Gains and Drawdowns with Standard DeviationsThis “Gains and Drawdowns with Standard Deviations” indicator helps in analyzing and visualizing the percentage gains and drawdown phases of a market or asset relative to its historical range. By calculating gains from the lowest low and drawdowns from the highest high over a specified lookback period, this indicator provides deeper insights into price movements and risk.
Key Features and Applications:
1. Gain and Drawdown Calculation:
• Gains: The indicator calculates the percentage gain from the lowest price point within a specific lookback period (e.g., 250 days).
• Drawdowns: Drawdowns are calculated as the percentage change from the highest point in the same period. This helps in identifying the maximum loss phases.
2. Standard Deviation:
• The indicator computes the standard deviation of both gains and drawdowns over a specified period (e.g., 250 days), allowing you to quantify volatility.
• Three bands (1st, 2nd, and 3rd standard deviations) are plotted for both gains and drawdowns, representing the frequency and magnitude of price movements within the normal volatility range.
3. Extreme Movements Highlighting:
• The indicator highlights extreme gains and drawdowns when they exceed user-defined thresholds. This helps in identifying significant market events or turning points.
4. Customizable Thresholds:
• Users can adjust the thresholds for extreme gains and drawdowns, as well as the lookback period for calculating gains, drawdowns, and standard deviations, making the indicator highly adaptable to specific needs.
Application in Portfolio Management:
The use of standard deviation in portfolio management is essential for assessing the risk and volatility of a portfolio. According to Modern Portfolio Theory (MPT) by Harry Markowitz, diversification of assets in a portfolio helps to minimize overall risk (especially the standard deviation), while maximizing returns. The standard deviation of a portfolio measures the volatility of its returns, with higher standard deviation indicating higher risk.
Scientific Source: Markowitz, H. M. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
Markowitz’s theory suggests that an optimized portfolio, by minimizing the standard deviation of returns and combining a diversified asset allocation, can achieve better risk-adjusted returns.
Conclusion:
This indicator is particularly useful for traders and portfolio managers who want to understand and visualize market risk and extreme events. By using gains, drawdowns, and volatility metrics, it allows for systematic monitoring and evaluation of price movements, leading to more informed decisions in trading or portfolio management. A comprehensive understanding of price behavior and volatility helps in optimizing risk management and making strategic market entries.
Key Features:
• Visualization of Gains and Drawdowns with color-coded highlights for extreme movements.
• Standard Deviation Calculations for detailed volatility analysis.
• Customizable Thresholds for identifying extreme market events.
This indicator is a valuable tool for analyzing market data from a scientific standpoint, improving risk management, and making data-driven decisions based on historical performance.
Statistics
Buy When There's Blood in the Streets StrategyStatistical Analysis of Drawdowns in Stock Markets
Drawdowns, defined as the decline from a peak to a trough in asset prices, are an essential measure of risk and market dynamics. Their statistical properties provide insights into market behavior during extreme stress periods.
Distribution of Drawdowns: Research suggests that drawdowns follow a power-law distribution, implying that large drawdowns, while rare, are more frequent than expected under normal distributions (Sornette et al., 2003).
Impacts of Extreme Drawdowns: During significant drawdowns (e.g., financial crises), the average recovery time is significantly longer, highlighting market inefficiencies and behavioral biases. For example, the 2008 financial crisis led to a 57% drawdown in the S&P 500, requiring years to recover (Cont, 2001).
Using Standard Deviations: Drawdowns exceeding two or three standard deviations from their historical mean are often indicative of market overreaction or capitulation, creating contrarian investment opportunities (Taleb, 2007).
Behavioral Finance Perspective: Investors often exhibit panic-selling during drawdowns, leading to oversold conditions that can be exploited using statistical thresholds like standard deviations (Kahneman, 2011).
Practical Implications: Studies on mean reversion show that extreme drawdowns are frequently followed by periods of recovery, especially in equity markets. This underpins strategies that "buy the dip" under specific, statistically derived conditions (Jegadeesh & Titman, 1993).
References:
Sornette, D., & Johansen, A. (2003). Stock market crashes and endogenous dynamics.
Cont, R. (2001). Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance.
Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable.
Kahneman, D. (2011). Thinking, Fast and Slow.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.
USDEGP Rate MultipleIndicator shows the ratio between USDEGP rate calculated using CIB GDR and the official rate. In no stress, value should be stable.
London USDEGP priceThis indicator calculates the hypothetical USDEGP price using CIB receipts price in London Stock Exchange and its price in EGX. Values are smoothed.
Tims Smart Money COT-IndexThe **Tims Smart Money COT Index** analyzes the positions of different groups of market participants from the COT report (Commercials, Large Specs, Small Specs). It calculates their net positions and scales them relative to extremes of the last 24 weeks. It indicates bullish and bearish zones to identify market sentiments.
