview of market trends through a matrix of ratios. The indicator automatically expands the matrix based on the number of assets chosen, simplifying the process of comparing multiple assets in terms of performance.
Key features include the ability to choose from a narrow selection of indicators to perform the ratio trend analysis, allowing users to apply well-defined metrics to their comparison.
Drawback: Due to the computational intensity involved in calculating ratios across many assets, the indicator has a limitation related to loading speed. TradingView has time limits for calculations, and for users on the basic (free) plan, this could result in frequent errors due to exceeded time limits. To use the indicator effectively, users with any paid plans should run it on timeframes higher than 8h (the lowest timeframe on which it managed to load with 40 assets), as lower timeframes may not reliably load.
Indicators:
RSI_raw: Simple function to calculate the Relative Strength Index (RSI) of a source (asset price).
RSI_sma: Calculates RSI followed by a Simple Moving Average (SMA).
RSI_ema: Calculates RSI followed by an Exponential Moving Average (EMA).
CCI: Calculates the Commodity Channel Index (CCI).
Fisher: Implements the Fisher Transform to normalize prices.
Utility Functions:
f_remove_exchange_name: Strips the exchange name from asset tickers (e.g., "INDEX:BTCUSD" to "BTCUSD").
f_remove_exchange_name(simple string name) =>
string parts = str.split(name, ":")
string result = array.size(parts) > 1 ? array.get(parts, 1) : name
result
f_get_price: Retrieves the closing price of a given asset ticker using request.security().
f_constant_src: Checks if the source data is constant by comparing multiple consecutive values.
Inputs:
General settings allow users to select the number of tickers for analysis (used_assets) and choose the trend indicator (RSI, CCI, Fisher, etc.).
Table settings customize how trend scores are displayed in terms of text size, header visibility, highlighting options, and top-performing asset identification.
The script includes inputs for up to 40 assets, allowing the user to select various cryptocurrencies (e.g., BTCUSD, ETHUSD, SOLUSD) or other assets for trend analysis.
Price Arrays:
Price values for each asset are stored in variables (price_a1 to price_a40) initialized as na. These prices are updated only for the number of assets specified by the user (used_assets).
Trend scores for each asset are stored in separate arrays
// declare price variables as "na"
var float price_a1 = na, var float price_a2 = na, var float price_a3 = na, var float price_a4 = na, var float price_a5 = na
var float price_a6 = na, var float price_a7 = na, var float price_a8 = na, var float price_a9 = na, var float price_a10 = na
var float price_a11 = na, var float price_a12 = na, var float price_a13 = na, var float price_a14 = na, var float price_a15 = na
var float price_a16 = na, var float price_a17 = na, var float price_a18 = na, var float price_a19 = na, var float price_a20 = na
var float price_a21 = na, var float price_a22 = na, var float price_a23 = na, var float price_a24 = na, var float price_a25 = na
var float price_a26 = na, var float price_a27 = na, var float price_a28 = na, var float price_a29 = na, var float price_a30 = na
var float price_a31 = na, var float price_a32 = na, var float price_a33 = na, var float price_a34 = na, var float price_a35 = na
var float price_a36 = na, var float price_a37 = na, var float price_a38 = na, var float price_a39 = na, var float price_a40 = na
// create "empty" arrays to store trend scores
var a1_array = array.new_int(40, 0), var a2_array = array.new_int(40, 0), var a3_array = array.new_int(40, 0), var a4_array = array.new_int(40, 0)
var a5_array = array.new_int(40, 0), var a6_array = array.new_int(40, 0), var a7_array = array.new_int(40, 0), var a8_array = array.new_int(40, 0)
var a9_array = array.new_int(40, 0), var a10_array = array.new_int(40, 0), var a11_array = array.new_int(40, 0), var a12_array = array.new_int(40, 0)
var a13_array = array.new_int(40, 0), var a14_array = array.new_int(40, 0), var a15_array = array.new_int(40, 0), var a16_array = array.new_int(40, 0)
var a17_array = array.new_int(40, 0), var a18_array = array.new_int(40, 0), var a19_array = array.new_int(40, 0), var a20_array = array.new_int(40, 0)
var a21_array = array.new_int(40, 0), var a22_array = array.new_int(40, 0), var a23_array = array.new_int(40, 0), var a24_array = array.new_int(40, 0)
var a25_array = array.new_int(40, 0), var a26_array = array.new_int(40, 0), var a27_array = array.new_int(40, 0), var a28_array = array.new_int(40, 0)
var a29_array = array.new_int(40, 0), var a30_array = array.new_int(40, 0), var a31_array = array.new_int(40, 0), var a32_array = array.new_int(40, 0)
var a33_array = array.new_int(40, 0), var a34_array = array.new_int(40, 0), var a35_array = array.new_int(40, 0), var a36_array = array.new_int(40, 0)
var a37_array = array.new_int(40, 0), var a38_array = array.new_int(40, 0), var a39_array = array.new_int(40, 0), var a40_array = array.new_int(40, 0)
f_get_price(simple string ticker) =>
request.security(ticker, "", close)
// Prices for each USED asset
f_get_asset_price(asset_number, ticker) =>
if (used_assets >= asset_number)
f_get_price(ticker)
else
na
// overwrite empty variables with the prices if "used_assets" is greater or equal to the asset number
if barstate.isconfirmed // use barstate.isconfirmed to avoid "na prices" and calculation errors that result in empty cells in the table
price_a1 := f_get_asset_price(1, asset1), price_a2 := f_get_asset_price(2, asset2), price_a3 := f_get_asset_price(3, asset3), price_a4 := f_get_asset_price(4, asset4)
price_a5 := f_get_asset_price(5, asset5), price_a6 := f_get_asset_price(6, asset6), price_a7 := f_get_asset_price(7, asset7), price_a8 := f_get_asset_price(8, asset8)
price_a9 := f_get_asset_price(9, asset9), price_a10 := f_get_asset_price(10, asset10), price_a11 := f_get_asset_price(11, asset11), price_a12 := f_get_asset_price(12, asset12)
price_a13 := f_get_asset_price(13, asset13), price_a14 := f_get_asset_price(14, asset14), price_a15 := f_get_asset_price(15, asset15), price_a16 := f_get_asset_price(16, asset16)
price_a17 := f_get_asset_price(17, asset17), price_a18 := f_get_asset_price(18, asset18), price_a19 := f_get_asset_price(19, asset19), price_a20 := f_get_asset_price(20, asset20)
price_a21 := f_get_asset_price(21, asset21), price_a22 := f_get_asset_price(22, asset22), price_a23 := f_get_asset_price(23, asset23), price_a24 := f_get_asset_price(24, asset24)
price_a25 := f_get_asset_price(25, asset25), price_a26 := f_get_asset_price(26, asset26), price_a27 := f_get_asset_price(27, asset27), price_a28 := f_get_asset_price(28, asset28)
price_a29 := f_get_asset_price(29, asset29), price_a30 := f_get_asset_price(30, asset30), price_a31 := f_get_asset_price(31, asset31), price_a32 := f_get_asset_price(32, asset32)
price_a33 := f_get_asset_price(33, asset33), price_a34 := f_get_asset_price(34, asset34), price_a35 := f_get_asset_price(35, asset35), price_a36 := f_get_asset_price(36, asset36)
price_a37 := f_get_asset_price(37, asset37), price_a38 := f_get_asset_price(38, asset38), price_a39 := f_get_asset_price(39, asset39), price_a40 := f_get_asset_price(40, asset40)
Universal Indicator Calculation (f_calc_score):
This function allows switching between different trend indicators (RSI, CCI, Fisher) for flexibility.
