Advanced Multi-Seasonality StrategyThe Multi-Seasonality Strategy is a trading system based on seasonal market patterns. Seasonality refers to recurring market trends driven by predictable calendar-based events. These patterns emerge due to economic cycles, corporate activities (e.g., earnings reports), and investor behavior around specific times of the year. Studies have shown that such effects can influence asset prices over defined periods, leading to opportunities for traders who exploit these patterns (Hirshleifer, 2001; Bouman & Jacobsen, 2002).
How the Strategy Works:
The strategy allows the user to define four distinct periods within a calendar year. For each period, the trader selects:
Entry Date (Month and Day): The date to enter the trade.
Holding Period: The number of trading days to remain in the trade after the entry.
Trade Direction: Whether to take a long or short position during that period.
The system is designed with flexibility, enabling the user to activate or deactivate each of the four periods. The idea is to take advantage of seasonal patterns, such as buying during historically strong periods and selling during weaker ones. A well-known example is the "Sell in May and Go Away" phenomenon, which suggests that stock returns are higher from November to April and weaker from May to October (Bouman & Jacobsen, 2002).
Seasonality in Financial Markets:
Seasonal effects have been documented across different asset classes and markets:
Equities: Stock markets tend to exhibit higher returns during certain months, such as the "January effect," where prices rise after year-end tax-loss selling (Haugen & Lakonishok, 1987).
Commodities: Agricultural commodities often follow seasonal planting and harvesting cycles, which impact supply and demand patterns (Fama & French, 1987).
Forex: Currency pairs may show strength or weakness during specific quarters based on macroeconomic factors, such as fiscal year-end flows or central bank policy decisions.
Scientific Basis:
Research shows that market anomalies like seasonality are linked to behavioral biases and institutional practices. For example, investors may respond to tax incentives at the end of the year, and companies may engage in window dressing (Haugen & Lakonishok, 1987). Additionally, macroeconomic factors, such as monetary policy shifts and holiday trading volumes, can also contribute to predictable seasonal trends (Bouman & Jacobsen, 2002).
Risks of Seasonal Trading:
While the strategy seeks to exploit predictable patterns, there are inherent risks:
Market Changes: Seasonal effects observed in the past may weaken or disappear as market conditions evolve. Increased algorithmic trading, globalization, and policy changes can reduce the reliability of historical patterns (Lo, 2004).
Overfitting: One of the risks in seasonal trading is overfitting the strategy to historical data. A pattern that worked in the past may not necessarily work in the future, especially if it was based on random chance or external factors that no longer apply (Sullivan, Timmermann, & White, 1999).
Liquidity and Volatility: Trading during specific periods may expose the trader to low liquidity, especially around holidays or earnings seasons, leading to slippage and larger-than-expected price swings.
Economic and Geopolitical Shocks: External events such as pandemics, wars, or political instability can disrupt seasonal patterns, leading to unexpected market behavior.
Conclusion:
The Multi-Seasonality Strategy capitalizes on the predictable nature of certain calendar-based patterns in financial markets. By entering and exiting trades based on well-established seasonal effects, traders can potentially capture short-term profits. However, caution is necessary, as market dynamics can change, and seasonal patterns are not guaranteed to persist. Rigorous backtesting, combined with risk management practices, is essential to successfully implementing this strategy.
References:
Bouman, S., & Jacobsen, B. (2002). The Halloween Indicator, "Sell in May and Go Away": Another Puzzle. American Economic Review, 92(5), 1618-1635.
Fama, E. F., & French, K. R. (1987). Commodity Futures Prices: Some Evidence on Forecast Power, Premiums, and the Theory of Storage. Journal of Business, 60(1), 55-73.
Haugen, R. A., & Lakonishok, J. (1987). The Incredible January Effect: The Stock Market's Unsolved Mystery. Dow Jones-Irwin.
Hirshleifer, D. (2001). Investor Psychology and Asset Pricing. Journal of Finance, 56(4), 1533-1597.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Sullivan, R., Timmermann, A., & White, H. (1999). Data-Snooping, Technical Trading Rule Performance, and the Bootstrap. Journal of Finance, 54(5), 1647-1691.
This strategy harnesses the power of seasonality but requires careful consideration of the risks and potential changes in market behavior over time.
在腳本中搜尋"Futures"
Statistical ArbitrageThe Statistical Arbitrage Strategy, also known as pairs trading, is a quantitative trading method that capitalizes on price discrepancies between two correlated assets. The strategy assumes that over time, the prices of these two assets will revert to their historical relationship. The core idea is to take advantage of mean reversion, a principle suggesting that asset prices will revert to their long-term average after deviating significantly.
Strategy Mechanics:
1. Selection of Correlated Assets:
• The strategy focuses on two historically correlated assets (e.g., equity index futures like Dow Jones Mini and S&P 500 Mini). These assets tend to move in the same direction due to similar underlying fundamentals, such as overall market conditions. By tracking their relative prices, the strategy seeks to exploit temporary mispricings.
2. Spread Calculation:
• The spread is the difference between the prices of the two assets. This spread represents the relationship between the assets and serves as the basis for determining when to enter or exit trades.
3. Mean and Standard Deviation:
• The historical average (mean) of the spread is calculated using a Simple Moving Average (SMA) over a chosen period. The strategy also computes the standard deviation (volatility) of the spread, which measures how far the spread has deviated from the mean over time. This allows the strategy to define statistically significant price deviations.
4. Entry Signal (Mean Reversion):
• A buy signal is triggered when the spread falls below the mean by a multiple (e.g., two) of the standard deviation. This indicates that one asset is temporarily undervalued relative to the other, and the strategy expects the spread to revert to its mean, generating profits as the prices converge.
5. Exit Signal:
• The strategy exits the trade when the spread reverts to the mean. At this point, the mispricing has been corrected, and the profit from the mean reversion is realized.
Academic Support:
Statistical arbitrage has been widely studied in finance and economics. Gatev, Goetzmann, and Rouwenhorst’s (2006) landmark study on pairs trading demonstrated that this strategy could generate excess returns in equity markets. Their research found that by focusing on historically correlated stocks, traders could identify pricing anomalies and profit from their eventual correction.
Additionally, Avellaneda and Lee (2010) explored statistical arbitrage in different asset classes and found that exploiting deviations in price relationships can offer a robust, market-neutral trading strategy. In these studies, the strategy’s success hinges on the stability of the relationship between the assets and the timely execution of trades when deviations occur.
Risks of Statistical Arbitrage:
1. Correlation Breakdown:
• One of the primary risks is the breakdown of correlation between the two assets. Statistical arbitrage assumes that the historical relationship between the assets will hold in the future. However, market conditions, company fundamentals, or external shocks (e.g., macroeconomic changes) can cause these assets to deviate permanently, leading to potential losses.
• For instance, if two equity indices historically move together but experience divergent economic conditions or policy changes, their prices may no longer revert to the expected mean.
2. Execution Risk:
• This strategy relies on efficient execution and tight spreads. In volatile or illiquid markets, the actual price at which trades are executed may differ significantly from expected prices, leading to slippage and reduced profits.
3. Market Risk:
• Although statistical arbitrage is designed to be market-neutral (i.e., not dependent on the overall market direction), it is not entirely risk-free. Systematic market shocks, such as financial crises or sudden shifts in market sentiment, can affect both assets simultaneously, causing the spread to widen rather than revert to the mean.
4. Model Risk:
• The assumptions underlying the strategy, particularly regarding mean reversion, may not always hold true. The model assumes that asset prices will return to their historical averages within a certain timeframe, but the timing and magnitude of mean reversion can be uncertain. Misestimating this timeframe can lead to extended drawdowns or unrealized losses.
5. Overfitting:
• Over-reliance on historical data to fine-tune the strategy parameters (e.g., the lookback period or standard deviation thresholds) may result in overfitting. This means that the strategy works well on past data but fails to perform in live markets due to changing conditions.
Conclusion:
The Statistical Arbitrage Strategy offers a systematic and quantitative approach to trading that capitalizes on temporary price inefficiencies between correlated assets. It has been proven to generate returns in academic studies and is widely used by hedge funds and institutional traders for its market-neutral characteristics. However, traders must be aware of the inherent risks, including correlation breakdown, execution risks, and the potential for prolonged deviations from the mean. Effective risk management, diversification, and constant monitoring are essential for successfully implementing this strategy in live markets.
SMT Divergences [OutOfOptions]Smart Money Technique (SMT) Divergence is designed to identify discrepancies between correlated assets within the same timeframe. It occurs when two related assets exhibit opposing signals, such as one forming a higher low while the other forms a lower low. This technique is particularly useful for anticipating market shifts or reversals before they become evident through other Premium Discount (PD) Arrays.
This indicator works by identifying the highs and lows that have formed for an asset on the current chart and the correlated symbol defined in the settings. Once a pivot on either asset is formed, it checks if the pivot has taken liquidity as identified by the previous pivot in the same direction (i.e., a new high taking out a previous high). If this is the case and the corresponding asset has not taken a similar pivot, the condition is determined to be a potential valid divergence. The indicator will then filter out SMTs formed by adjacent candles, requiring at least one candle difference between the candles forming the SMT.
If the “Candle Direction Validation” setting is enabled, the indicator will further check both assets to ensure that for bullish SMTs, the last high on both assets was formed by down candle, and for bearish SMTs, the low was formed by an up candle. This check can often eliminate low-probability SMTs that are frequently broken.
The referenced chart shows divergence between Nasdaq (NQ) and S&P 500 (ES) futures, which are normally closely correlated assets that move in the same direction. The lines shown represent bullish and bearish divergences between the two when they are formed. As you can see from the chart, SMT Divergences may not always indicate a reversal, or a reversal might be just a short-term retrace. Therefore, SMT Divergences should not be used independently. However, in conjunction with other PD arrays, they can provide strong confirmation of a change in market direction.
Configurability:
Pivot strength - Indicates how many bars to the left/right of a high for pivot to be considered, recommended to keep at 1 for maximum detection speed
Candle Direction Validation - Additional SMT validation to filter out weak/low-probability SMTs be examining candle direction
Line Styling for Bullish/Bearish SMTs - Ability to customize line style, color & width for bullish/bearish SMTs
Label Control - Whether or not to show SMT label and if shown what font size & color should be used
What makes this indicator different:
Unlike other SMT indicators, this indicators has additional built-in controls to remove low-probability SMTs
RSI/MFI Divergence Finder [idahodev]Monitoring RSI (Relative Strength Index) and MFI (Money Flow Index) divergences on a stock or index chart offers several benefits to traders and analysts. Let's break down the advantages:
Comprehensive Market View: Combining both indicators provides a more complete picture of market conditions, as they measure different aspects of price movement. RSI focuses on recent gains/losses relative to price change, while MFI incorporates volume data to assess money flow in and out of a security.