- Commercials (Smart Money)**: Often act against the trend, bullish from 80+.
- Large Specs (Retail Money)**: Trend-following, bullish from 80+.
- Small Specs**: Mostly impulsive, bullish from 80+.
The indicator helps to identify turning points in the market based on the behavior of the players.
COT Commercials Positions Table Der COT Commercials Opposite Positions Table for Forex ist ein umfangreicher TradingView-Indikator, der die Positionen der kommerziellen Marktteilnehmer (Commercials) im Rahmen des Commitments of Traders (COT)-Berichts darstellt. Er zeigt Long-, Short-, und Netto-Positionen sowie deren prozentuale Anteile für ausgewählte Märkte an.
Hauptmerkmale:
Datenquellenwahl: Unterstützt "Futures Only" und "Futures and Options".
Marktabdeckung: Umfasst Währungen, Rohstoffe, Indizes und Kryptowährungen.
Farbkodierung: Dynamische Farbverläufe zur Hervorhebung von Extremen bei Long-/Short-Positionen und Prozentsätzen.
Historische Daten: Zeigt Positionsdaten der letzten 10 Wochen an.
Anpassbare Tabelle: Klar strukturiert mit wichtigen Kennzahlen wie max./min. Positionen und Netto-Positionen.
Der Indikator ist besonders für Trader nützlich, die Marktstimmungen analysieren und Positionierungen großer Marktteilnehmer in ihre Handelsentscheidungen einbeziehen möchten.
Der Indikator ist hauptsächlich für Futures gedacht und funktioniert nur im 1 Woche Chart.
PR50 AutoI have observed price revisits the 50% Percentile Rank of previous pivot highs and lows with in a decay window of up to 288 bars, but notably 144 bars in a rather systematic way. When watching price, lines will breakout out towards price. a good indicator price and line will meet at some point. Continuation of trends can be seen when price is rejected of previous high/low. Price will also bounce between structure high and low 50% PR.
I provide a this script to auto calculate and populate these lines. The default decay is set to 288, and transparency of lines are determined by the decay.
i provide a levels indicator to show the percentage of lines where price is above and below the start point of the lines at full decay and half. With defaults, 288 and 144 bars respectfully. it is not an indicator to buy or sell, but to demonstrate price is either above or below the distribution of lines.
MA Ratio Weighted Trend System I [InvestorUnknown]The MA Ratio Weighted Trend System I combines slow and fast indicators to identify stable trends and capture potential market turning points. By dynamically adjusting the weight of fast indicators based on the Moving Average Ratio (MAR), the system aims to provide timely entry and exit signals while maintaining overall trend stability through slow indicators.
Slow and Fast Indicators with Dynamic Weighting
Slow Indicators: Designed for stable trend identification, these indicators maintain a constant weight in the overall signal calculation. They include:
DMI For Loop (Directional Movement Index)
CCI For Loop (Commodity Channel Index)
Aroon For Loop
Fast Indicators: Aim to detect rapid market changes and potential turning points. Their weights are dynamically adjusted based on the absolute value of the Moving Average Ratio (MAR). Fast indicators include:
ZLEMA For Loop (Zero-Lag Exponential Moving Average)
IIRF For Loop (Infinite Impulse Response Filter)
Dynamic Weighting Mechanism:
Moving Average Ratio (MAR) is calculated as the ratio of the price to its moving average, minus one (for simplicity and visualization).
Weight Calculation
Fast indicator weights are determined based on the absolute value of MAR, possibly with an offset to avoid scenarios where MAR follows rapid price reversals too closely:
// Function to calculate weights based on MAR
f_mar_weights(series float mar, simple int offset, simple float weight_thre) =>
o_mar = math.abs(mar )
float fast_weight = 0
float slow_weight = 1
if o_mar != 0
if weight_thre > 0
if o_mar <= weight_thre
fast_weight := o_mar
else
fast_weight := o_mar
Threshold-Based vs. Continuous Weighting:
Threshold-Based: Fast indicators receive weight only when the absolute MAR exceeds a user-defined threshold (weight_thre).
Continuous: By setting weight_thre to zero, fast indicators always receive some weight, though this may increase false signals.
Offset Mechanism
The offset parameter shifts the MAR used for weighting by a certain number of bars. This helps avoid situations where the MAR follows sudden price movements too closely, preventing fast indicators from failing to provide timely exit signals.
