Timed Ranges [mktrader]The Timed Ranges indicator helps visualize price ranges that develop during specific time periods. It's particularly useful for analyzing market behavior in instruments like NASDAQ, S&P 500, and Dow Jones, which often show reactions to sweeps of previous ranges and form reversals.
### Key Features
- Visualizes time-based ranges with customizable lengths (30 minutes, 90 minutes, etc.)
- Tracks high/low range development within specified time periods
- Shows multiple cycles per day for pattern recognition
- Supports historical analysis across multiple days
### Parameters
#### Settings
- **First Cycle (HHMM-HHMM)**: Define the time range of your first cycle. The duration of this range determines the length of all subsequent cycles (e.g., "0930-1000" creates 30-minute cycles)
- **Number of Cycles per Day**: How many consecutive cycles to display after the first cycle (1-20)
- **Maximum Days to Display**: Number of historical days to show the ranges for (1-50)
- **Timezone**: Select the appropriate timezone for your analysis
#### Style
- **Box Transparency**: Adjust the transparency of the range boxes (0-100)
### Usage Example
To track 30-minute ranges starting at market open:
1. Set First Cycle to "0930-1000" (creates 30-minute cycles)
2. Set Number of Cycles to 5 (will show ranges until 11:30)
3. The indicator will display:
- Range development during each 30-minute period
- Visual progression of highs and lows
- Color-coded cycles for easy distinction
### Use Cases
- Identify potential reversal points after range sweeps
- Track regular time-based support and resistance levels
- Analyze market structure within specific time windows
- Monitor range expansions and contractions during key market hours
### Tips
- Use in conjunction with volume analysis for better confirmation
- Pay attention to breaks and sweeps of previous ranges
- Consider market opens and key session times when setting cycles
- Compare range sizes across different time periods for volatility analysis
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Rolling Angled Volume Profile [Trendoscope®]🎲 Volume Profile Indicators
🎯Traditional Volume Profile
Volume profile indicators visually represent the distribution of volume across price levels. These indicators typically operate on horizontal price levels, making them effective in identifying supply and demand zones in ranging markets. However, they are less useful in trending markets where price movements follow a slope.
🎯The Need for Angled Volume Profiles
Just as support and resistance levels differ from trendlines, volume profile indicators require an equivalent method to account for volume distribution along a sloped trajectory. This would enable more accurate volume analysis in trending markets.
We identified the need of Angled Volume profile and have already published few indicators that implements the concept.
Angled Volume Profile calculates volume distribution along a slope. Users interact with the indicator by selecting the starting point, after which the volume profile is calculated for the selected trajectory.
Volume Forks is another tool that extends angled volume profile analysis, aligning volume profiles along the trajectory of pitchforks.
🎲 Rolling Volume Profile Indicator
The Rolling Volume Profile offers a new approach to angled volume profile calculations, addressing some limitations of earlier implementations:
🎯 Rolling Calculation
The volume profile is calculated for the last N bars of the instrument
The slope of the profile lines is determined by the closing prices of the starting and ending bars
Profiles are drawn in the direction of price movement between the start and end bars.
🎯 Dynamic Updates
As new bars are added, the calculations are updated, and the profile is redrawn based on the latest data.
This dynamic behavior earns it the name "Rolling Volume Profile."
🎯 Advantages Over Earlier Versions
Unlimited Profile Lines : Unlike previous implementations limited to 500 profile lines, this indicator uses polyline objects, overcoming the restriction.
Live Updates : Previous angled volume profile tools lacked real-time updates when new bars appeared. This limitation is resolved in the Rolling Volume Profile Indicator.
The Rolling Volume Profile provides an efficient and scalable solution for analyzing volume in trending markets.
🎯 Indicator Settings
Simple settings include few customisable options
Internals Elite NYSE [Beta]Overview:
This indicator is designed to provide traders with a quick overview of key market internals and metrics in a single, easy-to-read table displayed directly on the chart. It incorporates a variety of metrics that help gauge market sentiment, momentum, and overall market conditions.
The table dynamically updates in real-time and uses color-coding to highlight significant changes or thresholds, allowing traders to quickly interpret the data and make informed trading decisions.
Features:
Market Internals:
TICK: Measures the difference between the number of stocks ticking up versus those ticking down on the NYSE. Green or red background indicates if it crosses a user-defined threshold.
Advance/Decline (ADD): Shows the net number of advancing versus declining stocks on the NYSE. Color-coded to show positive, negative, or neutral conditions.
Volatility Metrics:
VIX Change (%): Displays the percentage change in the Volatility Index (VIX), a key gauge of market fear or complacency. Color-coded for direction.
VIX Price: Displays the current VIX price with thresholds to indicate low, medium, or high volatility.
Other Market Metrics:
DXY Change (%): Percentage change in the US Dollar Index (DXY), indicating dollar strength or weakness.
VWAP Deviation (%): Percentage of stocks above VWAP (Volume Weighted Average Price), helping traders assess intraday buying and selling pressure.
Asset-Specific Metrics:
BTCUSD Change (%): Percentage change in Bitcoin (BTC) price, useful for monitoring cryptocurrency sentiment.
SPY Change (%): Percentage change in the S&P 500 ETF (SPY), a proxy for the overall stock market.
Current Ticker Change (%): Percentage change in the currently selected ticker on the chart.
US10Y Change (%): Percentage change in the yield of the 10-Year US Treasury Note (TVC:US10Y), an important macroeconomic indicator.
Customizable Appearance:
Adjustable text size to suit your chart layout.
User-defined thresholds for key metrics (e.g., TICK, ADD, VWAP, VIX).
Dynamic Table Placement:
You can position the table anywhere on the chart: top-right, top-left, bottom-right, bottom-left, middle-right, or middle-left.
How to Use:
Add the Indicator to Your Chart:
Apply the indicator to your chart from the Pine Script editor in TradingView.
Customize the Inputs:
Adjust the thresholds for TICK, ADD, VWAP, and VIX according to your trading style.
Enable or disable the metrics you want to see in the table by toggling the display options for each metric (e.g., Show TICK, Show BTC, Show SPY).
Set the table placement to your preferred position on the chart.
Interpret the Table:
Look for color-coded cells to quickly identify significant changes or breaches of thresholds.
Positive values are typically shown in green, negative values in red, and neutral/insignificant changes in gray.
Use metrics like TICK and ADD to gauge market breadth and momentum.
Refer to VWAP deviation to assess intraday buying or selling pressure.
Monitor the VIX and US10Y changes to stay aware of macroeconomic and volatility shifts.
Incorporate Into Your Strategy:
Use the indicator alongside technical analysis to confirm setups or identify areas of caution.
Keep an eye on correlated metrics (e.g., VIX and SPY) for broader market context.