Enhanced Signal Accuracy: When divergences occur simultaneously in both RSI and MFI, it may be considered a stronger signal than if only one indicator showed divergence. This can potentially lead to more reliable trading decisions.
Identification of False Breakouts: Divergences between these indicators and price action can help identify false breakouts or misleading price movements that are not supported by underlying market strength or volume.
More Nuanced Market Understanding: By examining divergent behavior between money flow (MFI) and momentum (RSI), traders gain a more detailed comprehension of the interplay between these factors in shaping market trends.
Early Warning Signs: These divergences can act as early warning signs for potential trend reversals or changes in market sentiment, allowing traders to adjust their strategies proactively.
It's important to note that RSI/MFI divergences should be used as part of a broader trading strategy rather than solely relying on them for buy/sell signals. They can serve as valuable tools for confirming trends, identifying potential turning points, or warning against overbought/oversold conditions.
When using these indicators together, traders must be cautious of false signals, especially in choppy markets or during periods of high volatility. It's crucial to combine this analysis with other technical and fundamental factors before making trading decisions.
In summary, monitoring RSI/MFI divergences may offer a way to gain insights into the underlying strengths and weaknesses of market movements.
This utility differs from other in that it allows for a choke/threshold/sensitivity setting to help weed out noisy signals. This needs to be carefully adjusted per chart.
It also allows for tuning of the MFI smoothing length (number of bars on the current chart) as well as how many previous bars it will take into consideration when calculating RSI and MFI divergences. It will signal when it sees alignment forming between RSI and MFI divergences in a direction. You will likely need to tune this script's settings every few days or at least anytime there is a change in overall market behavior or sustained volatility.
Ultimately, the goal with this script is to provide an additional level of confirmation of weakness or strength. It should be combined with other indicators such as exhaustion, pivots, supply/demand, trendline breaks or tests, and structure changes, to name a few complementary tools or strategies. It's not meant to be a standalone buy/sell signal indicator!
Here are some settings for futures that may help you get started:
ES (4m chart)
RSI Length: 26
MFI Length: 8
MFI Smoothing Length: 32
Divergence Sensitivity: 124
Left Bars for Pivot: 10
Right Bars for Pivot: 1
NQ (4m chart)
RSI Length: 14
MFI Length: 14
MFI Smoothing Length: 21
Divergence Sensitivity: 400
Left Bars for Pivot: 21
Right Bars for Pivot: 1
YM (4m chart)
RSI Length: 14
MFI Length: 14
MFI Smoothing Length: 21
Divergence Sensitivity: 810
Left Bars for Pivot: 33
Right Bars for Pivot: 1
Advanced Economic Indicator by USCG_VetAdvanced Economic Indicator by USCG_Vet
tldr:
This comprehensive TradingView indicator combines multiple economic and financial metrics into a single, customizable composite index. By integrating key indicators such as the yield spread, commodity ratios, stock indices, and the Federal Reserve's QE/QT activities, it provides a holistic view of the economic landscape. Users can adjust the components and their weights to tailor the indicator to their analysis, aiding in forecasting economic conditions and market trends.
Detailed Description
Overview
The Advanced Economic Indicator is designed to provide traders and investors with a powerful tool to assess the overall economic environment. By aggregating a diverse set of economic indicators and financial market data into a single composite index, it helps identify potential turning points in the economy and financial markets.
Key Features:
Comprehensive Coverage: Includes 14 critical economic and financial indicators.
Customizable Components: Users can select which indicators to include.
Adjustable Weights: Assign weights to each component based on perceived significance.
Visual Signals: Clear plotting with threshold lines and background highlights.
Alerts: Set up alerts for when the composite index crosses user-defined thresholds.
Included Indicators
Yield Spread (10-Year Treasury Yield minus 3-Month Treasury Yield)
Copper/Gold Ratio
High Yield Spread (HYG/IEF Ratio)
Stock Market Performance (S&P 500 Index - SPX)
Bitcoin Performance (BLX)
Crude Oil Prices (CL1!)
Volatility Index (VIX)
U.S. Dollar Index (DXY)
Inflation Expectations (TIP ETF)
Consumer Confidence (XLY ETF)
Housing Market Index (XHB)
Manufacturing PMI (XLI ETF)
Unemployment Rate (Inverse SPY as Proxy)
Federal Reserve QE/QT Activities (Fed Balance Sheet - WALCL)
How to Use the Indicator
Configuring the Indicator:
Open Settings: Click on the gear icon (⚙️) next to the indicator's name.
Inputs Tab: You'll find a list of all components with checkboxes and weight inputs.
Including/Excluding Components
Checkboxes: Check or uncheck the box next to each component to include or exclude it from the composite index.
Default State: By default, all components are included.
Adjusting Component Weights:
Weight Inputs: Next to each component's checkbox is a weight input field.
Default Weights: Pre-assigned based on economic significance but fully adjustable.
Custom Weights: Enter your desired weight for each component to reflect your analysis.
Threshold Settings:
Bearish Threshold: Default is -1.0. Adjust to set the level below which the indicator signals potential economic downturns.
Bullish Threshold: Default is 1.0. Adjust to set the level above which the indicator signals potential economic upswings.
Setting the Timeframe:
Weekly Timeframe Recommended: Due to the inclusion of the Fed's balance sheet data (updated weekly), it's best to use this indicator on a weekly chart.
Changing Timeframe: Select 1W (weekly) from the timeframe options at the top of the chart.
Interpreting the Indicator:
Composite Index Line
Plot: The blue line represents the composite economic indicator.
Movement: Observe how the line moves relative to the threshold lines.
Threshold Lines
Zero Line (Gray Dotted): Indicates the neutral point.
Bearish Threshold (Red Dashed): Crossing below suggests potential economic weakness.
Bullish Threshold (Green Dashed): Crossing above suggests potential economic strength.
Background Highlights
Red Background: When the composite index is below the bearish threshold.
Green Background: When the composite index is above the bullish threshold.
No Color: When the composite index is between the thresholds.
Understanding the Components
1. Yield Spread
Description: The difference between the 10-year and 3-month U.S. Treasury yields.
Economic Significance: An inverted yield curve (negative spread) has historically preceded recessions.
2. Copper/Gold Ratio
Description: The price ratio of copper to gold.
Economic Significance: Copper is tied to industrial demand; gold is a safe-haven asset. The ratio indicates risk sentiment.
3. High Yield Spread (HYG/IEF Ratio)
Description: Ratio of high-yield corporate bonds (HYG) to intermediate-term Treasury bonds (IEF).
Economic Significance: Reflects investor appetite for risk; widening spreads can signal credit stress.
4. Stock Market Performance (SPX)
Description: S&P 500 Index levels.
Economic Significance: Broad measure of U.S. equity market performance.
5. Bitcoin Performance (BLX)
Description: Bitcoin Liquid Index price.
Economic Significance: Represents risk appetite in speculative assets.
6. Crude Oil Prices (CL1!)
Description: Front-month crude oil futures price.
Economic Significance: Influences inflation and consumer spending.
7. Volatility Index (VIX)
Description: Market's expectation of volatility (fear gauge).
Economic Significance: High VIX indicates market uncertainty; inverted in the indicator to align directionally.
8. U.S. Dollar Index (DXY)
Description: Value of the U.S. dollar relative to a basket of foreign currencies.
Economic Significance: Affects international trade and commodity prices; inverted in the indicator.
9. Inflation Expectations (TIP ETF)
Description: iShares TIPS Bond ETF prices.
Economic Significance: Reflects market expectations of inflation.
10. Consumer Confidence (XLY ETF)
Description: Consumer Discretionary Select Sector SPDR Fund prices.
Economic Significance: Proxy for consumer confidence and spending.
11. Housing Market Index (XHB)
Description: SPDR S&P Homebuilders ETF prices.
Economic Significance: Indicator of the housing market's health.
12. Manufacturing PMI (XLI ETF)
Description: Industrial Select Sector SPDR Fund prices.
Economic Significance: Proxy for manufacturing activity.
13. Unemployment Rate (Inverse SPY as Proxy)
Description: Inverse of the SPY ETF price.
Economic Significance: Represents unemployment trends; higher inverse SPY suggests higher unemployment.
14. Federal Reserve QE/QT Activities (Fed Balance Sheet - WALCL)
Description: Total assets held by the Federal Reserve.
Economic Significance: Indicates liquidity injections (QE) or withdrawals (QT); impacts interest rates and asset prices.
Customization and Advanced Usage
Adjusting Weights:
Purpose: Emphasize components you believe are more predictive or relevant.
Method: Increase or decrease the weight value next to each component.
Example: If you think the yield spread is particularly important, you might assign it a higher weight.
Disclaimer
This indicator is for educational and informational purposes only. It is not financial advice. Trading and investing involve risks, including possible loss of principal. Always conduct your own analysis and consult with a professional financial advisor before making investment decisions.
Weekly Initial BalanceWeekly Initial Balance Indicator
The Weekly Initial Balance (IB) indicator is a powerful tool designed for traders to identify key support and resistance levels based on the market's initial activity at the start of each week. By analyzing the first 30 hours of trading.
Key Features:
Customizable IB Period: Define the start hour and duration of the initial balance period to suit your trading schedule and the specific market you are analyzing, I have it set at 30 hours by default.
IB High, Low, and Midpoint Levels: Automatically plots the high, low, and midpoint of the IB period, providing immediate visual reference to critical price levels.
Extension Levels: Calculate and display extension levels based on customizable percentages (e.g., 50%, 100%, 150%), allowing you to project potential breakout targets and identify areas of interest beyond the initial balance range.
Dynamic Lines and Labels: The indicator updates in real-time, extending lines and repositioning labels as new bars form, ensuring you always have the most current information.
Customizable Appearance: Adjust line styles, widths, colors, and label positions to match your charting preferences and improve visual clarity.
How to Use:
Set the IB Parameters:
Week Start Hour (UTC): Specify the hour when the weekly IB period begins. I use 1800EST as that is when the futures market opens.
IB Duration in Hours: Define how many hours constitute the IB period.
Configure Extension Levels:
Input the desired extension percentages to calculate levels beyond the IB range.
Customize Visual Settings:
Adjust line colors, styles, widths, and label offsets to tailor the indicator's appearance.
Interpret the Levels:
Use the IB high and low as immediate support and resistance levels.
