[SGM Return Distribution]Code Description
This Pine Script™ is designed to analyze the distribution of historical returns of a financial asset and project future confidence levels. It uses statistical techniques to estimate the probability of winning and losing as well as displaying confidence bands and distribution statistics.
User Entries
Length (252): The number of days used to calculate statistics.
Offset (20): Offset used to project future values.
Projection Days (10): Number of days projected into the future.
Smoothing Confidence Levels (10): Smoothing confidence bands.
Display Settings
Plot Distribution: Shows the distribution of returns.
Show Probabilities: Shows winning and losing probabilities.
Show Distribution Stats: Shows distribution statistics.
Show Confidence Bands: Shows confidence bands.
Show Confidence Lines: Shows confidence lines.
Calculations and Features
Distribution of Yields:
Calculates logarithmic returns and their statistics (average, volatility, skewness, kurtosis).
Projects the average and volatility over the projected number of days.
Displays the distribution of returns as a histogram.
Confidence Interval:
Uses the inv_norm function to calculate Z scores for different confidence levels.
Calculates the upper and lower bounds of the confidence bands.
Probability Display:
Calculates and displays win and loss probabilities based on the distribution of returns.
Statistics Display:
Shows key statistics such as mean, volatility, skewness and kurtosis.
Trust Bands and Lines:
Shows confidence bands and lines based on calculated confidence levels.
Mathematical Assumptions Used
Logarithmic Returns: Returns are calculated using the logarithm of prices, which is common for financial time series because it makes returns independent of price level.
Normal Distribution for Confidence Bands: Confidence interval calculations are based on the assumption that returns follow a normal distribution.
Average and Volatility Projection: Average returns and volatility are projected over a future period assuming they remain constant.
Skewness and Kurtosis: Although these measures are calculated for understanding the distribution of returns, they are not used in box projections but can provide additional information about the distribution of historical returns.
Use in Trading
Risk Estimation: Confidence bands can help estimate likely future price levels, which is crucial for determining strike levels and risk management.
Risk Management: Use confidence bands to set stop-loss and take-profit levels.
Probability Analysis: Win and loss probabilities can help assess a position's likelihood of success.
Potential Problems
Assumption of Normality for Confidence Bands: Financial returns do not always follow a normal distribution, especially in the presence of extreme events (fat tails).
Stationarity: Assuming that return statistics (average, volatility) remain constant over time can be erroneous in volatile market periods.
Limited Historical Data: Using a limited history (252 days) may not capture all possible behaviors of the asset.
Input Parameters: Results can be sensitive to the input parameters chosen (length, offset, etc.).
Forecasting
ToxicJ3ster - Day Trading SignalsThis Pine Script™ indicator, "ToxicJ3ster - Signals for Day Trading," is designed to assist traders in identifying key trading signals for day trading. It employs a combination of Moving Averages, RSI, Volume, ATR, ADX, Bollinger Bands, and VWAP to generate buy and sell signals. The script also incorporates multiple timeframe analysis to enhance signal accuracy. It is optimized for use on the 5-minute chart.
Purpose:
This script uniquely combines various technical indicators to create a comprehensive and reliable day trading strategy. Each indicator serves a specific purpose, and their integration is designed to provide multiple layers of confirmation for trading signals, reducing false signals and increasing trading accuracy.
1. Moving Averages: These are used to identify the overall trend direction. By calculating short and long period Moving Averages, the script can detect bullish and bearish crossovers, which are key signals for entering and exiting trades.
2. RSI Filtering: The Relative Strength Index (RSI) helps filter signals by ensuring trades are only taken in favorable market conditions. It detects overbought and oversold levels and trends within the RSI to confirm market momentum.
3. Volume and ATR Conditions: Volume and ATR multipliers are used to identify significant market activity. The script checks for volume spikes and volatility to confirm the strength of trends and avoid false signals.
4. ADX Filtering: The ADX is used to confirm the strength of a trend. By filtering out weak trends, the script focuses on strong and reliable signals, enhancing the accuracy of trade entries and exits.
5. Bollinger Bands: Bollinger Bands provide additional context for the trend and help identify potential reversal points. The script uses Bollinger Bands to avoid false signals and ensure trades are taken in trending markets.
6. Higher Timeframe Analysis: This feature ensures that signals align with broader market trends by using higher timeframe Moving Averages for trend confirmation. It adds a layer of robustness to the signals generated on the 5-minute chart.
7. VWAP Integration: VWAP is used for intraday trading signals. By calculating the VWAP and generating buy and sell signals based on its crossover with the price, the script provides additional confirmation for trade entries.
8. MACD Analysis: The MACD line, signal line, and histogram are calculated to generate additional buy/sell signals. The MACD is used to detect changes in the strength, direction, momentum, and duration of a trend.
9. Alert System: Custom alerts are integrated to notify traders of potential trading opportunities based on the signals generated by the script.
How It Works:
- Trend Detection: The script calculates short and long period Moving Averages and identifies bullish and bearish crossovers to determine the trend direction.
- Signal Filtering: RSI, Volume, ATR, and ADX are used to filter and confirm signals, ensuring trades are taken in strong and favorable market conditions.
- Multiple Timeframe Analysis: The script uses higher timeframe Moving Averages to confirm trends, aligning signals with broader market movements.
- Additional Confirmations: VWAP, MACD, and Bollinger Bands provide multiple layers of confirmation for buy and sell signals, enhancing the reliability of the trading strategy.
Usage:
- Customize the input parameters to suit your trading strategy and preferences.
- Monitor the generated signals and alerts to make informed trading decisions.
- This script is made to work best on the 5-minute chart.
Disclaimer:
This indicator is not perfect and can generate false signals. It is up to the trader to determine how they would like to proceed with their trades. Always conduct thorough research and consider seeking advice from a financial professional before making trading decisions. Use this script at your own risk.
9:30 Opening Price MarkerIndicator Name: 9:30 Opening Price Marker
Description:
The "9:30 Opening Price Marker" is a custom indicator for TradingView that highlights the opening price at 9:30 AM in the UTC-4 time zone (Eastern Daylight Time) on the chart. It helps traders and analysts easily identify and track the price level at which the market opens each day.
Features:
Timezone Conversion: The indicator converts the current time to the UTC-4 timezone (Eastern Daylight Time) to accurately determine the 9:30 AM opening price.