Use BTCUSD or DXY as additional indicators of risk-on/risk-off sentiment.
Ideal Users:
Day Traders: Quickly gauge intraday market conditions and momentum.
Swing Traders: Identify broader sentiment shifts using metrics like ADD, DXY, and US10Y.
Macro Investors: Stay updated on key macroeconomic indicators like the 10-Year Treasury yield (US10Y) and the US Dollar Index (DXY).
This indicator serves as a comprehensive tool for understanding market conditions at a glance, enabling traders to act decisively based on the latest data.
Implied and Historical VolatilityAbstract
This TradingView indicator visualizes implied volatility (IV) derived from the VIX index and historical volatility (HV) computed from past price data of the S&P 500 (or any selected asset). It enables users to compare market participants' forward-looking volatility expectations (via VIX) with realized past volatility (via historical returns). Such comparisons are pivotal in identifying risk sentiment, volatility regimes, and potential mispricing in derivatives.
Functionality
Implied Volatility (IV):
The implied volatility is extracted from the VIX index, often referred to as the "fear gauge." The VIX represents the market's expectation of 30-day forward volatility, derived from options pricing on the S&P 500. Higher values of VIX indicate increased uncertainty and risk aversion (Whaley, 2000).
Historical Volatility (HV):
The historical volatility is calculated using the standard deviation of logarithmic returns over a user-defined period (default: 20 trading days). The result is annualized using a scaling factor (default: 252 trading days). Historical volatility represents the asset's past price fluctuation intensity, often used as a benchmark for realized risk (Hull, 2018).
Dynamic Background Visualization:
A dynamic background is used to highlight the relationship between IV and HV:
Yellow background: Implied volatility exceeds historical volatility, signaling elevated market expectations relative to past realized risk.
Blue background: Historical volatility exceeds implied volatility, suggesting the market might be underestimating future uncertainty.
Use Cases
Options Pricing and Trading:
The disparity between IV and HV provides insights into whether options are over- or underpriced. For example, when IV is significantly higher than HV, options traders might consider selling volatility-based derivatives to capitalize on elevated premiums (Natenberg, 1994).
Market Sentiment Analysis:
Implied volatility is often used as a proxy for market sentiment. Comparing IV to HV can help identify whether the market is overly optimistic or pessimistic about future risks.
Risk Management:
Institutional and retail investors alike use volatility measures to adjust portfolio risk exposure. Periods of high implied or historical volatility might necessitate rebalancing strategies to mitigate potential drawdowns (Campbell et al., 2001).
Volatility Trading Strategies:
Traders employing volatility arbitrage can benefit from understanding the IV/HV relationship. Strategies such as "long gamma" positions (buying options when IV < HV) or "short gamma" (selling options when IV > HV) are directly informed by these metrics.
Scientific Basis
The indicator leverages established financial principles:
Implied Volatility: Derived from the Black-Scholes-Merton model, implied volatility reflects the market's aggregate expectation of future price fluctuations (Black & Scholes, 1973).
Historical Volatility: Computed as the realized standard deviation of asset returns, historical volatility measures the intensity of past price movements, forming the basis for risk quantification (Jorion, 2007).
Behavioral Implications: IV often deviates from HV due to behavioral biases such as risk aversion and herding, creating opportunities for arbitrage (Baker & Wurgler, 2007).
Practical Considerations
Input Flexibility: Users can modify the length of the HV calculation and the annualization factor to suit specific markets or instruments.
Market Selection: The default ticker for implied volatility is the VIX (CBOE:VIX), but other volatility indices can be substituted for assets outside the S&P 500.
Data Frequency: This indicator is most effective on daily charts, as VIX data typically updates at a daily frequency.
Limitations
Implied volatility reflects the market's consensus but does not guarantee future accuracy, as it is subject to rapid adjustments based on news or events.
Historical volatility assumes a stationary distribution of returns, which might not hold during structural breaks or crises (Engle, 1982).
References
Black, F., & Scholes, M. (1973). "The Pricing of Options and Corporate Liabilities." Journal of Political Economy, 81(3), 637-654.
Whaley, R. E. (2000). "The Investor Fear Gauge." The Journal of Portfolio Management, 26(3), 12-17.
Hull, J. C. (2018). Options, Futures, and Other Derivatives. Pearson Education.
Natenberg, S. (1994). Option Volatility and Pricing: Advanced Trading Strategies and Techniques. McGraw-Hill.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (2001). The Econometrics of Financial Markets. Princeton University Press.
Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk. McGraw-Hill.
Baker, M., & Wurgler, J. (2007). "Investor Sentiment in the Stock Market." Journal of Economic Perspectives, 21(2), 129-151.
Candle VolumeThis indicator gives gives candle volume represented in X.Y format for simplicity.
100% = 1.0
20% = 0.2
Anything 10X is represented by an arrow up or down based on candle price delta open to close.
By default, a 500 candle lookback of volume is used excluding exteem outliers of 50.
You can adjust these in settings.
Dynamic Volatility Differential Model (DVDM)The Dynamic Volatility Differential Model (DVDM) is a quantitative trading strategy designed to exploit the spread between implied volatility (IV) and historical (realized) volatility (HV). This strategy identifies trading opportunities by dynamically adjusting thresholds based on the standard deviation of the volatility spread. The DVDM is versatile and applicable across various markets, including equity indices, commodities, and derivatives such as the FDAX (DAX Futures).
Key Components of the DVDM:
1. Implied Volatility (IV):
The IV is derived from options markets and reflects the market’s expectation of future price volatility. For instance, the strategy uses volatility indices such as the VIX (S&P 500), VXN (Nasdaq 100), or RVX (Russell 2000), depending on the target market. These indices serve as proxies for market sentiment and risk perception (Whaley, 2000).
2. Historical Volatility (HV):
The HV is computed from the log returns of the underlying asset’s price. It represents the actual volatility observed in the market over a defined lookback period, adjusted to annualized levels using a multiplier of \sqrt{252} for daily data (Hull, 2012).
3. Volatility Spread:
The difference between IV and HV forms the volatility spread, which is a measure of divergence between market expectations and actual market behavior.
4. Dynamic Thresholds:
Unlike static thresholds, the DVDM employs dynamic thresholds derived from the standard deviation of the volatility spread. The thresholds are scaled by a user-defined multiplier, ensuring adaptability to market conditions and volatility regimes (Christoffersen & Jacobs, 2004).
Trading Logic:
1. Long Entry:
A long position is initiated when the volatility spread exceeds the upper dynamic threshold, signaling that implied volatility is significantly higher than realized volatility. This condition suggests potential mean reversion, as markets may correct inflated risk premiums.