Signal Calculation
The final signal is a weighted average of the slow and fast indicators:
// Calculate Signal (as weighted average)
float sig = math.round(((DMI*slow_w) + (CCI*slow_w) + (Aroon*slow_w) + (ZLEMA*fast_w) + (IIRF*fast_w)) / (3*slow_w + 2*fast_w), 2)
Backtesting and Performance Metrics
Enables users to test the indicator's performance over historical data, comparing it to a buy-and-hold strategy.
Alerts
Set up alerts for when the signal crosses above or below the thresholds.
alertcondition(long_alert, "LONG (MAR Weighted Trend System)", "MAR Weighted Trend System flipped ⬆LONG⬆")
alertcondition(short_alert, "SHORT (MAR Weighted Trend System)", "MAR Weighted Trend System flipped ⬇Short⬇")
Important Notes
Customization: Due to the experimental nature of this indicator, users are strongly encouraged to adjust and calibrate the settings to align with their trading strategies and market conditions.
Default Settings Disclaimer: The default settings are not optimized or recommended for any specific use and serve only as placeholders for the indicator's publication.
Backtest Results Disclaimer: Historical backtest results are not indicative of future performance. Market conditions change, and past results do not guarantee future outcomes.
Sharpe Ratio Z-ScoreThe "Sharpe Ratio Z-Score" indicator is a powerful tool designed to measure risk-adjusted returns in financial assets. This script helps investors evaluate the performance of a security relative to its risk, using a Z-score based modification of the Sharpe Ratio. The indicator is suitable for assessing market environments and understanding periods of underperformance or overperformance relative to historical standards.
Features:
Risk Assessment and Scaling: The indicator calculates a modified version of the Sharpe Ratio
over a user-defined period. By using scaling and mean offset adjustments, it allows for better
fitting to different market conditions.
Customizable Settings:
Period Length: The number of bars used to calculate the Sharpe Ratio.
Mean Adjustment: Offset value to adjust the average return of the calculated Sharpe ratio.
Scale Factor: A multiplier for emphasizing or reducing the calculated score's impact.
Line Color: Easily customize the plot's appearance.
Visual Cues:
Plots horizontal lines and fills specific regions to visually represent significant Z-score levels.
Highlighted zones include risk thresholds, such as overbought (positive Z-scores) and oversold
(negative Z-scores) areas, using intuitive color fills:
Green for areas below -0.5 (potential buy opportunities).
Red for areas above 0.5 (potential sell opportunities).
Yellow for neutral zones between -0.5 and 0.5.
Use Cases:
Risk-Adjusted Decision Making: Understand when returns are favorable compared to risk, especially during volatile market conditions.
Timing Reversion to Mean: Use highlighted zones to identify potential reversion-to-mean scenarios.
Trend Analysis: Identify times when an asset's performance is significantly deviating from its
average risk-adjusted return.
How It Works:
The script computes the daily returns over a set period, calculates the standard deviation of
those returns, and then applies a modified Sharpe Ratio approach. The Z-score transformation
helps to visualize how far an asset's risk-adjusted return deviates from its historical average.
This "Sharpe Ratio Z-Score" indicator is well-suited for investors seeking to combine quantitative metrics with visual cues, enhancing decision-making for long and short positions while maintaining a risk-adjusted perspective.
Conditional Value at Risk (CVaR)This Pine Script implements the Conditional Value at Risk (CVaR), a risk metric that evaluates the potential losses in a financial portfolio beyond a certain confidence level, incorporating both the Value at Risk (VaR) and the expected loss given that the VaR threshold has been breached.
Key Features:
Input Parameters:
length: Defines the observation period in days (default is 252, typically used to represent the number of trading days in a year).
confidence: Specifies the confidence interval for calculating VaR and CVaR, with values between 0.5 and 0.99 (default is 0.95, indicating a 95% confidence level).
Logarithmic Returns Calculation: The script computes the logarithmic returns based on the daily closing prices, a common method to measure financial asset returns, given by:
Log Return=ln(PtPt−1)
Log Return=ln(Pt−1Pt)
where PtPt is the price at time tt, and Pt−1Pt−1 is the price at the previous time point.
VaR Calculation: Value at Risk (VaR) is estimated as the percentile of the returns array corresponding to the given confidence interval. This represents the maximum loss expected over a given time horizon under normal market conditions at the specified confidence level.
CVaR Calculation: The Conditional VaR (CVaR) is calculated as the average of the returns that fall below the VaR threshold. This represents the expected loss given that the loss has exceeded the VaR threshold.
Visualization: The script plots two key risk measures:
VaR: The maximum potential loss at the specified confidence level.
CVaR: The average of the losses beyond the VaR threshold.
The script also includes a neutral line at zero to help visualize the losses and their magnitude.