It uses a switch-case structure to calculate the indicator score, where a positive trend is denoted by 1 and a negative trend by 0. Each indicator has its own logic to determine whether the asset is trending up or down.
// use switch to allow "universality" in indicator selection
f_calc_score(source, trend_indicator, int_1, int_2) =>
int score = na
if (not f_constant_src(source)) and source > 0.0 // Skip if you are using the same assets for ratio (for example BTC/BTC)
x = switch trend_indicator
"RSI (Raw)" => RSI_raw(source, int_1)
"RSI (SMA)" => RSI_sma(source, int_1, int_2)
"RSI (EMA)" => RSI_ema(source, int_1, int_2)
"CCI" => CCI(source, int_1)
"Fisher" => Fisher(source, int_1)
y = switch trend_indicator
"RSI (Raw)" => x > 50 ? 1 : 0
"RSI (SMA)" => x > 50 ? 1 : 0
"RSI (EMA)" => x > 50 ? 1 : 0
"CCI" => x > 0 ? 1 : 0
"Fisher" => x > x ? 1 : 0
score := y
else
score := 0
score
Array Setting Function (f_array_set):
This function populates an array with scores calculated for each asset based on a base price (p_base) divided by the prices of the individual assets.
It processes multiple assets (up to 40), calling the f_calc_score function for each.
// function to set values into the arrays
f_array_set(a_array, p_base) =>
array.set(a_array, 0, f_calc_score(p_base / price_a1, trend_indicator, int_1, int_2))
array.set(a_array, 1, f_calc_score(p_base / price_a2, trend_indicator, int_1, int_2))
array.set(a_array, 2, f_calc_score(p_base / price_a3, trend_indicator, int_1, int_2))
array.set(a_array, 3, f_calc_score(p_base / price_a4, trend_indicator, int_1, int_2))
array.set(a_array, 4, f_calc_score(p_base / price_a5, trend_indicator, int_1, int_2))
array.set(a_array, 5, f_calc_score(p_base / price_a6, trend_indicator, int_1, int_2))
array.set(a_array, 6, f_calc_score(p_base / price_a7, trend_indicator, int_1, int_2))
array.set(a_array, 7, f_calc_score(p_base / price_a8, trend_indicator, int_1, int_2))
array.set(a_array, 8, f_calc_score(p_base / price_a9, trend_indicator, int_1, int_2))
array.set(a_array, 9, f_calc_score(p_base / price_a10, trend_indicator, int_1, int_2))
array.set(a_array, 10, f_calc_score(p_base / price_a11, trend_indicator, int_1, int_2))
array.set(a_array, 11, f_calc_score(p_base / price_a12, trend_indicator, int_1, int_2))
array.set(a_array, 12, f_calc_score(p_base / price_a13, trend_indicator, int_1, int_2))
array.set(a_array, 13, f_calc_score(p_base / price_a14, trend_indicator, int_1, int_2))
array.set(a_array, 14, f_calc_score(p_base / price_a15, trend_indicator, int_1, int_2))
array.set(a_array, 15, f_calc_score(p_base / price_a16, trend_indicator, int_1, int_2))
array.set(a_array, 16, f_calc_score(p_base / price_a17, trend_indicator, int_1, int_2))
array.set(a_array, 17, f_calc_score(p_base / price_a18, trend_indicator, int_1, int_2))
array.set(a_array, 18, f_calc_score(p_base / price_a19, trend_indicator, int_1, int_2))
array.set(a_array, 19, f_calc_score(p_base / price_a20, trend_indicator, int_1, int_2))
array.set(a_array, 20, f_calc_score(p_base / price_a21, trend_indicator, int_1, int_2))
array.set(a_array, 21, f_calc_score(p_base / price_a22, trend_indicator, int_1, int_2))
array.set(a_array, 22, f_calc_score(p_base / price_a23, trend_indicator, int_1, int_2))
array.set(a_array, 23, f_calc_score(p_base / price_a24, trend_indicator, int_1, int_2))
array.set(a_array, 24, f_calc_score(p_base / price_a25, trend_indicator, int_1, int_2))
array.set(a_array, 25, f_calc_score(p_base / price_a26, trend_indicator, int_1, int_2))
array.set(a_array, 26, f_calc_score(p_base / price_a27, trend_indicator, int_1, int_2))
array.set(a_array, 27, f_calc_score(p_base / price_a28, trend_indicator, int_1, int_2))
array.set(a_array, 28, f_calc_score(p_base / price_a29, trend_indicator, int_1, int_2))
array.set(a_array, 29, f_calc_score(p_base / price_a30, trend_indicator, int_1, int_2))
array.set(a_array, 30, f_calc_score(p_base / price_a31, trend_indicator, int_1, int_2))
array.set(a_array, 31, f_calc_score(p_base / price_a32, trend_indicator, int_1, int_2))
array.set(a_array, 32, f_calc_score(p_base / price_a33, trend_indicator, int_1, int_2))
array.set(a_array, 33, f_calc_score(p_base / price_a34, trend_indicator, int_1, int_2))
array.set(a_array, 34, f_calc_score(p_base / price_a35, trend_indicator, int_1, int_2))
array.set(a_array, 35, f_calc_score(p_base / price_a36, trend_indicator, int_1, int_2))
array.set(a_array, 36, f_calc_score(p_base / price_a37, trend_indicator, int_1, int_2))
array.set(a_array, 37, f_calc_score(p_base / price_a38, trend_indicator, int_1, int_2))
array.set(a_array, 38, f_calc_score(p_base / price_a39, trend_indicator, int_1, int_2))
array.set(a_array, 39, f_calc_score(p_base / price_a40, trend_indicator, int_1, int_2))
a_array
Conditional Array Setting (f_arrayset):
This function checks if the number of used assets is greater than or equal to a specified number before populating the arrays.