Monitor the midpoint for potential pivot areas.
Watch for price interactions with extension levels to anticipate breakouts or reversals.
Benefits:
Identify Key Weekly Levels: Understand the market's initial sentiment each week to gauge potential trends.
Enhance Trading Strategies: Incorporate the IB levels into your trading plan for better entry and exit points.
Adaptable to Various Markets: Suitable for Forex, commodities, indices, and other markets where weekly analysis is beneficial.
Wick/Tail Candle MeasurementsThis indicator runs on trading view. It was programmed with pine script v5.
Once the indicator is running you can scroll your chart to any year or date on the chart, then for the input select the date your interested in knowing the length of the tails and wicks from a bar and their lengths are measured in points.
To move the measurement, you can select the vertical bar built into the indicator AFTER clicking the green label and moving it around using the vertical bar *only*. You must click the vertical bar in the middle of the label to move the indicator calculation to another bar. You can also just select the date using the input as mentioned. This indicator calculates just one bar at a time.
measurements are from bar OPEN to bar HIGH for measured WICKS regardless of the bar being long or short and from bar OPEN to bar LOW for measured TAILS also regardless of the bar being long or short.
This indicator calculates tails and wicks including the bar body in the calculations. Basically showing you how much the market moved in a certain direction for the entire duration of that Doji candle.
Its designed to measure completed bars on the daily futures charts. (Dow Jones, ES&P500, Nasdaq, Russell 2000, etc) Although it may work well on other markets. The indicator could easily be tweaked in order to work well with other markets. It is not designed for forex markets currently.
Simultaneous INSIDE Bar Break IndicatorSimultaneous Inside Bar Break Indicator (SIBBI) for The Strat Community
Overview:
The Simultaneous Inside Bar Break Indicator (SIBBI) is designed to help traders using The Strat methodology identify one of the most powerful breakout patterns: the Simultaneous Inside Bar Break across multiple symbols. This indicator detects when all four user-selected symbols form inside bars on the previous candle and then break those inside bars in the same direction (either bullish or bearish) on the current candle.
Inside bars represent consolidation periods where price action does not break the high or low of the previous candle. When a simultaneous break occurs across multiple symbols, this often signals a strong move in the market, making this a key actionable signal in The Strat trading strategy.
Key Features:
Multi-Symbol Analysis: You can track up to four different symbols simultaneously. By default, the indicator comes with SPY, QQQ, IWM, and DIA, but you can modify these to track any other assets or symbols.
Inside Bar Detection: The indicator checks whether all four symbols have inside bars on the previous candle. It only triggers when all symbols meet this condition, making it a highly specific and reliable signal.
Simultaneous Break Detection: Once all symbols have inside bars, the indicator waits for a breakout in the same direction across all four symbols. A simultaneous bullish break (prices breaking above the previous candle’s high) triggers a green label, while a simultaneous bearish break (prices breaking below the previous candle’s low) triggers a red label.
Dynamic Label Timeframe: The indicator dynamically adjusts the timeframe in the label based on the user’s selected timeframe. This allows traders to know precisely which timeframe the break is occurring on. If the user selects "Chart Timeframe," the indicator will evolve with the current chart's timeframe, making it more versatile.
Timeframe Flexibility: The indicator can be set to analyze any timeframe—15-minute, 30-minute, 60-minute, daily, weekly, and so on. It only works for the specific timeframe you set it to in the settings. If set to "Chart Timeframe," the label will adapt dynamically based on the timeframe you are currently viewing.
Customizable Labels: The user can choose the size of the labels (tiny, small, or normal), ensuring that the visual output is tailored to individual preferences and chart layouts.
Best Use Case:
The Simultaneous Inside Bar Break Indicator is particularly powerful when applied to multiple timeframes. Here’s how to use it for maximum impact:
Multi-Timeframe Setup: Set the indicator on various timeframes (e.g., 15-minute, 30-minute, 60-minute, and daily) across multiple charts. This allows you to monitor different timeframes and identify when lower timeframe breaks trigger potential moves on higher timeframes.
Anticipating Strong Moves: When a simultaneous inside bar break occurs on one timeframe (e.g., 30-minute), keep an eye on the higher timeframes (e.g., 60-minute or daily) to see if those timeframes also break. This stacking of inside bar breaks can signal powerful market moves.
Higher Conviction Signals: The indicator is designed to provide high-conviction signals. Since it requires all four symbols to break in the same direction simultaneously, it reduces false signals and focuses on higher probability setups, which is crucial for traders using The Strat to time their trades effectively.
How the Indicator Works:
Inside Bar Formation: The indicator first checks that all four selected symbols had inside bars in the previous bar (i.e., the current high and low are contained within the previous bar’s high and low).
Simultaneous Break Detection: After detecting inside bars, the indicator checks if all four symbols break out in the same direction—bullish (breaking above the previous bar’s high) or bearish (breaking below the previous bar’s low).
Label Display: When a simultaneous inside bar break occurs, a label is plotted on the chart—either green for a bullish break (below the candle) or red for a bearish break (above the candle). The label will display the timeframe you set in the settings (e.g., "IBSB 60" for a 60-minute break).
Chart Timeframe Option: If you prefer, you can set the indicator to evolve with the chart’s current timeframe. In this mode, the label will not show a specific timeframe but will still display the simultaneous inside bar break when it occurs.
Recommendations for Usage:
Focus on Multiple Timeframes: The Strat methodology is all about understanding the relationship between different timeframes. Use this indicator on multiple timeframes to get a better picture of potential moves.
Pair with Other Strat Techniques: This indicator is most powerful when combined with other Strat tools, such as broadening formations, timeframe continuity, and actionable signals (e.g., 2-2 reversals). The simultaneous inside bar break can help confirm or invalidate other signals.
Customize Symbols and Timeframes: Although the default symbols are SPY, QQQ, IWM, and DIA, feel free to replace them with symbols more relevant to your trading. This indicator works well across equities, indices, futures, and forex pairs.
How to Set It Up:
Select Symbols: Choose four symbols that you want to track. These can be index ETFs (like SPY and QQQ), individual stocks, or any other tradable instruments.
Set Timeframe: In the indicator’s settings, choose a specific timeframe (e.g., 15-minute, 30-minute, daily). The label will reflect the selected timeframe, making it clear which time-based break you are seeing.
Optional - Chart Timeframe Mode: If you want the indicator to adapt to the chart’s current timeframe, select the "Chart Timeframe" option in the settings. The indicator will plot the breaks without showing a specific timeframe in the label.
Customize Label Size: Depending on your chart layout and personal preference, you can adjust the size of the labels (tiny, small, or normal) in the settings.
Conclusion:
The Simultaneous Inside Bar Break Indicator is a powerful tool for traders using The Strat methodology, offering a highly specific and reliable signal that can indicate potential large market moves. By monitoring multiple symbols and timeframes, you can gain deeper insight into the market's behavior and act with greater confidence. This indicator is ideal for traders looking to catch high-conviction moves and align their trades with broader market continuity.
Note: The indicator works best when paired with multi-timeframe analysis, allowing you to see how breaks on lower timeframes might influence larger trends. For traders who prefer simplicity, setting it to the "Chart Timeframe" mode offers flexibility while maintaining the core benefits of this indicator.
Connors VIX Reversal III invented by Dave LandryThis strategy is based on trading signals derived from the behavior of the Volatility Index (VIX) relative to its 10-day moving average. The rules are split into buying and selling conditions:
Buy Conditions:
The VIX low must be above its 10-day moving average.
The VIX must close at least 10% above its 10-day moving average.
If both conditions are met, a buy signal is generated at the market's close.
Sell Conditions:
The VIX high must be below its 10-day moving average.
The VIX must close at least 10% below its 10-day moving average.
If both conditions are met, a sell signal is generated at the market's close.
Exit Conditions:
For long positions, the strategy exits when the VIX trades intraday below its previous day’s 10-day moving average.
For short positions, the strategy exits when the VIX trades intraday above its previous day’s 10-day moving average.
This strategy is primarily a mean-reversion strategy, where the market is expected to revert to a more normal state after the VIX exhibits extreme behavior (i.e., large deviations from its moving average).
About Dave Landry
Dave Landry is a well-known figure in the world of trading, particularly in technical analysis. He is an author, trader, and educator, best known for his work on swing trading strategies. Landry focuses on trend-following and momentum-based techniques, teaching traders how to capitalize on shorter-term price swings in the market. He has written books like "Dave Landry on Swing Trading" and "The Layman's Guide to Trading Stocks," which emphasize practical, actionable trading strategies.
About Connors Research
Connors Research is a financial research firm known for its quantitative research in financial markets. Founded by Larry Connors, the firm specializes in developing high-probability trading systems based on historical market behavior. Connors’ work is widely respected for its data-driven approach, including systems like the RSI(2) strategy, which focuses on short-term mean reversion. The firm also provides trading education and tools for institutional and retail traders alike, emphasizing strategies that can be backtested and quantified.
Risks of the Strategy
While this strategy may appear to offer promising opportunities to exploit extreme VIX movements, it carries several risks:
Market Volatility: The VIX itself is a measure of market volatility, meaning the strategy can be exposed to sudden and unpredictable market swings. This can result in whipsaws, where positions are opened and closed in rapid succession due to sharp reversals in the VIX.
Overfitting: Strategies based on specific conditions like the VIX closing 10% above or below its moving average can be subject to overfitting, meaning they work well in historical tests but may underperform in live markets. This is a common issue in quantitative trading systems that are not adaptable to changing market conditions .
Mean-Reversion Assumption: The core assumption behind this strategy is that markets will revert to their mean after extreme movements. However, during periods of sustained trends (e.g., market crashes or rallies), this assumption may break down, leading to prolonged drawdowns.
Liquidity and Slippage: Depending on the asset being traded (e.g., S&P 500 futures, ETFs), liquidity issues or slippage could occur when executing trades at market close, particularly in volatile conditions. This could increase costs or worsen trade execution.
Scientific Explanation of the Strategy
The VIX is often referred to as the "fear gauge" because it measures the market's expectations of volatility based on options prices. Research has shown that the VIX tends to spike during periods of market stress and revert to lower levels when conditions stabilize . Mean reversion strategies like this one assume that extreme VIX levels are unsustainable in the long run, which aligns with findings from academic literature on volatility and market behavior.
Studies have found that the VIX is inversely correlated with stock market returns, meaning that higher VIX levels often correspond to lower stock prices and vice versa . By using the VIX’s relationship with its 10-day moving average, this strategy aims to capture reversals in market sentiment. The 10% threshold is designed to identify moments when the VIX is significantly deviating from its norm, signaling a potential reversal.