Visual Marker: It visually marks the opening price with a dotted line on the chart, making it prominent for quick reference.
Label: Additionally, it includes a label next to the opening price line, indicating "9:30 Opening Price", enhancing clarity and usability.
Overlay: The indicator is designed to overlay on the price chart, ensuring it doesn't clutter other technical analysis tools or indicators.
Usage:
Day-to-Day Analysis: Traders can use this indicator to quickly gauge market sentiment at the daily opening, which can influence intraday trading strategies.
Reference Point: Acts as a reference point for identifying price movements and potential trading opportunities relative to the day's opening price.
Time-Specific Insights: Provides insights into price action immediately following the market open, aiding in decision-making based on early trading activity.
Installation: Copy the provided Pine Script code into TradingView's Pine Editor, save the script as an indicator, and apply it to your chart.
Disclaimer : This indicator is intended for informational purposes only and should not be solely relied upon for trading decisions. Always consider multiple sources of information and perform thorough analysis before executing trades.
ARIMA Indicator with Optional SmoothingOverview
The ARIMA (AutoRegressive Integrated Moving Average) Indicator is a powerful tool used to forecast future price movements by combining differencing, autoregressive, and moving average components. This indicator is designed to help traders identify trends and potential reversal points by analyzing the historical price data.
Key Features
AutoRegressive Component (AR): Utilizes past values to predict future prices.
Moving Average Component (MA): Averages past price differences to smooth out noise.
Differencing: Reduces non-stationarity in the time series data.
Optional Smoothing: Applies EMA to the ARIMA output for a smoother signal.
Customizable Parameters: Allows users to adjust AR and MA orders, differencing periods, and smoothing lengths.
Concepts Underlying the Calculations
Differencing: Subtracts previous prices from current prices to remove trends and seasonality, making the data stationary.
AutoRegressive Component (AR): Predicts future prices based on a linear combination of past values.
Moving Average Component (MA): Uses past forecast errors to refine future predictions.
Exponential Moving Average (EMA): Applies more weight to recent prices, providing a smoother and more responsive signal.
How It Works
The ARIMA Indicator first calculates the differenced series to achieve stationarity. Then, it computes the simple moving average (SMA) of this differenced series. The indicator uses the AR and MA components to adjust the SMA, creating an approximation of the ARIMA model. Finally, an optional smoothing step using EMA can be applied to the ARIMA approximation to produce a smoother signal.
How Traders Can Use It
Traders can use the ARIMA Indicator to:
Identify Trends: Detect emerging trends by observing the direction of the ARIMA line.
Spot Reversals: Look for divergences between the ARIMA line and the price to identify potential reversal points.
Generate Trading Signals: Use crossovers between the ARIMA line and the price to generate buy or sell signals.
Filter Noise: Enable the optional smoothing to filter out market noise and focus on significant price movements.
Example Usage Instructions
Add the ARIMA Indicator to your chart.
Adjust the input parameters to suit your trading strategy:
Set the SMA Length (e.g., 14).
Choose the Differencing Period (e.g., 1).
Define the AR Order (p) and MA Order (q) (e.g., 1).
Configure the Smoothing Length if smoothing is desired (e.g., 5).
Enable or disable smoothing as needed.
Observe the ARIMA line (blue) and compare it to the price chart.
Use the ARIMA line to identify trends and potential reversals.
Implement trading decisions based on the ARIMA line’s behavior relative to the price.
Simple Risk-to-Reward Multiplier A simple R/R indicator that allows you to input your entry price and stop loss (in ticks). Then, your take profit levels are R-multipliers based on your stop loss. You can have up to 5 take profit levels on your chart. There is also a function to indicate if it is a long or short setup. You can also set alerts with this script, allowing you the ability not to have to stare at the charts all day.
[Suitable Hope] Crypto Upside Model 3.0The "Crypto Upside Model 3.0" indicator dynamically calculates the potential price of any cryptocurrency based on various percentages of Ethereum or Bitcoin's market capitalization.
By fetching and analyzing marketcap data from TradingView sources, it allows traders to visualize potential price targets if their chosen cryptocurrency reaches specific market dominance levels. This tool is designed for daily timeframe analysis and can be used to set informed price expectations and strategic investment goals, providing valuable insights for long-term investment planning.
Why using the Crypto Upside Model 3.0?
Strategic Planning: Helps traders and investors set realistic price targets and investment goals by visualizing potential market cap scenarios.
Informed Decision-Making: Provides a data-driven approach to understanding how a cryptocurrency might perform relative to major assets like Bitcoin and Ethereum.
Customizable Analysis: Allows users to choose different comparison assets (ETH or BTC) and visualize various market cap dominance percentages, offering tailored insights.
Daily Timeframe Focus: Ideal for swing traders and long-term investors who operate on a daily analysis timeframe, providing relevant and actionable data.
Bull Markets: Identify potential price targets if your cryptocurrency's market cap increases significantly.
Bear Markets: Assess how much value could be retained relative to major cryptocurrencies.
Strategic Entry/Exit Points: Use the visualized targets to plan entry or exit points in your trading strategy.
Comparative Advantage
Dynamic Adaptation: Unlike fixed indicators, this tool adapts to any active chart, making it versatile for multiple cryptocurrencies.
Market Cap Insights: Provides a unique perspective by linking price targets to market cap dominance, a critical factor in the crypto market.
User Instructions
Setup: Add the " Upside Model 3.0" indicator to your TradingView chart.
Configuration: Use the input settings to select the comparison cryptocurrency (ETH or BTC) and enable the desired market cap percentage plots.
Analysis: The indicator will display potential price targets based on the selected market cap percentages, providing a visual guide for setting price expectations.
Limitations
Marketcap Data Availability: The indicator relies on marketcap data from TradingView, which may not be available for all cryptocurrencies. If the data is unavailable, the indicator will not function for that asset. This tool is more likely to work with older, established cryptocurrencies, as marketcap data for newer cryptocurrencies may not yet be available.
Daily Timeframe Restriction: The indicator is designed to work exclusively on the daily timeframe, limiting its applicability for intraday trading.
Assumptions of Market Dynamics: The calculations assume a direct correlation between market dominance and price, which may not account for other market dynamics and external factors influencing prices.
Data Accuracy: The accuracy of the indicator depends on the reliability of the data provided by TradingView, which may sometimes experience delays or inaccuracies.
