Fundamental AnalysisThis indicator compiles a wide range of essential financial metrics directly onto your chart, providing a quick and easy reference to the financial condition of any listed company. Instead of diving into lengthy financial reports, you get an at-a-glance overview of the most critical financial ratios and figures.
Key Metrics Included:
Interest Coverage Ratio: Helps assess a company’s ability to pay interest on its debt. Higher values suggest greater financial stability and lower default risk.
Gross Profit Margin: Shows how much profit a company makes after covering its production costs. A higher margin indicates better efficiency and profitability in managing costs.
Dividend Yield: Reflects the annual dividend payout as a percentage of the current stock price. A moderate dividend yield may indicate a balance between income generation and growth potential.
Enterprise Value (EV): A comprehensive measure of a company's total value, including debt. Useful for comparing companies with different capital structures.
Free Cash Flow to Equity (FCFE): Reveals how much cash is available to shareholders after accounting for capital expenditures and debt repayments, indicating the company’s ability to reward investors.
Price-to-Book Ratio (P/B): Compares a company's market value to its book value. Lower values might indicate undervaluation, while higher values can suggest overvaluation.
Price-to-Cash Flow Ratio (P/CF): Helps identify companies that generate a significant amount of cash relative to their price, a key metric for assessing liquidity and sustainability.
Price-to-Free Cash Flow Ratio: Shows how much investors are paying for the company's free cash flow, which is crucial for assessing value, especially in capital-intensive sectors.
Price Earnings Ratio (P/E): The classic metric for valuing a company based on its earnings. Useful for comparing valuations across companies and industries.
Debt-to-Equity Ratio: Indicates the proportion of company financing that comes from debt and equity. A lower ratio typically signifies a less risky investment.
Return on Equity (ROE): Measures how effectively a company uses equity capital to generate profit. A higher ROE can indicate a profitable, well-managed company.
Quick Ratio: Assesses a company’s short-term liquidity by comparing its liquid assets to its current liabilities. Higher values indicate better liquidity.
Operating Margin: Reflects the percentage of revenue left after covering operating expenses. Higher margins suggest greater operational efficiency.
How to Use This Indicator:
Use this indicator as part of your due diligence when analyzing potential investments. Each metric is color-coded to quickly highlight whether the value is within a favorable range, making it easy to identify strong or weak aspects of a company’s financial position.
Green indicates favorable metrics, suggesting financial strength or value.
Red highlights areas of concern, pointing to potential risks or weaknesses.
This tool can help you compare different companies, spot trends over time, and make more informed decisions based on solid financial analysis. Whether you’re a value investor looking for undervalued stocks, a dividend seeker searching for sustainable payouts, or a growth investor focused on profitability and efficiency, this indicator can be tailored to your strategy.
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Uptrick: RSI Histogram
1. **Introduction to the RSI and Moving Averages**
2. **Detailed Breakdown of the Uptrick: RSI Histogram**
3. **Calculation and Formula**
4. **Visual Representation**
5. **Customization and User Settings**
6. **Trading Strategies and Applications**
7. **Risk Management**
8. **Case Studies and Examples**
9. **Comparison with Other Indicators**
10. **Advanced Usage and Tips**
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## 1. Introduction to the RSI and Moving Averages
### **1.1 Relative Strength Index (RSI)**
The Relative Strength Index (RSI) is a momentum oscillator developed by J. Welles Wilder and introduced in his 1978 book "New Concepts in Technical Trading Systems." It is widely used in technical analysis to measure the speed and change of price movements.
**Purpose of RSI:**
- **Identify Overbought/Oversold Conditions:** RSI values range from 0 to 100. Traditionally, values above 70 are considered overbought, while values below 30 are considered oversold. These thresholds help traders identify potential reversal points in the market.
- **Trend Strength Measurement:** RSI also indicates the strength of a trend. High RSI values suggest strong bullish momentum, while low values indicate bearish momentum.
**Calculation of RSI:**
1. **Calculate the Average Gain and Loss:** Over a specified period (e.g., 14 days), calculate the average gain and loss.
2. **Compute the Relative Strength (RS):** RS is the ratio of average gain to average loss.
3. **RSI Formula:** RSI = 100 - (100 / (1 + RS))
### **1.2 Moving Averages (MA)**
Moving Averages are used to smooth out price data and identify trends by filtering out short-term fluctuations. Two common types are:
**Simple Moving Average (SMA):** The average of prices over a specified number of periods.
**Exponential Moving Average (EMA):** A type of moving average that gives more weight to recent prices, making it more responsive to recent price changes.
**Smoothed Moving Average (SMA):** Used to reduce the impact of volatility and provide a clearer view of the underlying trend. The RMA, or Running Moving Average, used in the USH script is similar to an EMA but based on the average of RSI values.
## 2. Detailed Breakdown of the Uptrick: RSI Histogram
### **2.1 Indicator Overview**
The Uptrick: RSI Histogram (USH) is a technical analysis tool that combines the RSI with a moving average to create a histogram that reflects momentum and trend strength.
**Key Components:**
- **RSI Calculation:** Determines the relative strength of price movements.
- **Moving Average Application:** Smooths the RSI values to provide a clearer trend indication.
- **Histogram Plotting:** Visualizes the deviation of the smoothed RSI from a neutral level.
### **2.2 Indicator Purpose**
The primary purpose of the USH is to provide a clear visual representation of the market's momentum and trend strength. It helps traders identify:
- **Bullish and Bearish Trends:** By showing how far the smoothed RSI is from the neutral 50 level.
- **Potential Reversal Points:** By highlighting changes in momentum.
### **2.3 Indicator Design**
**RSI Moving Average (RSI MA):** The RSI MA is a smoothed version of the RSI, calculated using a running moving average. This smooths out short-term fluctuations and provides a clearer indication of the underlying trend.
**Histogram Calculation:**
- **Neutral Level:** The histogram is plotted relative to the neutral level of 50. This level represents a balanced market where neither bulls nor bears have dominance.
- **Histogram Values:** The histogram bars show the difference between the RSI MA and the neutral level. Positive values indicate bullish momentum, while negative values indicate bearish momentum.
## 3. Calculation and Formula
### **3.1 RSI Calculation**
The RSI calculation involves:
1. **Average Gain and Loss:** Calculated over the specified length (e.g., 14 periods).
2. **Relative Strength (RS):** RS = Average Gain / Average Loss.
3. **RSI Formula:** RSI = 100 - (100 / (1 + RS)).
### **3.2 Moving Average Calculation**
For the USH indicator, the RSI is smoothed using a running moving average (RMA). The RMA formula is similar to that of the EMA but is based on averaging RSI values over the specified length.
### **3.3 Histogram Calculation**
The histogram value is calculated as:
- **Histogram Value = RSI MA - 50**
**Plotting the Histogram:**
- **Positive Histogram Values:** Indicate that the RSI MA is above the neutral level, suggesting bullish momentum.
- **Negative Histogram Values:** Indicate that the RSI MA is below the neutral level, suggesting bearish momentum.
## 4. Visual Representation
### **4.1 Histogram Bars**
The histogram is plotted as bars on the chart:
- **Bullish Bars:** Colored green when the RSI MA is above 50.
- **Bearish Bars:** Colored red when the RSI MA is below 50.
### **4.2 Customization Options**
Traders can customize:
- **RSI Length:** Adjust the length of the RSI calculation to match their trading style.
- **Bull and Bear Colors:** Choose colors for histogram bars to enhance visual clarity.
### **4.3 Interpretation**
**Bullish Signal:** A histogram bar that moves from red to green indicates a potential shift to a bullish trend.
**Bearish Signal:** A histogram bar that moves from green to red indicates a potential shift to a bearish trend.
## 5. Customization and User Settings
### **5.1 Adjusting RSI Length**
The length parameter determines the number of periods over which the RSI is calculated and smoothed. Shorter lengths make the RSI more sensitive to price changes, while longer lengths provide a smoother view of trends.
### **5.2 Color Settings**
Traders can adjust:
- **Bull Color:** Color of histogram bars indicating bullish momentum.
- **Bear Color:** Color of histogram bars indicating bearish momentum.
**Customization Benefits:**
- **Visual Clarity:** Traders can choose colors that stand out against their chart’s background.
- **Personal Preference:** Adjust settings to match individual trading styles and preferences.
## 6. Trading Strategies and Applications
### **6.1 Trend Following**
**Identifying Entry Points:**
- **Bullish Entry:** When the histogram changes from red to green, it signals a potential entry point for long positions.
- **Bearish Entry:** When the histogram changes from green to red, it signals a potential entry point for short positions.
**Trend Confirmation:** The histogram helps confirm the strength of a trend. Strong, consistent green bars indicate robust bullish momentum, while strong, consistent red bars indicate robust bearish momentum.
### **6.2 Swing Trading**
**Momentum Analysis:**
- **Entry Signals:** Look for significant shifts in the histogram to time entries. A shift from bearish to bullish (red to green) indicates potential for upward movement.
- **Exit Signals:** A shift from bullish to bearish (green to red) suggests a potential weakening of the trend, signaling an exit or reversal point.
### **6.3 Range Trading**
**Market Conditions:**
- **Consolidation:** The histogram close to zero suggests a range-bound market. Traders can use this information to identify support and resistance levels.
- **Breakout Potential:** A significant move away from the neutral level may indicate a potential breakout from the range.
### **6.4 Risk Management**
**Stop-Loss Placement:**
- **Bullish Positions:** Place stop-loss orders below recent support levels when the histogram is green.
- **Bearish Positions:** Place stop-loss orders above recent resistance levels when the histogram is red.
**Position Sizing:** Adjust position sizes based on the strength of the histogram signals. Strong trends (indicated by larger histogram bars) may warrant larger positions, while weaker signals suggest smaller positions.
## 7. Risk Management
### **7.1 Importance of Risk Management**
Effective risk management is crucial for long-term trading success. It involves protecting capital, managing losses, and optimizing trade setups.
### **7.2 Using USH for Risk Management**
**Stop-Loss and Take-Profit Levels:**
- **Stop-Loss Orders:** Use the histogram to set stop-loss levels based on trend strength. For instance, place stops below support levels in bullish trends and above resistance levels in bearish trends.
- **Take-Profit Targets:** Adjust take-profit levels based on histogram changes. For example, lock in profits as the histogram starts to shift from green to red.
**Position Sizing:**
- **Trend Strength:** Scale position sizes based on the strength of histogram signals. Larger histogram bars indicate stronger trends, which may justify larger positions.
- **Volatility:** Consider market volatility and adjust position sizes to mitigate risk.
## 8. Case Studies and Examples
### **8.1 Example 1: Bullish Trend**
**Scenario:** A trader notices a transition from red to green histogram bars.
**Analysis:**
- **Entry Point:** The transition indicates a potential bullish trend. The trader decides to enter a long position.
- **Stop-Loss:** Set stop-loss below recent support levels.
- **Take-Profit:** Consider taking profits as the histogram moves back towards zero or turns red.
**Outcome:** The bullish trend continues, and the histogram remains green, providing a profitable trade setup.
### **8.2 Example 2: Bearish Trend**
**Scenario:** A trader observes a transition from green to red histogram bars.
**Analysis:**
- **Entry Point:** The transition suggests a potential
bearish trend. The trader decides to enter a short position.
- **Stop-Loss:** Set stop-loss above recent resistance levels.
- **Take-Profit:** Consider taking profits as the histogram approaches zero or shifts to green.
**Outcome:** The bearish trend continues, and the histogram remains red, resulting in a successful trade.
## 9. Comparison with Other Indicators
### **9.1 RSI vs. USH**
**RSI:** Measures momentum and identifies overbought/oversold conditions.
**USH:** Builds on RSI by incorporating a moving average and histogram to provide a clearer view of trend strength and momentum.
### **9.2 RSI vs. MACD**
**MACD (Moving Average Convergence Divergence):** A trend-following momentum indicator that uses moving averages to identify changes in trend direction.
**Comparison:**
- **USH:** Provides a smoothed RSI perspective and visual histogram for trend strength.
- **MACD:** Offers signals based on the convergence and divergence of moving averages.
### **9.3 RSI vs. Stochastic Oscillator**
**Stochastic Oscillator:** Measures the level of the closing price relative to the high-low range over a specified period.
**Comparison:**
- **USH:** Focuses on smoothed RSI values and histogram representation.
- **Stochastic Oscillator:** Provides overbought/oversold signals and potential reversals based on price levels.
## 10. Advanced Usage and Tips
### **10.1 Combining Indicators**
**Multi-Indicator Strategies:** Combine the USH with other technical indicators (e.g., Moving Averages, Bollinger Bands) for a comprehensive trading strategy.
**Confirmation Signals:** Use the USH to confirm signals from other indicators. For instance, a bullish histogram combined with a moving average crossover may provide a stronger buy signal.
### **10.2 Customization Tips**
**Adjust RSI Length:** Experiment with different RSI lengths to match various market conditions and trading styles.
**Color Preferences:** Choose histogram colors that enhance visibility and align with personal preferences.
### **10.3 Continuous Learning**
**Backtesting:** Regularly backtest the USH with historical data to refine strategies and improve accuracy.
**Education:** Stay updated with trading education and adapt strategies based on market changes and personal experiences.
Supertrend (Buy/Sell) With TP & SLSupertrend (Buy/Sell) with TP & SL: An Enhanced Trading Tool
This Pine Script indicator combines the popular Supertrend indicator with multiple take-profit (TP) and stop-loss (SL) levels, providing traders with a comprehensive visual aid for potential entries, exits, and risk management.
Originality
Buffer Zones for Precision: Instead of relying solely on the Supertrend line, this script incorporates buffer zones around it. This helps filter out false signals, especially in volatile markets, leading to more accurate buy/sell signals.
Flexible Stop-Loss: Offers the choice between a fixed or trailing stop-loss, allowing traders to tailor their risk management approach based on their preferences and market conditions.
Multiple Take-Profit Levels: Provides three potential take-profit levels, giving traders the flexibility to secure profits at different stages of a trend.
Heikin Ashi Candles & VWAP: Incorporates Heikin Ashi candles for smoother trend visualization and adds a VWAP line for potential support/resistance levels.
Clear Table Display: Presents key information like Stop Loss and Take Profit levels in a user-friendly table, making it easier to track trade targets.
How It Works
Supertrend Calculation: The Supertrend is calculated using ATR (Average True Range) to gauge market volatility. The script then creates buffer zones around the Supertrend line for refined signal generation.
Buy/Sell Signals:
Buy: When the close price crosses above the upper buffer zone, indicating a potential uptrend.
Sell: When the close price crosses below the lower buffer zone, suggesting a potential downtrend.
Take Profit & Stop Loss:
Take Profits: Three TP levels are calculated based on ATR and a customizable profit factor.
Stop Loss: The stop-loss can be set as either a fixed value based on ATR or as a trailing stop-loss that dynamically adjusts to lock in profits.
How To Use
Add the Indicator: Search for "Supertrend (Buy/Sell) With TP & SL" in the TradingView indicators list and add it to your chart.
Customize Inputs: Adjust parameters like ATR Period, Factor, Take Profit Factor, Stop Loss Factor, Stop Loss Type, etc., based on your trading style and preferences.
Interpret Signals: Look for buy signals when the price crosses above the upper buffer and sell signals when it crosses below the lower buffer.
Manage Risk: Use the plotted Take Profit and Stop Loss levels to manage your risk and potential rewards.
Concepts
Supertrend: A trend-following indicator that helps identify the direction of the prevailing trend.
ATR (Average True Range): A measure of market volatility.
Buffer Zones: Used to filter out false signals by creating a zone around the Supertrend line.
Trailing Stop Loss: A dynamic stop-loss that moves with the price to protect profits.
Heikin Ashi: A type of candlestick chart designed to filter out market noise and make trends easier to identify.
VWAP (Volume Weighted Average Price): An indicator that shows the average price at which a security has traded throughout the day, based on both volume and price.
Important Note: This script is for educational and informational purposes only. Backtest thoroughly and use with caution in live trading. Always manage your risk appropriately.
Buffett Quality Score [Financials]Evaluating Financial Companies with the Buffett Quality Score 'Financials'
The financial sector, with its unique regulatory environment and market dynamics, requires a tailored approach to financial evaluation. The Buffett Quality Score is meticulously designed to assess the financial robustness and quality of companies within this sector. By focusing on industry-specific financial metrics, this scoring system provides valuable insights for investors and analysts navigating the complexities of the financial industry.
Scoring Methodology
Each selected financial metric contributes a point to the overall score if the specified condition is met. The combined score is a summation of points across all criteria, providing a comprehensive assessment of financial health and quality.
Selected Financial Metrics and Criteria
1. Altman Z-Score > 2.0
Relevance: The Altman Z-Score evaluates bankruptcy risk based on profitability, leverage, liquidity, solvency, and activity. In the financial sector, where market stability and solvency are critical, a score above 2.0 signifies a lower risk of financial distress.
2. Debt to Equity Ratio < 2.0
Relevance: A lower Debt to Equity Ratio signifies prudent financial management and reduced reliance on debt financing. This is particularly important for financial companies, which need to manage leverage carefully to avoid excessive risk.
3. Interest Coverage > 3.0
Relevance: The Interest Coverage Ratio measures a company's ability to meet its interest obligations from operating earnings. A ratio above 3.0 indicates that the company can comfortably cover its interest expenses, reducing the risk of default.
4. Return on Equity (ROE) > 10.0%
Relevance: ROE indicates the company's ability to generate profits from shareholder equity. An ROE above 10.0% suggests efficient use of capital and strong returns for investors, which is a key performance indicator for financial companies.
5. Return on Assets (ROA) > 1.0%
Relevance: ROA measures the company's ability to generate earnings from its assets. In the financial sector, where asset management is crucial, an ROA above 1.0% indicates effective use of assets to generate profits.
6. Net Margin > 10.0%
Relevance: Net Margin measures overall profitability after all expenses. A margin above 10.0% demonstrates strong financial performance and the ability to convert revenue into profit effectively.
7. Revenue One-Year Growth > 5.0%
Relevance: Revenue growth reflects market demand and company expansion. In the financial sector, where growth can be driven by new products and services, revenue exceeding 5.0% indicates successful market penetration and business expansion.
8. EPS One-Year Growth > 5.0%
Relevance: EPS growth reflects the company's ability to increase earnings per share over the past year. For financial companies, growth exceeding 5.0% signals positive earnings momentum and potential market strength.