2. Short Entry:
A short position is initiated when the volatility spread falls below the lower dynamic threshold, indicating that implied volatility is significantly undervalued relative to realized volatility. This signals the possibility of increased market uncertainty.
3. Exit Conditions:
Positions are closed when the volatility spread crosses the zero line, signifying a normalization of the divergence.
Advantages of the DVDM:
1. Adaptability:
Dynamic thresholds allow the strategy to adjust to changing market conditions, making it suitable for both low-volatility and high-volatility environments.
2. Quantitative Precision:
The use of standard deviation-based thresholds enhances statistical reliability and reduces subjectivity in decision-making.
3. Market Versatility:
The strategy’s reliance on volatility metrics makes it universally applicable across asset classes and markets, ensuring robust performance.
Scientific Relevance:
The strategy builds on empirical research into the predictive power of implied volatility over realized volatility (Poon & Granger, 2003). By leveraging the divergence between these measures, the DVDM aligns with findings that IV often overestimates future volatility, creating opportunities for mean-reversion trades. Furthermore, the inclusion of dynamic thresholds aligns with risk management best practices by adapting to volatility clustering, a well-documented phenomenon in financial markets (Engle, 1982).
References:
1. Christoffersen, P., & Jacobs, K. (2004). The importance of the volatility risk premium for volatility forecasting. Journal of Financial and Quantitative Analysis, 39(2), 375-397.
2. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
3. Hull, J. C. (2012). Options, Futures, and Other Derivatives. Pearson Education.
4. Poon, S. H., & Granger, C. W. J. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41(2), 478-539.
5. Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
This strategy leverages quantitative techniques and statistical rigor to provide a systematic approach to volatility trading, making it a valuable tool for professional traders and quantitative analysts.
Volatility IndicatorThe volatility indicator presented here is based on multiple volatility indices that reflect the market’s expectation of future price fluctuations across different asset classes, including equities, commodities, and currencies. These indices serve as valuable tools for traders and analysts seeking to anticipate potential market movements, as volatility is a key factor influencing asset prices and market dynamics (Bollerslev, 1986).
Volatility, defined as the magnitude of price changes, is often regarded as a measure of market uncertainty or risk. Financial markets exhibit periods of heightened volatility that may precede significant price movements, whether upward or downward (Christoffersen, 1998). The indicator presented in this script tracks several key volatility indices, including the VIX (S&P 500), GVZ (Gold), OVX (Crude Oil), and others, to help identify periods of increased uncertainty that could signal potential market turning points.
Volatility Indices and Their Relevance
Volatility indices like the VIX are considered “fear gauges” as they reflect the market’s expectation of future volatility derived from the pricing of options. A rising VIX typically signals increasing investor uncertainty and fear, which often precedes market corrections or significant price movements. In contrast, a falling VIX may suggest complacency or confidence in continued market stability (Whaley, 2000).
The other volatility indices incorporated in the indicator script, such as the GVZ (Gold Volatility Index) and OVX (Oil Volatility Index), capture the market’s perception of volatility in specific asset classes. For instance, GVZ reflects market expectations for volatility in the gold market, which can be influenced by factors such as geopolitical instability, inflation expectations, and changes in investor sentiment toward safe-haven assets. Similarly, OVX tracks the implied volatility of crude oil options, which is a crucial factor for predicting price movements in energy markets, often driven by geopolitical events, OPEC decisions, and supply-demand imbalances (Pindyck, 2004).
Using the Indicator to Identify Market Movements
The volatility indicator alerts traders when specific volatility indices exceed a defined threshold, which may signal a change in market sentiment or an upcoming price movement. These thresholds, set by the user, are typically based on historical levels of volatility that have preceded significant market changes. When a volatility index exceeds this threshold, it suggests that market participants expect greater uncertainty, which often correlates with increased price volatility and the possibility of a trend reversal.
For example, if the VIX exceeds a pre-determined level (e.g., 30), it could indicate that investors are anticipating heightened volatility in the equity markets, potentially signaling a downturn or correction in the broader market. On the other hand, if the OVX rises significantly, it could point to an upcoming sharp movement in crude oil prices, driven by changing market expectations about supply, demand, or geopolitical risks (Geman, 2005).
Practical Application
To effectively use this volatility indicator in market analysis, traders should monitor the alert signals generated when any of the volatility indices surpass their thresholds. This can be used to identify periods of market uncertainty or potential market turning points across different sectors, including equities, commodities, and currencies. The indicator can help traders prepare for increased price movements, adjust their risk management strategies, or even take advantage of anticipated price swings through options trading or volatility-based strategies (Black & Scholes, 1973).
Traders may also use this indicator in conjunction with other technical analysis tools to validate the potential for significant market movements. For example, if the VIX exceeds its threshold and the market is simultaneously approaching a critical technical support or resistance level, the trader might consider entering a position that capitalizes on the anticipated price breakout or reversal.
Conclusion
This volatility indicator is a robust tool for identifying market conditions that are conducive to significant price movements. By tracking the behavior of key volatility indices, traders can gain insights into the market’s expectations of future price fluctuations, enabling them to make more informed decisions regarding market entries and exits. Understanding and monitoring volatility can be particularly valuable during times of heightened uncertainty, as changes in volatility often precede substantial shifts in market direction (French et al., 1987).
References
• Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Christoffersen, P. F. (1998). Evaluating Interval Forecasts. International Economic Review, 39(4), 841-862.
• Whaley, R. E. (2000). Derivatives on Market Volatility. Journal of Derivatives, 7(4), 71-82.
• Pindyck, R. S. (2004). Volatility and the Pricing of Commodity Derivatives. Journal of Futures Markets, 24(11), 973-987.
• Geman, H. (2005). Commodities and Commodity Derivatives: Modeling and Pricing for Agriculturals, Metals and Energy. John Wiley & Sons.
• Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
• French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected Stock Returns and Volatility. Journal of Financial Economics, 19(1), 3-29.
Economic RegimeThis indicator, "Economic Regime" , provides a comprehensive analysis of market conditions by combining multiple asset classes and financial metrics. It uses normalized scores and trend analysis to classify the current economic regime into one of four categories: Goldilocks, Reflation, Inflation, or Deflation. The classification is based on inputs like S&P 500 performance, bond yields, commodity prices, volatility indices, and sector ETFs. Additionally, it plots key financial spreads, including the yield spread (10Y-2Y) and credit spread (HYG-LQD), to offer deeper insights into liquidity and market sentiment. The background color dynamically reflects the identified economic regime, facilitating quick visual interpretation.