// only set values into arrays for USED assets
f_arrayset(asset_number, a_array, p_base) =>
if (used_assets >= asset_number)
f_array_set(a_array, p_base)
else
na
Main Logic
The main logic initializes arrays to store scores for each asset. Each array corresponds to one asset's performance score.
Setting Trend Values: The code calls f_arrayset for each asset, populating the respective arrays with calculated scores based on the asset prices.
Combining Arrays: A combined_array is created to hold all the scores from individual asset arrays. This array facilitates further analysis, allowing for an overview of the performance scores of all assets at once.
// create a combined array (work-around since pinescript doesn't support having array of arrays)
var combined_array = array.new_int(40 * 40, 0)
if barstate.islast
for i = 0 to 39
array.set(combined_array, i, array.get(a1_array, i))
array.set(combined_array, i + (40 * 1), array.get(a2_array, i))
array.set(combined_array, i + (40 * 2), array.get(a3_array, i))
array.set(combined_array, i + (40 * 3), array.get(a4_array, i))
array.set(combined_array, i + (40 * 4), array.get(a5_array, i))
array.set(combined_array, i + (40 * 5), array.get(a6_array, i))
array.set(combined_array, i + (40 * 6), array.get(a7_array, i))
array.set(combined_array, i + (40 * 7), array.get(a8_array, i))
array.set(combined_array, i + (40 * 8), array.get(a9_array, i))
array.set(combined_array, i + (40 * 9), array.get(a10_array, i))
array.set(combined_array, i + (40 * 10), array.get(a11_array, i))
array.set(combined_array, i + (40 * 11), array.get(a12_array, i))
array.set(combined_array, i + (40 * 12), array.get(a13_array, i))
array.set(combined_array, i + (40 * 13), array.get(a14_array, i))
array.set(combined_array, i + (40 * 14), array.get(a15_array, i))
array.set(combined_array, i + (40 * 15), array.get(a16_array, i))
array.set(combined_array, i + (40 * 16), array.get(a17_array, i))
array.set(combined_array, i + (40 * 17), array.get(a18_array, i))
array.set(combined_array, i + (40 * 18), array.get(a19_array, i))
array.set(combined_array, i + (40 * 19), array.get(a20_array, i))
array.set(combined_array, i + (40 * 20), array.get(a21_array, i))
array.set(combined_array, i + (40 * 21), array.get(a22_array, i))
array.set(combined_array, i + (40 * 22), array.get(a23_array, i))
array.set(combined_array, i + (40 * 23), array.get(a24_array, i))
array.set(combined_array, i + (40 * 24), array.get(a25_array, i))
array.set(combined_array, i + (40 * 25), array.get(a26_array, i))
array.set(combined_array, i + (40 * 26), array.get(a27_array, i))
array.set(combined_array, i + (40 * 27), array.get(a28_array, i))
array.set(combined_array, i + (40 * 28), array.get(a29_array, i))
array.set(combined_array, i + (40 * 29), array.get(a30_array, i))
array.set(combined_array, i + (40 * 30), array.get(a31_array, i))
array.set(combined_array, i + (40 * 31), array.get(a32_array, i))
array.set(combined_array, i + (40 * 32), array.get(a33_array, i))
array.set(combined_array, i + (40 * 33), array.get(a34_array, i))
array.set(combined_array, i + (40 * 34), array.get(a35_array, i))
array.set(combined_array, i + (40 * 35), array.get(a36_array, i))
array.set(combined_array, i + (40 * 36), array.get(a37_array, i))
array.set(combined_array, i + (40 * 37), array.get(a38_array, i))
array.set(combined_array, i + (40 * 38), array.get(a39_array, i))
array.set(combined_array, i + (40 * 39), array.get(a40_array, i))
Calculating Sums: A separate array_sums is created to store the total score for each asset by summing the values of their respective score arrays. This allows for easy comparison of overall performance.
Ranking Assets: The final part of the code ranks the assets based on their total scores stored in array_sums. It assigns a rank to each asset, where the asset with the highest score receives the highest rank.
// create array for asset RANK based on array.sum
var ranks = array.new_int(used_assets, 0)
// for loop that calculates the rank of each asset
if barstate.islast
for i = 0 to (used_assets - 1)
int rank = 1
for x = 0 to (used_assets - 1)
if i != x
if array.get(array_sums, i) < array.get(array_sums, x)
rank := rank + 1
array.set(ranks, i, rank)
Dynamic Table Creation
Initialization: The table is initialized with a base structure that includes headers for asset names, scores, and ranks. The headers are set to remain constant, ensuring clarity for users as they interpret the displayed data.
Data Population: As scores are calculated for each asset, the corresponding values are dynamically inserted into the table. This is achieved through a loop that iterates over the scores and ranks stored in the combined_array and array_sums, respectively.
Automatic Extending Mechanism
Variable Asset Count: The code checks the number of assets defined by the user. Instead of hardcoding the number of rows in the table, it uses a variable to determine the extent of the data that needs to be displayed. This allows the table to expand or contract based on the number of assets being analyzed.
Dynamic Row Generation: Within the loop that populates the table, the code appends new rows for each asset based on the current asset count. The structure of each row includes the asset name, its score, and its rank, ensuring that the table remains consistent regardless of how many assets are involved.