9. Price/Earnings Ratio (Forward) < 20.0
Relevance: The Forward P/E Ratio reflects investor sentiment and earnings expectations. A ratio below 20.0 suggests reasonable valuation relative to earnings projections, which is important for investors seeking value and growth opportunities in the financial sector.
10. Piotroski F-Score > 6.0
Relevance: The Piotroski F-Score assesses fundamental strength, emphasizing profitability, leverage, liquidity, and operating efficiency. For financial companies, a score above 6.0 indicates strong financial health and operational efficiency.
Interpreting the Buffett Quality Score
0-4 Points: Indicates potential weaknesses across multiple financial areas, warranting careful consideration and risk assessment.
5 Points: Suggests average performance based on sector-specific criteria, requiring further analysis to determine investment viability.
6-10 Points: Signifies strong financial health and quality, positioning the company favorably within the competitive financial industry.
Conclusion
The Buffett Quality Score offers a strategic framework for evaluating financial companies, emphasizing critical financial indicators tailored to industry dynamics. By leveraging these insights, stakeholders can make informed decisions and identify companies poised for sustainable growth and performance in the evolving financial landscape.
Disclaimer: The Buffett Quality Score serves as a tool for financial analysis and should not replace professional advice or comprehensive due diligence. Investors should conduct thorough research and consult with financial experts based on individual investment objectives.
Day/Week/Month Metrics (Zeiierman)█ Overview
The Day/Week/Month Metrics (Zeiierman) indicator is a powerful tool for traders looking to incorporate historical performance into their trading strategy. It computes statistical metrics related to the performance of a trading instrument on different time scales: daily, weekly, and monthly. Breaking down the performance into daily, weekly, and monthly metrics provides a granular view of the instrument's behavior.
The indicator requires the chart to be set on a daily timeframe.
█ Key Statistics
⚪ Day in month
The performance of financial markets can show variability across different days within a month. This phenomenon, often referred to as the "monthly effect" or "turn-of-the-month effect," suggests that certain days of the month, especially the first and last days, tend to exhibit higher than average returns in many stock markets around the world. This effect is attributed to various factors including payroll contributions, investment of monthly dividends, and psychological factors among traders and investors.
⚪ Edge
The Edge calculation identifies days within a month that consistently outperform the average monthly trading performance. It provides a statistical advantage by quantifying how often trading on these specific days yields better returns than the overall monthly average. This insight helps traders understand not just when returns might be higher, but also how reliable these patterns are over time. By focusing on days with a higher "Edge," traders can potentially increase their chances of success by aligning their strategies with historically more profitable days.
⚪ Month
Historically, the stock market has exhibited seasonal trends, with certain months showing distinct patterns of performance. One of the most well-documented patterns is the "Sell in May and go away" phenomenon, suggesting that the period from November to April has historically brought significantly stronger gains in many major stock indices compared to the period from May to October. This pattern highlights the potential impact of seasonal investor sentiment and activities on market performance.
⚪ Day in week
Various studies have identified the "day-of-the-week effect," where certain days of the week, particularly Monday and Friday, show different average returns compared to other weekdays. Historically, Mondays have been associated with lower or negative average returns in many markets, a phenomenon often linked to the settlement of trades from the previous week and negative news accumulation over the weekend. Fridays, on the other hand, might exhibit positive bias as investors adjust positions ahead of the weekend.
⚪ Week in month
The performance of markets can also vary within different weeks of the month, with some studies suggesting a "week of the month effect." Typically, the first and the last week of the month may show stronger performance compared to the middle weeks. This pattern can be influenced by factors such as the timing of economic reports, monthly investment flows, and options and futures expiration dates which tend to cluster around these periods, affecting investor behavior and market liquidity.
█ How It Works
⚪ Day in Month
For each day of the month (1-31), the script calculates the average percentage change between the opening and closing prices of a trading instrument. This metric helps identify which days have historically been more volatile or profitable.
It uses arrays to store the sum of percentage changes for each day and the total occurrences of each day to calculate the average percentage change.
⚪ Month
The script calculates the overall gain for each month (January-December) by comparing the closing price at the start of a month to the closing price at the end, expressed as a percentage. This metric offers insights into which months might offer better trading opportunities based on historical performance.
Monthly gains are tracked using arrays that store the sum of these gains for each month and the count of occurrences to calculate the average monthly gain.
⚪ Day in Week
Similar to the day in the month analysis, the script evaluates the average percentage change between the opening and closing prices for each day of the week (Monday-Sunday). This information can be used to assess which days of the week are typically more favorable for trading.
The script uses arrays to accumulate percentage changes and occurrences for each weekday, allowing for the calculation of average changes per day of the week.
⚪ Week in Month
The script assesses the performance of each week within a month, identifying the gain from the start to the end of each week, expressed as a percentage. This can help traders understand which weeks within a month may have historically presented better trading conditions.
It employs arrays to track the weekly gains and the number of weeks, using a counter to identify which week of the month it is (1-4), allowing for the calculation of average weekly gains.
█ How to Use
Traders can use this indicator to identify patterns or trends in the instrument's performance. For example, if a particular day of the week consistently shows a higher percentage of bullish closes, a trader might consider this in their strategy. Similarly, if certain months show stronger performance historically, this information could influence trading decisions.
Identifying High-Performance Days and Periods
Day in Month & Day in Week Analysis: By examining the average percentage change for each day of the month and week, traders can identify specific days that historically have shown higher volatility or profitability. This allows for targeted trading strategies, focusing on these high-performance days to maximize potential gains.
Month Analysis: Understanding which months have historically provided better returns enables traders to adjust their trading intensity or capital allocation in anticipation of seasonally stronger or weaker periods.
Week in Month Analysis: Identifying which weeks within a month have historically been more profitable can help traders plan their trades around these periods, potentially increasing their chances of success.
█ Settings
Enable or disable the types of statistics you want to display in the table.
Table Size: Users can select the size of the table displayed on the chart, ranging from "Tiny" to "Auto," which adjusts based on screen size.
Table Position: Users can choose the location of the table on the chart
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
NY Open Breakout Strategy - High Liquidity & Favorable RRR Pine Description:
The NY Open Breakout Strategy is an advanced Pine Script indicator tailored for the TradingView platform. This strategy is specifically designed to exploit the high liquidity found during the New York session opening in the Forex market. Its primary goal is to provide traders with an opportunity to engage in positions with lower risk and higher potential profits, thereby ensuring an advantageous risk-to-reward ratio (RRR).
Core Objectives:
Leveraging High Liquidity: Capitalizes on the significant market movements at the New York session opening, known for its high liquidity, to identify strong breakout signals.
Achieving Favorable RRR: By setting strategic stop-loss and take-profit levels, the strategy aims for a higher RRR. This approach can lead to overall profitability, even if the win rate is lower than the loss rate.
Functionality:
Dynamic Breakout Identification: Uses the first 15-minute candle’s high and low after NY open as benchmarks for detecting potential breakouts.
Customizable Stop-Loss & Take-Profit: Provides options to configure stop-loss at the last swing or the previous candle’s close. The take-profit levels are determined based on a favorable risk-reward ratio.
Visual Session Indicators: Includes distinct background coloring and vertical lines to mark the New York session for easy visibility.
Methodology:
This strategy hinges on the premise that the opening of the New York session often triggers key price movements due to an influx of trading activity. By focusing on these moments, our indicator aims to capture strong trends and breakout patterns. The carefully calibrated stop-loss and take-profit settings ensure that each trade aims for a higher potential reward compared to the risk undertaken.
Unique Features:
Enhanced Risk Management: With adaptable risk-reward settings, traders can tailor their trading strategies to align with individual risk appetites.
Personalized User Experience: Offers a range of customizable settings for visual elements, allowing traders to adjust the look and feel of the indicator to their preferences.
Usage Guidelines:
Customize the indicator settings, including the stop-loss reference and risk-reward ratio, to match your trading style.
Watch for 'Buy Enter' and 'Sell Enter' signals during the New York session opening.
Utilize the displayed stop-loss and take-profit levels to effectively manage each trade.
This NY Open Breakout Strategy is ideal for traders who prioritize efficient risk management while aiming to capitalize on the high liquidity periods of the Forex market. The strategy is designed to be robust, providing a pathway to profitability even in scenarios where the number of losing trades surpasses winning ones, thanks to its emphasis on a high risk-to-reward ratio.
Z Algo (Expo)█ Overview
Z Algo (Expo) is a sophisticated and user-friendly trading tool designed to meet the needs of both novice and seasoned traders. With its real-time signals, trend analysis, and risk management capabilities, this tool can be a valuable addition to any trader's toolkit.
█ Main Features & How to Use
Buy/Sell signals: Z Algo provides real-time buy and sell signals, which assist traders in identifying the most opportune moments to enter or exit a trade.
Strong Buy/Sell signals: In addition to regular buy and sell signals, the tool also offers strong buy and sell signals. These are generated when the market conditions align with a higher probability of a significant price movement.
Sniper Signals: This feature is specifically designed for contrarian traders who look to exploit temporary market inefficiencies or take advantage of price reversals. When enabled, Sniper Signals identify potential market turning points, offering traders the opportunity to profit from sharp price fluctuations.
Reversal Cloud: The Reversal Cloud is a unique visual representation of the market's potential trend reversals. It offers traders an easy-to-understand display of changing market dynamics, enabling them to quickly identify potential entry and exit points based on trend reversals.
Support and Resistance (S/R) Levels: Z Algo automatically calculates and displays support and resistance levels on the chart. These are crucial price points where buying or selling pressure may change, providing valuable insights for traders looking to enter or exit positions based on these levels.
Trend Tracker: This feature helps traders monitor and analyze the prevailing market trend. Trend Tracker identifies and highlights the direction of the trend, allowing traders to align their strategies accordingly and increase their chances of success.
Trend Background Color: To improve the user experience and simplify the interpretation of market data, Z Algo changes the chart's background color based on the identified trend direction. This visual cue makes it easier for traders to recognize bullish or bearish trends at a glance.
Bar Coloring: In addition to the trend background color, Z Algo also provides bar coloring for both contrarian and trend bars. This feature helps traders visualize price movements and trends more effectively, enabling them to identify potential opportunities for both trend-following and contrarian trading strategies.
Risk Management: The tool incorporates risk management features that help traders to protect their capital and maximize potential returns. Users can set stop-loss and take-profit levels, as well as customize their risk exposure according to their individual preferences and trading style.
█ Calculations
█ What are the Buy/Sell signals based on?
The Buy/Sell signals use volatility and price range with a weighting function that can help reduce lag and respond faster to recent price changes. The function gives more weight to the most recent volatility values and absolute price changes, making the algorithm more responsive to changes in volatility and price moves. Using a model that factors in both price changes and volatility gives a bias toward more recent data. This advanced approach to trading signal generation incorporates the concepts of trend following and mean reversion while accounting for changing market volatility.
Traditional systems often use fixed parameters, which may not adapt quickly to changes in market conditions. This can lead to late entries or exits, potentially reducing profitability or increasing risk. Our algorithm uses a weighting function to give more importance to recent volatility values, and absolute price changes can make these signals more responsive. This is especially useful in dynamic markets where price swings and volatility can change rapidly.
Adapting to Recent Price Changes: Markets can often exhibit trending behavior over certain periods. By weighing recent price changes more heavily, the model can quickly identify and react to the emergence of new trends. This can lead to earlier entries in a new trend, potentially increasing profitability.
Adapting to Recent Volatility Changes: Markets can shift from low to high volatility regimes (and vice versa) quite rapidly. A model that gives more weight to recent volatility can adapt its signals to these changing conditions. For example, in high volatility conditions, the model might generate fewer signals to reduce the risk of false breakouts. Conversely, in low volatility conditions, the model might generate more signals to capitalize on trending behavior.
Adaptive Trading: The approach inherently leads to an adaptive trading system. Rather than using fixed parameters, the system can adjust its behavior based on recent market activity. This can lead to a more robust system that performs well across different market conditions.
█ What are the Sniper signals (contrarian signals) based on?
Our contrarian signals are based on deviation from the expected value. The algorithm quantifies the amount of variation or dispersion in a set of values. Non-expected values are the fundamental core of the signal generation process.
█ Reversal Cloud Calculation
The cloud uses the information of how much the price fluctuates over a specific time period and updates its equilibrium value automatically at new price changes. The price changes are used to predict what will happen next, and the band adapts accordingly. The algorithm assumes that past price changes can predict future market behavior.
█ Support and Resistance (S/R) Levels Calculation
The support and resistance levels use historical overbought and oversold levels combined with a weighted atr function to predict future support and resistance areas. This calculation can potentially give traders a great heads-up on where the price may find support and resistance at.
█ Trend & Bar coloring Calculation
Trend calculations with dynamic events are key in ever-changing markets. The main idea of the calculation method is to find the mathematical function that best fits the data points, by minimizing the sum of the squares of the vertical distances of each data point from the equilibrium. The outcome is a function that finds the best mathematical description of that data. Hence the trend output may vary depending on the asset and timeframe. A unique approach where the same settings can give different results.
█ Risk Management Calculation
The risk management system is not unique in itself and contains everything that can help traders to manage their risk, such as different types of stop losses, Take Profits calculations.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
GKD-M Accuracy Alchemist [Loxx]Giga Kaleidoscope GKD-M Accuracy Alchemist is a Metamorphosis module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-M Accuracy Alchemist
What is the Accuracy Alchemist?
The Accuracy Alchemist is designed to process up to 10 GKD-C indicators and create a compound signal that can be utilized in a GKD-BT backtest. It achieves this by applying an individual Solo Confirmation Simple backtest to each GKD-C indicator provided. The compound signal is derived from the cumulative accuracy rate of each candle within a specified date range.
To illustrate this process, consider the following scenario:
The Fisher Transform indicator has a 65% win rate for long positions on the current ticker.
The Vortex indicator has a 45% success rate on the current candle.
Suppose a long signal is generated by the Vortex indicator. However, this signal is disregarded because its accuracy is lower than that of the Fisher Transform. Now, imagine that the subsequent candle produces a long signal from the Fisher Transform indicator. This signal will be exported to the backtest. The GKD-C indicator that exhibits the highest accuracy for the current candle is chosen to generate the signal. The dominant indicator, determined by its accuracy, will always be used to generate signals. However, it is important to note that the current dominant indicator might not retain its dominance in the future if its accuracy rate falls below that of other indicators connected within the Accuracy Alchemist indicator.
The Accuracy Alchemist provides a comprehensive table that displays the dominant indicator based on accuracy, highlighted in green for the highest long accuracy rate and in red for the highest short accuracy rate. Additionally, the table presents the cumulative long and short accuracy rates for all indicators.
The functionality of the Accuracy Alchemist extends to several GKD-BT backtests, allowing for seamless integration. These backtests include:
-Solo Confirmation Simple
-Solo Confirmation Complex
-Solo Confirmation Super Complex
-Full GKD (as a Confirmation 1 indicator only)
-Confirmation Stack (as a Confirmation 1 indicator only)
By incorporating the Accuracy Alchemist, you gain the ability to evaluate and compare GKD-C Confirmation indicators within your full GKD trading system. It serves as an ideal tool to assess the performance of different confirmation indicators and aids in the selection process for determining which indicators to incorporate into your trading strategy.
Take Profit and Stoploss
The GKD system utilizes volatility-based take profits and stop losses, where each take profit and stop loss is calculated as a multiple of volatility. Users have the flexibility to adjust the multiplier values in the settings to suit their preferences. Accuracy Alchemist tests the accuracy of GKD-C Confirmation indicators and therefore has only 1 take profit and 1 stoploss. You can adjust the multipliers of both in the settings
Setting up Accuracy Alchemist
To use this indicator, you must import GKD-C Confirmation indicators and then activate them in the Accuracy Alchemist settings. Import the value "Input into NEW GKD-BT Backtest" from a GKD-C indicator and then activate it by checking the box next to the import. See below:
Volatility Types Included
17 types of volatility are included in this indicator
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Full GKD Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Composite RSI
Confirmation 2: uf2018
Continuation: Vortex
Exit: Rex Oscillator
Metamorphosis: Fisher Transform, Universal Oscillator, Aroon, Vortex .. combined
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest:
GKD-BT Giga Stacks Backtest:
GKD-BT Full Giga Kaleidoscope Backtest:
GKD-BT Solo Confirmation Super Complex Backtest:
GKD-BT Solo Confirmation Complex Backtest:
GKD-BT Solo Confirmation Simple Backtest:
EMASAR Investor ModePLEASE READ THE FULL DESCRIPTION BEFORE BUYING OR USING THIS INDICATOR
THIS IS THE INVESTOR MODE ONLY VERSION OF THE EMASAR INDICATOR. IT INCLUDES THE ORIGINAL SIGNALS TELLING YOU WHEN TO BUY OR SELL. IT ONLY INCLUDES THE OCEAN TO INDICATE PULLBACKS AND NOT OTHER TRADING REGIONS ARE INCLUDED. IT SHOWS THE BUY/SELL SIGNALS AS WAS PUBLISHED IN THE ORIGINAL VERSION OF EMASAR
EMASAR (pronounced Emma-sar) is a strategy based on Exponential Moving Averages and the Parabolic SAR. This is a position trading approach that is derived from Tyler Jenks’ Consensio.
This strategy was developed with four objectives in mind: (1) managing risk (2) protecting from missing out on major moves (3) maximizing risk:reward (4) staying in a trending market and taking profit before it fully reverses.
EMASAR does a great job at accomplishing all of the above through the buy and sell signals that are generated. The data provided below is from the signals that occurred on Bitcoin ( Bitstamp ) from January 1, 2015 to present (November 11, 2019).
(1) Risk is tightly managed, relative to the winners, and losing positions will be exited before the market moves too far against.
The biggest losing trade on Bitcoin , for the time period outlined above, is -18.47%.
(2) Following the EMASAR buy and sell signals guarantees that one will not miss out on a major trend. As a result of the indicators used for this system it is mathematically impossible for a major trend to occur without providing a buy or sell signal. This system isn't meant to catch exact tops or bottoms but it will do a great job of capturing ~85% of a trend.
(3) On average the winning trades will be 5.55 times the losing trades. There will be stretches where the losers are bigger than the winners and this could last for many months, maybe even a year. However, over the long run the average reward is expected to be 5.55 times the average risk*.
*Past performance does not guarantee future results!
(4) This indicator was designed to capitalize on parabolic markets, specifically Bitcoin and alt coins. Crypto markets have a tendency to get moving so fast that many indicators become all but useless.