Heikin Ashi Candles - [Better Overlay]Heikin Ashi Candles - Better Overlay
Heikin Ashi candles are a unique charting technique designed to smooth price data, making it easier to identify trends and potential reversals. The "Heikin Ashi Candles - Better Overlay" indicator takes this concept further by introducing enhancements like a moving average based on the Heikin Ashi values and an overlay of actual price dynamics. This blog explores the functionality and features of this indicator.
Key Features
1. Heikin Ashi Candle Plotting
The indicator calculates Heikin Ashi values (open, high, low, and close) to plot candles directly on the chart. These candles provide a clearer view of market trends by reducing noise commonly seen in standard candlesticks.
- Heikin Ashi Close: The average of open, high, low, and close prices.
- Heikin Ashi Open: A smoothed value derived from the previous Heikin Ashi open and close values.
- Heikin Ashi High/Low: The highest and lowest prices between the Heikin Ashi open, close, and the actual high/low of the period.
The candle colors are intuitive:
- Green: Indicates bullish movement.
- Red: Indicates bearish movement.
The indicator uses semi-transparent candle bodies to ensure better visibility of the actual price chart underneath.
2. Heikin Ashi Moving Average
The indicator includes an optional moving average calculated from the Heikin Ashi values. This moving average helps traders identify the overall trend direction and its strength.
- The length of the moving average is adjustable via input settings.
- The color of the moving average line reflects its trend:
- Green: Uptrend.
- Red: Downtrend.
3. Dynamic Actual Price Line
To maintain a connection with real-time price data, the indicator overlays a dashed line representing the actual closing price of the asset. This feature provides valuable context when analyzing Heikin Ashi data, ensuring traders do not lose sight of the actual price levels.
Customization Options
The indicator offers several customization settings for better usability:
- Heikin Ashi Moving Average:
- Toggle to show or hide the moving average.
- Adjustable length for the moving average, ranging from 1 to 500 periods.
- Candle Styling:
- The colors and transparency levels of the candles are predefined to maintain chart clarity.
- Users can visually distinguish Heikin Ashi data from the actual price chart.
Practical Use Cases
1. Trend Identification
Heikin Ashi candles smooth out noise, making it easier to identify trends. Bullish and bearish candle coloring provides a quick visual cue for market sentiment.
2. Trend Strength and Reversals
The Heikin Ashi moving average serves as a reliable indicator of trend strength. A change in the color of the moving average can indicate a potential trend reversal.
3. Real-Time Price Reference
The dynamic price line ensures traders have a clear reference to the actual closing price, which is crucial for making informed decisions in real-time markets.
Conclusion
The "Heikin Ashi Candles - Better Overlay" indicator is a versatile tool for traders looking to combine the smoothing benefits of Heikin Ashi candles with the precision of real-time price data. Its additional features, like the Heikin Ashi moving average and dynamic price line, make it a comprehensive solution for both trend-following and real-time trading strategies.
This indicator is a great addition to any trader's toolkit, offering clarity and actionable insights without overcomplicating the chart. Give it a try to explore its potential in your trading journey.
Average Trading Volume per Minute & Suitable Position SizeDescription:
This indicator calculates an average trading volume per minute for the specified lookback period (default 377 bars). It then estimates a suitable position size in USD (or contracts on specific exchanges) by multiplying the average volume by a user-defined percentage (default 8%). The script discards extreme data points (top and bottom 20%) before finding the median, so it provides a more robust measure of typical volume.
How It Works:
1. Each bar’s volume is converted to a USD-based figure, either by taking volume directly (if the exchange quotes in USD) or multiplying volume by the midpoint price.
2. Values are stored in an array, which is then sorted to remove the most extreme 40% (20% from each tail). The remaining 60% is used to calculate a median.
3. You enter a position size percentage (e.g. 8%), and the script multiplies the median volume-per-minute by this percentage to get your recommended position size.
4. For certain exchanges like BitMEX/Deribit, the script adapts how it treats volume (in quotes vs. base), so it can display the final position size properly (USD or contracts).
5. The script displays the result in a small table on the chart, showing the recommended position size in USD (or, for some perpetual contracts, in contract units). If no valid data is available, it indicates “Data Invalid.”
Usage Tips:
• The default Position Size Percentage is 8%. You can adjust it higher for more aggressive trading or lower for smaller exposure.
• The default lookback (Average Calculation Period) is 377 bars. Experiment with different values (e.g. 200 or 500) to capture more or fewer historical bars.
• On certain exchanges and symbols (e.g. BitMEX or Deribit’s “.P” pairs), the script automatically switches how it calculates volume (USD vs. coin-based).
• If you see “Data Invalid,” it likely means the current symbol or timeframe lacks sufficient volume info, or you’re running it on a symbol like BTC.D.
Why This Helps:
• Many traders size positions by guesswork or a fixed fraction of their account. This script instead ties position size to actual average trading volume, ensuring your position is neither too large (risk of poor fills) nor too small (wasting leverage potential).
• Removing top/bottom outliers and using the median aims to give a stable volume measure—less influenced by sudden spikes or extremely quiet bars.
Feel free to tweak the inputs and experiment with different timeframes or pairs. By aligning your position size with typical market liquidity, you can potentially improve overall trade execution and manage risk more effectively.
Relative StrengthThis strategy employs a custom "strength" function to assess the relative strength of a user-defined source (e.g., closing price, moving average) compared to its historical performance over various timeframes (8, 34, 20, 50, and 200 periods). The strength is calculated as a percentage change from an Exponential Moving Average (EMA) for shorter timeframes and a Simple Moving Average (SMA) for longer timeframes. Weights are then assigned to each timeframe based on a logarithmic scale, and a weighted average strength is computed.
Key Features:
Strength Calculation:
Calculates the relative strength of the source using EMAs and SMAs over various timeframes.
Assigns weights to each timeframe based on a logarithmic scale, emphasizing shorter timeframes.
Calculates a weighted average strength for a comprehensive view.
Visualizations:
Plots the calculated strength as a line, colored green for positive strength and red for negative strength.
Fills the background area below the line with green for positive strength and red for negative strength, enhancing visualization.
Comparative Analysis:
Optionally displays the strength of Bitcoin (BTC), Ethereum (ETH), S&P 500, Nasdaq, and Dow Jones Industrial Average (DJI) for comparison with the main source strength.
Backtesting:
Allows users to specify a start and end time for backtesting the strategy's performance.
Trading Signals:
Generates buy signals when the strength turns positive from negative and vice versa for sell signals.
Entry and exit are conditional on the backtesting time range.
Basic buy and sell signal plots are commented out (can be uncommented for visual representation).
Risk Management:
Closes all open positions and cancels pending orders outside the backtesting time range.
Disclaimer:
Backtesting results do not guarantee future performance. This strategy is for educational purposes only and should be thoroughly tested and refined before risking capital.
Additional Notes:
- The strategy uses a custom "strength" function that can be further customized to explore different timeframes and weighting schemes.