Source and Scientific Background:
The concept of Value at Risk (VaR) was popularized by J.P. Morgan in the 1990s, and it has since become a widely-used tool for risk management (Jorion, 2007). Conditional Value at Risk (CVaR), also known as Expected Shortfall, addresses the limitation of VaR by considering the severity of losses beyond the VaR threshold (Rockafellar & Uryasev, 2002). CVaR provides a more comprehensive risk measure, especially in extreme tail risk scenarios.
References:
Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk. McGraw-Hill Education.
Rockafellar, R.T., & Uryasev, S. (2002). Conditional Value-at-Risk for General Loss Distributions. Journal of Banking & Finance, 26(7), 1443–1471.
DJ - SIP Returns with Configurable Features - v1This Pine Script calculates and displays the returns of a Systematic Investment Plan (SIP) with configurable features such as annual increments and additional investments based on market corrections. The script is designed to work on daily time frames and includes various input options for customization.
Key Features
Input Options:
SIP Start and End Dates: Define the period for the SIP.
SIP Period: Choose between weekly and monthly SIP intervals.
Initial SIP Amount: Set the starting amount for the SIP.
Annual Increment: Enable and set a yearly increment percentage for the SIP amount.
Increment Month: Specify the month for the yearly increment.
Correction-Based Investment: Enable additional investments based on market corrections.
Correction Trigger: Set the percentage drop in price to trigger additional investments.
Correction Investment Type: Choose between a lump sum or a multiple of the last SIP amount for additional investments.
Display
Displays the total invested amount, current value, SIP return percentage, and the number of correction triggers in the status line.
The table displays various SIP metrics
Manual Trading Checklist by Afnan TajuddinHey traders! This Trading Checklist indicator like your personal to-do list right on your chart! Here’s what it does:
Easy Tracking: Seven checkboxes to make sure you’ve done all your trading steps.
Colorful Signs: Green "✔" for done stuff and red "✘" for things you need to fix.
Make It Yours: Change where the table is on the chart, pick your favorite colors, and set the text size just how you like it.
Simple Setup: Rename the checklist items and toggle them on or off in the settings.
Clean Look: It stays neat on your chart without messing things up.
Whether you’re just starting out or you’ve been trading for a while, this checklist helps you stay organized and stick to your plan. Perfect for anyone who loves keeping things tidy and on track!
Important to Know: This checklist is not dynamic or automatic and not specific to any symbol. You need to manually check it every time for all the stocks you’re planning to trade. It won’t do the checking for you, so make sure to update it yourself! 🚨
RHR_CANDLELibrary "RHR_CANDLE"
Library for Expansion Contraction Indicator, a zero-lag dual perspective indicator based on Jake Bernstein’s principles of Moving Average Channel system.
calc(shortLookback, longLookback)
Calculates Expansion Contraction values.
Parameters:
shortLookback (int) : Integer for the short lookback calculation, defaults to 8
longLookback (int) : Integer for the long lookback calculation, defaults to 32
@return Returns array of Expansion Contraction values
stdevCalc(positiveShort, negativeShort, positiveLong, negativeLong, stdevLookback)
Calculates standard deviation lines based on Expansion Contraction Long and Short values.
Parameters:
positiveShort (float) : Float for the positive short XC value from calculation
negativeShort (float) : Float for the negative short XC value from calculation
positiveLong (float) : Float for the positive long XC value from calculation
negativeLong (float) : Float for the negative long XC value from calculation
stdevLookback (int) : Integer for the standard deviation lookback, defaults to 500
@return Returns array of standard deviation values
trend(positiveShort, negativeShort, positiveLong, negativeLong)
Determines if trend is strong or weak based on Expansion Contraction values.
Parameters:
positiveShort (float) : Float for the positive short XC value from calculation
negativeShort (float) : Float for the negative short XC value from calculation
positiveLong (float) : Float for the positive long XC value from calculation
negativeLong (float) : Float for the negative long XC value from calculation
@return Returns array of boolean values indicating strength or weakness of trend
Price & Volume HeatmapDescription:
Displays a heatmap (like TV's Stock Heatmap) for up to 40 symbols (either from 3 presets, or custom). It can show Price Change, Volume Change and Volume (in $). The text size for each symbol can auto-change based on whether it fits into the cell. Each cell shows the name of the symbol, and when hovered - it shows the value.
Inputs:
- Symbols -> which symbols to use (Custom, or predefined list of Stocks/Crypto/Forex)
- Data -> show Price Change (%), Volume Change (%) or Volume ($)
- Custom -> put your custom list of symbols here (comma separated without spaces, up to 40 symbols)
- Position -> heatmap position
- Height / Width -> height / width of the heatmap (% of indicator's space)
- Text Size -> can be constant (Tiny/Normal/etc) or automatically change based on the text of each cell (Auto/Auto (Smaller))
- Color -> text color
Notes:
It is not recommended to use the script on timeframes below 30 seconds, because it may be too slow there (since it's based on a table object, it might be slow).