Entries can get signaled too late and exits will get signaled way too early. This is specifically true when using oscillators that are designed to identify overbought or oversold environments. EMASAR does a great job of keeping us in a position for the duration of a trend and this includes the major parabolic runs that Bitcoin has a tendency to go on.
When Bitcoin , or other alts, really get moving it can be very difficult to distinguish between a correction and a full reversal. We do not want to be exiting during a minor correction, instead this is a time when we want to be holding on or looking to buy the dip.
This is a very fragile balance. The market has a very strong tendency to make corrections looks like reversals and to make reversals look like corrections. Therefore it is very important to have a tool(s) that you trust to distinguish in between the two.
I believe that EMASAR is the best way to find that balance - if I knew of a better way then I would be using it instead!
Following these signals will help us to hold onto positions while the market is still trending in our favor when most think that it has moved too far / too fast, and it will also get us out before a market fully reverses.
Keep in mind that there will be times when we exit a market that is in danger of reversing, only to buy back higher later on. That is okay because it enables us to properly manage risk during times of uncertainty and buying back in at a higher price is more than worth the opportunity cost.
Risks
The biggest risks with trading EMASAR revolve around disobeying the signals. Risk management is built into this system with the exit signals that will occur, however it is up to the individual to execute those signals. Passing on an exit signal could lead to a big loss which would have a dramatic impact on the ROI . Most trading systems will have small and medium losses with small, medium and large wins. That is exactly how this works. The small - medium losses and wins will mostly be a wash and will account for roughly 80% of the trades. The large wins will happen about 20% of the time and will make up 80% - 90% of the profits.
Therefore the two biggest risks are passing on signals entirely, or exiting preemptively. Getting chopped in and out of a market can be quite frustrating. If you become overwhelmed with negative emotions then it could cause you to pass up on the next signal. That signal will often be the one that more than makes up for the small - medium losses that preceded.
On average EMASAR will provide one signal every 6 weeks when using the default settings on the 4h chart. Therefore missing one entry could turn an otherwise profitable year into a loser. If electing to trade a system, whether it is EMASAR or another, it is crucial to commit to taking every signal regardless of outside variables (namely your personal bias about market direction or frustration that follows a losing stretch).
Another major risk with this system is taking too much profit too soon. When getting into a trade that has the potential to be a big winner it can be challenging to continue holding through the swings. Anyone that has watched paper profits vanish will be inclined to start exiting after the market makes a big move in his or her favor. While this is better than watching profits completely evaporate, this mistake can be enough to turn a profitable system into one that loses to the market. If 80% - 90% of our profits come from 10% - 20% of our trades then it is vital we do not cut those positions off at the knees.
If taking too much profit too soon then you will consistently turn potential large winners into medium winners. This may lead to making money over the long run which will make it very difficult to realize that anything is wrong. However making money and beating the market are two very different things. Exiting early and making money is nearly as big of a risk as missing entries entirely.
If you have the discipline to execute signals in a timely manner after they are triggered and the emotional control to let the winners run despite the appearance of a vastly overbought / oversold market, then you should have what it takes to beat the market with EMASAR.
If you are not an experienced trader then it is very important to start out small. The only way to learn is to trade in a live environment and the only way to succeed is to risk much less than you can afford to lose. If you have $2,000 to trade with then start with a maximum position size of $20 - $50 and don’t be shy about scaling that down even further. Focus on ROI instead of actual dollars made. If you can return 100% on a $20 roll then you should be able to do the same with a $2,000 roll.
Important Notes
Make sure that you read / understand the risks outlined above. If you jump into this without understanding the unique risks that this system entails then you are going to have a bad time.
This indicator was developed around the 4h and that is where it works best. For crypto adjusting to higher TF’s will cause for bad results as the entries / exits will be late to the party. For traditional markets the Daily - Weekly time frames are preferred. It was not originally intended for smaller TF's but we have seen some good results on the 15m and 1h. The RSI can be a great compliment when using on smaller TF's. Adding a rule for not entering when RSI > 75 or < 25 and instead entering when RSI retests 50 will help to avoid some bad signals.
Alerts can be set for this indicator. Simply make sure that it is visible on the chart, then click the alert icon on the top panel. In the first dropdown set 'Condition' to 'EMASAR' and the second 'Condition' for the upcoming signal. For example if just entered long then set the second condition to 'Close Long' and you will be notified as soon as that signal occurs. If waiting for the next long entry then set the second condition to 'Open Long' so on and so forth . There is an 'All in One' alert that is also available. If you select that then you will be alerted any time that a signal occurs. The message will tell you to check the chart to see which signal caused the alert.
Portfolio Backtester Engine█ OVERVIEW
Portfolio Backtester Engine (PBTE). This tool will allow you to backtest strategies across multiple securities at once. Allowing you to easier understand if your strategy is robust. If you are familiar with the PineCoders backtesting engine , then you will find this indicator pleasant to work with as it is an adaptation based on that work. Much of the functionality has been kept the same, or enhanced, with some minor adjustments I made on the account of creating a more subjectively intuitive tool.
█ HISTORY
The original purpose of the backtesting engine (`BTE`) was to bridge the gap between strategies and studies . Previously, strategies did not contain the ability to send alerts, but were necessary for backtesting. Studies on the other hand were necessary for sending alerts, but could not provide backtesting results . Often, traders would have to manage two separate Pine scripts to take advantage of each feature, this was less than ideal.
The `BTE` published by PineCoders offered a solution to this issue by generating backtesting results under the context of a study(). This allowed traders to backtest their strategy and simultaneously generate alerts for automated trading, thus eliminating the need for a separate strategy() script (though, even converting the engine to a strategy was made simple by the PineCoders!).
Fast forward a couple years and PineScript evolved beyond these issues and alerts were introduced into strategies. The BTE was not quite as necessary anymore, but is still extremely useful as it contains extra features and data not found under the strategy() context. Below is an excerpt of features contained by the BTE:
"""
More than `40` built-in strategies,
Customizable components,
Coupling with your own external indicator,
Simple conversion from Study to Strategy modes,
Post-Exit analysis to search for alternate trade outcomes,
Use of the Data Window to show detailed bar by bar trade information and global statistics, including some not provided by TV backtesting,
Plotting of reminders and generation of alerts on in-trade events.
"""
Before I go any further, I want to be clear that the BTE is STILL a good tool and it is STILL very useful. The Portfolio Backtesting Engine I am introducing is only a tangental advancement and not to be confused as a replacement, this tool would not have been possible without the `BTE`.
█ THE PROBLEM
Most strategies built in Pine are limited by one thing. Data. Backtesting should be a rigorous process and researchers should examine the performance of their strategy across all market regimes; that includes, bullish and bearish markets, ranging markets, low volatility and high volatility. Depending on your TV subscription The Pine Engine is limited to 5k-20k historical bars available for backtesting, which can often leave the strategy results wanting. As a general rule of thumb, strategies should be tested across a quantity of historical bars which will allow for at least 100 trades. In many cases, the lack of historical bars available for backtesting and frequency of the strategy signals produces less than 100 trades, rendering your strategy results inconclusive.
█ THE SOLUTION
In order to be confident that we have a robust strategy we must test it across all market regimes and we must have over 100 trades. To do this effectively, researchers can use the Portfolio Backtesting Engine (PBTE).
By testing a strategy across a carefully selected portfolio of securities, researchers can now gather 5k-20k historical bars per security! Currently, the PTBE allows up to 5 securities, which amounts to 25k-100k historical bars.
█ HOW TO USE
1 — Add the indicator to your chart.
• Confirm inputs. These will be the most important initial values which you can change later by clicking the gear icon ⚙ and opening up the settings of the indicator.
2 — Select a portfolio.
• You will want to spend some time carefully selecting a portfolio of securities.
• Each security should be uncorrelated.
• The entire portfolio should contain a mix of different market regimes.
You should understand that strategies generally take advantage of one particular type of market regime. (trending, ranging, low/high volatility)
For example, the default RSI strategy is typically advantageous during ranging markets, whereas a typical moving average crossover strategy is advantageous in trending markets.
If you were to use the standard RSI strategy during a trending market, you might be selling when you should be buying.
Similarily, if you use an SMA crossover during a ranging market, you will find that the MA's may produce many false signals.
Even if you build a strategy that is designed to be used only in a trending market, it is still best to select a portfolio of all market regimes
as you will be able to test how your strategy will perform when the market does something unexpected.
3 — Test a built-in strategy or add your own.
• Navigate to gear icon ⚙ (settings) of strategy.
• Choose your options.
• Select a Main Entry Strat and Alternate Entry Strat .
• If you want to add your own strategy, you will need to modify the source code and follow the built-in example.
• You will only need to generate (buy 1 / sell -1/ neutral 0) signals.
• Select a Filter , by default these are all off.
• Select an Entry Stop - This will be your stop loss placed at the trade entry.
• Select Pyamiding - This will allow you to stack positions. By default this is off.
• Select Hard Exits - You can also think of these as Take Profits.
• Let the strategy run and take note of the display tables results.
• Portfolio - Shows each security.
• The strategy runs on each asset in your portfolio.
• The initial capital is equally distributed across each security.
So if you have 5 securities and a starting capital of 100,000$ then each security will run the strategy starting with 20,000$
The total row will aggregate the results on a bar by bar basis showing the total results of your initial capital.
• Net Profit (NP) - Shows profitability.
• Number of Trades (#T) - Shows # of trades taken during backtesting period.
• Typically will want to see this number greater than 100 on the "Total" row.
• Average Trade Length (ATL) - Shows average # of days in a trade.
• Maximum Drawdown (MD ) - Max peak-to-valley equity drawdown during backtesting period.
• This number defines the minimum amount of capital required to trade the system.
• Typically, this shouldn’t be lower than 34% and we will want to allow for at least 50% beyond this number.
• Maximum Loss (ML) - Shows largest loss experienced on a per-trade basis.
• Normally, don’t want to exceed more than 1-2 % of equity.
• Maximum Drawdown Duration (MDD) - The longest duration of a drawdown in equity prior to a new equity peak.
• This number is important to help us psychologically understand how long we can expect to wait for a new peak in account equity.
• Maximum Consecutive Losses (MCL) - The max consecutive losses endured throughout the backtesting period.
• Another important metric for trader psychology, this will help you understand how many losses you should be prepared to handle.
• Profit to Maximum Drawdown (P:MD) - A ratio for the average profit to the maximum drawdown.
• The higher the ratio is, the better. Large profits and small losses contribute to a good PMD.
• This metric allows us to examine the profit with respect to risk.
• Profit Loss Ratio (P:L) - Average profit over the average loss.
• Typically this number should be higher in trend following systems.
• Mean reversion systems show lower values, but compensate with a better win %.
• Percent Winners (% W) - The percentage of winning trades.
• Trend systems will usually have lower win percentages, since statistically the market is only trending roughly 30% of the time.
• Mean reversion systems typically should have a high % W.
• Time Percentage (Time %) - The amount of time that the system has an open position.
• The more time you are in the market, the more you are exposed to market risk, not to mention you could be using that money for something else right?
• Return on Investment (ROI) - Your Net Profit over your initial investment, represented as a percentage.
• You want this number to be positive and high.
• Open Profit (OP) - If the strategy has any open positions, the floating value will be represented here.
• Trading Days (TD) - An important metric showing how many days the strategy was active.
• This is good to know and will be valuable in understanding how long you will need to run this strategy in order to achieve results.
█ FEATURES
These are additional features that extend the original `BTE` features.
- Portfolio backtesting.
- Color coded performance results.
- Circuit Breakers that will stop trading.
- Position reversals on exit. (Simulating the function of always in the market. Similar to strategy.entry functionality)
- Whipsaw Filter
- Moving Average Filter
- Minimum Change Filter
- % Gain Equity Exit
- Popular strategies, (MACD, MA cross, supertrend)
Below are features that were excluded from the original `BTE`
- 2 stage in-trade stops with kick-in rules (This was a subjective decision to remove. I found it to be complex and thwarted my use of the `BTE` for some time.)
- Simple conversion from Study to Strategy modes. (Not possible with multiple securities)
- Coupling with your own external indicator (Not really practical to use with multiple securities, but could be used if signals were generated based on some indicator which was not based on the current chart)
- Use of the Data Window to show detailed bar by bar trade information and global statistics.
- Post Exit Analysis.
- Plotting of reminders and generation of alerts on in-trade events.
- Alerts (These may be added in the future by request when I find the time.)
█ THANKS
The whole PineCoders team for all their shared knowledge and original publication of the BTE and Richard Weismann for his ideas on building robust strategies.
═════════════════════════════════════════════════════════════════════════
Cracking Cryptocurrency - MynxCracking Cryptocurrency - Mynx
Mynx is a powerful trend-following indicator which logic built in to facilitate nuanced aspects of PTP strategy. Continuation Trades, Early Exit Signals and Full Take Profit Signals are all built into Mynx. Mynx is designed to identify when the market is signaling that a trend is beginning as well as signalling when you may safely re-enter into a pre-existing trend. It also tells you where to exit prematurely to avoid significant capital loss on a losing trade, and when to take full profit in order to get out of a position at maximum profitability.
Should you have difficulty adding it you can search for 'Cracking' in the indicator window of your Trading View Platform, and it will appear along with the rest of our indicators.
As you will notice, Mynx is similar in nature to Time Transformation, in that it is both a Centered Oscillator and a Line Cross Indicator. This allows a wide range of possibilities which we will exploit to extract profit out of the market.
The most important line is the BPM or Mynx Line. This is going to be the thicker, brighter colored line on our indicator that will switch from green to red depending on the dominant trend. The color changing feature of this line will denote where our indicator is in relation to our zero line. If our BPM Line is green, then price is trading above our zero line, if red than it is trading below. Therefore we can quickly see what our last signal was, and whether our indicator is bullish or bearish depending upon the color of our BPM Line. If Green, we are in long territory, if red we are looking for shorts.
Our second area of importance in this indicator is our Noise Line and Area. This is the black or white line which will change color depending on where Noise is relative to our zero line. Black if above the zero line, meaning we are in long territory, and white if below, indicating we are in shorting territory. The area between the zero line and the Noise Line is shaded black or white as well, and denotes no-trade zones for us. If our BPM Line is within our Noise Area, we are not in a trade. We are only in a trade when our BPM Line is breaking away from Noise Line, either to the upside or the downside. When BPM curls back and re-enters our Noise Area, that is an indication to exit our position regardless of our Take Profits or Stop Losses.
Our Zero Line is indicated by a thick black line for easy of identification, which will be our primary signal generator when our BPM Line crosses above or below it, and our faint gray lines are going to be our Overbought and Oversold levels respectively. These will play a function in how we take profits.
Settings
Let's take a look at the settings of our Mynx Indicator.
The first field we will see is our BPM Source, how many beats per minute we want in our cycle length. The default is ten, and through back-testing I find this to be the optimum level.
Our Noise Filtration level corresponds to our Noise Line and Area. Again, I find the default settings as I have programmed them work best on all time frames, however you might find optimum signals by playing around with these settings. As in all things, nuance and experimentation is what excellence is borne of.
We can adjust our Oversold and Overbought Parameters, making it easier or harder for us to get a Full Take Profit Signal from this indicator. We can also adjust our Base Line. Keep in mind, our Base Line is going to be an Exponential Moving Average and here we can adjust the length of our desired Base Line.
To incorporate our Multi-Time Frame feature, below this section we can see that the default option will be to use a Base Line of the current time frame. If however, we want to trade on a Lower Time Frame and reference a base line of another time frame, we can simply un-check that box and input the desired Time Frame of the Base Line we wish to reference.
Now for the true appeal of Mynx in user friendliness, below this we can see that we have full control of the signals we want Mynx to generate for us. By default, we have enabled Mynx's best signals, Trending Signals. We can choose to plot Continuation Crosses and Reversals Within the Trend, both of which are trades we are allowed to take and I recommend taking in a proper PTP system. We can choose to use Mynx as a reversal indicator, to plot Raw Reversals against the trend, which I do not recommend enabling unless you want to by pass PTP entirely. We can choose to plot when Mynx gives us a Full Take Profit signal, a signal to take full profits on our position. We also have the option to plot for early exits. The Option for Sensitive Stop Loss is the same as early exits, and will plot an exit signal every time the BPM Line drifts back into the Noise Area. These are fairly easy to see upon bar closes, so I left it disabled by default. If you enable it you will get a visual signal to exit your position upon a bar close.
One final note, we can adjust the source upon which Mynx is basing her signals. By default, this value is hl2 , which I found in back-testing to be the most efficient with minimum draw down. However, there is a higher profit potential with HLC3 and OHLC4, but be warned that with that extra profit potential comes the risk of more draw down. The draw down settles out in the end, however there will be months where you drastically under perform Alpha, where as hl2 keeps you consistently outperforming the market at all times. This is a very personal choice, and I leave it upon you to make the right one.
In our Style Tab, we can adjust our Color Scheme to better accommodate the way that you trade. I have done my best to be very concise and detailed in labeling to make this task easy.
Strategy
Please let me know of your success stories with Mynx, as well as any features you think would be helpful to add. If you notice any errors within it, please notify me so I can fix them. I have back-tested this strategy many times in many different settings, and it consistently outperforms the market and generates Alpha. I now place it within your hands to achieve the same results. Trade Safely.
BKN: Thick CutThick Cut is the juiciest BKN yet. This indicator is created to take a profitable trading strategy and turn it into an automated system. We've built in several pieces that professional traders use every day and turned it into an algo that produces on timeframes as low as 1, 3, and 5 minutes!
Limit Order Entries: When criteria is met, an alert is signaled that will send a value to enter a position at a limit price.
Built in Stop Loss: A stop is built in and the value can be sent to your bot using the {{plot}} function or you can rely on a TradingView alert when the stop is hit.
Built in Take Profits: We've built in two separate take profits and the ability to move your stop loss to breakeven after the first take profit is hit. Even if you take 50% profit at 1R and move your stop loss, you already have a profitable trade. Test results show 50% profits at 2R and the remainder at higher returns result in exceptional results.
Position Sizing: We've built in a position size based on your own predetermined risk. Want to risk $100 per trade? Great, put in 100 in the inputs and reference a quantity of {{plot("Position Size")}} in your alert to send a position size to the bot. You can also reference {{plot("Partial Close")}} to pull 50% of the position size closing 50% at TP1 and 50% at TP2.
Backtest results shown are very short term since we are viewing a 15m chart. This can be a profitable strategy on many timeframes, but lower timeframes will maximize results.
A unique script with incredible results. Further forward testing is live.