However, academic research also highlights the limitations of relying on the VIX alone for trading signals. The VIX does not predict market direction, only volatility, meaning that it cannot indicate the magnitude of price movements . Furthermore, extreme VIX levels can persist longer than expected, particularly during financial crises.
In conclusion, while the strategy is grounded in well-established financial principles (e.g., mean reversion and the relationship between volatility and market performance), it carries inherent risks and should be used with caution. Backtesting and careful risk management are essential before applying this strategy in live markets.
Varanormal Mac N Cheez Strategy v1Mac N Cheez Strategy (Set a $200 Take profit Manually)
It's super cheesy. Strategy does the following:
Here's a detailed explanation of what the entire script does, including its key components, functionality, and purpose.
1. Strategy Setup and Input Parameters:
Strategy Name: The script is named "NQ Futures $200/day Strategy" and is set as an overlay, meaning all elements (like moving averages and signals) are plotted on the price chart.
Input Parameters:
fastLength: This sets the length of the fast moving average. The user can adjust this value, and it defaults to 9.
slowLength: This sets the length of the slow moving average. The user can adjust this value, and it defaults to 21.
dailyTarget: The daily profit target, which defaults to $200. If set to 0, this disables the daily profit target.
stopLossAmount: The fixed stop-loss amount per trade, defaulting to $100. This value is used to calculate how much you're willing to lose on a single trade.
trailOffset: This value sets the distance for a trailing stop. It helps protect profits by automatically adjusting the stop-loss as the price moves in your favor.
2. Calculating the Moving Averages:
fastMA: The fast moving average is calculated using the ta.sma() function on the close price with a period length of fastLength. The ta.sma() function calculates the simple moving average.
slowMA: The slow moving average is also calculated using ta.sma() but with the slowLength period.
These moving averages are used to determine trend direction and identify entry points.
3. Buy and Sell Signal Conditions:
longCondition: This is the buy condition. It occurs when the fast moving average crosses above the slow moving average. The script uses ta.crossover() to detect this crossover event.
shortCondition: This is the sell condition. It occurs when the fast moving average crosses below the slow moving average. The script uses ta.crossunder() to detect this crossunder event.
4. Executing Buy and Sell Orders:
Buy Orders: When the longCondition is true (i.e., fast MA crosses above slow MA), the script enters a long position using strategy.entry("Buy", strategy.long).
Sell Orders: When the shortCondition is true (i.e., fast MA crosses below slow MA), the script enters a short position using strategy.entry("Sell", strategy.short).
5. Setting Stop Loss and Trailing Stop:
Stop-Loss for Long Positions: The stop-loss is calculated as the entry price minus the stopLossAmount. If the price falls below this level, the trade is exited automatically.
Stop-Loss for Short Positions: The stop-loss is calculated as the entry price plus the stopLossAmount. If the price rises above this level, the short trade is exited.
Trailing Stop: The trail_offset dynamically adjusts the stop-loss as the price moves in favor of the trade, locking in profits while still allowing room for market fluctuations.
6. Conditional Daily Profit Target:
The script includes a daily profit target that automatically closes all trades once the total profit for the day reaches or exceeds the dailyTarget.
Conditional Logic:
If the dailyTarget is greater than 0, the strategy checks whether the strategy.netprofit (total profit for the day) has reached or exceeded the target.
If the strategy.netprofit >= dailyTarget, the script calls strategy.close_all(), closing all open trades for the day and stopping further trading.
If dailyTarget is set to 0, this logic is skipped, and the script continues trading without a daily profit target.
7. Plotting Moving Averages:
plot(fastMA): This plots the fast moving average as a blue line on the price chart.
plot(slowMA): This plots the slow moving average as a red line on the price chart. These help visualize the crossover points and the trend direction on the chart.
8. Plotting Buy and Sell Signals:
plotshape(): The script uses plotshape() to add visual markers when buy or sell conditions are met:
"Long Signal": When a buy condition (longCondition) is met, a green marker is plotted below the price bar with the label "Long".
"Short Signal": When a sell condition (shortCondition) is met, a red marker is plotted above the price bar with the label "Short".
These markers help traders quickly see when buy or sell signals occurred on the chart.
In addition, triangle markers are plotted:
Green Triangle: Indicates where a buy entry occurred.
Red Triangle: Indicates where a sell entry occurred.
Summary of What the Script Does:
Inputs: The script allows the user to adjust moving average lengths, daily profit targets, stop-loss amounts, and trailing stop offsets.
Signals: It generates buy and sell signals based on the crossovers of the fast and slow moving averages.
Order Execution: It executes long positions on buy signals and short positions on sell signals.
Stop-Loss and Trailing Stop: It sets dynamic stop-losses and uses a trailing stop to protect profits.
Daily Profit Target: The strategy stops trading for the day once the net profit reaches the daily target (unless the target is disabled by setting it to 0).
Visual Markers: It plots moving averages and buy/sell signals directly on the main price chart to aid in visual analysis.
This script is designed to trade based on moving average crossovers, with robust risk management features like stop-loss and trailing stops, along with an optional daily profit target to limit daily trading activity. Let me know if you need further clarification or want to adjust any specific part of the script!
Tomorrow Floor Pivots with CPR By Nifty ZThe colors for resistance and support levels have been updated to gradient reds and greens for clearer distinction.
The CPR band uses light blue and purple to stand out more effectively.
Here's a detailed explanation of the user inputs and the typical use of **Floor Pivots for Tomorrow’s Market Range** in a trading context, focusing on support, resistance, and breakout scenarios:
The script allows traders to customize key parameters for their analysis:
1. Pivot Timeframe:
- Users can select different timeframes for calculating floor pivots, such as 1 hour, 4 hours, daily, weekly, monthly, etc.
- This is crucial because the timeframe selection influences the granularity of the support and resistance levels for the next trading day.
- For instance, selecting a **Daily** timeframe will calculate floor pivots for the next trading day, while selecting **Weekly** will give levels for the upcoming week.
2. Show Floor Pivots:
- Users can toggle the visibility of the calculated **Floor Pivots**, which include resistance levels (R1, R2, R3, R4) and support levels (S1, S2, S3, S4).
3. Show CPR (Central Pivot Range):
- CPR (Central Pivot Range) is a key area where the price tends to consolidate.
- The script allows users to enable or disable the visibility of CPR, which consists of the BC (Bottom Central Pivot) and TC (Top Central Pivot).
4. Show Labels:
- Users can choose whether or not to display labels indicating the **Pivot**, **Support**, and Resistance levels on the chart. This can be helpful for visual analysis when day trading.
Understanding Floor Pivots
The Floor Pivots (Pivot, Resistance, and Support levels) for tomorrow's market range are calculated based on today’s high, low, and close. These levels help traders anticipate how the market may behave in the upcoming session.
1. Pivot:
- The Pivot Point is a central level, calculated as the average of the high, low, and close. It’s considered a reference point that determines the market’s overall bias.
- If the price is trading **above the pivot**, it generally suggests a **bullish** sentiment for the day.
- If the price is trading **below the pivot**, it suggests a **bearish** sentiment.
2. Resistance Levels (R1, R2, R3, R4):
- R1 is often the first area where price may stall in an uptrend. It represents the first major resistance level.
- **R2**, **R3**, and **R4** mark additional levels of resistance, progressively further away from the current price. These are used to project potential upward targets.
- These resistance levels are areas where the price might encounter selling pressure, especially during day trading.
3. **Support Levels (S1, S2, S3, S4):**
- Similarly, **S1** is the first area where the price might find support in a downtrend.
- **S2**, **S3**, and **S4** provide deeper support levels where the price may bounce from.
- These support zones are used by day traders to anticipate where the price might reverse upward.
### **Role of Resistance and Support in Day Trading**
- **Resistance Levels (R1, R2, R3, R4)** indicate potential areas where price could **stall** during an uptrend. These levels are useful for **short-term traders** looking to set exit points or identify reversal zones.
- **Support Levels (S1, S2, S3, S4)** highlight areas where the price could **find support** and potentially **bounce** higher. These levels are particularly helpful for identifying buy zones in a downtrend.
- If a price **breaks out** above the resistance levels or **breaks down** below the support levels, it often signals a strong trend continuation.
### **Understanding the Central Pivot Range (CPR)**
The **CPR** is formed by two key levels:
- **BC (Bottom Central Pivot):** The midpoint of the day’s high and low.
- **TC (Top Central Pivot):** The difference between the pivot and BC.
The CPR acts as a region of **consolidation** or **indecision** where the market is likely to stay within a narrow range. The width of the CPR gives traders a sense of volatility:
- A **narrow CPR** often signals that a **breakout** is imminent.
- A **wider CPR** suggests that the market could remain range-bound.
### **Market Sentiment Based on Floor Pivots**
The relationship between **today’s** and **tomorrow’s pivots** is crucial in determining the market sentiment for the next day.
1. **Bullish Case (Higher Highs):**
- If **tomorrow's pivot** is higher than **today's pivot**, it indicates a **bullish sentiment**. This suggests that the market is likely to trend upward in the next session.
- In a **bullish overlapping pivot range**, if **Day 1 (today)** is higher than **Day 2 (tomorrow)**, traders expect continued upward momentum.
2. **Bearish Case (Lower Lows):**
- Conversely, if **tomorrow's pivot** is lower than **today's pivot**, it suggests a **bearish sentiment** and that the market could trend downward in the next session.
- In a **bearish overlapping pivot range**, if **Day 1 (today)** is lower than **Day 2 (tomorrow)**, traders expect continued downward pressure.
### **Breakout Scenarios**
A breakout occurs when the price **violates either the support or resistance levels** significantly, indicating that the price is moving in the direction of the breakout.
1. **Bullish Breakout:**
- If the price consistently stays **above the CPR** and **resistance levels (R1, R2)**, it indicates a strong **bullish breakout**.
- This is especially true when the **CPR is narrow** for both days, signaling a buildup in price action and a potential breakout to the upside.
2. **Bearish Breakout:**
- If the price breaks **below the CPR** and **support levels (S1, S2)**, it indicates a **bearish breakout**.
- A narrow CPR on **both days** suggests that a breakout to the downside could be imminent.
3. **Neutral or Ranging Days:**
- Sometimes, the CPR stays **unchanged** for 4-5 days, indicating a period of **consolidation** where the price is moving within a tight range. This often leads to a significant breakout once the consolidation ends.