DTT Weekly Volatility Grid [Pro+] (NINE/ANARR)Introduction:
Automate Digital Time Theory (DTT) Weekly Models with the DTT Weekly Volatility Grid , leveraging the proprietary framework developed by Nine and Anarr. This tool allows to navigate the advanced landscape of Time-based statistical trading for futures, crypto, and forex markets.
Description:
Built on the Digital Time Theory (DTT), this script provides traders with a structured view of time and price interactions, ideal for swing insights. It divides the weekly range into Time models and inner intervals, empowering traders with data-driven insights to anticipate market expansions, detect Time-based distortions, and understand volatility fluctuations at specific Times during the trading week.
Key Features:
Time-Based Weekly Models and Volatility Awareness: The DTT Weekly Time Models automatically map onto your chart, highlighting critical volatility points in weekly sessions. These models help traders recognize potential shifts in the market, ideal for identifying larger, swing-oriented moves.
Average Model Range Probability (AMRP): The AMRP feature calculates the historical probability of reaching previous DTT Weekly Model Ranges. With AMRP and Standard Deviation metrics, traders can evaluate the likelihood of DTT model continuations or breaks, aligning their strategy with higher Timeframe volatility trends.
Root Candles and Liquidity Draws: Visualize Root Candles as liquidity draws, emphasizing premium and discount areas and marking the origin of a Time-based price movement. The tool allows traders to toggle features like opening prices and equilibrium points of each Root Candle. Observing accumulation or distribution zones around these candles provides crucial reference points for strategic swing entries and exits.
Extended Visualization of Weekly Model Ranges: Leverage previous weekly model ranges within the current Time model to observe historical high, low, and equilibrium levels. This feature aids traders in visualizing premium and discount ranges of prior models, pinpointing areas of liquidity and imbalance to watch.
Customization Options: Tailor Time intervals with a variety of line styles (solid, dashed, dotted) and colours to customize each model. Adjust settings to display specific historical weekly models, apply custom labels, and create a personalized view that suits your trading style and focus.
Lookback Periods and Model Count: Select customizable lookback periods to display past models, offering insights into market behaviour over a chosen historical range. This feature enables clean, organized charts and allows analysts to add more models for detailed backtesting and analysis.
Detailed Real-Time Data Table: The live data table provides easy access to AMRP and range data for selected models. This table highlights model targets and anticipated ranges, offering insights into whether previous models have exceeded historical volatility expectations or remained within them.
How Traders Can Use The DTT Weekly Volatility Grid Effectively:
Identifying Premium and Discount Zones: Track weekly ranges using Root Candles and previous model equilibrium levels to assess if prices are trading in premium or discount areas. This information helps framing the broader swing outlook.
Timing Trades Based on Volatility: Recognize potential exhaustion points through AMRP insights or completed model distortions that may signal new expansions. By observing inner intervals and Root Candles, traders can identify periods of high market activity, assisting in Timing weekly entries and exits.
Avoiding Low Volatility Phases: AMRP calculations can indicate periods when price action may slow or become choppy. If price remains within AMRP deviations or near them, traders can adjust risk or step aside, awaiting more favourable conditions for volatility-driven trades as new inner intervals or model roots appear.
Designed for Swing Traders and Higher Timeframes: The Weekly DTT Models are suited for those looking to study higher timeframe trends across futures, forex, and crypto markets. This tool equips traders with volatility-aware, and data-driven insights during extended market cycles.
Usage Guidance:
Add DTT Weekly Volatility Grid (NINE/ANARR) to your TradingView chart.
Customize your preferred time intervals, model history, and visual settings for your session.
Use the data table to track average model ranges and probabilities, ensuring you align your trades with key levels.
Incorporate DTT Weekly Volatility Grid (NINE/ANARR) into your existing strategies to fine-tune your view through based on data-driven insights into volatility and price behaviour.
Terms and Conditions
Our charting tools are products provided for informational and educational purposes only and do not constitute financial, investment, or trading advice. Our charting tools are not designed to predict market movements or provide specific recommendations. Users should be aware that past performance is not indicative of future results and should not be relied upon for making financial decisions. By using our charting tools, the purchaser agrees that the seller and the creator are not responsible for any decisions made based on the information provided by these charting tools. The purchaser assumes full responsibility and liability for any actions taken and the consequences thereof, including any loss of money or investments that may occur as a result of using these products. Hence, by purchasing these charting tools, the customer accepts and acknowledges that the seller and the creator are not liable nor responsible for any unwanted outcome that arises from the development, the sale, or the use of these products. Finally, the purchaser indemnifies the seller from any and all liability. If the purchaser was invited through the Friends and Family Program, they acknowledge that the provided discount code only applies to the first initial purchase of the Toodegrees Premium Suite subscription. The purchaser is therefore responsible for cancelling – or requesting to cancel – their subscription in the event that they do not wish to continue using the product at full retail price. If the purchaser no longer wishes to use the products, they must unsubscribe from the membership service, if applicable. We hold no reimbursement, refund, or chargeback policy. Once these Terms and Conditions are accepted by the Customer, before purchase, no reimbursements, refunds or chargebacks will be provided under any circumstances.