- Consider incorporating additional technical indicators or filters to refine the entry and exit signals.
- Backtesting with different parameters and market conditions is crucial for evaluating the strategy's robustness.
SnowglobeA fun Christmas publication where snowflakes fall to the bottom, as in a Snowglobe.
☃️ Shake Snowglobe
- Set the settings as desired.
Position the chart so the current real-time bar at the right is still visible; otherwise, the snowflakes will not move.
- Simple move the chart a bit, zoom, or adjust the settings if you want to start over.
'White Theme' users will experience black snow, while 'Dark Themers' will get white snow! 😄
🎄 Pine Script™
- If the 'Amount' is 500 or lower, only label.new() is used, if higher, box.new() with text comes also in play.
- The size of the text is set with numeric values, a new feature of Pine Script™ version 6!
☃️ Settings
Amount: Maximum amount of snowflakes
Moving Flakes: Maximum amount of moving snowflakes per tick move
Max Speed: Maximum speed of tumbling snowflakes
Drift: Maximum bar distance of snowflakes' drift
Happy Holidays! 🎅🏻🧑🏻🎄
Intrabar DistributionThe Intrabar Distribution publication is an extension of the Intrabar BoxPlot publication. Besides a boxplot, it showcases price and volume distribution using intrabar Lower Timeframe (LTF) values (close) which can be displayed on the chart or in a separate pane.
🔶 USAGE
Intrabar Distribution has several features, users can display:
Recent candle for comparison against the other features
Boxplot of recent candle
Price distribution (optionally displayed as a curve)
Volume distribution
🔹 Recent candle / Boxplot
The middle 50% intrabar close values (Interquartile range, or IQR) are shown as a box, where the upper limit is percentile 75 (p75), and the lower limit is percentile 25 (p25). The dashed lines show the addition/subtraction of 1.5*IQR. All values out of range are considered outliers. They are displayed as white dots within the IQR*1.5 range or white X's when beyond the IQR*3 range (extreme outliers).
By showing the middle 50% intrabar values through a box, we can more easily see where the intrabar activity is mainly situated.
Note in the example above an upward-directed candle with a negative volume delta, displayed as a red box and dot (see further).
As seen in the following example, compared against the recent candle (grey candle at the left), most of the intrabar activity lies just beneath the opening price.
Note that results will be more accurate when more data is available, which can be done by making the difference between the current timeframe and the intrabar timeframe large enough.
🔹 Price / Volume distribution
The price and volume distribution can be helpful for highlighting areas of interest.
Here, we can see two areas where intrabar closing prices are mainly positioned.
The following example shows three successive bars. The recent bar is displayed on the left side, together with the volume distribution. The boxplot and price distribution are displayed on the right.
You can see the difference between volume and price distribution.
At the first bar, most price activity is at the top, while most of the volume was generated at the bottom; in other words, the price got briefly in the bottom region, with high volume before it returned.
At the second bar, price and volume are relatively equally distributed, which fits for indecisiveness.
The third bar shows more volume at a higher region; most intrabar closing prices are above the closing price.
Following example shows the same with 'Curve shaped' enabled (Settings: 'Price Distribution')
When 'Curve shaped' is enabled, lines/labels are shown with the standard deviation distance.
A blue 'guide line' can be enabled for easier interpretation.
🔹 Volume Delta
When there is a discrepancy between the delta volume and direction of the candle, this will be displayed as follows:
Red candle: when the sum of the volume of green intrabars is higher than the sum of the volume of red intrabars, the 'mean dot' will be coloured green.
Green candle: when the sum of the volume of red intrabars is higher than the sum of the volume of green intrabars, the 'mean dot' will be coloured red.
🔶 DETAILS
The intrabar values are sorted and split in parts/sections. The number of values in each section is displayed as a white line
The same principle applies to volume distribution, where the sum of volume per section is displayed as an orange area.
The boxplot displays several price values
Last close price
Highest / lowest intrabar close price
Median
p25 / p75
🔹 LTF settings
When 'Auto' is enabled (Settings, LTF), the LTF will be the nearest possible x times smaller TF than the current TF. When 'Premium' is disabled, the minimum TF will always be 1 minute to ensure TradingView plans lower than Premium don't get an error.
Examples with current Daily TF (when Premium is enabled):
500 : 3 minute LTF
1500 (default): 1 minute LTF
5000: 30 seconds LTF (1 minute if Premium is disabled)
🔶 SETTINGS
Location: Chart / Pane (when pane is opted, move the indicator to a separate pane as well)
Parts: divides the intrabar close values into parts/sections
Offset: offsets every drawing at once
Width: width of drawings, only applicable on "location: chart"
Label size: size of price labels
🔹 LTF
LTF: LTF setting
Auto + multiple: Adjusts the initial set LTF
Premium: Enable when your TradingView plan is Premium or higher
🔹 Current Bar
Display toggle + color setting
Offset: offsets only the 'Current Bar' drawing
🔹 Intrabar Boxplot
Display toggle + Colors, dependable on different circumstances.
Up: Price goes up, with more bullish than bearish intrabar volume.
Up-: Price goes up, with more bearish than bullish intrabar volume.
Down: Price goes down, with more bearish than bullish intrabar volume.
Down+: Price goes down, with more bullish than bearish intrabar volume.
Offset: offsets only the 'Boxplot' drawing
🔹 Price distribution
Display toggle + Color.
Curve Shaped
Guide Lines: Display 2 blue lines
Display Price: Show price of 'x' standard deviation
Offset: offsets only the 'Price distribution' drawing
Label size: size of price labels (standard deviation)
🔹 Volume distribution
Display toggle + Color.
Offset: offsets only the 'Volume distribution' drawing
🔹 Table
Show TF: Show intrabar Timeframe.
Textcolor
Size Table: Text Size
Intrabar BoxPlotThe Intrabar BoxPlot publication highlights an uncommon technique by displaying statistical intrabar Lower Timeframe (LTF) values on the chart.
🔶 USAGE
🔹 Middle 50% Boxes
By showing the middle 50% intrabar values through a box, we can more easily see where the intrabar activity is mainly situated.
The middle 50% intrabar values are referred to from here on as Interquartile range (IQR).
In this example, the successive IQRs form a channel where the price eventually breaks out.
Disproportionately distributed values can give insights which can be used to find potential support/resistance areas.
IQR gaps can give valuable information as well. Potentially, the price can return to these gaps.
Seeing the IQR areas against regular candles gives an alternative image of the underlying price movements.
🔹 Highest volume Price level
The script displays the price level with the highest volume situated, dependable on the user's source setting. Setting the source at 'close' will only display intrabar close values; the same goes for high, low, ...