Strategic Application of Floor Pivots for Tomorrow
Traders use floor pivots to plan their next-day trades by:
- **Aligning with Market Sentiment:** Based on whether tomorrow’s pivot is higher or lower than today’s, traders can align their trades in the direction of the market’s overall bias.
- **Identifying Entry and Exit Points:** Resistance and support levels provide well-defined areas to enter or exit trades, making pivots essential for day trading strategies.
- **Anticipating Breakouts:** Monitoring the width of the CPR and the relation between pivots helps traders anticipate potential breakouts, allowing them to react quickly to sudden price movements.
By effectively using these pivots and understanding their significance, traders can improve their decision-making for short-term trades in the stock or futures markets.
Black-Scholes option price model & delta hedge strategyBlack-Scholes Option Pricing Model Strategy
The strategy is based on the Black-Scholes option pricing model and allows the calculation of option prices, various option metrics (the Greeks), and the creation of synthetic positions through delta hedging.
ATTENTION!
Trading derivative financial instruments involves high risks. The author of the strategy is not responsible for your financial results! The strategy is not self-sufficient for generating profit! It is created exclusively for constructing a synthetic derivative financial instrument. Also, there might be errors in the script, so use it at your own risk! I would appreciate it if you point out any mistakes in the comments! I would be even more grateful if you send the corrected code!
Application Scope
This strategy can be used for delta hedging short positions in sold options. For example, suppose you sold a call option on Bitcoin on the Deribit exchange with a strike price of $60,000 and an expiration date of September 27, 2024. Using this script, you can create a delta hedge to protect against the risk of loss in the option position if the price of Bitcoin rises.
Another example: Suppose you use staking of altcoins in your strategies, for which options are not available. By using this strategy, you can hedge the risk of a price drop (Put option). In this case, you won't lose money if the underlying asset price increases, unlike with a short futures position.
Another example: You received an airdrop, but your tokens will not be fully unlocked soon. Using this script, you can fully hedge your position and preserve their dollar value by the time the tokens are fully unlocked. And you won't fear the underlying asset price increasing, as the loss in the event of a price rise is limited to the option premium you will pay if you rebalance the portfolio.
Of course, this script can also be used for simple directional trading of momentum and mean reversion strategies!
Key Features and Input Parameters
1. Option settings:
- Style of option: "European vanilla", "Binary", "Asian geometric".
- Type of option: "Call" (bet on the rise) or "Put" (bet on the fall).
- Strike price: the option contract price.
- Expiration: the expiry date and time of the option contract.
2. Market statistic settings:
- Type of price source: open, high, low, close, hl2, hlc3, ohlc4, hlcc4 (using hl2, hlc3, ohlc4, hlcc4 allows smoothing the price in more volatile series).
- Risk-free return symbol: the risk-free rate for the market where the underlying asset is traded. For the cryptocurrency market, the return on the funding rate arbitrage strategy is accepted (a special function is written for its calculation based on the Premium Price).
- Volatility calculation model: realized (standard deviation over a moving period), implied (e.g., DVOL or VIX), or custom (you can specify a specific number in the field below). For the cryptocurrency market, the calculation of implied volatility is implemented based on the product of the realized volatility ratio of the considered asset and Bitcoin to the Bitcoin implied volatility index.
- User implied volatility: fixed implied volatility (used if "Custom" is selected in the "Volatility Calculation Method").
3. Display settings:
- Choose metric: what to display on the indicator scale – the price of the underlying asset, the option price, volatility, or Greeks (all are available).
- Measure: bps (basis points), percent. This parameter allows choosing the unit of measurement for the displayed metric (for all except the Greeks).
4. Trading settings:
- Hedge model: None (do not trade, default), Simple (just open a position for the full volume when the strike price is crossed), Synthetic option (creating a synthetic option based on the Black-Scholes model).
- Position side: Long, Short.
- Position size: the number of units of the underlying asset needed to create the option.
- Strategy start time: the moment in time after which the strategy will start working to create a synthetic option.
- Delta hedge interval: the interval in minutes for rebalancing the portfolio. For example, a value of 5 corresponds to rebalancing the portfolio every 5 minutes.
Post scriptum
My strategy based on the SegaRKO model. Many thanks to the author! Unfortunately, I don't have enough reputation points to include a link to the author in the description. You can find the original model via the link in the code, as well as through the search indicators on the charts by entering the name: "Black-Scholes Option Pricing Model". I have significantly improved the model: the calculation of volatility, risk-free rate and time value of the option have been reworked. The code performance has also been significantly optimized. And the most significant change is the execution, with which you can now trade using this script.
Muti TimeFrame 1st Minute High and a LowThis Pine Script code is designed to plot the high, close, and low prices at exactly 9:31 AM on any timeframe chart. Here's a breakdown of what the script does:
Inputs
Define the start time of the trading day (default: 9:30 AM)
Define the end time of the trading day (default: 4:00 PM)
Toggle to display daily open and close lines (default: true)
Toggle to extend lines for daily open and close (default: false)
Calculations
- Determines if the current bar is the first bar of the trading day (9:30 AM)
- Retrieves the high, close, and low prices at 9:31 AM for the current timeframe
- Plots these prices as crosses on the chart
- Draws lines for the 4 pm close and 9:30 am open, as well as lines for the high and low of the first candle
- Calculates the start and end times for a rectangle box and draws the box on the chart if the start price high and low are set
Features
- Plots the high, close, and low prices at exactly 9:31 AM on any timeframe chart
- Displays daily open and close lines
- Extends lines for daily open and close (optional)
- Draws a rectangle box around the first candle of the day (optional)
Markets
- Designed for use on various markets, including stocks, futures, forex, and crypto
This script is useful for traders who want to visualize the prices at the start of the trading day and track the market's movement throughout the day.
Weighted US Liquidity ROC Indicator with FED RatesThe Weighted US Liquidity ROC Indicator is a technical indicator that measures the Rate of Change (ROC) of a weighted liquidity index. This index aggregates multiple monetary and liquidity measures to provide a comprehensive view of liquidity in the economy. The ROC of the liquidity index indicates the relative change in this index over a specified period, helping to identify trend changes and market movements.
1. Liquidity Components:
The indicator incorporates various monetary and liquidity measures, including M1, M2, the monetary base, total reserves of depository institutions, money market funds, commercial paper, and repurchase agreements (repos). Each of these components is assigned a weight that reflects its relative importance to overall liquidity.
2. ROC Calculation:
The Rate of Change (ROC) of the weighted liquidity index is calculated by finding the difference between the current value of the index and its value from a previous period (ROC period), then dividing this difference by the value from the previous period. This gives the percentage increase or decrease in the index.
3. Visualization:
The ROC value is plotted as a histogram, with positive and negative changes indicated by different colors. The Federal Funds Rate is also plotted separately to show the impact of central bank policy on liquidity.
Discussion of the Relationship Between Liquidity and Stock Market Returns
The relationship between liquidity and stock market returns has been extensively studied in financial economics. Here are some key insights supported by scientific research:
Liquidity and Stock Returns:
Liquidity Premium Theory: One of the primary theories is the liquidity premium theory, which suggests that assets with higher liquidity typically offer lower returns because investors are willing to accept lower yields for more liquid assets. Conversely, assets with lower liquidity may offer higher returns to compensate for the increased risk associated with their illiquidity (Amihud & Mendelson, 1986).
Empirical Evidence: Research by Fama and French (1992) has shown that liquidity is an important factor in explaining stock returns. Their studies suggest that stocks with lower liquidity tend to have higher expected returns, aligning with the liquidity premium theory.
Market Impact of Liquidity Changes:
Liquidity Shocks: Changes in liquidity can impact stock returns significantly. For example, an increase in liquidity is often associated with higher stock prices, as it reduces the cost of trading and enhances market efficiency (Chordia, Roll, & Subrahmanyam, 2000). Conversely, a liquidity shock, such as a sudden decrease in market liquidity, can lead to higher volatility and lower stock prices.
Financial Crises: During financial crises, liquidity tends to dry up, leading to sharp declines in stock market returns. For instance, studies on the 2008 financial crisis illustrate how a reduction in market liquidity exacerbated the decline in stock prices (Brunnermeier & Pedersen, 2009).
Central Bank Policies and Liquidity:
Monetary Policy Impact: Central bank policies, such as changes in the Federal Funds Rate, directly influence market liquidity. Lower interest rates generally increase liquidity by making borrowing cheaper, which can lead to higher stock market returns. On the other hand, higher rates can reduce liquidity and negatively impact stock prices (Bernanke & Gertler, 1999).
Policy Expectations: The anticipation of changes in monetary policy can also affect stock market returns. For example, expectations of rate cuts can lead to a rise in stock prices even before the actual policy change occurs (Kuttner, 2001).
Key References:
Amihud, Y., & Mendelson, H. (1986). "Asset Pricing and the Bid-Ask Spread." Journal of Financial Economics, 17(2), 223-249.
Fama, E. F., & French, K. R. (1992). "The Cross-Section of Expected Stock Returns." Journal of Finance, 47(2), 427-465.
Chordia, T., Roll, R., & Subrahmanyam, A. (2000). "Market Liquidity and Trading Activity." Journal of Finance, 55(2), 265-289.
Brunnermeier, M. K., & Pedersen, L. H. (2009). "Market Liquidity and Funding Liquidity." Review of Financial Studies, 22(6), 2201-2238.
Bernanke, B. S., & Gertler, M. (1999). "Monetary Policy and Asset Prices." NBER Working Paper No. 7559.
Kuttner, K. N. (2001). "Monetary Policy Surprises and Interest Rates: Evidence from the Fed Funds Futures Market." Journal of Monetary Economics, 47(3), 523-544.
These studies collectively highlight how liquidity influences stock market returns and how changes in liquidity conditions, influenced by monetary policy and other factors, can significantly impact stock prices and market stability.
Last Candle OHLC (Ticks or Points)What the Code Does
1. **Draws Lines and Labels**:
- It draws lines on your chart to show the high, low, open, and close prices from the previous period (like the previous day or week).
- It also labels these lines with numbers that tell you how far the current price is from these levels.
2. **Shows Price Movement**:
- You can see how far the price has moved from these levels in terms of small price changes (ticks) or larger units (points).
- This helps you understand price movements and potential levels of support or resistance.
3. **Customizable**:
- You can choose whether to show these lines and labels, and you can select if you want to see the movement in ticks or points.
- The lines can extend into the future on your chart to help you anticipate where prices might be in the coming days.
### How It’s Useful:
1. **Identify Key Levels**:
- It helps you spot important price levels from past periods, which can act as support or resistance.
2. **Understand Price Movement**:
- You get a visual sense of how much the price has moved from key levels, which can help you gauge market volatility.