// Automatically extending table based on the number of used assets
var table table = table.new(position.bottom_center, 50, 50, color.new(color.black, 100), color.white, 3, color.white, 1)
if barstate.islast
if not hide_head
table.cell(table, 0, 0, "Universal Ratio Trend Matrix", text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.merge_cells(table, 0, 0, used_assets + 3, 0)
if not hide_inps
table.cell(table, 0, 1,
text = "Inputs: You are using " + str.tostring(trend_indicator) + ", which takes: " + str.tostring(f_get_input(trend_indicator)),
text_color = color.white, text_size = fontSize), table.merge_cells(table, 0, 1, used_assets + 3, 1)
table.cell(table, 0, 2, "Assets", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, 2, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.cell(table, 0, x + 3, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = f_asset_col(array.get(ranks, x)), text_size = fontSize)
for r = 0 to (used_assets - 1)
for c = 0 to (used_assets - 1)
table.cell(table, c + 1, r + 3, text = str.tostring(array.get(combined_array, c + (r * 40))),
text_color = hl_type == "Text" ? f_get_col(array.get(combined_array, c + (r * 40))) : color.white, text_size = fontSize,
bgcolor = hl_type == "Background" ? f_get_col(array.get(combined_array, c + (r * 40))) : na)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, x + 3, "", bgcolor = #010c3b)
table.cell(table, used_assets + 1, 2, "", bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 1, x + 3, "==>", text_color = color.white)
table.cell(table, used_assets + 2, 2, "SUM", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
table.cell(table, used_assets + 3, 2, "RANK", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 2, x + 3,
text = str.tostring(array.get(array_sums, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_sum(array.get(array_sums, x), array.get(ranks, x)))
table.cell(table, used_assets + 3, x + 3,
text = str.tostring(array.get(ranks, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_rank(array.get(ranks, x)))
Market Volatility Key: CHOP, ATR, VIX & 10Y BondThis script builds upon existing market analysis tools by providing a comprehensive dashboard that combines the Choppiness Index (CHOP), Average True Range (ATR) with a user-selectable timeframe, VIX (Volatility Index), and the 10-year US Treasury bond price in a compact tile format. The color-coded key provides quick visual cues for market conditions—highlighting whether the market is trending or consolidating—allowing traders to make informed decisions quickly.
For example, when trading the Nasdaq (NQ), you might use this indicator to help manage your scalping trades. If you trade on a 10-minute chart but set the ATR timeframe to 1 minute, it helps identify whether there is enough price movement to justify entering a trade. If the ATR is less than 10, it suggests there's not enough range for scalping opportunities, and you may choose to stay out of the trade.
This expanded indicator integrates and enhances existing concepts to deliver a well-rounded view of volatility, trend strength, and market conditions all in one glance, making it an essential tool for both trend-following and scalping strategies.
Statistics plot1. setting the price range
At the beginning of the script, set the price range (interval). Price ranges are used to divide prices into several groups (buckets) and record how many prices have been reached within each group. For example, setting the price range to “10” will divide the price into intervals 0-10, 10-20, 20-30, and so on.
The price range can also be set manually by the user or automatically calculated based on the initial price. This allows for flexibility in adjusting price ranges for different assets and different time frames.
2. aggregate the number of times a price is reached
Record how many times the price reached each price range (e.g., 100-110, 110-120, etc.). This aggregate data is stored in a data structure called an array.
Each element of the array corresponds to a price range, and when a price reaches that range, the corresponding array value is incremented by one. This process is performed in real time, tracking price movements.
3. initializing and extending price ranges
The first bar of the script (when the chart is first loaded) divides the price ranges into several groups and initializes a count of 0 for each range.
When a price reaches a new range, the array is expanded as needed to add the new price range. This allows the script to work with any price movement, even if the price range continues to grow.
4. visualize the number of price arrivals with a histogram
The aggregated number of arrivals per price range is visually displayed in the form of a histogram. This histogram is designed to allow the user to see at a glance which price range is being reached most frequently.
For example, if prices frequently reach the 100-110 range, the histogram bar corresponding to that range will appear higher than the other ranges. This allows you to visually identify price “dwell points” or support and resistance levels.
5. display of moving averages
A moving average (MA) of the number of times a price has been reached is drawn above the histogram. Moving averages are indicators that show a smooth trend for the number of price arrivals and are useful for understanding the overall direction of price movements.
The duration of the moving average (how many data points it is calculated based on) can be set by the user. This allows for flexible analysis of short or long term price trends. 6.
6. price range tracking and labeling
The script keeps track of which price range the current price is located in. Based on this, information related to the current price range is displayed on the chart as labels.
In particular, labels indicate the beginning and end points of the price range, including which range the price was in at the beginning and which range the price reached at the end. These labels are a useful feature to visually identify price ranges on the chart.
7. labeling of current price range
To confirm which price range the current price is in, when a price reaches a specific price range, a label corresponding to that price range is displayed. This label indicates the position of the price in real-time, allowing traders to visually track where the current price is in the area.
8. calculating the start and end points of the range
The script calculates the start and end points of a range with a non-zero number of price arrivals to find the minimum and maximum of the range. This calculation allows you to see where prices are concentrated within a range.
9. out-of-range price processing
When a price reaches outside the range, the script automatically adds the array element corresponding to that price range and inserts the data in the appropriate location for the count. This allows the script to follow the price as it moves unexpectedly.
Supertrend Scanner on ChartThis Indicator is Used to scan 10 stock on chart.
Supertrend is widely used indicator on tradingview. So we have used the originals indicator codes of supertrend by tradingview here. Background color has been changed as per supertrend trrend.
Problem : Sometime trader wants to track multiple stocks supertrend at a time. Mostly those stock are of same sector. To track all the stocks of same sector in one chart , trader has to open multiple charts for that.
Solution : This indicator pointout where other stocks has changed the trend. Like if you see "SBIN" written in GEREEN at bottom of the candle , that means on that particular candle SBIN supertrend has changed to positive. Similarly if you see "KOTAK" written in RED at top of the candle the means supertrend has changed to Negative on that particular candle. Its so easy to trace 10 stock on same chart which stocks labelling.
How to use :
When you trade on any index , then apply all the index constituents stock on this indicator. When Index changes the trend and that change in trend is confirmed by other constituents ( like 7/10 confirmed ) then that is confirmed trend. If all the constituents are on same direction than that's the confirmed trend.