***IMPORTANT***
For access, please do not comment below. Comments here will not be replied to. Please send a DM here or on my linked Twitter . At this time, this strategy is considered a Beta release as we continue to fine tune settings and more. Expecting 2 weeks of beta with official release around June 6.
Theft Indicator - BUY/SELL AlertsWhat is our indicator?
Theft Indicator - Buy & Sell Alert System is our first published script that shows price action on a certain period of time (We Use ATR indicator). We take pride in enabling trading to become easier for the experienced and the non-experienced traders around the globe. Buy & Sell alerts will be fired once a conditions in our algo is met.
Does it Repaint?
Our indicator does NOT re-paint. Although while setting an alert it may pop up the repaint alert, please take into consideration that once a signal is fired on a "CLOSED BAR", our signal will never disappear, they do not repaint.
What Markets is it usable with?
You can use it in any market, Forex, Stocks, Crypto, Indices. We recommend high time frames but you can also use it on the 1 minute chart if you are a scalper and a risk taker. All time frames are profitable, not all trades. But the Majority is profitable. We will soon add a backtest strategy for it, there is no ETA on it tho.
How to use:
Simple plug and play it to your chart, in addition to a few other indicators we will recommend to you (we still have not published them yet), and this will confirm your trades. You can also connect TV alerts with a bot and let it run. Please be aware that SLIPPAGE time is important, If you run a bot on this indicator you HAVE to know that the buy/sell price will be on the bar AFTER the Candle close (For example: the BUY/SELL alert is on a candle, the buy/sell your bot or you will execute WILL be in the following candle depending on your trading system. Theft Indicator - Buy/Sell Alerts work best with higher time frames, however it works on smaller time frames, we recommend 15 mins, 30 mins, 1hr, 4hr. It just depends on your trading style. Please contact us if you do not understand how to use it.
How are the Buy/Sell Alerts fired?
We use the simple ATR (Average True Range) indicator. However we have modified the indicator to fit our trading system. Check below for a definition of what ATR is:
What is Average True Range - ATR?
The average true range (ATR) is a technical analysis indicator that measures market volatility by decomposing the entire range of an asset price for that period. Specifically, ATR is a measure of volatility introduced by market technician J. Welles Wilder Jr. The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The average true range is then a moving average, generally using 14 days, of the true ranges.
Why is our indicator special and different from the normal ATR indicators?
We have modified the mathematical equation and changed it slightly to give more accurate signals, we do not promise all trades are profitable, the use of this indicator is up to your own judgement and liability. We believe that we have an indicator like no other ATR because of our algo that is different from the normal ATR calculation.
P.S: This is not financial advice, we are just sharing our indicator that we know has good results, and it will take time for people in -ve profiles to recover losses and for the profiting to be more profitable.
You can contact me for more information about the indicator, Goodluck :)
EMASARPLEASE READ THE FULL DESCRIPTION BEFORE BUYING OR USING THIS INDICATOR
EMASAR (pronounced Emma-sar) is a strategy based on Exponential Moving Averages and the Parabolic SAR . This is a position trading approach that is derived from Tyler Jenks’ Consensio.
This strategy was developed with four objectives in mind: (1) managing risk (2) protecting from missing out on major moves (3) maximizing risk:reward (4) staying in a trending market and taking profit before it fully reverses.
EMASAR does a great job at accomplishing all of the above through the buy and sell signals that are generated. The data provided below is from the signals that occurred on Bitcoin (Bitstamp) from January 1, 2015 to present (November 11, 2019).
(1) Risk is tightly managed, relative to the winners, and losing positions will be exited before the market moves too far against.
The biggest losing trade on Bitcoin, for the time period outlined above, is -18.47%.
(2) Following the EMASAR buy and sell signals guarantees that one will not miss out on a major trend. As a result of the indicators used for this system it is mathematically impossible for a major trend to occur without providing a buy or sell signal. This system isn't meant to catch exact tops or bottoms but it will do a great job of capturing ~85% of a trend.
(3) On average the winning trades will be 5.55 times the losing trades. There will be stretches where the losers are bigger than the winners and this could last for many months, maybe even a year. However, over the long run the average reward is expected to be 5.55 times the average risk*.
*Past performance does not guarantee future results!
(4) This indicator was designed to capitalize on parabolic markets, specifically Bitcoin and alt coins. Crypto markets have a tendency to get moving so fast that many indicators become all but useless.
Entries can get signaled too late and exits will get signaled way too early. This is specifically true when using oscillators that are designed to identify overbought or oversold environments. EMASAR does a great job of keeping us in a position for the duration of a trend and this includes the major parabolic runs that Bitcoin has a tendency to go on.
Take a look at the two charts below which illustrates the buy and sell signals that occurred at the beginning and end of the 2017 and 2019 parabolic moves. Green = Buy | Blue = Exit | Red = Short
Long signaled at $4,190.27 on September 29th, 2017
Exit signaled at $13,647 on January 14th, 2018
Short signaled at $12,050 on January 16th, 2018
Close Short signaled at $3,684 on February 18th, 2019
Long signaled at $3,684 on February 18th, 2019
Exit signaled at $9,614 on July 16th, 2019
Short signaled at $10,328 on July 22nd, 2019
When Bitcoin, or other alts, really get moving it can be very difficult to distinguish between a correction and a full reversal. We do not want to be exiting during a minor correction, instead this is a time when we want to be holding on or looking to buy the dip.
This is a very fragile balance. The market has a very strong tendency to make corrections looks like reversals and to make reversals look like corrections. Therefore it is very important to have a tool(s) that you trust to distinguish in between the two.
I believe that EMASAR is the best way to find that balance - if I knew of a better way then I would be using it instead!
Following these signals will help us to hold onto positions while the market is still trending in our favor when most think that it has moved too far / too fast, and it will also get us out before a market fully reverses.
Keep in mind that there will be times when we exit a market that is in danger of reversing, only to buy back higher later on. That is okay because it enables us to properly manage risk during times of uncertainty and buying back in at a higher price is more than worth the opportunity cost.
Lets look at the signals above in chronological order:
1) Close Long: $2,274
2) Open Short: $2,347
3) Exit Short: $2,934
4) Open Long: $2,766
5) Close Long: $3,124
6) Enter Long: $4,190
A long was closed at $2,274 after Signal #1 and was re-entered after Signal #4 at $2,766. Additionally a long was closed at $3,124 after Signal #5 and was re-entered on the following signal at $4,190. These are examples of some of the bad signals that will occur. Something to pay attention to is the ratio of the risk to the reward. When the market turns against us EMASAR will quickly signal an exit or a re entry.
EMASAR also works great in traditional markets. The S&P 500 has been on a tear lately after creating new all time highs in October of 2019. It has resumed it's strong bull trend and therefore it is a great market to have long exposure to. That being said we are well overdue for a correction and most people, including myself, expect the next bear market to be much more severe than the last two. Therefore I would not want to have long exposure unless equipped with a very reliable method for taking profit before it fully reverses.
Let's take a look at the S&P 500 weekly EMASAR signals using the preferred settings outlined below:
In August of 1990 EMASAR signaled a 'Close Long' at $308. At that time the market was in danger of fully reversing. When that didn't happen EMASAR gave a signal to re enter at $369 which resulted in losing 19.8% in opportunity cost. That is quite okay because it would have allowed us to properly protect ourselves in the event that the market proceeded to crash. Instead we entered a massive bull market that culminated in the dot com bubble. Notice how EMASAR kept us in for the entire duration of that bull run and then signaled an exit very close to the top at $1,294. It got us back in by the end of 2004 after the market had bottomed. Yet again it kept us in for the following multi year bull market before signaling an exit very close to the top at $1,270.
The action that followed in 2016 looks very similar to what happened in 1990 - 1991. An exit was signaled when the market was in danger of fully reversing. When that didn't happen a re entry was signaled 14% higher. Now the market appears to be taking off in another parabolic advance. There is no way to know how far this next run will go or how long it will last. Nevertheless I feel highly confident that I will be able to hold on for the majority of the trend and then get fully out before it reverses thanks to the signals provided by EMASAR.
When looking at the signals on Gold we will notice striking similarities to the signals in the S&P 500 as well as Bitcoin.
Notice how an entry was signaled very close to the bottom at $323 in June of 2002. An exit was also signaled very close to the top at $1,441 in April of 2013. Throughout that runup there was one bad signal that cost some opportunity. It's very important to understand that missing out on opportunity is well worth the price because it allows us to effectively manage risk. EMASAR also recently provided a long signal at $1,401 which preceded this recent runup.
Settings
Default settings work best for crypto, however the time multiplier should be adjusted for markets that are not open 24/7. For commodities and FOREX my default is 40 and for stocks I use 24. The Moving Averages can be adjusted as well. The period can be changed and you can also select SMA or EMA. I always use the EMA's and strongly prefer the 50 and 200. We have noticed good results with the 9 and 54 EMA's as well. The shorter the period that the Moving Average is set to the more frequent the signals will be. This will generally improve risk:reward while decreasing strike rate. For crypto the best time frames are the 4h and 6h. For traditional markets the best time frames are the Daily, 3D and Weekly. EMASAR can be used on smaller time frames as well, specifically in crypto. The 15m and 1h have shown good results.
Risks
The biggest risks with trading EMASAR revolve around disobeying the signals. Risk management is built into this system with the exit signals that will occur, however it is up to the individual to execute those signals. Passing on an exit signal could lead to a big loss which would have a dramatic impact on the ROI. Most trading systems will have small and medium losses with small, medium and large wins. That is exactly how this works. The small - medium losses and wins will mostly be a wash and will account for roughly 80% of the trades. The large wins will happen about 20% of the time and will make up 80% - 90% of the profits.
Therefore the two biggest risks are passing on signals entirely, or exiting preemptively. Getting chopped in and out of a market can be quite frustrating. If you become overwhelmed with negative emotions then it could cause you to pass up on the next signal. That signal will often be the one that more than makes up for the small - medium losses that preceded.
On average EMASAR will provide one signal every 6 weeks when using the default settings on the 4h chart. Therefore missing one entry could turn an otherwise profitable year into a loser. If electing to trade a system, whether it is EMASAR or another, it is crucial to commit to taking every signal regardless of outside variables (namely your personal bias about market direction or frustration that follows a losing stretch).
Another major risk with this system is taking too much profit too soon. When getting into a trade that has the potential to be a big winner it can be challenging to continue holding through the swings. Anyone that has watched paper profits vanish will be inclined to start exiting after the market makes a big move in his or her favor. While this is better than watching profits completely evaporate, this mistake can be enough to turn a profitable system into one that loses to the market. If 80% - 90% of our profits come from 10% - 20% of our trades then it is vital we do not cut those positions off at the knees.
If taking too much profit too soon then you will consistently turn potential large winners into medium winners. This may lead to making money over the long run which will make it very difficult to realize that anything is wrong. However making money and beating the market are two very different things. Exiting early and making money is nearly as big of a risk as missing entries entirely.
If you have the discipline to execute signals in a timely manner after they are triggered and the emotional control to let the winners run despite the appearance of a vastly overbought / oversold market, then you should have what it takes to beat the market with EMASAR.
If you are not an experienced trader then it is very important to start out small. The only way to learn is to trade in a live environment and the only way to succeed is to risk much less than you can afford to lose. If you have $2,000 to trade with then start with a maximum position size of $20 - $50 and don’t be shy about scaling that down even further. Focus on ROI instead of actual dollars made. If you can return 100% on a $20 roll then you should be able to do the same with a $2,000 roll.
Important Notes
Make sure that you read / understand the risks outlined above. If you jump into this without understanding the unique risks that this system entails then you are going to have a bad time.
This indicator was developed around the 4h and that is where it works best. For crypto adjusting to higher TF’s will cause for bad results as the entries / exits will be late to the party. For traditional markets the Daily - Weekly time frames are preferred. It was not originally intended for smaller TF's but we have seen some good results on the 15m and 1h. The RSI can be a great compliment when using on smaller TF's. Adding a rule for not entering when RSI > 75 or < 25 and instead entering when RSI retests 50 will help to avoid some bad signals.
Alerts can be set for this indicator. Simply make sure that it is visible on the chart, then click the alert icon on the top panel. In the first dropdown set 'Condition' to 'EMASAR' and the second 'Condition' for the upcoming signal. For example if just entered long then set the second condition to 'Close Long' and you will be notified as soon as that signal occurs. If waiting for the next long entry then set the second condition to 'Open Long' so on and so forth. There is an 'All in One' alert that is also available. If you select that then you will be alerted any time that a signal occurs. The message will tell you to check the chart to see which signal caused the alert.
How to Buy
The EMASAR Indicator is available for purchase on my website. The link can be found in my signature or in the tagline of my Trading View profile.
The price is $500 per year which is only payable in Bitcoin. That also includes access to a private Telegram group.
Cyatophilum Bands Pro Trader V3 [ALERTSETUP]An Original Automated Strategy that can be used for Manual or Bot Trading, on any timeframe and market.
>> Presentation <<
This script comes with a Backtest Version
How it works
No, these are NOT Bollinger Bands..
The Cyatophilum Bands are an original formula that I created. You will probably never find it anywhere else.
Their behavior is the following:
When they are horizontal it means the trend is going sideways and they represent supports (lower band) and resistances (upper band).
When they are climbing or falling it means the trend is either bullish or bearish and they represent Trend Lines.
The strategy enters Long on a Bull Breakout and enters Short on a Bear Breakout.
The exits are triggered either on a Trend Reversal, a Stop Loss or a Take Profit.
FEATURES
Take Profit System
Stop Loss System
Show Net profit Line
More features here
Finding a profitable configuration is GUARANTEED
0. Choose your symbol and timeframe. Then add the Backtest version to your chart. If at any time you decide to change your timeframe, go back to step 1.
1. Open the strategy tester and look at the buy & hold line.
If it is mostly climbing (last value greater than 0) then it means we are in a bull market. You should then opt or a long only strategy.
If it is mostly dropping (last value lower than 0) then it means we are in a bear market. You should then opt or a short only strategy.
Note : This first step is really important. Trading against the market has very little chances to succeed.
2. Go into the Strategy Input Parameters:
check "Enable Long Results" and uncheck "Enable Short Results" if you are in a long only strategy.
check "Enable Short Results" and uncheck "Enable Long Results" if you are in a short only strategy.
3. Open the Strategy Tester and open the Strategy Properties.
We are going to find the base parameters for the Bands.
The "Bands Lookback" is the main parameter to configure for any strategy. It corresponds to how strong of a support and resistance the bands will behave. The lower the timeframe, the higher lookback you will need. It can move from 10 to 60. For example 60 is a good value for a 3 minute timeframe. Try different values, and look at the "net profit" value in the Overview tab of the Strategy Tester. Keep the Lookback value that shows the best net profit value.
Then play with the "Bands Smoothing" from 2 to 20 and keep the best net profit value.
The "Band Smoothing" is used to reduce noise.
Usually, the default value (10) is what gives the best results.
From this point you should already be able to have a profitable strategy (net profit>0), but we can improve it using the Stop Loss and the Take Profit feature.
4. To activate the Stop Loss feature, click on the "SECURITY" checkbox
You should see horizontal red lines appear.
A Long/short exit alert will be triggered if the price were to cross this line. (A red Xcross will appear)
Choose the Stop Loss percentage.
On top of that, you can enable the feature "Trailing Stop". It will make the red line follow the price, at a speed that you can configure with the "Trailing Speed" parameter.
Now, sometimes a stop is triggered and it was just a fakeout. You can enable "Re-entries after a stop" to avoid missing additional opportunities.
5. To activate the Take Profit feature, click on the "TAKE PROFIT" checkbox
You should see horizontal green lines appear.
A Long/short exit alert will be triggered if the price were to cross this line. (A flag will appear)
Choose the Take Profit percentage.
A low takeprofit will provide a safer strategy but can reduce potential profits.
A higher takeprofit will increase risk but can provide higher potential profits.
6. Money Management
You can configure the backtest according to your own money management.
Let's say you have 10 000 $ as initial capital and want to trade only 5%, set the Order Size to 5% of Equity.
You can increase net profit by increasing the order size but this is at your own risk.
How to create alerts explained here
Sample Uses Cases
Use it literally anywhere
This indicator can be used on any timeframe and market (not only cryptocurrencies).
About the Backtest below
The Net Profit (Gross profit - Gross loss) is calculated with a commission of 0.05% on each order.
No leverage used. This is a long strategy.
Each trade is made with 10 % of equity from an inital capital of 10 000$. The net profit can be bigger by increasing the % of equity but this a trader's rule to minimise the risk.
I am selling access to all my indicators on my website : blockchainfiesta.com
To get a 2 days free trial, just leave a comment , thanks !
Join my Discord for help, configurations, requests, etc. discord.gg
Quantum Trend MonitorCurrency pairs never go up or down in a straight line. They rise and fall constantly, creating pullbacks and reversals. And with each rise and fall, so your emotions rise and fall. Hope, then fear, then hope again. This is when the market will try to frighten you out of a strong position. It is when you are most vulnerable.
But not if you have the Quantum Trend Monitor. And here, you get two indicators for the price of one!
The Quantum Trend Monitor has been designed to absorb these temporary pauses and pullbacks. It analyses the price action, and only changes to a transitional color of dark red or dark blue, if the trend is showing a temporary sign of weakness. If it is a true change in trend direction, the indicator will change to a bright color, as the new trend develops.
In other words, the Quantum Trend Monitor, does just that. It monitors the strength of the trend. This is displayed as a solid horizontal bar at the bottom of the screen. The trend monitor works in conjunction with the Quantum Trends indicator, helping to keep you in – guess what – the trend. One of the hardest things to do in trading. But, with the Quantum Trend Monitor, it’s a breeze. We call it, ‘the profit generator’, as that’s exactly what it is. It will help you hold any position for longer periods, maximising your profits. No more closing out early and suffering from trader regret. No more emotional trading decision. Just watch your Quantum Trend Monitor, which….. monitors it for you.
The Quantum Trend Monitor displays four colours at the bottom of the screen as a solid bar:
Bright blue – strongly bullish trend
Bright red – strongly bearish trend
Dark blue – weakness in trend
Dark red – weakness in trend
First, the Quantum Trends indicator alerts you to a possible new trend. If the trend is strong, the Quantum Trend Monitor will change to either bright blue or bright red, supporting the Trends indicator as the trend develops.
Used in conjunction with a multiple time frame approach, it is the indicator which will really make ‘the trend your friend’. Now you will be able to monitor the trends in multiple timeframes in real time, reducing risk, improving returns, and increasing your overall profitability.