As seen in the above example, the volume levels can aid in finding support/resistance.
🔹 Median
The location of the median off all intrabar values is displayed as a coloured dot: green when the close price is higher than the opening price and red if otherwise. The median can give valuable insights into price movements.
🔹 Outliers
Medium (white dots) and extreme (white X) outliers, in combination with the IQR box, can help identify potential areas of interest.
🔹 Volume Delta
When there is a discrepancy between the delta volume and direction of the candle, this will be displayed as follows:
Green candle: when the sum of the volume of red intrabars is higher than the sum of the volume of green intrabars, the candle will be coloured orange.
Red candle: when the sum of the volume of green intrabars is higher than the sum of the volume of red intrabars, the candle will be coloured blue.
🔹 Highlight Boxplot only
Probably the easiest way to display boxplot only is by changing the Bar's style to Bars .
🔶 DETAILS
All intrabar values (Lower TimeFrame - LTF) are sorted and evaluated. Values can be close , high , low , ... by selecting this in Settings ( source ).
The middle 50% of all values are displayed as a box; this contains the values between percentile 25 (p25) and percentile 75 (p75). The value of percentile rank 75 means 75% of all values are lower. The value of percentile rank 25 means 25% of all values are lower, or 75% is higher.
The difference between p75 and p25 is also known as Interquartile range (IQR)
IQR is used to check for outliers.
Wiki: Boxplot , Interquartile range
Extreme high: maximum value, higher than p75 + IQR*3
Max outlier high: maximum value, higher than p75 + IQR*1.5 but lower than p75 + IQR*3
Max: maximum value, lower than p75 + IQR*1.5
Min: minimum value, higher than p25 - IQR*1.5
Min outlier low: minimum value, lower than p25 - IQR*1.5 but higher than p25 - IQR*3
Extreme low: minimum value, lower than p25 - IQR*3
Max and min must not be interpreted with the current candle high/low.
🔹 Example: Length of chart-puppets
The following example can make it easier to digest. Forty "chart-puppets" are sorted by their length.
The p25 value is 97
The p50 value is 120
The p75 value is 149
75% of all "chart-puppets" are smaller than p75, and 25% is larger than p75.
50% of all "chart-puppets" are smaller than p50, and 50% is larger than p50 (= median).
25% of all "chart-puppets" are smaller than p25, and 75% is larger than p25.
IQR = 149 - 97 = 52
Extreme outlier limit max: p75 + IQR*3 = 149 + 52*3 = 305
Mild outlier limit max: p75 + IQR*1.5 = 149 + 52*1.5 = 227
Mild outlier limit min: p25 - IQR*1.5 = 97 - 52*1.5 = 19
Extreme outlier limit min: p25 - IQR*3 = 97 - 52*3 = -59
In this example there are no outliers to be found, all values are located between p25 - IQR*1.5 (19) and p75 + IQR*1.5. (227)
🔹 Source settings
Note that results are dependable on the chosen source (settings). When, for example, close is chosen as the source, only intrabar close prices are included. This means a low or high can stretch further then the min or max.
Here we can see different results with different source settings
🔹 LTF settings
When 'Auto' is enabled (Settings, LTF), the LTF will be the nearest possible x times smaller TF than the current TF. When 'Premium' is disabled, the minimum TF will always be 1 minute to ensure TradingView plans lower than Premium don't get an error.
Examples with current Daily TF (when Premium is enabled):
500 : 3 minute LTF
1500 (default): 1 minute LTF
5000: 30 seconds LTF (1 minute if Premium is disabled)
🔶 SETTINGS
Source: Set source at close, high, low,...
🔹 LTF
LTF: LTF setting
Auto + multiple: Adjusts the initial set LTF
Premium: Enable when your TradingView plan is Premium or higher
🔹 Intrabar Delta : Colors, dependable on different circumstances.
Up: Price goes up, with more bullish than bearish intrabar volume.
Up-: Price goes up, with more bearish than bullish intrabar volume.
Down: Price goes down, with more bearish than bullish intrabar volume.
Down+: Price goes down, with more bullish than bearish intrabar volume.
🔹 Table
Show table: Show details at the top right corner
Show TF: Show LTF at the bottom right corner
Text color/table size
See DETAILS for more information
VIX OscillatorOVERVIEW
Plots an oscillating value as a percentage, derived from the VIX and VIX3M . This can help identify broader market trends and pivots on higher time frames (ie. 1D), useful when making swing trades.
DATA & MATH
The VIX is a real-time index of expected S&P 500 volatility over the next 30 days, derived from option prices with near-term expirations. Similarly, the VIX3M measures expected volatility over the next 90 days.
Dividing one by the other yields an oscillating value, normalizing the relative strength of the expected volatility. Most commonly the VIX is divided by the VIX3M. However, because the VIX is inversely correlated to market sentiment (typically), this indicator divides the VIX3M by the VIX to visually correlate the plot direction with the anticipated market direction. Further, it subtracts 1.1 from the quotient to visually center the plot, and multiplies that difference by 100 to amplify the value as a percentage:
( VIX3M / VIX - 1.1 ) * 100
This variation makes identifying sentiment extremes easier within a buy-low-sell-high paradigm, where values below zero are bearish and values above zero are bullish.
PLOTS
Two plots are used, maximizing data fidelity and convenience. Candles are used to accurately reflect the quantized math and a Linear Regression is used to simplify contextualization. If you're not familiar with what a Linear Regression is, you can think of it like a better moving average. High / Low zones are also plotted to help identify sentiment extremes.
This combination allows you to quickly identify the expected sentiment (bullish / bearish) and its relative value (normal / extreme), which you can then use to anticipate if a trend continuation or pivot is more likely.
INPUTS
Candle colors (rise and fall)
Linear regression colors and length
Zone thresholds and zero line
Correlated Imbalance Detector# Correlated Imbalance Detector
This indicator helps traders identify strong market movements while avoiding fakeouts by detecting correlated imbalances across two trading instruments. By requiring confirmation from correlated markets like major indices (ES, NQ) or related forex pairs, it filters out potential false signals.
## What it Does
The indicator analyzes price action patterns known as 'imbalances' on two correlated instruments simultaneously. An imbalance occurs when there's a significant gap between price levels that hasn't been filled, indicating strong buying or selling pressure. By requiring both instruments to show the same pattern, it helps eliminate false breakouts and fakeouts.