3. **Plan Trades**:
- By seeing where the price has been and how it has moved, you can better plan your trades, like deciding where to enter or exit based on these levels.
4. **Flexible for Different Markets**:
- It works across various markets, like stocks, futures, and forex, adjusting to the specific characteristics of each instrument.
In short, this tool helps you visualize and understand past price movements and levels on your chart, aiding in your trading decisions.
Partial Profit Calculator [TFO]This indicator was built to help calculate the outcome of trades that utilize multiple profit targets and/or multiple entries.
In its simplest form, we can have a single entry and a single profit target. As shown below in this long trade example, the indicator will draw risk and reward boxes (red and green, respectively) with several annotations. On the left-hand side, all entries will be displayed (in this case there is only one entry, "E1"). On the bottom, the "SL" label indicates the trade's stop loss placement. On the top, all target prices are displayed (in this case there is only one target, "TP1"). Lastly, on the right-hand side a label will display the total R that is to be expected from a winning trade, where R is one's unit of risk.
In the following example, we have two target prices - one at 18600 and one at 18700. You can input as many target prices as you'd like, separated by commas, i.e. "18600,18700" in this example. Make sure the values are separated by commas only, and not spaces, new lines, etc. As a result, we can see that the indicator draws where our profit targets would be with respect to our entry, E1. The indicator assumes that equal parts of the trade position are taken off at each target price. In this example on Nasdaq futures (NQ1!), since we have 2 target prices, this would be equivalent to assuming that we take exactly half the trade position off at TP1, and the remaining half of the position at TP2.
If we wanted to take more of the position off at a certain target, we could simply duplicate the target price. Here I set the target prices to "18600,18600,18700" to enforce that two thirds of the position be taken off at TP1 and TP2, while the remaining third gets taken off at TP3.
We can also show outcome annotations to describe how much R is generated from each possible trade outcome. Using the below chart as an example, the stop loss indicates a -1R loss. The total R from this trade criteria is 1.33 R, and each target price shows how much R is being generated if one were to take off an equal part of the position at said target prices. In this case, we would generate 0.17 R from taking one third of the position off at TP1, another 0.5 R from taking one third of the position off at TP2, and another 0.67 R from taking the remaining one third of the position off at TP3, all adding up to the total R indicated on the right-hand side label.
Using multiple entries works the same way as using multiple target prices, where the input should indicate each entry price separated by commas. In this example I've used "18550,18450" to achieve an average price of 18500, as indicated by the "E_avg" label that appears when more than one entry price is utilized. We can also opt to display risk as dollars instead of R values, where you can input your desired risk per trade, and all values are shown as dollar amounts instead of R multiples, as shown below with a risk per trade of $100.
This is meant to be an educational tool for trades that utilize multiple profit targets and/or entries. Hope you like it!
Volatility Adaptive Signal Tracker (VAST)The Adaptive Trend Following Buy/Sell Signals Pine Script is designed to help traders identify and capitalize on market trends using an adaptive trend-following strategy. This script focuses on generating reliable buy and sell signals by analyzing market trends and volatility. It simplifies the trading process by providing clear signals without plotting additional lines, making it easy to use and interpret.
Key Features:
Adaptive Trend Following:
The script employs an adaptive trend-following approach that leverages market volatility to generate buy and sell signals. This method is effective in both trending and volatile markets.
Inputs and Customization:
The script includes customizable parameters for the Simple Moving Average (SMA) length, the Average True Range (ATR) length, and the ATR multiplier. These inputs allow traders to adjust the sensitivity of the signals to match their trading style and market conditions.
Signal Generation:
Buy Signal: Generated when the closing price crosses above the upper adaptive band, indicating a potential upward trend.
Sell Signal: Generated when the closing price crosses below the lower adaptive band, indicating a potential downward trend.
Visual Signals:
The script uses plotshape to mark buy signals with green labels below the bars and sell signals with red labels above the bars. This clear visual representation helps traders quickly identify trading opportunities.
Alert Conditions:
The script sets up alert conditions for both buy and sell signals. Traders can use these alerts to receive notifications when a signal is generated, ensuring they do not miss any trading opportunities.
How It Works:
SMA Calculation: The script calculates the Simple Moving Average (SMA) over a specified period, which helps in identifying the general trend direction.
ATR Calculation: The Average True Range (ATR) is calculated to measure market volatility.
Adaptive Bands: Upper and lower adaptive bands are created by adding and subtracting a multiple of the ATR to the SMA, respectively.
Signal Logic: Buy signals are generated when the closing price crosses above the upper band, while sell signals are generated when the closing price crosses below the lower band.
Example Use Case:
A trader looking to capitalize on medium-term trends in the Nifty futures market can use this script to receive timely buy and sell signals. By customizing the SMA length and ATR parameters, the trader can fine-tune the script to match their trading strategy, ensuring they enter and exit trades at optimal points.
Benefits:
Simplicity: The script provides clear buy and sell signals without cluttering the chart with additional lines or indicators.
Adaptability: Customizable parameters allow traders to adapt the script to various market conditions and trading styles.
Alerts: Built-in alert conditions ensure traders receive timely notifications, helping them to act quickly on trading signals.
How to Use:
Open TradingView: Go to the TradingView website and log in.
Create a New Chart: Click on the “Chart” button to open a new chart.
Open the Pine Script Editor: Click on the “Pine Editor” tab at the bottom of the chart.
Create a New Script: Delete any default code in the Pine Script editor and paste the provided script.
Add to Chart: Click on the “Add to Chart” button to compile and add the script to your chart.
Save the Script: Click “Save” and name the script.
Set Alerts: Right-click on the chart, select “Add Alert,” and choose the appropriate condition to set alerts for buy and sell signals.
CVD with Moving Average (Trend Colors) [SYNC & TRADE]Yesterday I wrote a simple and easy code for the indicator "Cumulative Delta Volume with a moving average" using AI.
Introduction:
Delta is the difference between buys and sells. If there are more purchases, the delta is positive, if there are more sales, the delta is negative. We look at each candle separately on a particular time frame, which does not give us an overall picture over time.
Cumulative volume delta is in many ways an extension of volume delta, but it covers longer periods of time and provides different trading signals. Like the volume delta indicator, the Cumulative Volume Delta (CVD) indicator measures the relationship between buying and selling pressure, but does not focus on one specific candle (or other chart element), but rather gives a picture over time.
What did you want to get?
I have often seen that they tried to attach RSI and the Ichimoku cloud to the cumulative delta of volume, but I have never seen a cumulative delta of volume with a moving average. A moving average that takes data from the cumulative volume delta will be different from the moving average of the underlying asset. It has been noted that often at the intersection of the cumulative volume delta and the moving average, this is a more accurate signal to buy or sell than the same intersections for the underlying asset.
Initially, 5 moving averages were made with values of 21, 55, 89, 144 and 233, but I realized that this overloads the chart. It is easier to change the length of the moving average depending on the time frame you are using than to overload the chart. The final version with one moving SMA, EMA, RMA, WMA, HMA.
The logic for applying a moving average to a cumulative volume delta:
You choose a moving average, just like you would on your underlying asset. Use the moving average you like and the period you are used to working with. Each TF has its own settings.
What we see on the graph:
This is not an oscillator, but an adapted version for a candlestick chart (line only). Using it, you can clearly see where the market is moving based on the cumulative volume delta. The cool thing is that you can include your moving average applied to the cumulative volume delta. Thanks to this, you can see a trend movement, a return to the moving average to continue the trend.
Opportunities not lost:
The most interesting thing is that it remains possible to observe the divergence of the asset and the cumulative delta of the volume. This gives a great advantage. Those who have not worked with divergence do not rush into it right away. There may be 3 peaks in divergence (as with oversold/overbought), but it works many times more clearly than RSI and MACD.
Here's a good example on the daily chart. The moment we were all waiting for 75,000. The cumulative Delta Volume fell with each peak, while the price chart (tops) were approximately level.
Usually they throw (allow to buy) without volume for sales (delta down, price up) in order to merge at a more interesting price. And they also drain without the volume of purchases for a squeeze (price down / delta up) and again I buy back at a more interesting price. There are more complex estimation options; you can read about the divergence of the cumulative delta of the CVD volume. I just recommend doing a backtest.
Recommendations:
One more moment. Use the indicator on the stock exchange, where there is the most money, by turnover and by asset. Choose Binance, not Bybit. Those. choose the BTC asset, for example, but on the Binance exchange. Not futures, but spot.
The greater the turnover on the exchange for an asset, and the fewer opportunities to enter with leverage, the less volatile the price and the more beautiful and accurate the chart.
Works on all assets. There is a subscription limit (the number of calculated bars) that has little effect on anything. Can be applied to any asset where there is volume (not SPX, but ES1, not MOEX, but MX1!).
Перевод на русский.
Вчера написал с помощью AI простой и легкий код индикатора "Кумулятивная Дельта Объема со скользящей средней".
Введение:
Дельта (Delta) — это разница между покупками и продажами. Если покупок больше — дельта положительная, если больше продаж — дельта отрицательная. Мы смотрим на каждую свечу отдельно на том или ином таймфрейме, что не дает нам общей картины во времени.
Кумулятивная дельта объема — во многом продолжение дельты объёмов, но она включает более длительные периоды времени и дает другие торговые сигналы. Как и индикатор дельты объёма, индикатор кумулятивной дельты объема (Cumulative Volume Delta, CVD) измеряет связь между давлением покупателей и продавцов, но при этом не фокусируется на одной конкретной свече (или другом элементе графика), а дает картину во времени.
Что хотел получить?
Часто видел, что к кумулятивной детьте объема пытались прикрепить RSI и облако ишимоку, но никогда не видел кумулятивную дельту объема со скользящей средней. Скользящая средняя которая берет данные от кумулятивной дельты объема будет отличатся от скользящей средней основного актива. Было замечено, что часто в местах пересечения кумулятивной дельты объема и скользящей средней - это более точный сигнал к покупке или продаже, чем такие же пересечения по основному активу.
Изначально было сделанно 5 скользящих со значениями 21, 55, 89, 144 и 233, но я понял, что это перегружает график. Проще менять длину скользящей средней от используемого таймфрейма, чем перегружать график. Финальный вариант с одной скользящей SMA, EMA, RMA, WMA, HMA.
Логика применения скользящей средней к кумулятивной дельте объема:
Вы выбираете скользящую среднюю, так же как и на основном активе. Применяйте ту скользящую среднюю, которая вам нравится и период, с которым привыкли работать. На каждом TF свои настройки.