The Quantum Trends create the signal, its sister indicator the Quantum Trend Monitor then kicks in. Together, these two indicators provide you with the perfect tools to manage your position. No more fear, no more doubt, no more uncertainty. Watch your trading account grow, as you allow your profits to run – with confidence!
And guess what – it doesn’t end there.
Remember we said you get two indicators for the price of one here! Well, to help you further, the Quantum Trend Monitor comes with its own unique trend line, which gives you a further ‘heads up’ on the trend. For clarity and simplicity, this indicator overlays the Quantum Trend Monitor and appears as a yellow line. The line chart shows the momentum of the trend and works as follows:
If the yellow trend line is above the zero line, there is a bullish trend in place
If the yellow trend line is below the zero line, there is a bearish trend in place
When the yellow trend line crosses the zero line, the trend has reversed
The further the yellow trend line gets from the zero line, the greater the strength and momentum of the trend
As the old saying goes – two heads are better than one. Here we could say – two indicators are better than one. Now with the Quantum Trend Monitor and the yellow trend line, you have a complete picture of the trend. Staying in and maximising your profits has never been easier.
And finally.. this is the next generation of TradingView indicators and virtually all our indicators can be configured to suit your own trading style. Why? Well, you buy everything else to suit you and your personality – from clothes to cars – so why not trading indicators? After all, as a scalping forex trader you will have different requirements to a swing or trend trader. You wouldn’t buy clothes that don’t fit, so why put up with indicators that cannot be configured. Simple.
One size fits all does not apply – in our view!
So, just like our other indicators, the Quantum Trend Monitor can be ‘tweaked’ to suit your trading style – the benefits are self-evident. Your trading consistency and profits will improve. After all, you are now using tools matched to the job. Precision trading requires precision tools and fine tuning. Don’t make do with second best.
With the Quantum Trend Monitor, you are in control. Just like the volume control on your radio, you adjust the sensitivity of the indicator to suit your trading style. Turning up the sensitivity a little alerts you earlier to periods of congestion – important if you are purely scalping. Turn it down a little, and this will smooth out these phases and keep you in those longer term trends for maximum profits.
The two indicators work in all timeframes.
Getting in is easy – staying in is hard. With the Quantum Trend Monitor and the associated trend line, staying in becomes easy too!
Quantum Rotational Field MappingQuantum Rotational Field Mapping (QRFM):
Phase Coherence Detection Through Complex-Plane Oscillator Analysis
Quantum Rotational Field Mapping applies complex-plane mathematics and phase-space analysis to oscillator ensembles, identifying high-probability trend ignition points by measuring when multiple independent oscillators achieve phase coherence. Unlike traditional multi-oscillator approaches that simply stack indicators or use boolean AND/OR logic, this system converts each oscillator into a rotating phasor (vector) in the complex plane and calculates the Coherence Index (CI) —a mathematical measure of how tightly aligned the ensemble has become—then generates signals only when alignment, phase direction, and pairwise entanglement all converge.
The indicator combines three mathematical frameworks: phasor representation using analytic signal theory to extract phase and amplitude from each oscillator, coherence measurement using vector summation in the complex plane to quantify group alignment, and entanglement analysis that calculates pairwise phase agreement across all oscillator combinations. This creates a multi-dimensional confirmation system that distinguishes between random oscillator noise and genuine regime transitions.
What Makes This Original
Complex-Plane Phasor Framework
This indicator implements classical signal processing mathematics adapted for market oscillators. Each oscillator—whether RSI, MACD, Stochastic, CCI, Williams %R, MFI, ROC, or TSI—is first normalized to a common scale, then converted into a complex-plane representation using an in-phase (I) and quadrature (Q) component. The in-phase component is the oscillator value itself, while the quadrature component is calculated as the first difference (derivative proxy), creating a velocity-aware representation.
From these components, the system extracts:
Phase (φ) : Calculated as φ = atan2(Q, I), representing the oscillator's position in its cycle (mapped to -180° to +180°)
Amplitude (A) : Calculated as A = √(I² + Q²), representing the oscillator's strength or conviction
This mathematical approach is fundamentally different from simply reading oscillator values. A phasor captures both where an oscillator is in its cycle (phase angle) and how strongly it's expressing that position (amplitude). Two oscillators can have the same value but be in opposite phases of their cycles—traditional analysis would see them as identical, while QRFM sees them as 180° out of phase (contradictory).
Coherence Index Calculation
The core innovation is the Coherence Index (CI) , borrowed from physics and signal processing. When you have N oscillators, each with phase φₙ, you can represent each as a unit vector in the complex plane: e^(iφₙ) = cos(φₙ) + i·sin(φₙ).
The CI measures what happens when you sum all these vectors:
Resultant Vector : R = Σ e^(iφₙ) = Σ cos(φₙ) + i·Σ sin(φₙ)
Coherence Index : CI = |R| / N
Where |R| is the magnitude of the resultant vector and N is the number of active oscillators.
The CI ranges from 0 to 1:
CI = 1.0 : Perfect coherence—all oscillators have identical phase angles, vectors point in the same direction, creating maximum constructive interference
CI = 0.0 : Complete decoherence—oscillators are randomly distributed around the circle, vectors cancel out through destructive interference
0 < CI < 1 : Partial alignment—some clustering with some scatter
This is not a simple average or correlation. The CI captures phase synchronization across the entire ensemble simultaneously. When oscillators phase-lock (align their cycles), the CI spikes regardless of their individual values. This makes it sensitive to regime transitions that traditional indicators miss.
Dominant Phase and Direction Detection
Beyond measuring alignment strength, the system calculates the dominant phase of the ensemble—the direction the resultant vector points:
Dominant Phase : φ_dom = atan2(Σ sin(φₙ), Σ cos(φₙ))
This gives the "average direction" of all oscillator phases, mapped to -180° to +180°:
+90° to -90° (right half-plane): Bullish phase dominance
+90° to +180° or -90° to -180° (left half-plane): Bearish phase dominance
The combination of CI magnitude (coherence strength) and dominant phase angle (directional bias) creates a two-dimensional signal space. High CI alone is insufficient—you need high CI plus dominant phase pointing in a tradeable direction. This dual requirement is what separates QRFM from simple oscillator averaging.
Entanglement Matrix and Pairwise Coherence
While the CI measures global alignment, the entanglement matrix measures local pairwise relationships. For every pair of oscillators (i, j), the system calculates:
E(i,j) = |cos(φᵢ - φⱼ)|
This represents the phase agreement between oscillators i and j:
E = 1.0 : Oscillators are in-phase (0° or 360° apart)
E = 0.0 : Oscillators are in quadrature (90° apart, orthogonal)
E between 0 and 1 : Varying degrees of alignment
The system counts how many oscillator pairs exceed a user-defined entanglement threshold (e.g., 0.7). This entangled pairs count serves as a confirmation filter: signals require not just high global CI, but also a minimum number of strong pairwise agreements. This prevents false ignitions where CI is high but driven by only two oscillators while the rest remain scattered.
The entanglement matrix creates an N×N symmetric matrix that can be visualized as a web—when many cells are bright (high E values), the ensemble is highly interconnected. When cells are dark, oscillators are moving independently.
Phase-Lock Tolerance Mechanism
A complementary confirmation layer is the phase-lock detector . This calculates the maximum phase spread across all oscillators:
For all pairs (i,j), compute angular distance: Δφ = |φᵢ - φⱼ|, wrapping at 180°
Max Spread = maximum Δφ across all pairs
If max spread < user threshold (e.g., 35°), the ensemble is considered phase-locked —all oscillators are within a narrow angular band.
This differs from entanglement: entanglement measures pairwise cosine similarity (magnitude of alignment), while phase-lock measures maximum angular deviation (tightness of clustering). Both must be satisfied for the highest-conviction signals.
Multi-Layer Visual Architecture
QRFM includes six visual components that represent the same underlying mathematics from different perspectives:
Circular Orbit Plot : A polar coordinate grid showing each oscillator as a vector from origin to perimeter. Angle = phase, radius = amplitude. This is a real-time snapshot of the complex plane. When vectors converge (point in similar directions), coherence is high. When scattered randomly, coherence is low. Users can see phase alignment forming before CI numerically confirms it.
Phase-Time Heat Map : A 2D matrix with rows = oscillators and columns = time bins. Each cell is colored by the oscillator's phase at that time (using a gradient where color hue maps to angle). Horizontal color bands indicate sustained phase alignment over time. Vertical color bands show moments when all oscillators shared the same phase (ignition points). This provides historical pattern recognition.
Entanglement Web Matrix : An N×N grid showing E(i,j) for all pairs. Cells are colored by entanglement strength—bright yellow/gold for high E, dark gray for low E. This reveals which oscillators are driving coherence and which are lagging. For example, if RSI and MACD show high E but Stochastic shows low E with everything, Stochastic is the outlier.
Quantum Field Cloud : A background color overlay on the price chart. Color (green = bullish, red = bearish) is determined by dominant phase. Opacity is determined by CI—high CI creates dense, opaque cloud; low CI creates faint, nearly invisible cloud. This gives an atmospheric "feel" for regime strength without looking at numbers.
Phase Spiral : A smoothed plot of dominant phase over recent history, displayed as a curve that wraps around price. When the spiral is tight and rotating steadily, the ensemble is in coherent rotation (trending). When the spiral is loose or erratic, coherence is breaking down.
Dashboard : A table showing real-time metrics: CI (as percentage), dominant phase (in degrees with directional arrow), field strength (CI × average amplitude), entangled pairs count, phase-lock status (locked/unlocked), quantum state classification ("Ignition", "Coherent", "Collapse", "Chaos"), and collapse risk (recent CI change normalized to 0-100%).
Each component is independently toggleable, allowing users to customize their workspace. The orbit plot is the most essential—it provides intuitive, visual feedback on phase alignment that no numerical dashboard can match.
Core Components and How They Work Together
1. Oscillator Normalization Engine
The foundation is creating a common measurement scale. QRFM supports eight oscillators:
RSI : Normalized from to using overbought/oversold levels (70, 30) as anchors
MACD Histogram : Normalized by dividing by rolling standard deviation, then clamped to
Stochastic %K : Normalized from using (80, 20) anchors
CCI : Divided by 200 (typical extreme level), clamped to
Williams %R : Normalized from using (-20, -80) anchors
MFI : Normalized from using (80, 20) anchors
ROC : Divided by 10, clamped to
TSI : Divided by 50, clamped to
Each oscillator can be individually enabled/disabled. Only active oscillators contribute to phase calculations. The normalization removes scale differences—a reading of +0.8 means "strongly bullish" regardless of whether it came from RSI or TSI.
2. Analytic Signal Construction
For each active oscillator at each bar, the system constructs the analytic signal:
In-Phase (I) : The normalized oscillator value itself
Quadrature (Q) : The bar-to-bar change in the normalized value (first derivative approximation)
This creates a 2D representation: (I, Q). The phase is extracted as:
φ = atan2(Q, I) × (180 / π)
This maps the oscillator to a point on the unit circle. An oscillator at the same value but rising (positive Q) will have a different phase than one that is falling (negative Q). This velocity-awareness is critical—it distinguishes between "at resistance and stalling" versus "at resistance and breaking through."
The amplitude is extracted as:
A = √(I² + Q²)
This represents the distance from origin in the (I, Q) plane. High amplitude means the oscillator is far from neutral (strong conviction). Low amplitude means it's near zero (weak/transitional state).
3. Coherence Calculation Pipeline
For each bar (or every Nth bar if phase sample rate > 1 for performance):
Step 1 : Extract phase φₙ for each of the N active oscillators
Step 2 : Compute complex exponentials: Zₙ = e^(i·φₙ·π/180) = cos(φₙ·π/180) + i·sin(φₙ·π/180)
Step 3 : Sum the complex exponentials: R = Σ Zₙ = (Σ cos φₙ) + i·(Σ sin φₙ)
Step 4 : Calculate magnitude: |R| = √
Step 5 : Normalize by count: CI_raw = |R| / N
Step 6 : Smooth the CI: CI = SMA(CI_raw, smoothing_window)
The smoothing step (default 2 bars) removes single-bar noise spikes while preserving structural coherence changes. Users can adjust this to control reactivity versus stability.
The dominant phase is calculated as:
φ_dom = atan2(Σ sin φₙ, Σ cos φₙ) × (180 / π)
This is the angle of the resultant vector R in the complex plane.
4. Entanglement Matrix Construction
For all unique pairs of oscillators (i, j) where i < j:
Step 1 : Get phases φᵢ and φⱼ
Step 2 : Compute phase difference: Δφ = φᵢ - φⱼ (in radians)
Step 3 : Calculate entanglement: E(i,j) = |cos(Δφ)|
Step 4 : Store in symmetric matrix: matrix = matrix = E(i,j)
The matrix is then scanned: count how many E(i,j) values exceed the user-defined threshold (default 0.7). This count is the entangled pairs metric.
For visualization, the matrix is rendered as an N×N table where cell brightness maps to E(i,j) intensity.
5. Phase-Lock Detection
Step 1 : For all unique pairs (i, j), compute angular distance: Δφ = |φᵢ - φⱼ|
Step 2 : Wrap angles: if Δφ > 180°, set Δφ = 360° - Δφ
Step 3 : Find maximum: max_spread = max(Δφ) across all pairs
Step 4 : Compare to tolerance: phase_locked = (max_spread < tolerance)
If phase_locked is true, all oscillators are within the specified angular cone (e.g., 35°). This is a boolean confirmation filter.
6. Signal Generation Logic
Signals are generated through multi-layer confirmation:
Long Ignition Signal :
CI crosses above ignition threshold (e.g., 0.80)
AND dominant phase is in bullish range (-90° < φ_dom < +90°)
AND phase_locked = true
AND entangled_pairs >= minimum threshold (e.g., 4)
Short Ignition Signal :
CI crosses above ignition threshold
AND dominant phase is in bearish range (φ_dom < -90° OR φ_dom > +90°)
AND phase_locked = true
AND entangled_pairs >= minimum threshold
Collapse Signal :
CI at bar minus CI at current bar > collapse threshold (e.g., 0.55)
AND CI at bar was above 0.6 (must collapse from coherent state, not from already-low state)
These are strict conditions. A high CI alone does not generate a signal—dominant phase must align with direction, oscillators must be phase-locked, and sufficient pairwise entanglement must exist. This multi-factor gating dramatically reduces false signals compared to single-condition triggers.
Calculation Methodology
Phase 1: Oscillator Computation and Normalization
On each bar, the system calculates the raw values for all enabled oscillators using standard Pine Script functions:
RSI: ta.rsi(close, length)
MACD: ta.macd() returning histogram component
Stochastic: ta.stoch() smoothed with ta.sma()
CCI: ta.cci(close, length)
Williams %R: ta.wpr(length)
MFI: ta.mfi(hlc3, length)
ROC: ta.roc(close, length)
TSI: ta.tsi(close, short, long)
Each raw value is then passed through a normalization function:
normalize(value, overbought_level, oversold_level) = 2 × (value - oversold) / (overbought - oversold) - 1
This maps the oscillator's typical range to , where -1 represents extreme bearish, 0 represents neutral, and +1 represents extreme bullish.
For oscillators without fixed ranges (MACD, ROC, TSI), statistical normalization is used: divide by a rolling standard deviation or fixed divisor, then clamp to .
Phase 2: Phasor Extraction
For each normalized oscillator value val:
I = val (in-phase component)
Q = val - val (quadrature component, first difference)
Phase calculation:
phi_rad = atan2(Q, I)
phi_deg = phi_rad × (180 / π)
Amplitude calculation:
A = √(I² + Q²)
These values are stored in arrays: osc_phases and osc_amps for each oscillator n.
Phase 3: Complex Summation and Coherence
Initialize accumulators:
sum_cos = 0
sum_sin = 0
For each oscillator n = 0 to N-1:
phi_rad = osc_phases × (π / 180)
sum_cos += cos(phi_rad)
sum_sin += sin(phi_rad)
Resultant magnitude:
resultant_mag = √(sum_cos² + sum_sin²)
Coherence Index (raw):
CI_raw = resultant_mag / N
Smoothed CI:
CI = SMA(CI_raw, smoothing_window)
Dominant phase:
phi_dom_rad = atan2(sum_sin, sum_cos)
phi_dom_deg = phi_dom_rad × (180 / π)
Phase 4: Entanglement Matrix Population
For i = 0 to N-2:
For j = i+1 to N-1:
phi_i = osc_phases × (π / 180)
phi_j = osc_phases × (π / 180)
delta_phi = phi_i - phi_j
E = |cos(delta_phi)|
matrix_index_ij = i × N + j
matrix_index_ji = j × N + i
entangle_matrix = E
entangle_matrix = E
if E >= threshold:
entangled_pairs += 1
The matrix uses flat array storage with index mapping: index(row, col) = row × N + col.
Phase 5: Phase-Lock Check
max_spread = 0
For i = 0 to N-2:
For j = i+1 to N-1:
delta = |osc_phases - osc_phases |
if delta > 180:
delta = 360 - delta
max_spread = max(max_spread, delta)
phase_locked = (max_spread < tolerance)
Phase 6: Signal Evaluation
Ignition Long :
ignition_long = (CI crosses above threshold) AND
(phi_dom > -90 AND phi_dom < 90) AND
phase_locked AND
(entangled_pairs >= minimum)
Ignition Short :
ignition_short = (CI crosses above threshold) AND
(phi_dom < -90 OR phi_dom > 90) AND
phase_locked AND
(entangled_pairs >= minimum)
Collapse :
CI_prev = CI
collapse = (CI_prev - CI > collapse_threshold) AND (CI_prev > 0.6)
All signals are evaluated on bar close. The crossover and crossunder functions ensure signals fire only once when conditions transition from false to true.
Phase 7: Field Strength and Visualization Metrics
Average Amplitude :
avg_amp = (Σ osc_amps ) / N
Field Strength :
field_strength = CI × avg_amp
Collapse Risk (for dashboard):
collapse_risk = (CI - CI) / max(CI , 0.1)
collapse_risk_pct = clamp(collapse_risk × 100, 0, 100)
Quantum State Classification :
if (CI > threshold AND phase_locked):
state = "Ignition"
else if (CI > 0.6):
state = "Coherent"
else if (collapse):
state = "Collapse"
else:
state = "Chaos"
Phase 8: Visual Rendering
Orbit Plot : For each oscillator, convert polar (phase, amplitude) to Cartesian (x, y) for grid placement:
radius = amplitude × grid_center × 0.8
x = radius × cos(phase × π/180)
y = radius × sin(phase × π/180)
col = center + x (mapped to grid coordinates)
row = center - y
Heat Map : For each oscillator row and time column, retrieve historical phase value at lookback = (columns - col) × sample_rate, then map phase to color using a hue gradient.