By continuing to use these charting tools, the user acknowledges and agrees to the Terms and Conditions outlined in this legal disclaimer.
COT Report Indicator with Speculator Net PositionsThe COT Report Indicator with Speculator Net Positions is designed to give traders insights into the behavior of large market participants, particularly speculators, based on the Commitment of Traders (COT) report data. This indicator visualizes the long and short positions of non-commercial traders, allowing users to gauge the sentiment and positioning of large speculators in key markets, such as Gold, Silver, Crude Oil, S&P 500, and currency pairs like EURUSD, GBPUSD, and others.
The indicator provides three essential components:
Net Long Position (Green) - Displays the total long positions held by speculators.
Net Short Position (Purple) - Shows the total short positions held by speculators.
Net Difference (Long - Short) (Yellow) - Illustrates the difference between long and short positions, helping users identify whether speculators are more bullish or bearish on the asset.
Recommended Timeframes:
Best Timeframes: Weekly and Monthly
The COT report data is released on a weekly basis, making higher timeframes like the Weekly and Monthly charts ideal for this indicator. These timeframes provide a more accurate reflection of the underlying trends in speculator positioning, avoiding the noise present in lower timeframes.
How to Use:
Market Sentiment: Use this indicator to gauge the sentiment of large speculators, who often drive market trends. A strong net long position can indicate bullish sentiment, while a high net short position might suggest bearish sentiment.
Trend Reversal Signals: Sudden changes in the net difference between long and short positions may indicate potential trend reversals.
Confirmation Tool: Pair this indicator with your existing analysis to confirm the strength of a trend or identify overbought/oversold conditions based on speculator activity.
Supported Symbols:
This indicator currently supports a range of commodities and currency pairs, including:
Gold ( OANDA:XAUUSD )
Silver ( OANDA:XAGUSD )
Crude Oil ( TVC:USOIL )
Natural Gas ( NYMEX:NG1! )
S&P 500 ( SP:SPX )
Dollar Index ( TVC:DXY )
EURUSD ( FX:EURUSD )
GBPUSD ( FX:GBPUSD )
GBPJPY( FX:GBPJPY )
By providing clear insight into the positions of large speculators, this indicator is a powerful tool for traders looking to align with institutional sentiment and enhance their trading strategy.
Financial X-RayThe Financial X-Ray is an advanced indicator designed to provide a thorough analysis of a company's financial health and market performance. Its primary goal is to offer investors and analysts a quick yet comprehensive overview of a company's financial situation by combining various key financial ratios and metrics.
How It Works
Data Collection: The indicator automatically extracts a wide range of financial data for the company, covering aspects such as financial strength, profitability, valuation, growth, and operational efficiency.
Sector-Specific Normalization: A unique feature of this indicator is its ability to normalize metrics based on the company's industry sector. This approach allows for more relevant comparisons between companies within the same sector, taking into account industry-specific characteristics.
Standardized Scoring: Each metric is converted to a score on a scale of 0 to 10, facilitating easy comparison and rapid interpretation of results.
Multidimensional Analysis: The indicator doesn't focus on just one financial dimension but offers an overview by covering several crucial aspects of a company's performance.
Fair Value Calculation: Using financial data and market conditions, the indicator provides an estimate of the company's fair value, offering a reference point for assessing current valuation.
Visual Presentation: Results are displayed directly on the TradingView chart in a tabular format, allowing for quick and efficient reading of key information.
Advantages for Users
Time-Saving: Instead of manually collecting and analyzing numerous financial data points, users get an instant comprehensive overview.
Contextual Analysis: Sector-specific normalization allows for a better understanding of the company's performance relative to its peers.
Flexibility: Users can choose which metrics to display, customizing the analysis to their specific needs.
Objectivity: By relying on quantitative data and standardized calculations, the indicator offers an objective perspective on the company's financial health.
Decision Support: The fair value estimate and normalized scores provide valuable reference points for investment decision-making.