### Key Features:
- Detects bullish and bearish imbalances across two correlated instruments
- Filters out fakeouts through correlation confirmation
- Uses candlestick direction for additional validation
- Simple visual signals with customizable colors
### Signals:
- Green square: Bullish imbalance detected on both instruments
- Red square: Bearish imbalance detected on both instruments
## Avoiding Fakeouts
The indicator's core strength lies in its correlation requirement:
- A signal only appears when both instruments show the same pattern
- Reduces false signals that might appear on a single instrument
- Helps validate genuine market moves through correlation
- Particularly effective in filtering out noise in choppy markets
## Index Correlation and Bias
Major indices often show strong correlation in their movements:
- ES (S&P 500 futures) and NQ (Nasdaq futures) typically move together
- When both show the same imbalance pattern, it significantly reduces the chance of a fakeout
- Use this correlation to confirm your market bias and strengthen your trading decisions
## Settings
- Correlated Symbol: Enter the symbol you want to correlate with
- Bearish Color: Customize the color for bearish signals
- Bullish Color: Customize the color for bullish signals
## Usage Tips
1. Particularly effective with correlated indices (ES/NQ)
2. Use to confirm your existing market bias
3. Best used on higher timeframes (H1 and above)
4. Wait for confirmation from both instruments to avoid fakeouts
5. Consider overall market context when interpreting signals
6. Use the absence of correlation as a warning sign for potential fakeouts
Note: This indicator is designed to help filter out false signals through correlation. It works best as part of your broader market analysis and should align with your trading bias and strategy.
Scatter Plot with Symbol or Data Source InputsDescription of setting items
Use Symbol for X Data?
Type: Checkbox (input.bool)
Explanation: Selects whether the data used for the X axis is obtained from a “symbol” or a “data source”.
If true: data for the X axis will be taken from a symbol (e.g. stock ticker).
If false: X axis data will be taken from the specified data source (e.g., closing price or volume).
Use Symbol for Y Data?
type: checkbox (input.bool)
Explanation: Selects whether the data used for the Y axis is retrieved from a “symbol” or a “data source”.
If true: Y-axis data is obtained from symbols.
If false: Data for the Y axis is obtained from the specified data source.
Select Ticker Symbol for X Data
type: symbol input (input.symbol)
description: selects the symbol to be used for the X axis (default is “AAPL”).
If “Use Symbol for X Data?” is set to true, this symbol will be used as the data for the X axis.
Select Ticker Symbol for Y Data
Type: Symbol input (input.symbol)
description: selects the symbol to be used for the Y axis (default is “GOOG”).
If “Use Symbol for Y Data?” is set to true, this symbol will be used as the data for the Y axis.
X Data Source
type: data source input (input.source)
description: specifies the data source to be used for the X axis.
Default is “close” (closing price).
Other possible values include open, high, low, volume, etc.
Y Data Source
Type: data source input (input.source)
Description: Specifies the data source to be used for the Y axis.
Default is “volume” (volume).
Other possible values include open, high, low, close, etc.
X Offset
type: integer input (input.int)
description: sets the offset value of the X axis.
This shifts the position of the X axis on the grid. The range is from -500 to 500.
Y Offset
Type: Integer input (constant)
description: offset value for y-axis.
Defaults to 0, but can be changed to adjust the Y axis position.
grid_width
type: integer input (input.int)
description: sets the width of the grid.
The default is 200. Increasing the value results in a finer grid.
grid_height
type: integer input (input.int)
description: sets the height of the grid.
Defaults to 200. Increasing the value results in a finer grid.
Frequency of updates
type: integer input (input.int)
description: set frequency of updates.
The higher the frequency of updates, the more bars will be used to calculate minimum and maximum values.
X Tick Interval
type: integer input (input.int)
description: sets the tick interval for the X axis.
The default is 10. To increase the number of ticks, decrease the value.
Y Tick Interval
Box border color
type: select color (input.color)
description: select color for grid box border
Default is blue.
Explanation of usage
To use symbol data: Set Use Symbol for X Data?
When “Use Symbol for X Data?” and “Use Symbol for Y Data?” are set to true, the data of the specified symbol is displayed on each axis. For example, you can use “AAPL” (Apple's stock price data) for the X axis and “GOOG” (Google's stock price data) for the Y axis.
To set the symbol, select the desired ticker in Select Ticker Symbol for X Data and Select Ticker Symbol for Y Data.
To use a data source: select the
You can set Use Symbol for X Data? and Use Symbol for Y Data? to false and use the data source specified in X Data Source or Y Data Source instead (e.g., closing price or volume).
Change Grid Size:.
Set the width and height of the grid with grid_width and grid_height. Larger values allow for more detailed scatter plots.
Set Tick Intervals: Set the X Tick Interval and Y Tick Interval.
Adjust X Tick Interval and Y Tick Interval to change the tick spacing on the X and Y axes.
Data Range Adjustment: Adjust the Frequency of updates to change the frequency of updates.
The Frequency of updates can be changed to control how often the data range is updated. The higher this value, the more historical data is considered and displayed.
Box Color.
Box Border Color allows you to change the color of the box border.
This script is useful for visualizing different symbols and data sources, especially to show the relationship between financial data.
Caution.
Some data may exceed the memory size, but the scale is the same, so you will know most of the locations.
*I made it myself because I could not find anything to draw a scatter plot. You can also compare more than 3 pieces of data by displaying more than one scatter plot. Here is how to do it. Set X or Y as the reference data. Set the data you want to compare to the one that is not the standard. Next, set the same indicator and set the reference to another set of data you wish to compare. Now you can compare the three sets of data. It is effective to change the color of the display box to prevent the user from not knowing which is which. Thus, you should be able to compare more than 3 pieces of data, so give it a try.
Trend Condition [TradersPro]
OVERVIEW
The Trend Condition Indicator measures the strength of the bullish or bearish trend by using a ribbon pattern of exponential moving averages and scoring system. Trend cycles naturally expand and contract as a normal part of the cycle. It is the rhythm of the market. Perpetual expansion and contraction of trend.
As trend cycles develop the indicator shows a compression of the averages. These compression zones are key locations as trends typically expand from there. The expansion of trend can be up or down.
As the trend advances the ribbon effect of the indicator can be seen as each average expands with the price action. Once they have “fanned” the probability of the current trend slowing is high.
This can be used to recognize a powerful trend may be concluding. Traders can tighten stops, exit positions or utilize other prudent strategies.
CONCEPTS
Each line will display green if it is higher than the prior period and red if it is lower than the prior period. If the average is green it is considered bullish and will score one point in the bullish display. Red lines are considered bearish and will score one point in the bearish display.
The indicator can then be used at a quick glance to see the number of averages that are bullish and the number that are bearish.
A trader may use these on any tradable instrument. They can be helpful in stock portfolio management when used with an index like the S&P 500 to determine the strength of the current market trend. This may affect trade decisions like possession size, stop location and other risk factors.