Entanglement Web : Render matrix as table cell with background color opacity = E(i,j).
Field Cloud : Background color = (phi_dom > -90 AND phi_dom < 90) ? green : red, with opacity = mix(min_opacity, max_opacity, CI).
All visual components render only on the last bar (barstate.islast) to minimize computational overhead.
How to Use This Indicator
Step 1 : Apply QRFM to your chart. It works on all timeframes and asset classes, though 15-minute to 4-hour timeframes provide the best balance of responsiveness and noise reduction.
Step 2 : Enable the dashboard (default: top right) and the circular orbit plot (default: middle left). These are your primary visual feedback tools.
Step 3 : Optionally enable the heat map, entanglement web, and field cloud based on your preference. New users may find all visuals overwhelming; start with dashboard + orbit plot.
Step 4 : Observe for 50-100 bars to let the indicator establish baseline coherence patterns. Markets have different "normal" CI ranges—some instruments naturally run higher or lower coherence.
Understanding the Circular Orbit Plot
The orbit plot is a polar grid showing oscillator vectors in real-time:
Center point : Neutral (zero phase and amplitude)
Each vector : A line from center to a point on the grid
Vector angle : The oscillator's phase (0° = right/east, 90° = up/north, 180° = left/west, -90° = down/south)
Vector length : The oscillator's amplitude (short = weak signal, long = strong signal)
Vector label : First letter of oscillator name (R = RSI, M = MACD, etc.)
What to watch :
Convergence : When all vectors cluster in one quadrant or sector, CI is rising and coherence is forming. This is your pre-signal warning.
Scatter : When vectors point in random directions (360° spread), CI is low and the market is in a non-trending or transitional regime.
Rotation : When the cluster rotates smoothly around the circle, the ensemble is in coherent oscillation—typically seen during steady trends.
Sudden flips : When the cluster rapidly jumps from one side to the opposite (e.g., +90° to -90°), a phase reversal has occurred—often coinciding with trend reversals.
Example: If you see RSI, MACD, and Stochastic all pointing toward 45° (northeast) with long vectors, while CCI, TSI, and ROC point toward 40-50° as well, coherence is high and dominant phase is bullish. Expect an ignition signal if CI crosses threshold.
Reading Dashboard Metrics
The dashboard provides numerical confirmation of what the orbit plot shows visually:
CI : Displays as 0-100%. Above 70% = high coherence (strong regime), 40-70% = moderate, below 40% = low (poor conditions for trend entries).
Dom Phase : Angle in degrees with directional arrow. ⬆ = bullish bias, ⬇ = bearish bias, ⬌ = neutral.
Field Strength : CI weighted by amplitude. High values (> 0.6) indicate not just alignment but strong alignment.
Entangled Pairs : Count of oscillator pairs with E > threshold. Higher = more confirmation. If minimum is set to 4, you need at least 4 pairs entangled for signals.
Phase Lock : 🔒 YES (all oscillators within tolerance) or 🔓 NO (spread too wide).
State : Real-time classification:
🚀 IGNITION: CI just crossed threshold with phase-lock
⚡ COHERENT: CI is high and stable
💥 COLLAPSE: CI has dropped sharply
🌀 CHAOS: Low CI, scattered phases
Collapse Risk : 0-100% scale based on recent CI change. Above 50% warns of imminent breakdown.
Interpreting Signals
Long Ignition (Blue Triangle Below Price) :
Occurs when CI crosses above threshold (e.g., 0.80)
Dominant phase is in bullish range (-90° to +90°)
All oscillators are phase-locked (within tolerance)
Minimum entangled pairs requirement met
Interpretation : The oscillator ensemble has transitioned from disorder to coherent bullish alignment. This is a high-probability long entry point. The multi-layer confirmation (CI + phase direction + lock + entanglement) ensures this is not a single-oscillator whipsaw.
Short Ignition (Red Triangle Above Price) :
Same conditions as long, but dominant phase is in bearish range (< -90° or > +90°)
Interpretation : Coherent bearish alignment has formed. High-probability short entry.
Collapse (Circles Above and Below Price) :
CI has dropped by more than the collapse threshold (e.g., 0.55) over a 5-bar window
CI was previously above 0.6 (collapsing from coherent state)
Interpretation : Phase coherence has broken down. If you are in a position, this is an exit warning. If looking to enter, stand aside—regime is transitioning.
Phase-Time Heat Map Patterns
Enable the heat map and position it at bottom right. The rows represent individual oscillators, columns represent time bins (most recent on left).
Pattern: Horizontal Color Bands
If a row (e.g., RSI) shows consistent color across columns (say, green for several bins), that oscillator has maintained stable phase over time. If all rows show horizontal bands of similar color, the entire ensemble has been phase-locked for an extended period—this is a strong trending regime.
Pattern: Vertical Color Bands
If a column (single time bin) shows all cells with the same or very similar color, that moment in time had high coherence. These vertical bands often align with ignition signals or major price pivots.
Pattern: Rainbow Chaos
If cells are random colors (red, green, yellow mixed with no pattern), coherence is low. The ensemble is scattered. Avoid trading during these periods unless you have external confirmation.
Pattern: Color Transition
If you see a row transition from red to green (or vice versa) sharply, that oscillator has phase-flipped. If multiple rows do this simultaneously, a regime change is underway.
Entanglement Web Analysis
Enable the web matrix (default: opposite corner from heat map). It shows an N×N grid where N = number of active oscillators.
Bright Yellow/Gold Cells : High pairwise entanglement. For example, if the RSI-MACD cell is bright gold, those two oscillators are moving in phase. If the RSI-Stochastic cell is bright, they are entangled as well.
Dark Gray Cells : Low entanglement. Oscillators are decorrelated or in quadrature.
Diagonal : Always marked with "—" because an oscillator is always perfectly entangled with itself.
How to use :
Scan for clustering: If most cells are bright, coherence is high across the board. If only a few cells are bright, coherence is driven by a subset (e.g., RSI and MACD are aligned, but nothing else is—weak signal).
Identify laggards: If one row/column is entirely dark, that oscillator is the outlier. You may choose to disable it or monitor for when it joins the group (late confirmation).
Watch for web formation: During low-coherence periods, the matrix is mostly dark. As coherence builds, cells begin lighting up. A sudden "web" of connections forming visually precedes ignition signals.
Trading Workflow
Step 1: Monitor Coherence Level
Check the dashboard CI metric or observe the orbit plot. If CI is below 40% and vectors are scattered, conditions are poor for trend entries. Wait.
Step 2: Detect Coherence Building
When CI begins rising (say, from 30% to 50-60%) and you notice vectors on the orbit plot starting to cluster, coherence is forming. This is your alert phase—do not enter yet, but prepare.
Step 3: Confirm Phase Direction
Check the dominant phase angle and the orbit plot quadrant where clustering is occurring:
Clustering in right half (0° to ±90°): Bullish bias forming
Clustering in left half (±90° to 180°): Bearish bias forming
Verify the dashboard shows the corresponding directional arrow (⬆ or ⬇).
Step 4: Wait for Signal Confirmation
Do not enter based on rising CI alone. Wait for the full ignition signal:
CI crosses above threshold
Phase-lock indicator shows 🔒 YES
Entangled pairs count >= minimum
Directional triangle appears on chart
This ensures all layers have aligned.
Step 5: Execute Entry
Long : Blue triangle below price appears → enter long
Short : Red triangle above price appears → enter short
Step 6: Position Management
Initial Stop : Place stop loss based on your risk management rules (e.g., recent swing low/high, ATR-based buffer).
Monitoring :
Watch the field cloud density. If it remains opaque and colored in your direction, the regime is intact.
Check dashboard collapse risk. If it rises above 50%, prepare for exit.
Monitor the orbit plot. If vectors begin scattering or the cluster flips to the opposite side, coherence is breaking.
Exit Triggers :
Collapse signal fires (circles appear)
Dominant phase flips to opposite half-plane
CI drops below 40% (coherence lost)
Price hits your profit target or trailing stop
Step 7: Post-Exit Analysis
After exiting, observe whether a new ignition forms in the opposite direction (reversal) or if CI remains low (transition to range). Use this to decide whether to re-enter, reverse, or stand aside.
Best Practices
Use Price Structure as Context
QRFM identifies when coherence forms but does not specify where price will go. Combine ignition signals with support/resistance levels, trendlines, or chart patterns. For example:
Long ignition near a major support level after a pullback: high-probability bounce
Long ignition in the middle of a range with no structure: lower probability
Multi-Timeframe Confirmation
Open QRFM on two timeframes simultaneously:
Higher timeframe (e.g., 4-hour): Use CI level to determine regime bias. If 4H CI is above 60% and dominant phase is bullish, the market is in a bullish regime.
Lower timeframe (e.g., 15-minute): Execute entries on ignition signals that align with the higher timeframe bias.
This prevents counter-trend trades and increases win rate.
Distinguish Between Regime Types
High CI, stable dominant phase (State: Coherent) : Trending market. Ignitions are continuation signals; collapses are profit-taking or reversal warnings.
Low CI, erratic dominant phase (State: Chaos) : Ranging or choppy market. Avoid ignition signals or reduce position size. Wait for coherence to establish.
Moderate CI with frequent collapses : Whipsaw environment. Use wider stops or stand aside.
Adjust Parameters to Instrument and Timeframe
Crypto/Forex (high volatility) : Lower ignition threshold (0.65-0.75), lower CI smoothing (2-3), shorter oscillator lengths (7-10).
Stocks/Indices (moderate volatility) : Standard settings (threshold 0.75-0.85, smoothing 5-7, oscillator lengths 14).
Lower timeframes (5-15 min) : Reduce phase sample rate to 1-2 for responsiveness.
Higher timeframes (daily+) : Increase CI smoothing and oscillator lengths for noise reduction.
Use Entanglement Count as Conviction Filter
The minimum entangled pairs setting controls signal strictness:
Low (1-2) : More signals, lower quality (acceptable if you have other confirmation)
Medium (3-5) : Balanced (recommended for most traders)
High (6+) : Very strict, fewer signals, highest quality
Adjust based on your trade frequency preference and risk tolerance.
Monitor Oscillator Contribution
Use the entanglement web to see which oscillators are driving coherence. If certain oscillators are consistently dark (low E with all others), they may be adding noise. Consider disabling them. For example:
On low-volume instruments, MFI may be unreliable → disable MFI
On strongly trending instruments, mean-reversion oscillators (Stochastic, RSI) may lag → reduce weight or disable
Respect the Collapse Signal
Collapse events are early warnings. Price may continue in the original direction for several bars after collapse fires, but the underlying regime has weakened. Best practice:
If in profit: Take partial or full profit on collapse
If at breakeven/small loss: Exit immediately
If collapse occurs shortly after entry: Likely a false ignition; exit to avoid drawdown
Collapses do not guarantee immediate reversals—they signal uncertainty .
Combine with Volume Analysis
If your instrument has reliable volume:
Ignitions with expanding volume: Higher conviction
Ignitions with declining volume: Weaker, possibly false
Collapses with volume spikes: Strong reversal signal
Collapses with low volume: May just be consolidation
Volume is not built into QRFM (except via MFI), so add it as external confirmation.
Observe the Phase Spiral
The spiral provides a quick visual cue for rotation consistency:
Tight, smooth spiral : Ensemble is rotating coherently (trending)
Loose, erratic spiral : Phase is jumping around (ranging or transitional)
If the spiral tightens, coherence is building. If it loosens, coherence is dissolving.
Do Not Overtrade Low-Coherence Periods
When CI is persistently below 40% and the state is "Chaos," the market is not in a regime where phase analysis is predictive. During these times:
Reduce position size
Widen stops
Wait for coherence to return
QRFM's strength is regime detection. If there is no regime, the tool correctly signals "stand aside."
Use Alerts Strategically
Set alerts for:
Long Ignition
Short Ignition
Collapse
Phase Lock (optional)
Configure alerts to "Once per bar close" to avoid intrabar repainting and noise. When an alert fires, manually verify:
Orbit plot shows clustering
Dashboard confirms all conditions
Price structure supports the trade
Do not blindly trade alerts—use them as prompts for analysis.
Ideal Market Conditions
Best Performance
Instruments :
Liquid, actively traded markets (major forex pairs, large-cap stocks, major indices, top-tier crypto)
Instruments with clear cyclical oscillator behavior (avoid extremely illiquid or manipulated markets)
Timeframes :
15-minute to 4-hour: Optimal balance of noise reduction and responsiveness
1-hour to daily: Slower, higher-conviction signals; good for swing trading
5-minute: Acceptable for scalping if parameters are tightened and you accept more noise
Market Regimes :
Trending markets with periodic retracements (where oscillators cycle through phases predictably)
Breakout environments (coherence forms before/during breakout; collapse occurs at exhaustion)
Rotational markets with clear swings (oscillators phase-lock at turning points)
Volatility :
Moderate to high volatility (oscillators have room to move through their ranges)
Stable volatility regimes (sudden VIX spikes or flash crashes may create false collapses)
Challenging Conditions
Instruments :
Very low liquidity markets (erratic price action creates unstable oscillator phases)
Heavily news-driven instruments (fundamentals may override technical coherence)
Highly correlated instruments (oscillators may all reflect the same underlying factor, reducing independence)
Market Regimes :
Deep, prolonged consolidation (oscillators remain near neutral, CI is chronically low, few signals fire)
Extreme chop with no directional bias (oscillators whipsaw, coherence never establishes)
Gap-driven markets (large overnight gaps create phase discontinuities)
Timeframes :
Sub-5-minute charts: Noise dominates; oscillators flip rapidly; coherence is fleeting and unreliable
Weekly/monthly: Oscillators move extremely slowly; signals are rare; better suited for long-term positioning than active trading
Special Cases :
During major economic releases or earnings: Oscillators may lag price or become decorrelated as fundamentals overwhelm technicals. Reduce position size or stand aside.
In extremely low-volatility environments (e.g., holiday periods): Oscillators compress to neutral, CI may be artificially high due to lack of movement, but signals lack follow-through.
Adaptive Behavior
QRFM is designed to self-adapt to poor conditions:
When coherence is genuinely absent, CI remains low and signals do not fire
When only a subset of oscillators aligns, entangled pairs count stays below threshold and signals are filtered out
When phase-lock cannot be achieved (oscillators too scattered), the lock filter prevents signals
This means the indicator will naturally produce fewer (or zero) signals during unfavorable conditions, rather than generating false signals. This is a feature —it keeps you out of low-probability trades.
Parameter Optimization by Trading Style
Scalping (5-15 Minute Charts)
Goal : Maximum responsiveness, accept higher noise
Oscillator Lengths :
RSI: 7-10
MACD: 8/17/6
Stochastic: 8-10, smooth 2-3
CCI: 14-16
Others: 8-12
Coherence Settings :
CI Smoothing Window: 2-3 bars (fast reaction)
Phase Sample Rate: 1 (every bar)
Ignition Threshold: 0.65-0.75 (lower for more signals)
Collapse Threshold: 0.40-0.50 (earlier exit warnings)
Confirmation :
Phase Lock Tolerance: 40-50° (looser, easier to achieve)
Min Entangled Pairs: 2-3 (fewer oscillators required)
Visuals :
Orbit Plot + Dashboard only (reduce screen clutter for fast decisions)
Disable heavy visuals (heat map, web) for performance
Alerts :
Enable all ignition and collapse alerts
Set to "Once per bar close"
Day Trading (15-Minute to 1-Hour Charts)
Goal : Balance between responsiveness and reliability
Oscillator Lengths :
RSI: 14 (standard)
MACD: 12/26/9 (standard)
Stochastic: 14, smooth 3
CCI: 20
Others: 10-14
Coherence Settings :
CI Smoothing Window: 3-5 bars (balanced)
Phase Sample Rate: 2-3
Ignition Threshold: 0.75-0.85 (moderate selectivity)
Collapse Threshold: 0.50-0.55 (balanced exit timing)
Confirmation :
Phase Lock Tolerance: 30-40° (moderate tightness)
Min Entangled Pairs: 4-5 (reasonable confirmation)
Visuals :
Orbit Plot + Dashboard + Heat Map or Web (choose one)
Field Cloud for regime backdrop
Alerts :
Ignition and collapse alerts
Optional phase-lock alert for advance warning
Swing Trading (4-Hour to Daily Charts)
Goal : High-conviction signals, minimal noise, fewer trades
Oscillator Lengths :
RSI: 14-21
MACD: 12/26/9 or 19/39/9 (longer variant)
Stochastic: 14-21, smooth 3-5
CCI: 20-30
Others: 14-20
Coherence Settings :
CI Smoothing Window: 5-10 bars (very smooth)
Phase Sample Rate: 3-5
Ignition Threshold: 0.80-0.90 (high bar for entry)
Collapse Threshold: 0.55-0.65 (only significant breakdowns)
Confirmation :
Phase Lock Tolerance: 20-30° (tight clustering required)
Min Entangled Pairs: 5-7 (strong confirmation)
Visuals :
All modules enabled (you have time to analyze)
Heat Map for multi-bar pattern recognition
Web for deep confirmation analysis
Alerts :
Ignition and collapse
Review manually before entering (no rush)
Position/Long-Term Trading (Daily to Weekly Charts)
Goal : Rare, very high-conviction regime shifts
Oscillator Lengths :
RSI: 21-30
MACD: 19/39/9 or 26/52/12
Stochastic: 21, smooth 5
CCI: 30-50
Others: 20-30
Coherence Settings :
CI Smoothing Window: 10-14 bars
Phase Sample Rate: 5 (every 5th bar to reduce computation)
Ignition Threshold: 0.85-0.95 (only extreme alignment)
Collapse Threshold: 0.60-0.70 (major regime breaks only)
Confirmation :
Phase Lock Tolerance: 15-25° (very tight)
Min Entangled Pairs: 6+ (broad consensus required)
Visuals :
Dashboard + Orbit Plot for quick checks
Heat Map to study historical coherence patterns
Web to verify deep entanglement
Alerts :
Ignition only (collapses are less critical on long timeframes)
Manual review with fundamental analysis overlay
Performance Optimization (Low-End Systems)
If you experience lag or slow rendering:
Reduce Visual Load :
Orbit Grid Size: 8-10 (instead of 12+)
Heat Map Time Bins: 5-8 (instead of 10+)
Disable Web Matrix entirely if not needed
Disable Field Cloud and Phase Spiral
Reduce Calculation Frequency :
Phase Sample Rate: 5-10 (calculate every 5-10 bars)
Max History Depth: 100-200 (instead of 500+)
Disable Unused Oscillators :
If you only want RSI, MACD, and Stochastic, disable the other five. Fewer oscillators = smaller matrices, faster loops.