Disclamer : This indicator is for education purpose , for any profit or loss , we are not responsible. Trade on your own risk.
Export Candles DataThis program is written in Pine Script (version 5) and is designed to retrieve candlestick data (open, high, low, and close prices) from the TradingView chart. The data is displayed in a table located in the upper right corner of the chart.
Main Functions of the Program:
Retrieving candlestick data: The program processes data for the last 10 candlesticks on the selected timeframe (e.g., hourly, minute, etc.) in the TradingView chart. For each candlestick, it retrieves:
Time of the candle's close
Opening price
Highest price during the period
Lowest price during the period
Closing price
Displaying data in a table: The data is presented in a compact table located in the upper right corner of the chart. The table contains 5 columns:
Time of the candle's close (formatted as yyyy-MM-dd HH:mm)
Opening price
Highest price
Lowest price
Closing price
Clearing the table every 50 bars: To prevent the table from becoming overloaded, it clears itself every 50 bars, starting from the first row and first column.
Data updates dynamically: The table dynamically updates, displaying the latest 10 candles, allowing traders to track current market changes.
Application:
This indicator is useful for traders who want a quick view of key candlestick parameters directly on the chart.
The indicator can be easily applied to any instrument or index in TradingView, such as the IMOEX index.
The table view makes it easy to quickly analyze market movements without needing to inspect each candle individually.
How the Program Works:
On each new bar, the program checks the current bar's index.
The program clears the table if 50 bars have passed since the last clearing.
It writes the data of the last 10 candlesticks into the table: the time of the candle's close, opening price, highest and lowest prices, and closing price.
The table updates automatically and continuously displays the latest data.
This indicator is suitable for both short-term and long-term market analysis, providing a convenient and efficient way to monitor price movements directly on the chart.
BTC Arcturus IndicatorBTC Arcturus Indicator: This indicator is designed to create buy and sell signals based on the market value of Bitcoin. It also predicts potential market tops with the Pi Cycle Top indicator.
How Does It Work?
1. MVRVZ (Market Value to Realized Value-Z Score) Calculation:
MC: Bitcoin's market cap (Market Cap) is pulled daily from Glassnode data.
MCR: Realized Market Cap of Bitcoin is taken daily from Coinmetrics data.
MVRVZ: It is calculated by dividing the difference between Bitcoin's market value and realized market value by one standard deviation. This value indicates whether the market is overvalued or undervalued.
2. Reception and Warning Signals:
Buy Signal: When MVRVZ falls below the -0.255 threshold value, the indicator gives a "Buy" signal. This indicates that Bitcoin is undervalued and may be a buying opportunity.
Warning Signal: A warning signal turns on when MVRVZ exceeds the threshold value of 2.765. This indicates that the market is approaching saturation and caution is warranted.
3. Tracking the Highest MVRVZ Value:
The indicator records the highest MVRVZ value in the last 10 candlesticks. This value is used to determine whether the market has reached its highest risk levels.
4. Warning Display:
If the MVRVZ value matches the highest value in the last 10 bars and this warning has not been displayed before, a "Warning" signal is displayed.
Once the warning signal is shown, no further warnings are shown for 10 candles.
5. Pi Cycle Top Indicator:
Pi Cycle Top: This indicator predicts Bitcoin tops by comparing two moving averages (350-day and 111-day). If the short-term moving average falls below the long-term moving average, this is considered a sell signal.
The indicator displays this signal with the label "Sell", indicating a potential market top.
User Guide:
Green Buy Signal: It means Bitcoin is cheap and offers a buying opportunity.
Yellow Warning Signal: Indicates that Bitcoin has reached possible profit taking points and caution should be exercised.
Red Sell Signal: Indicates that Bitcoin has reached market saturation and it may be appropriate to sell.
Pivot Channel Breaks [BigBeluga]Pivot Channel Break
The Pivot Channel Break indicator identifies key pivot points and creates a dynamic channel based on these pivots. It detects breakouts from this channel, providing potential entry and exit signals for traders.
🔵 How to Use
Channel Identification:
- Upper and lower channel lines drawn based on pivot highs and lows
- Channel width dynamically adjusted using ATR-like calculation
Breakout Signals:
- Upward breakout: Price closes above upper channel line
- Downward breakout: Price closes below lower channel line
- Signals shown as X marks on the chart
Pivot Points:
- High pivots marked with "H" triangles
- Low pivots marked with "L" triangles
Support & Resistance:
- Optional signals when price touches but doesn't break channel lines
Trend Visualization:
- Optional bar coloring based on the most recent breakout direction
🔵 Customization
• Pivot Right: Lookback period for pivot detection (default: 10)
• Pivot Left: Forward period for pivot confirmation (default: 40)
• Channel Width: Multiplier for channel width calculation (default: 1.0)
• Support & Resistance Signals: Toggle additional touch signals
• Bar Color: Enable/disable trend-based bar coloring
Calculation:
Detect pivot highs and lows using specified lookback periods
Calculate channel basis using 10-period SMA of close prices
Determine channel width using ATR-like calculation: RMA(high - low, 10) * width multiplier
Set channel lines based on pivot points and calculated deviations
Identify breakouts when price crosses beyond channel lines
The Pivot Channel Break indicator offers a dynamic approach to identifying potential trend changes and breakout opportunities. It combines pivot point analysis with a flexible channel calculation, providing traders with a visual tool for market structure analysis. Use this indicator in conjunction with other technical analysis methods to confirm signals and manage risk effectively.
Dual Chain StrategyDual Chain Strategy - Technical Overview
How It Works:
The Dual Chain Strategy is a unique approach to trading that utilizes Exponential Moving Averages (EMAs) across different timeframes, creating two distinct "chains" of trading signals. These chains can work independently or together, capturing both long-term trends and short-term price movements.
Chain 1 (Longer-Term Focus):
Entry Signal: The entry signal for Chain 1 is generated when the closing price crosses above the EMA calculated on a weekly timeframe. This suggests the start of a bullish trend and prompts a long position.
bullishChain1 = enableChain1 and ta.crossover(src1, entryEMA1)
Exit Signal: The exit signal is triggered when the closing price crosses below the EMA on a daily timeframe, indicating a potential bearish reversal.
exitLongChain1 = enableChain1 and ta.crossunder(src1, exitEMA1)
Parameters: Chain 1's EMA length is set to 10 periods by default, with the flexibility for user adjustment to match various trading scenarios.