Tomas' Financial Conditions Z Score"The indicator is a composite z-score comprised of the following four components (equally-weighted):
Credit spreads - ICE BofA High Yield Option Adjusted Spread (BAMLH0A0HYM2) and ICE BofA Corporate Index Option Adjusted Spread (BAMLC0A0CM)
Volatility indexes - VIX (S&P 500 implied volatility) and MOVE (US Treasury bond implied volatility)
I've got it set to a 160-day lookback period, which I think is roughly the best setting after some tinkering.
When the z-score is above zero, it throws a red signal - and when the z-score is below zero, it throws a green signal.
This indicator is a follow-on from the "traffic light financial conditions indicator" that I wrote a thread about a couple of months ago.
I moved on from that previous indicator because it is based on the Federal Reserve's NFCI, which is regularly revised, but I didn't take that into account at the time.
So not a great real-time indicator, if the signal can be subsequently revised in the opposite direction weeks later.
This new indicator is based on real-time market data, so there's no revisions, and it also updates daily, as opposed to weekly for the NFCI"
ATR SL Band (No-Repaint, Multi-Timeframe) + Risk per ContractThis indicator draws a non-repainting band for ATR-based Stoploss placement.
If used on Futures, it shows the distance + risk from the previous candle close, as well as from the current price.
The risk value is automatically calculated for the following symbols:
(Micro) ES (S&P 500)
(Micro) NQ (NASDAQ 100)
(Micro) YM (Dow Jones Industrial Average / US30)
The timeframe can be set individually. It is not recommended to use a lower timeframe than the chart timeframe as values differ from the actual timeframe's ATR SL in this case.
Optimal MA FinderIntroduction to the "Optimal MA Finder" Indicator
The "Optimal MA Finder" is a powerful and versatile tool designed to help traders optimize their moving average strategies. This script combines flexibility, precision, and automation to identify the most effective moving average (MA) length for your trading approach. Whether you're aiming to improve your long-only strategy or implement a buy-and-sell methodology, the "Optimal MA Finder" is your go-to solution for enhanced decision-making.
What Does It Do?
The script evaluates a wide range of moving average lengths, from 10 to 500, to determine which one produces the best results based on historical data. By calculating critical metrics such as the total number of trades and the profit factor for each MA length, it identifies the one that maximizes profitability. It supports both simple moving averages (SMA) and exponential moving averages (EMA), allowing you to tailor the analysis to your preferred method.
The logic works by backtesting each MA length against the price data and assessing the performance under two strategies:
Buy & Sell: Includes both long and short trades.
Long Only: Focuses solely on long positions for more conservative strategies.
Once the optimal MA length is identified, the script overlays it on the chart, highlighting periods when the price crosses over or under the optimal MA, helping traders identify potential entry and exit points.
Why Is It Useful?
This indicator stands out for its ability to automate a task that is often labor-intensive and subjective: finding the best MA length. By providing a clear, data-driven answer, it saves traders countless hours of manual testing while significantly enhancing the accuracy of their strategies. For example, instead of guessing whether a 50-period EMA is more effective than a 200-period SMA, the "Optimal MA Finder" will pinpoint the exact length and type of MA that has historically yielded the best results for your chosen strategy.
Key Benefits:
Precision: Identifies the MA length with the highest profit factor for maximum profitability.
Automation: Conducts thorough backtesting without manual effort.
Flexibility: Adapts to your preferred MA type (SMA or EMA) and trading strategy (Buy & Sell or Long Only).
Real-Time Feedback: Provides actionable insights by plotting the optimal MA directly on your chart and highlighting relevant trading periods.
Example of Use: Imagine you're trading a volatile stock and want to optimize your long-only strategy. By applying the "Optimal MA Finder," you discover that a 120-period EMA results in the highest profit factor. The indicator plots this EMA on your chart, showing you when to consider entering or exiting positions based on price movements relative to the EMA.
In short, the "Optimal MA Finder" empowers traders by delivering data-driven insights and improving the effectiveness of trading strategies. Its clear logic, combined with robust automation, makes it an invaluable tool for both novice and experienced traders seeking consistent results.
MES Position Sizing EstimatorDescription and Use:
Here is an indicator which aims to help all Micro-ES futures traders who struggle with risk management! I created this indicator designed as a general guideline to help short term traders (designed for 1 minute candles) determine how many contracts to trade on the MES for their desired profit target.
To use the indicator, simply go to MES on the 1 minute timeframe, apply the indicator, and enter your Holding Period (how long you want to have your position open for), Value Per Tick
(usually 1.25 for MES since one point is $5) and your target PnL for the trade in the inputs tab.
It will then show in a table the recommended position sizing, as well as the estimated price change for your holding period. Additionally, there are two plotted lines also showing the position sizing and estimated price change historically.
How the indicator works
On the technical level, I made calculations for this indicator using Python. I downloaded 82 days of 1 minute OHLC data from TradingView, and then ran regression (log-transformed linear regression specifically) to calculate how the average price change in MES futures scales with the amount of time a position is held for, and then ran these regressions for every hour of the day. I then copied the equations from those regressions into Pinescript, and used the assumption that:
position size = target PnL / (estimated price change for time * tick value)
Therefore, Choosing the number of contracts to trade position sizing for Micro E-mini S&P 500 Futures (MES) based on time of day, holding period, and tick value. This tool leverages historical volatility patterns and log-transformed linear regression models to provide precise recommendations tailored to your trading strategy.
If you want to check out how the regression code worked in python, it is all open source and available on my Github repository for it .
Notes:
The script assumes a log-normal distribution of price movements and is intended as an educational tool to aid in risk management.
It is not a standalone trading system and should be used in conjunction with other trading strategies and risk assessments.
Past performance is not indicative of future results, and traders should exercise caution and adjust their strategies based on personal risk tolerance.
This script is open-source and available for use and modification by the TradingView community. It aims to provide a valuable resource for traders seeking to enhance their risk management practices through data-driven insights.
Romantic Information CoefficientThis script calculates the Mutual Information (MI) between the closing prices of two assets over a defined lookback period. Mutual Information is a measure of the shared information between two time-series datasets. A higher MI indicates a stronger relationship between the two assets.
Key Features:
Ticker Inputs: You can select the tickers for two assets. For example, SPY (S&P 500 ETF) and AAPL (Apple stock) can be compared.
Lookback Period: Choose the number of bars to look back and calculate the Mutual Information. A larger lookback period incorporates more data, but may be less responsive to recent price changes.
Bins for Discretization: Control the level of granularity for discretizing the asset prices. More bins result in a more detailed MI calculation but can also reduce the signal-to-noise ratio.
Color Coded MI: The MI plot dynamically changes color to provide visual feedback on whether the relationship between the two assets is strengthening (red) or weakening (blue).
Only for educational purposes. Not in anyway, investment advice.