Customization and Evolution
One of the major strengths of this indicator is its open-source nature. Users can modify the code to adjust normalization methods, add new metrics, or adapt the display to their preferences. This flexibility allows the indicator to evolve and continuously improve through community contributions.
In summary, the Financial X-Ray is a powerful tool that combines automation, contextual analysis, and customization to provide investors with a clear and comprehensive view of companies' financial health, facilitating informed decision-making in financial markets.
This Financial X-Ray indicator is provided for informational and educational purposes only. It should not be considered as financial advice or a recommendation to buy or sell any security. The data and calculations used in this indicator may not be accurate or up-to-date. Users should always conduct their own research and consult with a qualified financial advisor before making any investment decisions. The creator of this indicator is not responsible for any losses or damages resulting from its use.
Asset Correlation with XAU/USD (Macroeconomics X Gold)This Pine Script calculates the correlation of economic assets with gold (XAU/USD), including indicators such as the DXY, the S&P 500, the US 10-year yield (US10Y), oil (USOIL), the USD/JPY pair, and the AUD/USD pair. The goal is to analyze the impact of these variables on the price of gold, particularly in a macroeconomic context.
Main Features:
Asset Monitoring: The script monitors 24-hour variations of six key assets (DXY, S&P 500, US10Y, USOIL, USDJPY, AUDUSD), along with the price of XAU/USD.
Percentage Change Calculation: The percentage change for each asset is calculated based on the previous day's close, compared to the most recent 5-minute close.
Direction Determination: The direction of each asset (whether the change is positive, negative, or neutral) is calculated and used to determine the potential impact on the price of gold.
Interactive Tables: The results of directions, variations, and impacts are displayed in a table on the screen, with each asset being evaluated by its weight (influence on gold) and direction. The table also includes arrows indicating the impact of each asset on the price of gold, based on the correlation between them.
Dominance: The overall dominance of gold is calculated based on the weights and directions of the assets, generating a result that reflects whether gold is trending upwards or downwards due to the other observed assets. An arrow symbol indicates whether the dominance is positive (⬆️), negative (⬇️), or neutral (—).
Table Details:
The table displays the monitored assets, their assigned weights, the direction (arrows up, down, or neutral), the percentage change of each asset, and the impact of these assets on the price of gold.
The last column shows the "dominance" overall, with the final impact of these assets on the direction of the XAU/USD price.
Usage: This script is useful for traders and analysts who want to monitor how different macroeconomic factors (such as the value of the dollar, the S&P 500, US interest rates, oil prices, and currency pairs) influence the price of gold. It provides a clear view of how these assets correlate with gold, helping to make more informed decisions in the market.
For a better view of the table, right-click on >> visual order >> bring it to the top.
Asset Corr. with BTC/USD (Macroeconomics X BTC)This indicator provides a comprehensive analysis of the correlation between multiple assets (DXY, Gold, S&P 500, US10Y, and USDT Dominance) and their potential impact on the BTC/USD price. The script calculates the 24-hour percentage variation of these assets, determines their direction (bullish, bearish, or neutral), and displays this information in a table, helping traders assess how each asset is influencing BTC.
How the Script Works:
Asset Monitoring:
The script tracks the following assets:
DXY: The U.S. Dollar Index.
Gold (XAUUSD): The price of gold in U.S. dollars.
S&P 500 (SP500): A stock market index of U.S. companies.
US10Y: U.S. 10-year treasury yield.
USDT Dominance (USDT.D): The market dominance of USDT (Tether) in the crypto market.
Variation Calculation:
The script calculates the percentage variation for each asset over the last 24 hours using the close price of the previous day and the current close price on the 5-minute chart.
Based on the variation, the script determines the direction of each asset:
Bullish (1): Positive variation.
Bearish (-1): Negative variation.
Neutral (0): No significant change.
Impact Assessment:
The script uses weighted values for each asset to calculate its potential impact on BTC. The assets are given different weights:
DXY = 3
Gold = 2
S&P 500 = 2
US10Y = 3
USDT.D = 3
The direction and correlation of each asset are assessed to determine whether they are having a positive or negative impact on BTC. This impact is represented by arrows in the table.
Table Display:
The script displays a table on the chart, providing detailed information for each asset:
Asset: The name of the asset being analyzed.
Weight (Wgt): The assigned weight of the asset.
Direction (Dir): The current direction of the asset (up, down, or neutral).
24h Variation (Var %): The percentage change of the asset over the last 24 hours.
BTC Impact: The predicted impact of each asset on BTC, based on its direction and correlation.
Dominance Calculation:
A final "Dominance" score is calculated by summing the weighted values of each asset's direction and correlation with BTC.
This result is displayed in the table, providing a clear indication of whether the overall market sentiment is bullish or bearish for BTC.