Chain 2 (Shorter-Term Focus):
Entry Signal: Chain 2 generates an entry signal when the closing price crosses above the EMA on a 12-hour timeframe. This setup is designed to capture quicker, shorter-term movements.
bullishChain2 = enableChain2 and ta.crossover(src2, entryEMA2)
Exit Signal: The exit signal occurs when the closing price falls below the EMA on a 9-hour timeframe, indicating the end of the shorter-term trend.
exitLongChain2 = enableChain2 and ta.crossunder(src2, exitEMA2)
Parameters: Chain 2's EMA length is set to 9 periods by default, and can be customized to better align with specific market conditions or trading strategies.
Key Features:
Dual EMA Chains: The strategy's originality shines through its dual-chain configuration, allowing traders to monitor and react to both long-term and short-term market trends. This approach is particularly powerful as it combines the strengths of trend-following with the agility of momentum trading.
Timeframe Flexibility: Users can modify the timeframes for both chains, ensuring the strategy can be tailored to different market conditions and individual trading styles. This flexibility makes it versatile for various assets and trading environments.
Independent Trade Logic: Each chain operates independently, with its own set of entry and exit rules. This allows for simultaneous or separate execution of trades based on the signals from either or both chains, providing a robust trading system that can handle different market phases.
Backtesting Period: The strategy includes a configurable backtesting period, enabling thorough performance assessment over a historical range. This feature is crucial for understanding how the strategy would have performed under different market conditions.
time_cond = time >= startDate and time <= finishDate
What It Does:
The Dual Chain Strategy offers traders a distinctive trading tool that merges two separate EMA-based systems into one cohesive framework. By integrating both long-term and short-term perspectives, the strategy enhances the ability to adapt to changing market conditions. The originality of this script lies in its innovative dual-chain design, providing traders with a unique edge by allowing them to capitalize on both significant trends and smaller, faster price movements.
Whether you aim to capture extended market trends or take advantage of more immediate price action, the Dual Chain Strategy provides a comprehensive solution with a high degree of customization and strategic depth. Its flexibility and originality make it a valuable tool for traders seeking to refine their approach to market analysis and execution.
How to Use the Dual Chain Strategy
Step 1: Access the Strategy
Add the Script: Start by adding the Dual Chain Strategy to your TradingView chart. You can do this by searching for the script by name or using the link provided.
Select the Asset: Apply the strategy to your preferred trading pair or asset, such as #BTCUSD, to see how it performs.
Step 2: Configure the Settings
Enable/Disable Chains:
The strategy is designed with two independent chains. You can choose to enable or disable each chain depending on your trading style and the market conditions.
enableChain1 = input.bool(true, title='Enable Chain 1')
enableChain2 = input.bool(true, title='Enable Chain 2')
By default, both chains are enabled. If you prefer to focus only on longer-term trends, you might disable Chain 2, or vice versa if you prefer shorter-term trades.
Set EMA Lengths:
Adjust the EMA lengths for each chain to match your trading preferences.
Chain 1: The default EMA length is 10 periods. This chain uses a weekly timeframe for entry signals and a daily timeframe for exits.
len1 = input.int(10, minval=1, title='Length Chain 1 EMA', group="Chain 1")
Chain 2: The default EMA length is 9 periods. This chain uses a 12-hour timeframe for entries and a 9-hour timeframe for exits.
len2 = input.int(9, minval=1, title='Length Chain 2 EMA', group="Chain 2")
Customize Timeframes:
You can customize the timeframes used for entry and exit signals for both chains.
Chain 1:
Entry Timeframe: Weekly
Exit Timeframe: Daily
tf1_entry = input.timeframe("W", title='Chain 1 Entry Timeframe', group="Chain 1")
tf1_exit = input.timeframe("D", title='Chain 1 Exit Timeframe', group="Chain 1")
Chain 2:
Entry Timeframe: 12 Hours
Exit Timeframe: 9 Hours
tf2_entry = input.timeframe("720", title='Chain 2 Entry Timeframe (12H)', group="Chain 2")
tf2_exit = input.timeframe("540", title='Chain 2 Exit Timeframe (9H)', group="Chain 2")
Set the Backtesting Period:
Define the period over which you want to backtest the strategy. This allows you to see how the strategy would have performed historically.
startDate = input.time(timestamp('2015-07-27'), title="StartDate")
finishDate = input.time(timestamp('2026-01-01'), title="FinishDate")
Step 3: Analyze the Signals
Understand the Entry and Exit Signals:
Buy Signals: When the price crosses above the entry EMA, the strategy generates a buy signal.
bullishChain1 = enableChain1 and ta.crossover(src1, entryEMA1)
Sell Signals: When the price crosses below the exit EMA, the strategy generates a sell signal.
bearishChain2 = enableChain2 and ta.crossunder(src2, entryEMA2)
Review the Visual Indicators:
The strategy plots buy and sell signals on the chart with labels for easy identification:
BUY C1/C2 for buy signals from Chain 1 and Chain 2.
SELL C1/C2 for sell signals from Chain 1 and Chain 2.
This visual aid helps you quickly understand when and why trades are being executed.
Step 4: Optimize the Strategy
Backtest Results:
Review the strategy’s performance over the backtesting period. Look at key metrics like net profit, drawdown, and trade statistics to evaluate its effectiveness.
Adjust the EMA lengths, timeframes, and other settings to see how changes affect the strategy’s performance.
Customize for Live Trading:
Once satisfied with the backtest results, you can apply the strategy settings to live trading. Remember to continuously monitor and adjust as needed based on market conditions.
Step 5: Implement Risk Management
Use Realistic Position Sizing:
Keep your risk exposure per trade within a comfortable range, typically between 1-2% of your trading capital.
Set Alerts:
Set up alerts for buy and sell signals, so you don’t miss trading opportunities.
Paper Trade First:
Consider running the strategy in a paper trading account to understand its behavior in real market conditions before committing real capital.