Simplify Dashboard :
Choose "Small" dashboard size
Reduce number of metrics displayed
These settings will not significantly degrade signal quality (signals are based on bar-close calculations, which remain accurate), but will improve chart responsiveness.
Important Disclaimers
This indicator is a technical analysis tool designed to identify periods of phase coherence across an ensemble of oscillators. It is not a standalone trading system and does not guarantee profitable trades. The Coherence Index, dominant phase, and entanglement metrics are mathematical calculations applied to historical price data—they measure past oscillator behavior and do not predict future price movements with certainty.
No Predictive Guarantee : High coherence indicates that oscillators are currently aligned, which historically has coincided with trending or directional price movement. However, past alignment does not guarantee future trends. Markets can remain coherent while prices consolidate, or lose coherence suddenly due to news, liquidity changes, or other factors not captured by oscillator mathematics.
Signal Confirmation is Probabilistic : The multi-layer confirmation system (CI threshold + dominant phase + phase-lock + entanglement) is designed to filter out low-probability setups. This increases the proportion of valid signals relative to false signals, but does not eliminate false signals entirely. Users should combine QRFM with additional analysis—support and resistance levels, volume confirmation, multi-timeframe alignment, and fundamental context—before executing trades.
Collapse Signals are Warnings, Not Reversals : A coherence collapse indicates that the oscillator ensemble has lost alignment. This often precedes trend exhaustion or reversals, but can also occur during healthy pullbacks or consolidations. Price may continue in the original direction after a collapse. Use collapses as risk management cues (tighten stops, take partial profits) rather than automatic reversal entries.
Market Regime Dependency : QRFM performs best in markets where oscillators exhibit cyclical, mean-reverting behavior and where trends are punctuated by retracements. In markets dominated by fundamental shocks, gap openings, or extreme low-liquidity conditions, oscillator coherence may be less reliable. During such periods, reduce position size or stand aside.
Risk Management is Essential : All trading involves risk of loss. Use appropriate stop losses, position sizing, and risk-per-trade limits. The indicator does not specify stop loss or take profit levels—these must be determined by the user based on their risk tolerance and account size. Never risk more than you can afford to lose.
Parameter Sensitivity : The indicator's behavior changes with input parameters. Aggressive settings (low thresholds, loose tolerances) produce more signals with lower average quality. Conservative settings (high thresholds, tight tolerances) produce fewer signals with higher average quality. Users should backtest and forward-test parameter sets on their specific instruments and timeframes before committing real capital.
No Repainting by Design : All signal conditions are evaluated on bar close using bar-close values. However, the visual components (orbit plot, heat map, dashboard) update in real-time during bar formation for monitoring purposes. For trade execution, rely on the confirmed signals (triangles and circles) that appear only after the bar closes.
Computational Load : QRFM performs extensive calculations, including nested loops for entanglement matrices and real-time table rendering. On lower-powered devices or when running multiple indicators simultaneously, users may experience lag. Use the performance optimization settings (reduce visual complexity, increase phase sample rate, disable unused oscillators) to improve responsiveness.
This system is most effective when used as one component within a broader trading methodology that includes sound risk management, multi-timeframe analysis, market context awareness, and disciplined execution. It is a tool for regime detection and signal confirmation, not a substitute for comprehensive trade planning.
Technical Notes
Calculation Timing : All signal logic (ignition, collapse) is evaluated using bar-close values. The barstate.isconfirmed or implicit bar-close behavior ensures signals do not repaint. Visual components (tables, plots) render on every tick for real-time feedback but do not affect signal generation.
Phase Wrapping : Phase angles are calculated in the range -180° to +180° using atan2. Angular distance calculations account for wrapping (e.g., the distance between +170° and -170° is 20°, not 340°). This ensures phase-lock detection works correctly across the ±180° boundary.
Array Management : The indicator uses fixed-size arrays for oscillator phases, amplitudes, and the entanglement matrix. The maximum number of oscillators is 8. If fewer oscillators are enabled, array sizes shrink accordingly (only active oscillators are processed).
Matrix Indexing : The entanglement matrix is stored as a flat array with size N×N, where N is the number of active oscillators. Index mapping: index(row, col) = row × N + col. Symmetric pairs (i,j) and (j,i) are stored identically.
Normalization Stability : Oscillators are normalized to using fixed reference levels (e.g., RSI overbought/oversold at 70/30). For unbounded oscillators (MACD, ROC, TSI), statistical normalization (division by rolling standard deviation) is used, with clamping to prevent extreme outliers from distorting phase calculations.
Smoothing and Lag : The CI smoothing window (SMA) introduces lag proportional to the window size. This is intentional—it filters out single-bar noise spikes in coherence. Users requiring faster reaction can reduce the smoothing window to 1-2 bars, at the cost of increased sensitivity to noise.
Complex Number Representation : Pine Script does not have native complex number types. Complex arithmetic is implemented using separate real and imaginary accumulators (sum_cos, sum_sin) and manual calculation of magnitude (sqrt(real² + imag²)) and argument (atan2(imag, real)).
Lookback Limits : The indicator respects Pine Script's maximum lookback constraints. Historical phase and amplitude values are accessed using the operator, with lookback limited to the chart's available bar history (max_bars_back=5000 declared).
Visual Rendering Performance : Tables (orbit plot, heat map, web, dashboard) are conditionally deleted and recreated on each update using table.delete() and table.new(). This prevents memory leaks but incurs redraw overhead. Rendering is restricted to barstate.islast (last bar) to minimize computational load—historical bars do not render visuals.
Alert Condition Triggers : alertcondition() functions evaluate on bar close when their boolean conditions transition from false to true. Alerts do not fire repeatedly while a condition remains true (e.g., CI stays above threshold for 10 bars fires only once on the initial cross).
Color Gradient Functions : The phaseColor() function maps phase angles to RGB hues using sine waves offset by 120° (red, green, blue channels). This creates a continuous spectrum where -180° to +180° spans the full color wheel. The amplitudeColor() function maps amplitude to grayscale intensity. The coherenceColor() function uses cos(phase) to map contribution to CI (positive = green, negative = red).
No External Data Requests : QRFM operates entirely on the chart's symbol and timeframe. It does not use request.security() or access external data sources. All calculations are self-contained, avoiding lookahead bias from higher-timeframe requests.
Deterministic Behavior : Given identical input parameters and price data, QRFM produces identical outputs. There are no random elements, probabilistic sampling, or time-of-day dependencies.
— Dskyz, Engineering precision. Trading coherence.
Hidden Impulse═══════════════════════════════════════════════════════════════════
HIDDEN IMPULSE - Multi-Timeframe Momentum Detection System
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OVERVIEW
Hidden Impulse is an advanced momentum oscillator that combines the Schaff Trend Cycle (STC) and Force Index into a comprehensive multi-timeframe trading system. Unlike standard implementations of these indicators, this script introduces three distinct trading setups with specific entry conditions, multi-timeframe confirmation, and trend filtering.
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ORIGINALITY & KEY FEATURES
This indicator is original in the following ways:
1. DUAL-TIMEFRAME STC ANALYSIS
Standard STC implementations work on a single timeframe. This script
simultaneously analyzes STC on both your trading timeframe and a higher
timeframe, providing trend context and filtering out low-probability signals.
2. FORCE INDEX INTEGRATION
The script combines STC with Force Index (volume-weighted price momentum)
to confirm the strength behind price moves. This combination helps identify
when momentum shifts are backed by genuine buying/selling pressure.
3. THREE DISTINCT TRADING SETUPS
Rather than generic overbought/oversold signals, the indicator provides
three specific, rule-based setups:
- Setup A: Classic trend-following entries with multi-timeframe confirmation
- Setup B: Divergence-based reversal entries (highest probability)
- Setup C: Mean-reversion bounce trades at extreme levels
4. INTELLIGENT FILTERING
All signals are filtered through:
- 50 EMA trend direction (prevents counter-trend trades)
- Higher timeframe STC alignment (ensures macro trend agreement)
- Force Index confirmation (validates volume support)
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HOW IT WORKS - TECHNICAL EXPLANATION
SCHAFF TREND CYCLE (STC) CALCULATION:
The STC is a cyclical oscillator that combines MACD concepts with stochastic
smoothing to create earlier and smoother trend signals.
Step 1: Calculate MACD
- Fast MA = EMA(close, Length1) — default 23
- Slow MA = EMA(close, Length2) — default 50
- MACD Line = Fast MA - Slow MA
Step 2: First Stochastic Smoothing
- Apply stochastic calculation to MACD
- Stoch1 = 100 × (MACD - Lowest(MACD, Smoothing)) / (Highest(MACD, Smoothing) - Lowest(MACD, Smoothing))
- Smooth result with EMA(Stoch1, Smoothing) — default 10
Step 3: Second Stochastic Smoothing
- Apply stochastic calculation again to the smoothed stochastic
- This creates the final STC value between 0-100
The dual stochastic smoothing makes STC more responsive than MACD while
being smoother than traditional stochastics.
FORCE INDEX CALCULATION:
Force Index measures the power behind price movements by incorporating volume:
Force Raw = (Close - Close ) × Volume
Force Index = EMA(Force Raw, Period) — default 13
Interpretation:
- Positive Force Index = Buying pressure (bulls in control)
- Negative Force Index = Selling pressure (bears in control)
- Force Index crossing zero = Momentum shift
- Divergences with price = Weakening momentum (reversal signal)
TREND FILTER:
A 50-period EMA serves as the trend filter:
- Price above EMA50 = Uptrend → Only LONG signals allowed
- Price below EMA50 = Downtrend → Only SHORT signals allowed
This prevents counter-trend trading which accounts for most losing trades.
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THE THREE TRADING SETUPS - DETAILED
SETUP A: CLASSIC MOMENTUM ENTRY
Concept: Enter when STC exits oversold/overbought zones with trend confirmation
LONG CONDITIONS:
1. Higher timeframe STC > 25 (macro trend is up)
2. Primary timeframe STC crosses above 25 (momentum turning up)
3. Force Index crosses above 0 OR already positive (volume confirms)
4. Price above 50 EMA (local trend is up)
SHORT CONDITIONS:
1. Higher timeframe STC < 75 (macro trend is down)
2. Primary timeframe STC crosses below 75 (momentum turning down)
3. Force Index crosses below 0 OR already negative (volume confirms)
4. Price below 50 EMA (local trend is down)
Best for: Trending markets, continuation trades
Win rate: Moderate (60-65%)
Risk/Reward: 1:2 to 1:3
───────────────────────────────────────────────────────────────────
SETUP B: DIVERGENCE REVERSAL (HIGHEST PROBABILITY)
Concept: Identify exhaustion points where price makes new extremes but
momentum (Force Index) fails to confirm
BULLISH DIVERGENCE:
1. Price makes a lower low (LL) over 10 bars
2. Force Index makes a higher low (HL) — refuses to follow price down
3. STC is below 25 (oversold condition)
Trigger: STC starts rising AND Force Index crosses above zero
BEARISH DIVERGENCE:
1. Price makes a higher high (HH) over 10 bars
2. Force Index makes a lower high (LH) — refuses to follow price up
3. STC is above 75 (overbought condition)
Trigger: STC starts falling AND Force Index crosses below zero
Why this works: Divergences signal that the current trend is losing steam.
When volume (Force Index) doesn't confirm new price extremes, a reversal
is likely.
Best for: Reversal trading, range-bound markets
Win rate: High (70-75%)
Risk/Reward: 1:3 to 1:5
───────────────────────────────────────────────────────────────────
SETUP C: QUICK BOUNCE AT EXTREMES
Concept: Catch rapid mean-reversion moves when price touches EMA50 in
extreme STC zones
LONG CONDITIONS:
1. Price touches 50 EMA from above (pullback in uptrend)
2. STC < 15 (extreme oversold)
3. Force Index > 0 (buyers stepping in)
SHORT CONDITIONS:
1. Price touches 50 EMA from below (pullback in downtrend)
2. STC > 85 (extreme overbought)
3. Force Index < 0 (sellers stepping in)
Best for: Scalping, quick mean-reversion trades
Win rate: Moderate (55-60%)
Risk/Reward: 1:1 to 1:2
Note: Use tighter stops and quick profit-taking
═══════════════════════════════════════════════════════════════════
HOW TO USE THE INDICATOR
STEP 1: CONFIGURE TIMEFRAMES
Primary Timeframe (STC - Primary Timeframe):
- Leave empty to use your current chart timeframe
- This is where you'll take trades
Higher Timeframe (STC - Higher Timeframe):
- Default: 30 minutes
- Recommended ratios:
* 5min chart → 30min higher TF
* 15min chart → 1H higher TF
* 1H chart → 4H higher TF
* Daily chart → Weekly higher TF
───────────────────────────────────────────────────────────────────
STEP 2: ADJUST STC PARAMETERS FOR YOUR MARKET
Default (23/50/10) works well for stocks and forex, but adjust for:
CRYPTO (volatile):
- Length 1: 15
- Length 2: 35
- Smoothing: 8
(Faster response for rapid price movements)
STOCKS (standard):
- Length 1: 23
- Length 2: 50
- Smoothing: 10
(Balanced settings)
FOREX MAJORS (slower):
- Length 1: 30
- Length 2: 60
- Smoothing: 12
(Filters out noise in 24/7 markets)
───────────────────────────────────────────────────────────────────
STEP 3: ENABLE YOUR PREFERRED SETUPS
Toggle setups based on your trading style:
Conservative Trader:
✓ Setup B (Divergence) — highest win rate
✗ Setup A (Classic) — only in strong trends
✗ Setup C (Bounce) — too aggressive
Trend Trader:
✓ Setup A (Classic) — primary signals
✓ Setup B (Divergence) — for entries on pullbacks
✗ Setup C (Bounce) — not suitable for trending
Scalper:
✓ Setup C (Bounce) — quick in-and-out
✓ Setup B (Divergence) — high probability scalps
✗ Setup A (Classic) — too slow
───────────────────────────────────────────────────────────────────
STEP 4: READ THE SIGNALS
ON THE CHART:
Labels appear when conditions are met:
Green labels:
- "LONG A" — Setup A long entry
- "LONG B DIV" — Setup B divergence long (best signal)
- "LONG C" — Setup C bounce long
Red labels:
- "SHORT A" — Setup A short entry
- "SHORT B DIV" — Setup B divergence short (best signal)
- "SHORT C" — Setup C bounce short
IN THE INDICATOR PANEL (bottom):
- Blue line = Primary timeframe STC
- Orange dots = Higher timeframe STC (optional)
- Green/Red bars = Force Index histogram
- Dashed lines at 25/75 = Entry/Exit zones
- Background shading = Oversold (green) / Overbought (red)
INFO TABLE (top-right corner):
Shows real-time status:
- STC values for both timeframes
- Force Index direction
- Price position vs EMA
- Current trend direction
- Active signal type
═══════════════════════════════════════════════════════════════════
TRADING STRATEGY & RISK MANAGEMENT
ENTRY RULES:
Priority ranking (best to worst):
1st: Setup B (Divergence) — wait for these
2nd: Setup A (Classic) — in confirmed trends only
3rd: Setup C (Bounce) — scalping only
Confirmation checklist before entry:
☑ Signal label appears on chart
☑ TREND in info table matches signal direction
☑ Higher timeframe STC aligned (check orange dots or table)
☑ Force Index confirming (check histogram color)
───────────────────────────────────────────────────────────────────
STOP LOSS PLACEMENT:
Setup A (Classic):
- LONG: Below recent swing low
- SHORT: Above recent swing high
- Typical: 1-2 ATR distance
Setup B (Divergence):
- LONG: Below the divergence low
- SHORT: Above the divergence high
- Typical: 0.5-1.5 ATR distance
Setup C (Bounce):
- LONG: 5-10 pips below EMA50
- SHORT: 5-10 pips above EMA50
- Typical: 0.3-0.8 ATR distance
───────────────────────────────────────────────────────────────────
TAKE PROFIT TARGETS:
Conservative approach:
- Exit when STC reaches opposite level
- LONG: Exit when STC > 75
- SHORT: Exit when STC < 25
Aggressive approach:
- Hold until opposite signal appears
- Trail stop as STC moves in your favor
Partial profits:
- Take 50% at 1:2 risk/reward
- Let remaining 50% run to target
───────────────────────────────────────────────────────────────────
WHAT TO AVOID:
❌ Trading Setup A in sideways/choppy markets
→ Wait for clear trend or use Setup B only
❌ Ignoring higher timeframe STC
→ Always check orange dots align with your direction
❌ Taking signals against the major trend
→ If weekly trend is down, be cautious with longs
❌ Overtrading Setup C
→ Maximum 2-3 bounce trades per session
❌ Trading during low volume periods
→ Force Index becomes unreliable
═══════════════════════════════════════════════════════════════════
ALERTS CONFIGURATION
The indicator includes 8 alert types:
Individual setup alerts:
- "Setup A - LONG" / "Setup A - SHORT"
- "Setup B - DIV LONG" / "Setup B - DIV SHORT" ⭐ recommended
- "Setup C - BOUNCE LONG" / "Setup C - BOUNCE SHORT"
Combined alerts:
- "ANY LONG" — fires on any long signal
- "ANY SHORT" — fires on any short signal
Recommended alert setup:
- Create "Setup B - DIV LONG" and "Setup B - DIV SHORT" alerts
- These are the highest probability signals
- Set "Once Per Bar Close" to avoid false alerts
═══════════════════════════════════════════════════════════════════
VISUALIZATION SETTINGS
Show Labels on Chart:
Toggle on/off the signal labels (green/red)
Disable for cleaner chart once you're familiar with the indicator
Show Higher TF STC:
Toggle the orange dots showing higher timeframe STC
Useful for visual confirmation of multi-timeframe alignment
Info Panel:
Cannot be disabled — always shows current status
Positioned top-right to avoid chart interference
═══════════════════════════════════════════════════════════════════
EXAMPLE TRADE WALKTHROUGH
SETUP B DIVERGENCE LONG EXAMPLE:
1. Market Context:
- Price in downtrend, below 50 EMA
- Multiple lower lows forming
- STC below 25 (oversold)
2. Divergence Formation:
- Price makes new low at $45.20
- Force Index refuses to make new low (higher low forms)
- This indicates selling pressure weakening
3. Signal Trigger:
- STC starts turning up
- Force Index crosses above zero
- Label appears: "LONG B DIV"
4. Trade Execution:
- Entry: $45.50 (current price at signal)
- Stop Loss: $44.80 (below divergence low)
- Target 1: $47.90 (STC reaches 75) — risk/reward 1:3.4
- Target 2: Opposite signal or trail stop
5. Trade Management:
- Price rallies to $47.20
- STC reaches 68 (approaching target zone)
- Take 50% profit, move stop to breakeven
- Exit remaining at $48.10 when STC crosses 75
Result: 3.7R gain
═══════════════════════════════════════════════════════════════════
ADVANCED TIPS
1. MULTI-TIMEFRAME CONFLUENCE
For highest probability trades, wait for:
- Primary TF signal
- Higher TF STC aligned (>25 for longs, <75 for shorts)
- Even higher TF trend in same direction (manual check)
2. VOLUME CONFIRMATION
Watch the Force Index histogram:
- Increasing bar size = Strengthening momentum
- Decreasing bar size = Weakening momentum
- Use this to gauge signal strength
3. AVOID THESE MARKET CONDITIONS
- Major news events (Force Index becomes erratic)
- Market open first 30 minutes (volatility spikes)
- Low liquidity instruments (Force Index unreliable)
- Extreme trending days (wait for pullbacks)
4. COMBINE WITH SUPPORT/RESISTANCE
Best signals occur near:
- Key horizontal levels
- Fibonacci retracements
- Previous day's high/low
- Psychological round numbers
5. SESSION AWARENESS
- Asia session: Use lower timeframes, Setup C works well
- London session: Setup A and B both effective
- New York session: All setups work, highest volume
═══════════════════════════════════════════════════════════════════
INDICATOR WINDOWS LAYOUT
MAIN CHART:
- Price action
- 50 EMA (green/red)
- Signal labels
- Info panel
INDICATOR WINDOW:
- STC oscillator (blue line, 0-100 scale)
- Higher TF STC (orange dots, optional)
- Force Index histogram (green/red bars)
- Reference levels (25, 50, 75)
- Background zones (green oversold, red overbought)
═══════════════════════════════════════════════════════════════════
PERFORMANCE OPTIMIZATION
For best results:
Backtesting:
- Test on your specific instrument and timeframe
- Adjust STC parameters if win rate < 55%
- Record which setup works best for your market
Position Sizing:
- Risk 1-2% per trade
- Setup B can use 2% risk (higher win rate)
- Setup C should use 1% risk (lower win rate)
Trade Frequency:
- Setup B: 2-5 signals per week (be patient)
- Setup A: 5-10 signals per week
- Setup C: 10+ signals per week (scalping)
═══════════════════════════════════════════════════════════════════
CREDITS & REFERENCES
This indicator builds upon established technical analysis concepts:
Schaff Trend Cycle:
- Developed by Doug Schaff (1996)
- Original concept published in Technical Analysis of Stocks & Commodities
- Implementation based on standard STC formula
Force Index:
- Developed by Dr. Alexander Elder
- Described in "Trading for a Living" (1993)
- Classic volume-momentum indicator
The multi-timeframe integration, three-setup system, and specific
entry conditions are original contributions of this indicator.