Что мы видим на графике:
Это не осциллятор, а адаптированная версия к свечному графику (только линия). С помощью него вы можете наглядно посмотреть куда движется рынок по кумулятивной дельте объема. Самое интересное, что вы можете включить свою скользящую среднюю, применимую к кумулятивной дельте объема. Благодаря этому вы можете видеть трендовое движение, возврат к средней скользящей для продолжения тренда.
Не потерянные возможности:
Самое интересное, что осталась возможность наблюдать за дивергенцией актива и кумулятивной дельтой объема. Это дает большое преимущество. Те кто не работал с дивергенцией не бросайтесь на нее сразу. Может быть и 3 пика в дивергенции (как с перепроданностью / перекупленностью), но работает в разы четче чем RSI и MACD.
Вот хороший пример на дневном графике. Момент когда мы все ждали 75000. Кумулятивная Дельта Объема падала с каждым пиком, в то время как ценовой график (вершины) были примерно на уровне.
Обычно закидывают (разрешают покупать) без объема на продажи (дельта вниз цена вверх), чтобы слить по более интересной цене. И также сливают без объема покупок для сквиза (цена вниз / дельта вверх) и опять откупаю по более интересной цене. Существуют более сложные варианты оценки, можете почитать про дивергенцию кумулятивной дельты объема CVD. Только рекомендую сделать бэктест.
Рекомендации:
Еще момент. Используйте индикатор, на бирже, там где больше всего денег, по обороту и по активу. Выбирайте не Bybit, а Binance. Т.е. выбираете актив BTC, к примеру, но на бирже Binance. Не фьючерс, а спот.
Чем более большие обороты на бирже, по активу, и меньше возможностей заходить с плечами, тем менее волатильная цена и более красивый и точный график.
Работает на всех активах. Есть ограничение по подписке (количество рассчитываемых баров) мало влияет на что. Можно применить к любому активу где есть объем (не SPX, а ES1, не MOEX, а MX1!).
Tapak 20RThis strategy originally developed by Jatrader. Kudos to him for giving me chance to develop this indicator.
This script should be use Light Crude Oil Futures 20 Range chart. (This strategy only proven for 20R range chart, Crude Oil.)
How it works?
If current 20R candle is closed green, the closing value must be higher than previous candle to take long position.
If not, it stays as previous direction.
If current candle is closed red, the closing value must be lower than previous candle to take short position.
If not, it stays as previous direction.
How to use this indicator?
1. First, determine the stoploss point from high or low candle.(if current candle is green, stoploss is set higher than high candle and vice versa)
2. Determine how many tick you want to allowed for stoploss, how much profit (ticks) you want to achieve.
3. Determine the color and thickness of each line.
The table will display all value involved with this strategy such as entry value, stoploss value and target profit value.
Please kept in mind that, this is scalping strategy. So, the recommended target profit should be around 10 - 20 ticks.
Thank you.
COT-NocTradingIndicator Description:
Commitments of Traders (COT) Data Indicator
The Commitments of Traders (COT) Data Indicator on TradingView provides insights into market sentiment based on the weekly CFTC (Commodity Futures Trading Commission) reports. It plots three key lines derived from this data, offering valuable information for traders seeking to understand positioning trends among large speculators, commercial hedgers, and small traders.
Lines Plotted:
Commercials: Reflects positions held by commercial entities engaged in the production or sale of the underlying commodity. Their positions often act as a hedge against physical market exposure.
Non Commercials: Represents positions held by large speculators, typically hedge funds and large financial institutions, who often take more significant positions based on their market outlook.
Retail Traders: Shows positions held by small traders, including individual retail traders and smaller institutional players, providing insights into the broader retail sentiment.
Labeling:
Each line is accompanied by a label to clearly identify its corresponding group, enhancing clarity and ease of interpretation for traders analyzing the indicator.
Usage:
Trend Confirmation: Monitor the positioning of commercial and non commercial relative to retail traders to confirm trends and potential reversals.
Sentiment Analysis: Assess shifts in market sentiment based on changes in positioning across different trader categories.
Trading Signals: Use crossovers, divergences, and extreme positioning relative to historical data to generate potential trading signals.
This indicator is valuable for traders looking to incorporate institutional positioning data into their trading strategies, offering a deeper understanding of market dynamics beyond price action alone.
Strategic Multi-Step Supertrend - Strategy [presentTrading]The code is mainly developed for me to stimulate the multi-step taking profit function for strategies. The result shows the drawdown can be reduced but at the same time reduced the profit as well. It can be a heuristic for futures leverage traders.
█ Introduction and How it is Different
The "Strategic Multi-Step Supertrend" is a trading strategy designed to leverage the power of multiple steps to optimize trade entries and exits across the Supertrend indicator. Unlike traditional strategies that rely on single entry and exit points, this strategy employs a multi-step approach to take profit, allowing traders to lock in gains incrementally. Additionally, the strategy is adaptable to both long and short trades, providing a comprehensive solution for dynamic market conditions.
This template strategy lies in its dual Supertrend calculation, which enhances the accuracy of trend detection and provides more reliable signals for trade entries and exits. This approach minimizes false signals and increases the overall profitability of trades by ensuring that positions are entered and exited at optimal points.
BTC 6h L/S Performance
█ Strategy, How It Works: Detailed Explanation
The "Strategic Multi-Step Supertrend Trader" strategy utilizes two Supertrend indicators calculated with different parameters to determine the direction and strength of the market trend. This dual approach increases the robustness of the signals, reducing the likelihood of entering trades based on false signals. Here is a detailed breakdown of how the strategy operates:
🔶 Supertrend Indicator Calculation
The Supertrend indicator is a trend-following overlay on the price chart, typically used to identify the direction of the trend. It is calculated using the Average True Range (ATR) to ensure that the indicator adapts to market volatility. The formula for the Supertrend indicator is:
Upper Band = (High + Low) / 2 + (Factor * ATR)
Lower Band = (High + Low) / 2 - (Factor * ATR)
Where:
- High and Low are the highest and lowest prices of the period.
- Factor is a user-defined multiplier.
- ATR is the Average True Range over a specified period.
The Supertrend changes its direction based on the closing price in relation to these bands.
🔶 Entry-Exit Conditions
The strategy enters long positions when both Supertrend indicators signal an uptrend, and short positions when both indicate a downtrend. Specifically:
- Long Condition: Supertrend1 < 0 and Supertrend2 < 0
- Short Condition: Supertrend1 > 0 and Supertrend2 > 0
- Long Exit Condition: Supertrend1 > 0 and Supertrend2 > 0
- Short Exit Condition: Supertrend1 < 0 and Supertrend2 < 0
🔶 Multi-Step Take Profit Mechanism
The strategy features a multi-step take profit mechanism, which allows traders to lock in profits incrementally. This is achieved through four user-configurable take profit levels. For each level, the strategy specifies a percentage increase (for long trades) or decrease (for short trades) in the entry price at which a portion of the position is exited:
- Step 1: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent1 / 100)
- Step 2: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent2 / 100)
- Step 3: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent3 / 100)
- Step 4: Exit a portion of the trade at Entry Price * (1 + Take Profit Percent4 / 100)
This staggered exit strategy helps in locking profits at multiple levels, thereby reducing risk and increasing the likelihood of capturing the maximum possible profit from a trend.
BTC Local
█ Trade Direction
The strategy is highly flexible, allowing users to specify the trade direction. There are three options available:
- Long Only: The strategy will only enter long trades.
- Short Only: The strategy will only enter short trades.
- Both: The strategy will enter both long and short trades based on the Supertrend signals.
This flexibility allows traders to adapt the strategy to various market conditions and their own trading preferences.
█ Usage
1. Add the strategy to your trading platform and apply it to the desired chart.
2. Configure the take profit settings under the "Take Profit Settings" group.
3. Set the trade direction under the "Trade Direction" group.
4. Adjust the Supertrend settings in the "Supertrend Settings" group to fine-tune the indicator calculations.
5. Monitor the chart for entry and exit signals as indicated by the strategy.
█ Default Settings
- Use Take Profit: True
- Take Profit Percentages: Step 1 - 6%, Step 2 - 12%, Step 3 - 18%, Step 4 - 50%
- Take Profit Amounts: Step 1 - 12%, Step 2 - 8%, Step 3 - 4%, Step 4 - 0%
- Number of Take Profit Steps: 3
- Trade Direction: Both
- Supertrend Settings: ATR Length 1 - 10, Factor 1 - 3.0, ATR Length 2 - 11, Factor 2 - 4.0
These settings provide a balanced starting point, which can be customized further based on individual trading preferences and market conditions.
Sharpe and Sortino Ratios█ OVERVIEW
This indicator calculates the Sharpe and Sortino ratios using a chart symbol's periodic price returns, offering insights into the symbol's risk-adjusted performance. It features the option to calculate these ratios by comparing the periodic returns to a fixed annual rate of return or the returns from another selected symbol's context.
█ CONCEPTS
Returns, risk, and volatility
The return on an investment is the relative gain or loss over a period, often expressed as a percentage. Investment returns can originate from several sources, including capital gains, dividends, and interest income. Many investors seek the highest returns possible in the quest for profit. However, prudent investing and trading entails evaluating such returns against the associated risks (i.e., the uncertainty of returns and the potential for financial losses) for a clearer perspective on overall performance and sustainability.
The profitability of an investment typically comes at the cost of enduring market swings, noise, and general uncertainty. To navigate these turbulent waters, investors and portfolio managers often utilize volatility , a measure of the statistical dispersion of historical returns, as a foundational element in their risk assessments because it provides a tangible way to gauge the uncertainty in returns. High volatility suggests increased uncertainty and, consequently, higher risk, whereas low volatility suggests more stable returns with minimal fluctuations, implying lower risk. These concepts are integral components in several risk-adjusted performance metrics, including the Sharpe and Sortino ratios calculated by this indicator.
Risk-free rate
The risk-free rate represents the rate of return on a hypothetical investment carrying no risk of financial loss. This theoretical rate provides a benchmark for comparing the returns on a risky investment and evaluating whether its excess returns justify the risks. If an investment's returns are at or below the theoretical risk-free rate or the risk premium is below a desired amount, it may suggest that the returns do not compensate for the extra risk, which might be a call to reassess the investment.
Since the risk-free rate is a theoretical concept, investors often utilize proxies for the rate in practice, such as Treasury bills and other government bonds. Conventionally, analysts consider such instruments "risk-free" for a domestic holder, as they are a form of government obligation with a low perceived likelihood of default.