Currently available cryptocurrencies: Bitcoin, Ethereum, Solana, Binance Coin, Cardano, Ripple, Polkadot, Avalanche, Chainlink, Litecoin, Dogecoin, Terra, Uniswap, VeChain, Stellar, Internet Computer, Hedera, Filecoin, Monero, Aave, TRON, NEAR Protocol, Compound, Maker,... For all compatible cryptocurrencies, please consult CRYPTOCAP's documentation.
Final notes
Although various sources ask a payment or user data for similar kind of private indicators, this one is entirely free and open source. "Uncanny" isn't it? I hope this indicator will provide you value. Feel free to leave a message if you have any questions or constructive feedback.
Examples of how I use this indicator
When using ETH's historical price as a reference compared to Bitcoin's marketcap, we can notice that price generally has been held between the +-30% and 50% lines of BTC's marketcap. If history is repeating again, we can expect major resistances around the 50% looking ahead into the future. This for me would be a great area to potentially reduce my ETH spot position.
When using SOL's historical price action, we can notice that the 15% line of ETH's marketcap has been a top in the previous cycle. Today SOL (July 2024), is back at this level. Could this be a top again or could price break this 15% level and head perhaps towards 30% which currently sits around $260? Time will tell.
These are 2 simple example of how I interpret the data. I'm keen to hear what other findings with other pairs you can find.
Sector Analysis This indicator offers a straightforward yet effective way to analyze and compare the performance of various sectors within the market. By normalizing and plotting sector-specific data as lines on the chart, it enables users to quickly assess sector rotations, relative strength, and potential shifts in market dynamics. The sector labels further enhance usability by clearly identifying each line’s corresponding sector, facilitating easy interpretation and analysis.
Risk Radar ProThe "Risk Radar Pro" indicator is a sophisticated tool designed to help investors and traders assess the risk and performance of their investments over a specified period. This presentation will explain each component of the indicator, how to interpret the results, and the advantages compared to traditional metrics.
The "Risk Radar Pro" indicator includes several key metrics:
● Beta
● Maximum Drawdown
● Compound Annual Growth Rate (CAGR)
● Annualized Volatility
● Dynamic Sharpe Ratio
● Dynamic Sortino Ratio
Each of these metrics is dynamically calculated using data from the entire selected period, providing a more adaptive and accurate measure of performance and risk.
1. Start Date
● Description: The date from which the calculations begin.
● Interpretation: This allows the user to set a specific period for analysis, ensuring that all metrics reflect the performance from this point onward.
2. Beta
● Description: Beta measures the volatility or systematic risk of the instrument relative to a reference index (e.g., SPY).
● Interpretation: A beta of 1 indicates that the instrument moves with the market. A beta greater than 1 indicates more volatility than the market, while a beta less than 1 indicates less volatility.
● Advantages: Unlike classic beta, which typically uses fixed historical intervals, this dynamic beta adjusts to market changes over the entire selected period, providing a more responsive measure.
3. Maximum Drawdown
● Description: The maximum observed loss from a peak to a trough before a new peak is achieved.
● Interpretation: This shows the largest single drop in value during the specified period. It is a critical measure of downside risk.
● Advantages: By tracking the maximum drawdown dynamically, the indicator can provide timely alerts when significant losses occur, allowing for better risk management.
4. Annualized Performance
● Description: The mean annual growth rate of the investment over the specified period.
● Interpretation: The Annualized Performance represents the smoothed annual rate at which the investment would have grown if it had grown at a steady rate.
● Advantages: This dynamic calculation reflects the actual long-term growth trend of the investment rather than relying on a fixed time frame.
5. Annualized Volatility
● Description: Measures the degree of variation in the instrument's returns over time, expressed as a percentage.
● Interpretation: Higher volatility indicates greater risk, as the investment's returns fluctuate more.
● Advantages: Annualized volatility calculated over the entire selected period provides a more accurate measure of risk, as it includes all market conditions encountered during that time.
6. Dynamic Sharpe Ratio
● Description: Measures the risk-adjusted return of an investment relative to its volatility.
● Choice of Risk-Free Rate Ticker: Users can select a ticker symbol to represent the risk-free rate in Sharpe ratio calculations. The default option is US03M, representing the 3-month US Treasury bill.
● Interpretation: A higher Sharpe ratio indicates better risk-adjusted returns. This ratio accounts for the risk-free rate to provide a comparison with risk-free investments.
● Advantages: By using returns and volatility over the entire period, the dynamic Sharpe ratio adjusts to changes in market conditions, offering a more accurate measure than traditional static calculations.
7. Dynamic Sortino Ratio
● Description: Similar to the Sharpe ratio, but focuses only on downside risk.
Interpretation: A higher Sortino ratio indicates better risk-adjusted returns, focusing solely on negative returns, which are more relevant to risk-averse investors.
● Choice of Risk-Free Rate Ticker: Similarly, users can choose a ticker symbol for the risk-free rate in Sortino ratio calculations. By default, this is also set to US03M.
● Advantages: This ratio's dynamic calculation considering the downside deviation over the entire period provides a more accurate measure of risk-adjusted returns in volatile markets.
Comparison with Basic Metrics
● Static vs. Dynamic Calculations: Traditional metrics often use fixed historical intervals, which may not reflect current market conditions. The dynamic calculations in "Risk Radar Pro" adjust to market changes, providing more relevant and timely information.
● Comprehensive Risk Assessment: By including metrics like maximum drawdown, Sharpe ratio, and Sortino ratio, the indicator provides a holistic view of both upside potential and downside risk.
● User Customization: Users can customize the start date, reference index, risk-free rate, and table position, tailoring the indicator to their specific needs and preferences.
Conclusion
The "Risk Radar Pro" indicator is a powerful tool for investors and traders looking to assess and manage risk more effectively. By providing dynamic, comprehensive metrics, it offers a significant advantage over traditional static calculations, ensuring that users have the most accurate and relevant information to make informed decisions.
The "Risk Radar Pro" indicator provides analytical tools and metrics for informational purposes only. It is not intended as financial advice. Users should conduct their own research and consider their individual risk tolerance and investment objectives before making any investment decisions based on the indicator's outputs. Trading and investing involve risks, including the risk of loss. Past performance is not indicative of future results.