This dual-layered approach offers a distinct advantage: it enables the strategy to adapt to varying market conditions by capturing both broad trends and immediate price action without one chain's activity impacting the other's decision-making process. The independence of these chains in executing transactions adds a level of sophistication and flexibility that is rarely seen in more conventional trading systems, making the Dual Chain Strategy not just unique, but a powerful tool for traders seeking to navigate complex market environments.
S&P Short-Range Oscillator**SHOULD BE USED ON THE S&P 500 ONLY**
The S&P Short-Range Oscillator (SRO), inspired by the principles of Jim Cramer's oscillator, is a technical analysis tool designed to help traders identify potential buy and sell signals in the stock market, specifically for the S&P 500 index. The SRO combines several market indicators to provide a normalized measure of market sentiment, assisting traders in making informed decisions.
The SRO utilizes two simple moving averages (SMAs) of different lengths: a 5-day SMA and a 10-day SMA. It also incorporates the daily price change and market breadth (the net change of closing prices). The 5-day and 10-day SMAs are calculated based on the closing prices. The daily price change is determined by subtracting the opening price from the closing price. Market breadth is calculated as the difference between the current closing price and the previous closing price.
The raw value of the oscillator, referred to as SRO Raw, is the sum of the daily price change, the 5-day SMA, the 10-day SMA, and the market breadth. This raw value is then normalized using its mean and standard deviation over a 20-day period, ensuring that the oscillator is centered and maintains a consistent scale. Finally, the normalized value is scaled to fit within the range of -15 to 15.
When interpreting the SRO, a value below -5 indicates that the market is potentially oversold, suggesting it might be a good time to start buying stocks as the market could be poised for a rebound. Conversely, a value above 5 suggests that the market is potentially overbought. In this situation, it may be prudent to hold on to existing positions or consider selling if you have substantial gains.
The SRO is visually represented as a blue line on a chart, making it easy to track its movements. Red and green horizontal lines mark the overbought (5) and oversold (-5) levels, respectively. Additionally, the background color changes to light red when the oscillator is overbought and light green when it is oversold, providing a clear visual cue.
By incorporating the S&P Short-Range Oscillator into your trading strategy, you can gain valuable insights into market conditions and make more informed decisions about when to buy, sell, or hold your stocks. However, always consider other market factors and perform your own analysis before making any trading decisions.
The S&P Short-Range Oscillator is a powerful tool for traders looking to gain insights into market sentiment. It provides clear buy and sell signals through its combination of multiple indicators and normalization process. However, traders should be aware of its lagging nature and potential complexity, and use it in conjunction with other analysis methods for the best results.
Disclaimer
The S&P Short-Range Oscillator is for informational purposes only and should not be considered financial advice. Trading involves risk, and you should conduct your own research or consult a financial advisor before making investment decisions. The author is not responsible for any losses incurred from using this indicator. Use at your own risk.
Equal Highs and Lows {Reh's and Rel's }# Equal Highs and Lows {Reh's and Rel's} Indicator
## Overview
The "Equal Highs and Lows {Reh's and Rel's}" indicator is designed to identify and mark equal highs and lows on a price chart. It detects both exact and relative equal levels, draws lines connecting these levels, and optionally labels them. This tool can help traders identify potential support and resistance zones based on historical price levels.
## Key Features
1. **Exact and Relative Equality**: Detects both precise price matches and relative equality within a specified threshold.
2. **Customizable Appearance**: Allows users to adjust colors, line styles, and widths.
3. **Dynamic Line Management**: Automatically extends or removes lines based on ongoing price action.
4. **Labeling System**: Optional labels to identify types of equal levels (e.g., "Equal High", "REH/Equal High").
5. **Flexible Settings**: Adjustable parameters for lookback periods, maximum bars apart, and relative equality thresholds.
## User Inputs
### Appearance
- `lineColorHigh`: Color for lines marking equal highs (default: red)
- `lineColorLow`: Color for lines marking equal lows (default: green)
- `lineWidth`: Thickness of the lines (range: 1-5, default: 1)
- `lineStyle`: Style of the lines (options: Solid, Dash, Dotted)
- `showLabels`: Toggle to show or hide labels for equal highs and lows
### Settings
- `lookbackLength`: Number of bars to look back for finding equal highs and lows (default: 200)
- `maxBarsApart`: Maximum number of bars apart for equal highs/lows to be considered (range: 2-10, default: 5)
### Relative Equality
- `considerRelativeEquals`: Enable detection of relative equal highs and lows
- `thresholdIndex`: Maximum tick difference for relative equality in index instruments (range: 1-10, default: 2)
- `thresholdStocks`: Maximum tick difference for relative equality in stock instruments (range: 5-200, step: 5, default: 10)
## How It Works
The indicator scans historical price data to identify equal or relatively equal highs and lows. It draws lines connecting these levels and updates them as new price data comes in. Lines are extended if the level holds and removed if the price breaks through. The tool adapts to different market conditions by allowing adjustments to the equality thresholds for various instrument types.
## Practical Use
Traders can use this indicator to:
- Identify potential support and resistance levels
- Spot areas where price might react based on historical turning points
- Enhance their understanding of price structure and repetitive patterns
## Disclaimer
This indicator is provided as a tool to assist in identifying potential price levels of interest. It is not financial advice. Users should not rely solely on this or any single indicator for trading decisions. Always conduct thorough analysis, consider multiple factors, and be aware that past price behavior does not guarantee future results. All trading involves risk.
Ripster MTF CloudsDescription:
MTF EMA Cloud By Ripster
EMA Cloud System is a Trading System Invented by Ripster where areas are shaded between two desired EMAs. The concept implies the EMA cloud area serves as support or resistance for Intraday & Swing Trading. This can be utilized effectively on 10 Min for day trading and 1Hr/Daily for Swings. Ripster himself utilizes various combinations of the 5-12, 34-50, 8-9, 20-21 EMA clouds but the possibilities are endless to find what works best for you.
“Ideally, 5-12 or 5-13 EMA cloud acts as a fluid trendline for day trades. 8-9 EMA Clouds can be used as pullback Levels –(optional). Additionally, a high level price over or under 34-50 EMA clouds confirms either bullish or bearish bias on the price action for any timeframe” – Ripster
This indicator is an extension of the Ripster EMA Clouds. It allows you to visualize Exponential Moving Average (EMA) clouds from any time frame on your current chart, regardless of the chart's own time frame. This functionality is especially useful for traders who want to monitor higher time frame trends and support/resistance levels while trading on lower time frames.