How to Use the Script:
Add the Indicator: Apply the script to any chart with a 5-minute timeframe. The indicator works by analyzing the correlation of multiple assets with BTC, so it is best used for short-term traders looking to gauge BTC's price movement based on broader market trends.
Interpret the Table: The table shows the direction, variation, and impact of each asset on BTC. The "Dominance" row at the end of the table provides an overall sentiment score, helping traders understand whether the broader market is leaning bullish or bearish on BTC.
Monitor the Correlation: By tracking the assets with the highest weights and monitoring their influence on BTC, traders can make informed decisions on potential BTC price movements.
Key Concepts:
Asset Correlation: The script monitors multiple key assets that typically influence BTC's price, including the U.S. Dollar Index, Gold, S&P 500, US10Y, and USDT Dominance.
Impact Assessment: Uses weighted calculations to assess how each asset’s direction affects BTC.
Dominance Score: Provides a summary score of overall market sentiment, helping traders understand the broader influence on BTC.
Short-Term Trading: This tool is optimized for short-term traders who want to gauge market sentiment and its effect on BTC in real time.
For a better view of the table, right-click on >> visual order >> bring it to the top.
Fed Fund Futures Custom AverageThis indicator helps traders track the expected average interest rate for the upcoming 12 months based on Fed Fund Futures. It calculates the average price of the next 12 monthly futures contracts and also shows the spread against the 1-Year US Treasury yield (US01Y). This can be useful for understanding market expectations regarding interest rate changes and identifying trading opportunities related to interest rate movements.
BarRange StrategyHello,
This is a long-only, volatility-based strategy that analyzes the range of the previous bar (high - low).
If the most recent bar’s range exceeds a threshold based on the last X bars, a trade is initiated.
You can customize the lookback period, threshold value, and exit type.
For exits, you can choose to exit after X bars or when the close price exceeds the previous bar’s high.
The strategy is designed for instruments with a long-term upward-sloping curves, such as ES1! or NQ1!. It may not perform well on other instruments.
Commissions are set to $2.50 per side ($5.00 per round trip).
Recommended timeframes are 1h and higher. With adjustments to the lookback period and threshold, it could potentially achieve similar results on lower timeframes as well.
Z Value AlertZ Value Alert analyzes daily price movements by evaluating fluctuations relative to historical volatility. It calculates the daily percentage change in the closing price, the average of this change over 252 days, and the standard deviation. Using these values, a Z-Score is calculated, indicating how much the current price change deviates from the historical range of fluctuations.
The user can set a threshold in standard deviations (Z-Score). When the absolute Z-Score exceeds this threshold, a significant movement is detected, indicating increased volatility. The Z-Score is visualized as a histogram, and an alert can be triggered when a significant movement occurs.
The number of trading days used to calculate historical volatility is adjustable, allowing the Sigma Move Alert to be tailored to various trading strategies and analysis periods.
Additionally, a dropdown option for the calculation method is available in the input menu, allowing the user to select between:
Normal: Calculates the percentage change in closing prices without using the logarithm.
Logarithmic: Uses the natural logarithm of daily returns. This method is particularly suitable for longer timeframes and scientific analyses, as logarithmic returns are additive.
These comprehensive features allow for precise customization of the Sigma Move Alert to individual needs and specific market conditions.
lib_momentumLibrary "lib_momentum"
This library calculates the momentum, derived from a sample range of prior candles. Depending on set MomentumType it either deduces the momentum from the price, volume, or a product of both. If price/product are selected, you can choose from SampleType if only candle body, full range from high to low or a combination of both (body counts full, wicks half for each direction) should be used. Optional: You can choose to normalize the results, dividing each value by its average (normalization_ma_length, normalization_ma). This will allow comparison between different instruments. For the normalization Moving Average you can choose any currently supported in my lib_no_delay.
get_momentum(momentum_type, sample_type, sample_length, normalization_ma_length, normalization_ma)
Parameters:
momentum_type (series MomentumType) : select one of MomentumType. to sample the price, volume or a product of both
sample_type (series SampleType) : select one of SampleType. to sample the body, total range from high to low or a combination of both (body count full, wicks half for each direction)
sample_length (simple int) : how many candles should be sampled (including the current)
normalization_ma_length (simple int) : if you want to normalize results (momentum / momentum average) this sets the period for the average. (default = 0 => no normalization)
normalization_ma (simple MovingAverage enum from robbatt/lib_no_delay/9) : is the type of moving average to normalize / compare with
Returns: returns the current momentum where the total line is not just (up - down) but also sampled over the sample_length and can therefore be used as trend indicator. If up/down fail to reach total's level it's a sign of decreasing momentum, if up/down exceed total the trend it's a sign of increasing momentum.