Symbolik SequentialThe Symbolik Sequential indicator aims to identify potential trend exhaustion points and trend reversals in the market. It consists of two main components: Setup and Countdown.
Setup:
A bullish setup occurs when there are 9 consecutive closes lower than the close 4 bars earlier.
A bearish setup occurs when there are 9 consecutive closes higher than the close 4 bars earlier.
When a setup is completed, it's called "perfected" and is shown as a triangle on the chart (green triangle for bullish, red for bearish).
Countdown:
After a setup is perfected, a countdown begins.
For a bullish countdown, it counts up to 13 when the close is lower than the low two bars earlier.
For a bearish countdown, it counts up to 13 when the close is higher than the high two bars earlier.
Countdown numbers are displayed on the chart (green at the bottom for bullish, red at the top for bearish).
Why it Signals:
The indicator signals potential trend exhaustion and reversal points.
A perfected setup (triangle) suggests that the current trend might be losing momentum.
A completed countdown (reaching 13) indicates a higher probability of a trend reversal.
Trend is weakening when both bearish and bullish countdowns are occurring at the same time.
How to Best Use It:
Setup Signals:
When you see a green triangle (bullish setup), it might indicate a potential bottom. Consider looking for buying opportunities.
When you see a red triangle (bearish setup), it might indicate a potential top. Consider looking for selling opportunities.
However, don't rely solely on these signals; use them in conjunction with other technical analysis tools.
Countdown:
As the countdown progresses, be increasingly cautious about the current trend.
When the countdown reaches 13, it signals a high probability of a trend reversal. Consider closing positions or preparing for a potential reversal.
Conflicting Signals:
The indicator doesn't show a bullish setup signal if there's an active bearish countdown, and vice versa. This helps avoid conflicting signals.
CNN Fear and Greed IndexThe “CNN Fear and Greed Index” indicator in this context is designed to gauge market sentiment based on a combination of several fundamental indicators. Here’s a breakdown of how this indicator works and what it represents:
Components of the Indicator:
1. Stock Price Momentum:
• Calculates the momentum of the S&P 500 index relative to its 125-day moving average. Momentum is essentially the rate of acceleration or deceleration of price movements over time.
2. Stock Price Strength:
• Measures the breadth of the market by comparing the number of stocks hitting 52-week highs versus lows. This provides insights into the overall strength or weakness of the market trend.
3. Stock Price Breadth:
• Evaluates the volume of shares trading on the rise versus the falling volume. Higher volume on rising days suggests positive market breadth, while higher volume on declining days indicates negative breadth.
4. Put and Call Options Ratio (Put/Call Ratio):
• This ratio indicates the sentiment of investors in the options market. A higher put/call ratio typically signals increased bearish sentiment (more puts relative to calls) and vice versa.
5. Market Volatility (VIX):
• Also known as the “fear gauge,” the VIX measures the expected volatility in the market over the next 30 days. Higher VIX values indicate higher expected volatility and often correlate with increased fear or uncertainty in the market.
6. Safe Haven Demand:
• Compares the returns of stocks (represented by S&P 500) versus safer investments like 10-year Treasury bonds. Higher returns on bonds relative to stocks suggest a flight to safety or risk aversion.
7. Junk Bond Demand:
• Measures the spread between yields on high-yield (junk) bonds and investment-grade bonds. Widening spreads may indicate increasing risk aversion as investors demand higher yields for riskier bonds.
Normalization and Weighting:
• Normalization: Each component is normalized to a scale of 0 to 100 using a function that adjusts the range based on historical highs and lows of the respective indicator.
• Weighting: The user can adjust the relative importance (weight) of each component using input parameters. This customization allows for different interpretations of market sentiment based on which factors are considered more influential.
Fear and Greed Index Calculation:
• The Fear and Greed Index is calculated as a weighted average of all normalized components. This index provides a single numerical value that summarizes the overall sentiment of the market based on the selected indicators.
Usage:
• Visualization: The indicator plots the Fear and Greed Index and its components on the chart. This allows traders and analysts to visually assess the sentiment trends over time.
• Analysis: Changes in the Fear and Greed Index can signal shifts in market sentiment. For example, a rising index may indicate increasing greed and potential overbought conditions, while a falling index may suggest increasing fear and potential oversold conditions.
• Customization: Traders can customize the indicator by adjusting the weights assigned to each component based on their trading strategies and market insights.
By integrating multiple fundamental indicators into a single index, the “CNN Fear and Greed Index” provides a comprehensive snapshot of market sentiment, helping traders make informed decisions about market entry, exit, and risk management strategies.
Fourier Extrapolation of PriceOverview
The "Fourier Extrapolation of Price" indicator utilizes Fourier Transform methods to analyze and predict future price movements based on historical data. By decomposing price series into their frequency components, this indicator provides a forecast of future price trends, making it a powerful tool for traders seeking advanced analytical techniques.
Key Features
Fourier Transform Analysis: Applies Discrete Fourier Transform (DFT) to the price series to identify frequency components.
Price Prediction: Forecasts future prices based on the dominant frequencies detected in the historical data.
Customizable Parameters: Allows users to set the length of historical data for analysis and the forecast period.
Visual Representation: Plots historical and forecasted prices for easy comparison.
How It Works
The indicator first normalizes the price series by subtracting the mean. It then applies the Discrete Fourier Transform (DFT) to the normalized data, extracting the real and imaginary parts. The magnitude and phase of these components are used to forecast future prices through an inverse DFT. Finally, the forecasted prices are denormalized and plotted alongside the historical prices on the chart.
Usage Instructions
Configure Parameters: Set the length of the historical data (DFT Length) and the forecast period (Forecast Length) to suit your analysis.
Apply to Chart: Add the indicator to your chart to start the analysis. Note that the computation may take a minute to complete due to the complexity of the Fourier Transform.
Analyze Results: Review the plotted forecasted prices (in red) alongside the historical prices (in blue) to identify potential future trends.
Trading Decisions: Use the forecasted price trends to inform your trading decisions, such as identifying potential entry and exit points based on predicted market movements.
Note : Due to the computational complexity of the Fourier Transform, the prediction may take a minute to load. Please be patient as the indicator processes the data to provide accurate forecasts.
This indicator is useful for traders who:
Advanced Analysis: Seek advanced mathematical techniques for market analysis.
Trend Prediction: Want to forecast future price movements based on historical data.