What does this code do?
The Ripster MTF Clouds indicator displays two sets of EMA clouds. Each set consists of a short EMA and a long EMA. By default, the indicator uses Daily 20/21 and 50/55 EMAs, but you can customize these settings to fit your trading strategy. The EMAs are plotted on your chart along with their corresponding clouds, colored for easy differentiation:
EMA 1 (default 50/55): Plotted in blue.
EMA 2 (default 20/21): Plotted in teal.
The indicator uses the security function to fetch EMA values from higher time frames and plots them on your current chart, allowing you to see how these higher time frame EMAs interact with your current time frame's price action.
How to use this indicator:
Adjust Resolution:
Set the "Resolution" input to the time frame from which you want to fetch EMA values. For example, set it to "1H" if you want to see 1-hour EMAs on your current chart.
Customize EMAs:
Modify the "EMA 1 Short Length" and "EMA 1 Long Length" inputs to change the default 50/55 EMAs.
Adjust the "EMA 2 Short Length" and "EMA 2 Long Length" inputs to change the default 20/21 EMAs.
Monitor Clouds:
The indicator fills the area between the short and long EMAs, creating a cloud that helps visualize the trend. A blue cloud indicates the area between the EMA 1 pair, while a teal cloud indicates the area between the EMA 2 pair.
Use Multiple Instances:
You can add multiple instances of this indicator to your chart to monitor multiple higher time frames simultaneously. For instance, one instance can show daily clouds while another shows hourly clouds.
Integration with Trading Strategy:
Use this indicator to identify higher time frame trends and support/resistance levels, which can help improve your trading decisions on lower time frames.
For example, you can go long when the stock is above the 50-55 EMA clouds and 20-21 EMA clouds with daily resolution on a 10-minute chart and short when it is below it.
Similarly, you can short a stock under the 1-hour 34/50 EMA clouds while still trading on a 10-minute chart.
Bollinger Bands Fast Trend Indicator [DCD]Description:
The Bollinger Bands Fast Trend Detector indicator is an advanced tool designed to provide traders with more precise trend detection and clearer entry and exit signals. This script builds upon the traditional Bollinger Bands indicator by adding customizable standard deviations and incorporating multiple moving averages to enhance the accuracy of the signals.
Main Features:
1. **Customizable Bollinger Bands**:
- Each Bollinger Band has its own standard deviation setting, allowing for more granular control and better trend detection.
- The short Bollinger Band is set to a 10-period SMA for faster trend recognition.
2. **Multiple Moving Averages**:
- The indicator includes several types of moving averages (SMA, EMA, LSMA, HMA, WMA) applied to the Bollinger Trend value, giving traders flexibility to choose the best fit for their strategy.
3. **Crossover and Crossdown Detection**:
- The script identifies crossover and crossdown points between the Bollinger Trend value and the selected moving average, marking potential buy and sell signals with green and red circles, respectively.
4. **Color-Coded Histogram**:
- The histogram bars are color-coded to indicate the strength and direction of the trend, making it easy to visualize market conditions at a glance.
Instructions:
1. **Adding the Script to Your Chart**:
- Open your TradingView chart and add the Bollinger Bands Fast Trend Detector indicator.
2. **Adjusting Parameters**:
- Customize the Bollinger Bands and moving average settings according to your trading preferences:
- `Short BB Length` (default: 10): Adjusts the length of the short Bollinger Band.
- `Long BB Length` (default: 50): Adjusts the length of the long Bollinger Band.
- `StdDev` (for both bands): Sets the standard deviation multiplier.
- `Moving Average Type`: Choose between SMA, EMA, LSMA, HMA, and WMA.
- `Moving Average Length` (default: 14): Sets the length of the moving average.
3. **Interpreting the Output**:
- Observe the BBTrend and moving average plots on your chart.
- Look for green circles indicating crossover points (potential buy signals) and red circles indicating crossdown points (potential sell signals).
- Use the color-coded histogram bars to assess the strength and direction of the trend.
Configurable Parameters:
- `shortLengthInput` (default: 10): Length of the short Bollinger Band.
- `longLengthInput` (default: 50): Length of the long Bollinger Band.
- `shortDevMultInput` (default: 1.0): Standard deviation multiplier for the short Bollinger Band.
- `longDevMultInput` (default: 2.0): Standard deviation multiplier for the long Bollinger Band.
- `maTypeInput` (default: SMA): Type of moving average (options: SMA, EMA, LSMA, HMA, WMA).
- `maLengthInput` (default: 14): Length of the moving average.
Code Explanation:
The script calculates two sets of Bollinger Bands with distinct lengths and standard deviations. The difference between the lower bands and upper bands is normalized by the short middle band to compute the BBTrend value. A selected moving average is then applied to this BBTrend value. The script plots the BBTrend, the moving average, and uses color-coded histogram bars to represent trend strength and direction. It also identifies and marks crossover and crossdown points to provide potential trading signals.
Disclaimer:
This script is for educational purposes only and should not be considered financial advice. Always perform your own analysis before making any trading decisions.
Average price in candlePlots the average price a security had within a candle.
So, for example: If you have a 15m candle, and price stayed near the candle's high for 10 out of the 15 minutes, you would expect the average price top be near the candle's high as well. And that's actually how it is.
(Blue 10:30 candle in the screenshot.)
I think this "average in-candle price" could be a useful thing to know. You can't gather this information from the normal chart (as you would need to go to a lower timeframe). Plus this plot can be smoother than plots of, say, closing price or hl2.
The calculation happens in a lower timeframe which is selected automatically. There is a precision selector which allows you to influence this. By default, at least 10 values are sampled for each candle.
If you have TradingView Premium, the script is able to use second-based intervals to look inside 1m candles. (Tick the corresponding checkbox to enable this.)
How it works: I collect timestamps and hl2 values from the lower timeframe. (I figure hl2 is the best choice here because a close is so arbitrary. If we don't have further information, our guess is the average is simply exactly in the middle of the candle's range.)
Then I throw the last close and current close (from the CHART timeframe) into the mix and calculate an average of the prices we collected weighted by the duration that price was in effect.
tl;dr; It's just math baby