The average yield on short-term Treasury bills, influenced by economic conditions, monetary policies, and inflation expectations, has historically hovered around 2-3% over the long term. This range also aligns with central banks' inflation targets. As such, one may interpret a value within this range as a minimum proxy for the risk-free rate, as it may correspond to the minimum rate required to maintain purchasing power over time. This indicator uses a default value of 2% as the risk-free rate in its Sharpe and Sortino ratio calculations. Users can adjust this value from the "Risk-free rate of return" input in the "Settings/Inputs" tab.
Sharpe and Sortino ratios
The Sharpe and Sortino ratios are two of the most widely used metrics that offer insight into an investment's risk-adjusted performance . They provide a standardized framework to compare the effectiveness of investments relative to their perceived risks. These metrics can help investors determine whether the returns justify the risks taken to achieve them, promoting more informed investment decisions.
Both metrics measure risk-adjusted performance similarly. However, they have some differences in their formulas and their interpretation:
1. Sharpe ratio
The Sharpe ratio , developed by Nobel laureate William F. Sharpe, measures the performance of an investment compared to a theoretically risk-free asset, adjusted for the investment risk. The ratio uses the following formula:
Sharpe Ratio = (𝑅𝑎 − 𝑅𝑓) / 𝜎𝑎
Where:
• 𝑅𝑎 = Average return of the investment
• 𝑅𝑓 = Theoretical risk-free rate of return
• 𝜎𝑎 = Standard deviation of the investment's returns (volatility)
A higher Sharpe ratio indicates a more favorable risk-adjusted return, as it signifies that the investment produced higher excess returns per unit of increase in total perceived risk.
2. Sortino ratio
The Sortino ratio is a modified form of the Sharpe ratio that only considers downside volatility , i.e., the volatility of returns below the theoretical risk-free benchmark. Although it shares close similarities with the Sharpe ratio, it can produce very different values, especially when the returns do not have a symmetrical distribution, since it does not penalize upside and downside volatility equally. The ratio uses the following formula:
Sortino Ratio = (𝑅𝑎 − 𝑅𝑓) / 𝜎𝑑
Where:
• 𝑅𝑎 = Average return of the investment
• 𝑅𝑓 = Theoretical risk-free rate of return
• 𝜎𝑑 = Downside deviation (standard deviation of negative excess returns, or downside volatility)
The Sortino ratio offers an alternative perspective on an investment's return-generating efficiency since it does not consider upside volatility in its calculation. A higher Sortino ratio signifies that the investment produced higher excess returns per unit of increase in perceived downside risk.
The risk-free rate (𝑅𝑓) in the numerator of both ratio formulas acts as a baseline for comparing an investment's performance to a theoretical risk-free alternative. By subtracting the risk-free rate from the expected return (𝑅𝑎−𝑅𝑓), the numerator essentially represents the risk premium of the investment.
Comparison with another symbol
In addition to the conventional Sharpe and Sortino ratios, which compare an instrument's returns to a risk-free rate, this indicator can also compare returns to a user-specified benchmark symbol , allowing the calculation of Information ratios .
An Information ratio is a generalized form of the Sharpe ratio that compares an investment's returns to a risky benchmark , such as SPY, rather than a risk-free rate. It measures the investment's active return (the difference between its returns and the benchmark returns) relative to its tracking error (i.e., the volatility of the active return) using the following formula:
𝐼𝑅 = (𝑅𝑝 − 𝑅𝑏) / 𝑇𝐸
Where:
• 𝑅𝑝 = Average return on the portfolio or investment
• 𝑅𝑏 = Average return from the benchmark instrument
• 𝑇𝐸 = Tracking error (volatility of 𝑅𝑝 − 𝑅𝑏)
Comparing returns to a benchmark instrument rather than a theoretical risk-free rate offers unique insights into risk-adjusted performance. Higher Information ratios signify that the investment produced higher active returns per unit of increase in risk relative to the benchmark. Conventional choices for non-risk-free benchmarks include major composite indices like the S&P 500 and DJIA, as the resulting ratios can provide insight into the effectiveness of an investment relative to the broader market.
Users can enable this generalized calculation for both the Sharpe and Sortino ratios by selecting the "Benchmark symbol returns" option from the "Benchmark type" dropdown in the "Settings/Inputs" tab.
It's crucial to note that this indicator compares the charts symbol's rate of change (return) to the rate of change in the benchmark symbol. Consequently, not all symbols available on TradingView are suitable for use with these ratios due to the nature of what their values represent. For instance, using a bond as a benchmark will produce distorted results since each bar's values represent yields rather than prices, meaning it compares the rate of change in the yield. To maintain consistency and relevance in the calculated ratios, ensure the values from the compared symbols strictly represent price information.
█ FEATURES
This indicator provides traders with two widely used metrics for assessing risk-adjusted performance, generalized to allow users to compare the chart symbol's price returns to a fixed risk-free rate or the returns from another risky symbol. Below are the key features of this indicator:
Timeframe selection
The "Returns timeframe" input determines the timeframe of the calculated price returns. Users can select any value greater than or equal to the chart's timeframe. The default timeframe is "1M".
Periodic returns tracking
This indicator compounds and collects requested price returns from the selected timeframe over monthly or daily periods, similar to how the Broker Emulator works when calculating strategy performance metrics on trade data. It employs the following logic:
• Track returns over monthly periods if the chart's data spans at least two months.
• Track returns over daily periods if the chart's data spans at least two days but not two months.
• Do not track or collect returns if the data spans less than two days, as the amount of data is insufficient for meaningful ratio calculations.
The indicator uses the returns collected from up to a specified number of periods to calculate the Sharpe and Sortino ratios, depending on the available historical data. It also uses these periodic returns to calculate the average returns it displays in the Data Window.
Users can control the maximum number of periods the indicator analyzes with the "Max no. of periods used" input in the "Settings/Inputs" tab. The default value is 60 periods.
Benchmark specification
The "Benchmark return type" input specifies the benchmark type the indicator compares to the chart symbol's returns in the ratio calculations. It features the following two options:
• "Risk-free rate of return (%)": Compares the price returns to a user-specified annual rate of return representing a theoretical risk-free rate (e.g., 2%).
• "Benchmark symbol return": Compares the price returns to a selected benchmark symbol (e.g., "AMEX:SPY) to calculate Information ratios.
When comparing a chart symbol's returns to a specified benchmark symbol, this indicator aligns the times of data points from the benchmark with the times of data points from the chart's symbol to facilitate a fair comparison between symbols with different active sessions.
Visualization and display
• The indicator displays the periodic returns requested from the specified "Returns timeframe" in a separate pane. The plot includes dynamic colors to signify positive and negative returns.
• When the "Returns timeframe" value represents a higher timeframe, the indicator displays background highlights on the main chart pane to signify when a new value is available and whether the return is positive or negative.
• When the specified benchmark return type is a benchmark symbol, the indicator displays the requested symbol's returns in the separate pane as a gray line for visual comparison.
• Within the separate pane, the indicator displays a single-cell table that shows the base period it uses for periodic returns, the number of periods it uses in the calculation, the timeframe of the requested data, and the calculated Sharpe and Sortino ratios.
• The Data Window displays the chart symbol and benchmark returns, their periodic averages, and the Sharpe and Sortino ratios.
█ FOR Pine Script™ CODERS
• This script utilizes the functions from our RiskMetrics library to determine the size of the periods, calculate and collect periodic returns, and compute the Sharpe and Sortino ratios.
• The `getAlignedPrices()` function in this script requests price data for the chart's symbol and a benchmark symbol with consistent time alignment by utilizing spread symbols , which helps facilitate a fair comparison between different symbol types. Retrieving prices from spreads avoids potential information loss and data misalignment that can otherwise occur when using separate requests from each symbol's context when those symbols have different sessions or data times.
• For consistency, the `getAlignedPrices()` function includes extended hours and dividend adjustment modifiers in its data requests. Additionally, it includes other settings inherited from the chart's context, such as "settlement-as-close" preferences for fair comparison between futures instruments.
• This script uses the `changePercent()` function from our ta library to calculate the percentage changes of the requested data.
• The newly released `force_overlay` parameter in display-related functions allows indicators to display visuals on the main chart and a separate pane simultaneously. We use the parameter in this script's bgcolor() call to display background highlights on the main chart.
Look first. Then leap.
VIX Percentile Rank HistogramVIX Percentile Rank Histogram
The VIX Percentile Rank Histogram provides a visual representation of the CBOE Volatility Index (VIX) percentile rank over a customizable lookback period, helping traders gauge market sentiment and make informed trading decisions.
Overview:
This indicator calculates the percentile rank of the VIX over a specified lookback period and displays it as a histogram. The histogram helps traders understand whether the current VIX level is relatively high or low compared to its recent history. This information is particularly useful for timing entries and exits in the S&P 500 or related ETFs and Mega Caps.
How It Works:
VIX Data Integration: The script fetches daily VIX close prices, regardless of the chart you are viewing, to analyze market volatility.
Percentile Rank Calculation: The indicator calculates the rank percentile of the VIX over the chosen lookback period.
Histogram Visualization: The histogram plots the difference between the flipped VIX percentile rank and 50, showing green bars for ranks below 50 (indicating lower market volatility) and red bars for ranks above 50 (indicating higher market volatility).
Usage:
This indicator is most effective when trading the S&P 500 (SPX, SPY, ES1!) or ETFs and Mega Caps that closely follow the S&P 500. It provides insight into market sentiment, helping traders make more informed decisions.
Timing Entries and Exits: Green histogram readings suggest it's a good time to enter or hold long positions, while red readings suggest considering exits or short positions.
Market Sentiment: A high VIX percentile rank (red bars) indicates market fear and uncertainty, while a low percentile rank (green bars) suggests investor confidence and reduced volatility.
Key Features:
Customizable Lookback Period: The default lookback period is set to 20 days, but can be adjusted based on the trader's average trade duration. For example, if your trades typically last 20 days, a 20-day lookback period helps contextualize the VIX level relative to its recent history.
Histogram Visualization: The histogram provides a clear visual representation of market volatility.
Green Bars: Indicate a lower-than-median VIX percentile rank, suggesting reduced market volatility.
Red Bars: Indicate a higher-than-median VIX percentile rank, suggesting increased market volatility.
Threshold Line: A dashed gray line at the 0 level serves as a visual reference for the median VIX rank.
Important Note:
This indicator always shows readings from the VIX, regardless of the chart you are viewing. For example, if you are looking at Natural Gas futures, this indicator will provide no relevant data. It works best when trading the S&P 500 or related ETFs and Mega Caps.