Customizable Analysis: Prefer customizable parameters for tailored analysis.
Visual Insights: Appreciate visual representation of historical and forecasted prices for better decision-making.
Gap Trend Lines by @eyemaginativeSummary:
The "Gap Trend Lines" script is designed to identify and visualize gaps between the close of one candle and the opening of the next on a TradingView chart. It draws extended trend lines to visually connect these gaps, helping traders to identify significant price movements between consecutive candles.
Functionality:
Indicator Setup:
The script is set as an overlay indicator on the main chart.
It includes settings for maximum line and label counts, ensuring efficient performance.
Parameter Customization:
Gap Threshold: Defines the minimum gap size considered significant.
Line Colors: Allows customization of colors for small and large gaps.
Line Thickness and Style: Provides options to adjust the thickness and style (solid, dotted, dashed) of the trend lines.
Drawing Extended Trend Lines:
For each bar (candlestick) on the chart, the script checks if there is a gap between the previous candle's close and the current candle's open.
If a gap is detected (i.e., close != open), it determines the size of the gap.
Depending on the size relative to the defined threshold, it selects the appropriate color (small or large gap).
It then draws an extended trend line that starts from the close of the previous candle (bar_index , close ) and extends to the open of the current candle (bar_index, open).
The trend line is drawn with the specified thickness, color, and style.
Dynamic Line Attribute Changes:
The script includes a function (changeLineAttributes()) that periodically changes the color and style of the trend lines.
By default, it changes the color every 4 hours (adjustable), alternating between green and the original color.
Enhanced Functionality:
Handles both small and large gaps with different visual cues (colors).
Supports extended trend lines that span both past and future directions (extend=extend.both), ensuring visibility across the entire chart.
Usage:
Traders can use the "Gap Trend Lines" script to:
Identify and analyze gaps between candlesticks.
Visualize significant price movements or breaks in continuity.
Customize the appearance of trend lines for better clarity and analysis.
By utilizing this script, traders can gain insights into price gap dynamics directly on TradingView charts, aiding in decision-making and strategy development.
Exponential Grid [Phi, Pi, Euler]If you disagree with one of the EMH principles that price is too random, then by definition you must agree that historic price has deterministic function to a scenario ahead.
I personally believe that constants like phi, pi and e can mimic exponential growth of the price.
In this script, first grid is based on the Lowest price multiplied with self fraction of the constant.
For example:
If you are familiar with fib ratio 1.272, then you must know that it is 1.618 to the power of 0.5.
With default settings of exponent step 0.25
First grid = Lowest price x phi^0.25
Second grid = Lowest price x phi^0.25x2
Third grid = Lowest price x phi^0.25x3 and so on
The script will automatically find the lowest price and update the grid values.
Or you can set up your custom Lowest price manually if you feel like the All Time Low level loses its relevance value after long period.
There are 64 grids including Lowest price level. And it wasn't by a chance. Pine Script has a limitation of max 64 plots. Number of grids shown in the chart depends on the highest price. Once price breaks above ATH a couple of next grids will be plotted automatically. In most cases if everything is plotted, the chart appears squeezed and you'll need to zoom in to see it. Therefore, I adjusted it relatively to the scale of the chart for the comfort.
In some cases 64 plots aren't enough to cover the whole chart. For example, let's take a look at NVIDIA chart:
Since the price has started with 0.0333, it is way too small to cover all with default settings.
We are left with 2 choices:
Either Enable "Round"
OR increase Exponent Step (from 0.25 to 0.5 in the particular example below)
If you set constant to pi or e which is a bigger number than phi, expect the gaps to be bigger. To reduce it to a more gradual way of expansion you can decrease Exponent Step.
Earnings Beat IndicatorThis indicator seeks to predict whether a stock will beat or miss earnings by forecasting revenues, and subsequently net income, using linear regression. The y-values of this regression are revenues and the x-axis is an economic series of your choosing. Double-click the status line (the words "US" and "GDP") to change economic datasets. The full list of economic datasets available in TradingView is in the Help Center.
Instructions:
1. Double-click on the status line (the fields "US" and "GDP"). The inputs tab will pop up.
2. Type in the country and data codes for the economic datasets you believe have the highest correlation with revenues and net margins respectively.
3. Check the correlation coefficient between financial data and economic data by interpreting the white and gray numbers on the status line - white for the correlation coefficient between revenues and your chosen economic dataset, and gray for the correlation coefficient between net margins and your chosen economic dataset. These numbers should be as close to +1 or -1 as possible.
4. Interpret the results - the blue number indicates whether revenues will beat estimates and the green number indicates whether earnings will beat estimates. A 1 for both outputs indicates a double beat, a 1 and a 0 indicates a revenue beat but not an earnings beat, a 0 and a 1 indicates an earnings beat but not a revenue beat, and a 0 and a 0 indicates a double miss.
- DickZhones
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.
ADX and SADX, SDIThe indicator aims to analyze and visualize the Average Directional Index (ADX) and its smoothed versions, along with directional indicators (DI) to help traders identify trend strength and potential buy/sell signals.
Indicator Settings:
The indicator is named "ADX and SADX, SDI" and is set to display prices with a precision of 2 decimal places.
Users can customize the ADX smoothing length, DI length, ADX smoothing period, and DI smoothing period through input variables.
Directional Movement (DM) Calculation:
The function dirmov calculates the positive and negative directional movements (DM) and the smoothed values of the positive directional index (DI+) and negative directional index (DI-).
This is done using the average true range (ATR) to normalize the DM values.
Average Directional Index (ADX) Calculation:
The function adx calculates the ADX, which measures the strength of a trend.
It uses the DI+ and DI- values to compute the ADX value.
Smoothed ADX and DI Calculation:
The ADX values are further smoothed using a simple moving average (SMA).
The DI difference is also smoothed and used to determine the trend direction.
Buy and Sell Signals:
A buy signal is generated when the DI+ crosses above DI- and the smoothed DI difference is increasing.
A sell signal is generated when the DI- crosses above DI+ and the smoothed DI difference is decreasing.
Plotting:
The ADX, smoothed ADX, smoothed DI difference (SPM), DI+, and DI- values are plotted on the chart.
Horizontal lines are drawn to indicate threshold levels (e.g., level 22).
Background and bar colors change based on buy (lime) and sell (maroon) signals to visually indicate these conditions.
Purpose of the Code:
This Pine Script code is used to create a custom indicator on TradingView that helps traders identify the strength and direction of a trend. The Average Directional Index (ADX) is used to measure trend strength, while the Directional Indicators (DI+ and DI-) are used to determine the direction of the trend. The smoothed versions of these indicators (SADX and SDI) provide additional confirmation and smoothing to reduce noise and false signals. Traders can use the buy and sell signals generated by this indicator to make informed trading decisions based on the trend strength and direction.
Important Note:
This script is provided for educational purposes and does not constitute financial advice. Traders and investors should conduct their research and analysis before making any trading decisions.
Median Analyst ConsensusThe Median Analyst Consensus Indicator provides an unbiased, easy-to-interpret view of market sentiment by leveraging TradingView's comprehensive financial data library. This tool displays the median 12-month price target and the percentage difference from the current price directly on your charts.
Key Features
1. Accurate Market Sentiment: By consolidating analyst ratings and price targets from multiple reputable sources like Bloomberg, Refinitiv (formerly Thomson Reuters), S&P Capital IQ, and Morningstar, this indicator displays the median analyst consensus. Using the median ensures outlier ratings don't skew the overall sentiment, providing a more robust representation.
2. Simplicity at a Glance: View the median 12-month price target and percentage difference from the current price directly on your chart. No need to juggle multiple reports - key insights are surfaced within your normal trading workflow.
3. Data-Driven Transparency: If no analyst data is available for a particular asset, the indicator will not display, ensuring you only see reliable information. The number of contributing analysts is also shown for context.
Why the Median?
The median is favored over the mean to minimize the impact of outlier ratings that could distort the consensus view. By taking the middle value across all analyst projections, the median provides a more stable, outlier-resistant measure of market sentiment.
Powered by TradingView Data
This indicator taps into TradingView's financial data library, which aggregates analyst ratings, estimates, and recommendations from leading institutional data providers. TradingView sources this data from firms like FactSet, Bloomberg, Refinitiv, S&P Capital IQ, and Morningstar, ensuring a comprehensive and trusted view of analyst sentiment.
The library provides variables like:
syminfo.recommendations_buy
syminfo.recommendations_sell
syminfo.target_price_median
syminfo.recommendations_buy_strong
syminfo.recommendations_sell_strong
The indicator calculates and displays the median of these analyst inputs.
Usage
The indicator displays:
The median 12-month price target across analysts
The percentage difference between the price target and current price
The number of contributing analyst estimates
If no analyst data is available, the indicator does not display, ensuring full transparency.
The Median Analyst Consensus Indicator provides an unbiased, easy-to-interpret view of market sentiment by leveraging TradingView's comprehensive financial data library. This tool offers a new perspective on potential trade opportunities directly on your charts.
Disclaimer
While the data is sourced from reputable providers, analyst forecasts should not be construed as investment recommendations. This indicator aims to synthesize market opinions, but investment decisions are solely your responsibility. As with any analytical tool, you should conduct your own research and risk assessments before executing any trades.
ΔYoY(Economics)Year over year indicator which will benchmark the most recent data vs 1 year lookback; Will automate the lookback for quarterly and monthly data based on timeframe selected (3M for quarterly, 1M for monthly). Tradingview will aggregate weekly data into a monthly data point. SMA applied to get the average over some x period.
Ln(close)Natural log indicator for normalizing data. SMA applied so you can take the average of that normalization factor. Personally use it for US economic data where the value is very large (GDI, Fed Balance Sheet, USM2 etc.) and the year over year delta is not pertinent (USM2) or not available (GDI.. although I did make an indicator to get YoY :D). Any additional ideas leave a comment and I'll take a look.
Nasan Moving Average with ForecastThe "Nasan Moving Average with Forecast" indicator is a technical analysis forecasting tool that combines the principles of historical data analysis and random walk theory. It calculates a customized moving average (Nasan Moving Average) by integrating price data and statistical measures and projects future price points by generating forecast values within calculated volatility bounds, creating a dynamic and insightful visualization of potential market movements. This indicator to blend past market behavior with probabilistic future trends to enhance forecasting.
Input Parameters:
len: Differencing length (default 21, Use a minimum of 5 and for lower time frames less than 15 min use values between 300 -3000)
len1: Correction Factor Length 1 (default 21, this determines the length of the MA you want , eg. 10 MA, 50 MA, 100 MA, )
len2: Correction Factor Length 2 (default 9, this works best if it is ~ </=1/2 of len1 )
len3: Smoothing Length (default 5, I would not change this and only use if I want to introduce lag where you want to use it for cross over strategies).
forecast_points: Number of points to forecast (default 30).
m: Multiplier for standard deviation (default 2.5).
bl: Block length for calculating max/min values (default 100).
use_calculated_max_min: Boolean to decide whether to use calculated max/min values.
Nasan Moving Average Calculation:
Calculates the simple moving average (mean) and standard deviation (sd) of the typical price (hlc3).
Computes intermediate variables (a, b, c, etc.) based on log transformation and cumulative sum.
Applies weighted moving averages (wma) to these intermediate variables to smooth them and derive the final value c6.
Plots c6 as the Nasan Moving Average if the bar is confirmed. To learn more see Nasan Moving Average.
Forecast Points Calculation:
Calculates maximum (max_val) and minimum (min_val) values for the forecast, either using a fixed value or based on standard deviation and a multiplier.
Initializes an array to store forecast values and creates polyline objects for plotting.
If the current bar is one of the last three bars and confirmed:
Clears and reinitializes the polyline.
Initializes the first forecast value from the cumulative sum c.
Generates subsequent forecast values using a random value within the range .
Updates the forecast array and plots the forecast points as an orange curved polyline.
Plotting Max/Min Values:
Plots max_val and min_val as green and red lines, respectively, to indicate the bounds of the forecast range.
Components of the Forecasting Model
Historical Dependence:
Nasan Moving Average Calculation: The script calculates a custom moving average (c6) that incorporates historical price data (hlc3), standard deviations (sd), and weighted moving averages (wma). This part of the code processes historical data to create a smoothed representation of the price trend.
Max/Min Value Calculation: The maximum (max_val) and minimum (min_val) values for the forecast can be calculated based on the historical standard deviation of a transformed variable b over a block length (bl). This introduces historical volatility into the bounds for the forecast.
Random Walk Model:
Random Value Generation: Within the forecast points calculation, a random value (random_val) is generated for each forecast point within the range . This random value introduces stochasticity into the model, characteristic of a random walk process.
Cumulative Sum for Forecasting: The script uses a cumulative sum (prev_f + random_val) to generate the next forecast point (next_f). This is a typical approach in random walk models where each new point is based on the previous point plus some random noise.
Explanation of the Forecast Model
Random Walk Characteristics: Each new forecast point is generated by adding a random value to the previous point, making the model a random walk with drift, where the drift is influenced by historical correction factors (c1, c4).
Historical and Statistical Dependence: The bounds of the random values and the initial conditions are derived from historical data, ensuring that the forecast respects historical volatility and trends.
The forecasting model in the script is a hybrid approach: It uses a random walk to generate future points, characterized by adding random values to the previous forecasted value.
The historical and statistical dependence is incorporated through initial conditions, scaling factors, and bounds derived from historical price data and its statistical properties.
This combination ensures that the forecasts are not purely stochastic but are grounded in historical price behavior, making the model more robust and potentially more accurate in reflecting market conditions.
ALT Trend DetectionALT Trend Detection Indicator
Overview:
The "ALT Trend Detection" indicator is designed to help traders analyze the relationship between Bitcoin's dominance, Bitcoin's price, and the potential impact on altcoin prices. This indicator uses various time frames and average true range (ATR) calculations to detect trends and provide insights into the altcoin market conditions based on Bitcoin's movements.
How It Works:
BTC Dominance and Price Data:
The indicator fetches Bitcoin dominance data (percentage of the total cryptocurrency market cap that Bitcoin represents) and Bitcoin price data using the selected time frame.
It calculates whether Bitcoin dominance and price are trending up, down, or remaining stable based on ATR calculations.
Altcoin Trend Detection:
The indicator then evaluates different scenarios based on the combination of Bitcoin dominance and price movements. These scenarios help predict the potential impact on altcoin prices.
For instance, if Bitcoin dominance is up and Bitcoin price is up, it might indicate a bearish trend for altcoins. Conversely, if Bitcoin dominance is down and Bitcoin price is up, it might indicate a bullish trend for altcoins (altseason).
Table Display:
The indicator displays a table on the chart that summarizes the current conditions for Bitcoin dominance, Bitcoin price, and the expected impact on altcoins. Each cell in the table is color-coded to provide a quick visual representation of the trends.
Usage:
Add the indicator to your TradingView chart.
Customize the time frame, ATR multiplier, table position, table size, and background color as per your preference.
Observe the table displayed on the chart. It shows the current state of Bitcoin dominance, Bitcoin price, and the potential trend for altcoin prices based on predefined scenarios.
Use this information to make informed trading decisions about altcoins. For example, if the table shows "ALT SEASON" in green, it might be a good time to consider investing in altcoins.
By analyzing the interaction between Bitcoin dominance and price, this indicator helps traders identify potential opportunities and risks in the altcoin market.
Anchored Monte Carlo Shuffled Projection [LuxAlgo]The Anchored Monte Carlo Shuffled Projection tool randomly simulates future price points based on historical bar movements made before a user-anchored point in time.
By anchoring our data and projections to a single point in time, users can better understand and reflect on how the price played out while taking into consideration our random simulations.
🔶 USAGE
After selecting the indicator to apply to the chart, you will be prompted to "Set the Anchor Point". Do so by clicking on the desired location on your chart, only time is used as the anchor point.
Note: To select a new anchor point when applied to the chart, click on the 'More' dropdown next to the indicator status bar (○○○), then select "Reset points...".
Alternate Method: You are also able to click and drag the vertical line that displays on the anchor point bar when the indicator is highlighted.
By randomly simulating bar movements, a range is developed of potential price action which could be utilized to locate future price development as well as potential support/resistance levels.
Performing numerous simulations and taking the average at each step will converge toward the result highlighted by the "Average Line", and can point out where the price might develop, assuming the trend and amount of volatility persist.
Current closing price + Sum of changes in the calculation window
This constraint will cause the simulations always to display an endpoint consistent with the current lookback's slope.
While this may be helpful to some traders, this indicator includes an option to produce a less biased range, as seen below:
🔶 DETAILS
The Anchored Monte Carlo Shuffled Projection tool creates simulations based on prices within a user-set lookback window originating at the specified anchor point. Simulations are done as follows:
Collect each bar's price changes in the user-set window.
Randomize the order of each change in the window.
Project the cumulative sum of the shuffled changes from the current closing price.
Collect data on each point along the way.
This is the process for the Default calculation; for the 'Randomize Direction' calculation, when added onto the front for every other change, the value is inverted, creating the randomized endpoints for each simulation.
The script contains each simulation's data for that bar, with a maximum of 1000 simulations.
To get a glimpse behind the scenes, each simulation (up to 99) can be viewed using the 'Visualize Simulations' Options, as seen below.
Because the script holds the full simulation data, the script can also calculate this data, such as standard deviations.
In this script the Standard deviation lines are the average of all standard deviations across the vertical data groups, this provides a singular value that can be displayed a distance away from the simulation center line.
🔶 SETTINGS
Lookback: Sets the number of Bars to include in calculations.
Simulation Count: Sets the number of randomized simulations to calculate. (Max 1000)
Randomize Direction: See Details Above. Creates a more 'Normalized' Distribution
Visualize Simulations: See Details Above. Turns on Visualizations, and colors are randomly generated. Visualized max does not cap the calculated max. If 1000 simulations are used, the data will be from 1000 simulations, however, only the last 99 simulations will be visualized.
🔹 Standard Deviations
Standard Deviation Multiplier: Sets the multiplier to use for the Standard Deviation distance away from the center line.
🔹 Style
Extend Lines: Extends the Simulated Value Lines into the future for further reference and analysis.
Velocity And Acceleration with Strategy: Traders Magazine◙ OVERVIEW
Hi, Ivestors and Traders... This Indicator, the focus is Scott Cong's article in the Stocks & Commodities September issue, “VAcc: A Momentum Indicator Based On Velocity And Acceleration”. I have also added a trading strategy for you to benefit from this indicator. First of all, let's look at what the indicator offers us and what its logic is. First, let's focus on the logic of the strategy.
◙ CONCEPTS
Here is a new indicator based on some simple physics concepts that is easy to use, responsive and precise. Learn how to calculate and use it.
The field of physics gives us some important principles that are highly applicable to analyzing the markets. In this indicator, I will present a momentum indicator. Scott Cong developed based on the concepts of velocity and acceleration this indicator. Of the many characteristics of price that traders and analysts often study, rate and rate of change are useful ones. In other words, it’s helpful to know: How fast is price moving, and is it speeding up or slowing down? How is price changing from one period to the next? The indicator I’m introducing here is calculated using the current bar (C) and every bar of a lookback period from the current bar. He named the indicator the VAcc since it’s based on the average of velocity line (av) and acceleration line (Acc) over the lookback period. For longer periods, the VAcc behaves the same way as the MACD, only it’s simpler, more responsive, and more precise. Interestingly, for shorter periods, VAcc exhibits characteristics of an oscillator, such as the stochastics oscillator.
◙ CALCULATION
The calculation of VAcc involves the following steps:
1. Relatively weighted average where the nearer price has the largest influence.
weighted_avg (float src, int length) =>
float sum = 0.0
for _i = 1 to length
float diff = (src - src ) / _i
sum += diff
sum /= length
2. The Velocity Average is smoothed with an exponential moving average. Now it get:
VAcc (float src, int period, int smoothing) =>
float vel = ta.ema(weighted_avg(src, period), smoothing)
float acc = weighted_avg(vel, period)
3. Similarly, accelerations for each bar within the lookback period and scale factor are calculated as:
= VAcc(src, length1, length2)
av /= (length1 * scale_factor)
◙ STRATEGY
In fact, Scott probably preferred to use it in periods 9 and 26 because it was similar to Macd and used the ratio of 0.5. However, I preferred to use the 8 and 21 periods to provide signals closer to the stochastic oscillator in the short term and used the 0.382 ratio. The logic of the strategy is this
Long Strategy → acc(Acceleration Line) > 0.1 and av(Velocity Average Line) > 0.1(Long Factor)
Short strategy → acc(Acceleration Line) < -0.1 and av(Velocity Average Line) < -0.1(Long Factor)
Here, you can change the Short Factor and Long Factor as you wish and produce more meaningful results that are closer to your own strategy.
I hope you benefits...
◙ GENEL BAKIŞ
Merhaba Yatırımcılar ve Yatırımcılar... Bu Gösterge, Scott Cong'un Stocks & Emtia Eylül sayısındaki “VAcc: Hız ve İvmeye Dayalı Bir Momentum Göstergesi” başlıklı makalesine odaklanmaktadır. Bu göstergeden faydalanabilmeniz için bir ticaret stratejisi de ekledim. Öncelikle göstergenin bize neler sunduğuna ve mantığının ne olduğuna bakalım. Öncelikle stratejinin mantığına odaklanalım.
◙ KAVRAMLAR
İşte kullanımı kolay, duyarlı ve kesin bazı basit fizik kavramlarına dayanan yeni bir gösterge. Nasıl hesaplanacağını ve kullanılacağını öğrenin.
Fizik alanı bize piyasaları analiz etmede son derece uygulanabilir bazı önemli ilkeler verir. Bu göstergede bir momentum göstergesi sunacağım. Scott Cong bu göstergeyi hız ve ivme kavramlarına dayanarak geliştirdi. Yatırımcıların ve analistlerin sıklıkla incelediği fiyatın pek çok özelliği arasında değişim oranı ve oranı yararlı olanlardır. Başka bir deyişle şunu bilmek faydalı olacaktır: Fiyat ne kadar hızlı hareket ediyor ve hızlanıyor mu, yavaşlıyor mu? Fiyatlar bir dönemden diğerine nasıl değişiyor? Burada tanıtacağım gösterge, mevcut çubuk (C) ve mevcut çubuktan bir yeniden inceleme döneminin her çubuğu kullanılarak hesaplanır. Göstergeye, yeniden inceleme dönemi boyunca hız çizgisinin (av) ve ivme çizgisinin (Acc) ortalamasına dayandığı için VAcc adını verdi. Daha uzun süreler boyunca VACc, MACD ile aynı şekilde davranır, yalnızca daha basit, daha duyarlı ve daha hassastır. İlginç bir şekilde, daha kısa süreler için VAcc, stokastik osilatör gibi bir osilatörün özelliklerini sergiliyor.
◙ HESAPLAMA
VAcc'nin hesaplanması aşağıdaki adımları içerir:
1. Yakın zamandaki fiyatın en büyük etkiye sahip olduğu göreceli ağırlıklı ortalamayı hesaplatıyoruz.
weighted_avg (float src, int length) =>
float sum = 0.0
for _i = 1 to length
float diff = (src - src ) / _i
sum += diff
sum /= length
2. Hız Ortalamasına üstel hareketli ortalamayla düzleştirme uygulanır. Şimdi bu şekilde aşağıdaki kod ile bunu şöyle elde ediyoruz:
VAcc (float src, int period, int smoothing) =>
float vel = ta.ema(weighted_avg(src, period), smoothing)
float acc = weighted_avg(vel, period)
3. Benzer şekilde, yeniden inceleme süresi ve ölçek faktörü içindeki her bir çubuk için fiyattaki ivmelenler yada momentum şu şekilde hesaplanır:
= VAcc(src, length1, length2)
av /= (length1 * scale_factor)
◙ STRATEJİ
Aslında Scott muhtemelen Macd'e benzediği ve 0,5 oranını kullandığı için 9. ve 26. periyotlarda kullanmayı tercih etmişti. Ancak kısa vadede stokastik osilatöre daha yakın sinyaller sağlamak için 8 ve 21 periyotlarını kullanmayı tercih ettim ve 0,382 oranını kullandım. Stratejinin mantığı şu
Uzun Strateji → acc(İvme Çizgisi) > 0,1 ve av(Hız Ortalama Çizgisi) > 0,1(Uzun Faktör)
Kısa strateji → acc(İvme Çizgisi) < -0,1 ve av(Hız Ortalama Çizgisi) < -0,1(Uzun Faktör)
Burada Kısa Faktör ve Uzun Faktör' ü dilediğiniz gibi değiştirip, kendi stratejinize daha yakın, daha anlamlı sonuçlar üretebilirsiniz.
umarım faydasını görürsün...