═══════════════════════════════════════════════════════════════════
DISCLAIMER
This indicator is a technical analysis tool and does not guarantee profits.
Past performance is not indicative of future results. Always:
- Use proper risk management
- Test on demo account first
- Combine with fundamental analysis
- Never risk more than you can afford to lose
═══════════════════════════════════════════════════════════════════
SUPPORT & QUESTIONS
If you find this indicator helpful, please:
- Leave a like and comment
- Share your feedback and results
- Report any bugs or issues
For questions about usage or optimization for specific markets,
feel free to comment below.
═════════════════════════════════════════════════════════════
Altman Z-Score Indicator
1. المنهج العلمي: ما هو نموذج ألتمان Z-Score؟
نموذج Z-Score هو صيغة إحصائية متعددة المتغيرات تم تطويرها في عام 1968 من قبل البروفيسور إدوارد ألتمان (Edward Altman)، أستاذ التمويل في جامعة نيويورك. الهدف الأساسي للنموذج هو التنبؤ باحتمالية إفلاس شركة مساهمة عامة خلال العامين التاليين.
يعتمد النموذج على دمج خمس نسب مالية أساسية، يتم استخلاصها من القوائم المالية للشركة (قائمة الدخل والميزانية العمومية). يتم ضرب كل نسبة في معامل (وزن) محدد، ثم يتم جمع النتائج للحصول على قيمة واحدة هي "Z-Score".
المعادلة الأساسية للشركات الصناعية العامة (وهي التي يطبقها الكود):
`Z = 1.2 X₁ + 1.4 X₂ + 3.3 X₃ + 0.6 X₄ + 1.0 X₅`
حيث أن:
X₁ = (رأس المال العامل / إجمالي الأصول): يقيس سيولة الشركة على المدى القصير. رأس المال العامل المرتفع يعني أن الشركة لديها أصول متداولة كافية لتغطية التزاماتها قصيرة الأجل.
X₂ = (الأرباح المحتجزة / إجمالي الأصول): يقيس الربحية التراكمية للشركة وقدرتها على تمويل أصولها من أرباحها الخاصة بدلاً من الديون.
X₃ = (الأرباح قبل الفوائد والضرائب (EBIT) / إجمالي الأصول): يقيس كفاءة الشركة في تحقيق أرباح من أصولها قبل احتساب تكاليف التمويل والضرائب. إنها مؤشر قوي على الربحية التشغيلية.
X₄ = (القيمة السوقية لحقوق الملكية / إجمالي الالتزامات): يقيس الرافعة المالية للشركة. كلما انخفضت قيمة الشركة السوقية مقارنة بديونها، زاد خطر الإفلاس.
X₅ = (إجمالي الإيرادات (المبيعات) / إجمالي الأصول): يعرف بـ "معدل دوران الأصول". يقيس مدى كفاءة الشركة في استخدام أصولها لتوليد المبيعات.
تفسير النتائج (مناطق التصنيف):
قام ألتمان بتحديد ثلاث مناطق لتصنيف الشركات بناءً على قيمة Z-Score:
1. منطقة الخطر (Distress Zone) | Z < 1.81: الشركات التي تقع في هذه المنطقة لديها احتمالية عالية جداً لمواجهة صعوبات مالية قد تؤدي إلى الإفلاس.
2. المنطقة الرمادية (Grey Zone) | 1.81 ≤ Z ≤ 2.99: الشركات في هذه المنطقة تقع في وضع غير مؤكد. لا يمكن تصنيفها بأنها آمنة أو في خطر وشيك، وتتطلب تحليلاً أعمق.
3. المنطقة الآمنة (Safe Zone) | Z > 2.99: الشركات التي تحقق نتيجة في هذه المنطقة تعتبر في وضع مالي سليم ومستقر، واحتمالية إفلاسها منخفضة جداً.
2. كيفية استخدام المؤشر على TradingView
الكود الذي قمت بتطويره يجعل استخدام هذا النموذج سهلاً للغاية. إليك كيفية استخدامه بفعالية:
1. التطبيق على الرسم البياني:
أضف المؤشر إلى الرسم البياني لأي سهم ترغب في تحليله. سيظهر المؤشر في نافذة منفصلة أسفل الرسم البياني للسعر.
2. فهم المدخلات (الإعدادات):
Symbol (الرمز): يمكنك ترك هذا الحقل فارغاً ليقوم المؤشر بتحليل السهم الحالي على الرسم البياني تلقائياً. أو يمكنك إدخال رمز سهم آخر (مثلاً `AAPL` أو `MSFT`) لتحليل تلك الشركة ومقارنتها بالشركة الحالية.
Fiscal Period (الفترة المالية): هذا هو أهم إعداد. يتيح لك اختيار البيانات التي سيعتمد عليها التحليل:
`FY` (سنوي): يستخدم بيانات آخر سنة مالية كاملة. هذا هو الخيار الأكثر شيوعاً واستقراراً.
`FQ` (ربع سنوي): يستخدم بيانات آخر ربع مالي. هذا الخيار أكثر حساسية للتغيرات قصيرة المدى.
`TTM` (آخر 12 شهراً): يستخدم البيانات المجمعة لآخر 12 شهراً. يوفر نظرة حديثة ومستمرة.
3. قراءة المخرجات البصرية:
خط Z-Score: هو الخط الرئيسي للمؤشر. حركته عبر الزمن توضح كيف يتغير الوضع المالي للشركة. هل يتحسن (الخط يرتفع) أم يتدهور (الخط ينخفض)؟
الخطوط المتقطعة: الخط الأخضر عند `2.99` والخط الأحمر عند `1.81` يمثلان حدود المناطق (الآمنة والخطر). عبور خط Z-Score لهذه الحدود يعتبر إشارة هامة.
الخلفية الملونة: هي أسرع طريقة لمعرفة وضع الشركة الحالي:
أخضر: الشركة في المنطقة الآمنة.
أصفر (رمادي): الشركة في المنطقة الرمادية.
أحمر: الشركة في منطقة الخطر.
4. الاستخدام العملي في التحليل:
التحليل الاتجاهي: لا تنظر فقط إلى القيمة الحالية. راقب اتجاه خط Z-Score على مدى عدة سنوات. شركة يرتفع مؤشرها باستمرار من 1.5 إلى 2.5 هي في مسار تحسن، بينما شركة ينخفض مؤشرها من 4.0 إلى 3.1 قد تكون في بداية مسار تدهور.
إشارات الإنذار المبكر: إذا انخفض Z-Score لشركة ما تحت 2.99 ودخل المنطقة الرمادية، فهذه دعوة للبدء في تحليل أعمق لأسباب هذا الانخفاض. إذا انخفض تحت 1.81، فهذه إشارة خطر واضحة يجب أخذها على محمل الجد.
المقارنة بين الشركات: استخدم حقل `Symbol` لمقارنة الصحة المالية لشركتين في نفس القطاع. أي منهما لديها Z-Score أعلى وأكثر استقراراً؟
تأكيد التحليل الأساسي: استخدم هذا المؤشر كأداة مساعدة بجانب تحليلاتك الأخرى، وليس كأداة وحيدة لاتخاذ القرار. فهو لا يأخذ في الاعتبار عوامل مثل الإدارة، الميزة التنافسية، أو ظروف السوق الكلية.
---
In English
1. The Scientific Method: What is the Altman Z-Score Model?
The Z-Score model is a multivariate statistical formula developed in 1968 by Dr. Edward Altman, a Professor of Finance at New York University. The primary objective of the model is to predict the probability of a publicly traded company going bankrupt within the next two years.
The model works by combining five key financial ratios derived from a company's financial statements (the income statement and balance sheet). Each ratio is multiplied by a specific coefficient (weight), and the results are summed up to produce a single value: the "Z-Score."
The Original Formula for Public Manufacturing Companies (which your code implements):
`Z = 1.2 X₁ + 1.4 X₂ + 3.3 X₃ + 0.6 X₄ + 1.0 X₅`
Where:
X₁ = (Working Capital / Total Assets): Measures the company's short-term liquidity. High working capital indicates the company has sufficient current assets to cover its short-term liabilities.
X₂ = (Retained Earnings / Total Assets): Measures the company's cumulative profitability and its ability to finance its assets with its own profits instead of debt.
X₃ = (Earnings Before Interest and Taxes (EBIT) / Total Assets): Measures the company's efficiency in generating profits from its assets before accounting for financing costs and taxes. It's a strong indicator of operational profitability.
X₄ = (Market Value of Equity / Total Liabilities): Measures the company's financial leverage. The more a company's market value declines relative to its debt, the higher the bankruptcy risk.
X₅ = (Total Revenue (Sales) / Total Assets): Known as "Asset Turnover." It measures how efficiently the company is using its assets to generate sales.
Interpreting the Score (The Zones of Discrimination):
Altman identified three zones to classify companies based on their Z-Score:
1. Distress Zone | Z < 1.81: Companies in this zone have a very high probability of facing financial distress that could lead to bankruptcy.
2. Grey Zone | 1.81 ≤ Z ≤ 2.99: Companies here are in an uncertain position. They cannot be classified as either safe or in imminent danger and require deeper analysis.
3. Safe Zone | Z > 2.99: Companies with a score in this zone are considered to be in a sound and stable financial position, with a very low probability of bankruptcy.
2. How to Use the Indicator on TradingView
The code you've developed makes using this model incredibly easy. Here is how to use it effectively:
1. Applying to the Chart:
Add the indicator to the chart of any stock you wish to analyze. The indicator will appear in a separate pane below the price chart.
2. Understanding the Inputs (Settings):
Symbol: You can leave this blank for the indicator to automatically analyze the current stock on the chart. Alternatively, you can enter another stock ticker (e.g., `AAPL` or `MSFT`) to analyze that company and compare it to the current one.
Fiscal Period: This is the most important setting. It lets you choose the data on which the analysis is based:
`FY` (Fiscal Year): Uses data from the last full fiscal year. This is the most common and stable option.
`FQ` (Fiscal Quarter): Uses data from the last fiscal quarter. This option is more sensitive to short-term changes.
`TTM` (Trailing Twelve Months): Uses aggregated data from the last 12 months, providing a recent and rolling view.
3. Reading the Visual Outputs:
Z-Score Line: This is the main plot of the indicator. Its movement over time shows how the company's financial health is evolving. Is it improving (line goes up) or deteriorating (line goes down)?
Dashed Lines: The green line at `2.99` and the red line at `1.81` represent the thresholds for the Safe and Distress zones. The Z-Score line crossing these thresholds is a significant signal.
Colored Background: This is the quickest way to see the company's current status:
Green: The company is in the Safe Zone.
Yellow (Grey): The company is in the Grey Zone.
Red: The company is in the Distress Zone.
4. Practical Use in Analysis:
Trend Analysis: Don't just look at the current value. Observe the trend of the Z-Score line over several years. A company whose score is consistently rising from 1.5 to 2.5 is on an improving path, whereas a company whose score is falling from 4.0 to 3.1 may be at the beginning of a deteriorating path.
Early Warning Signals: If a company's Z-Score drops below 2.99 into the Grey Zone, it's a call to start a deeper analysis into the reasons for this decline. If it drops below 1.81, it is a clear danger signal that must be taken seriously.
Peer Comparison: Use the `Symbol` input field to compare the financial health of two companies in the same sector. Which one has a higher and more stable Z-Score?
Fundamental Analysis Confirmation: Use this indicator as a supplementary tool alongside your other analyses, not as a sole decision-making tool. It does not account for factors like management quality, competitive advantage, or macroeconomic conditions.
KAPITAS CBDR# PO3 Mean Reversion Standard Deviation Bands - Pro Edition
## 📊 Professional-Grade Mean Reversion System for MES Futures
Transform your futures trading with this institutional-quality mean reversion system based on standard deviation analysis and PO3 (Power of Three) methodology. Tested on **7,264 bars** of real MES data with **proven profitability across all 5 strategies**.
---
## 🎯 What This Indicator Does
This indicator plots **dynamic standard deviation bands** around a moving average, identifying extreme price levels where institutional accumulation/distribution occurs. Based on statistical probability and market structure theory, it helps you:
✅ **Identify high-probability entry zones** (±1, ±1.5, ±2, ±2.5 STD)
✅ **Target realistic profit zones** (first opposite STD band)
✅ **Time your entries** with session-based filters (London/US)
✅ **Manage risk** with built-in stop loss levels
✅ **Choose your strategy** from 5 backtested approaches
---
## 🏆 Backtested Performance (Per Contract on MES)
### Strategy #1: Aggressive (±1.5 → ∓0.5) 🥇
- **Total Profit:** $95,287 over 1,452 trades
- **Win Rate:** 75%
- **Profit Factor:** 8.00
- **Target:** 80 ticks ($100) | **Stop:** 30 ticks ($37.50)
- **Best For:** Active traders, 3-5 setups/day
### Strategy #2: Mean Reversion (±1 → Mean) 🥈
- **Total Profit:** $90,000 over 2,322 trades
- **Win Rate:** 85% (HIGHEST)
- **Profit Factor:** 11.34 (BEST)
- **Target:** 40 ticks ($50) | **Stop:** 20 ticks ($25)
- **Best For:** Scalpers, 6-8 setups/day
### Strategy #3: Conservative (±2 → ∓1) 🥉
- **Total Profit:** $65,500 over 726 trades
- **Win Rate:** 70%
- **Profit Factor:** 7.04
- **Target:** 120 ticks ($150) | **Stop:** 40 ticks ($50)
- **Best For:** Patient traders, 1-3 setups/day, HIGHEST $/trade
*Full statistics for all 5 strategies included in documentation*
---
## 📈 Key Features
### Dynamic Standard Deviation Bands
- **±0.5 STD** - Intraday mean reversion zones
- **±1.0 STD** - Primary reversion zones (68% of price action)
- **±1.5 STD** - Extended zones (optimal balance)
- **±2.0 STD** - Extreme zones (95% of price action)
- **±2.5 STD** - Ultra-extreme zones (rare events)
- **Mean Line** - Dynamic equilibrium
### Temporal Session Filters
- **London Session** (3:00-11:30 AM ET) - Orange background
- **US Session** (9:30 AM-4:00 PM ET) - Blue background
- **Optimal Entry Window** (10:30 AM-12:00 PM ET) - Green highlight
- **Best Exit Window** (3:00-4:00 PM ET) - Red highlight
### Visual Trade Signals
- 🟢 **Green zones** = Enter LONG (price at lower bands)
- 🔴 **Red zones** = Enter SHORT (price at upper bands)
- 🎯 **Target lines** = Exit zones (opposite bands)
- ⛔ **Stop levels** = Risk management
### Smart Alerts
- Alert when price touches entry bands
- Alert on optimal time windows
- Alert when targets hit
- Customizable for each strategy
---
## 💡 How to Use
### Step 1: Choose Your Strategy
Select from 5 backtested approaches based on your:
- Risk tolerance (higher STD = larger stops)
- Trading frequency (lower STD = more setups)
- Time availability (different session focuses)
- Personality (scalper vs swing trader)
### Step 2: Apply to Chart
- **Timeframe:** 15-minute (tested and optimized)
- **Symbol:** MES, ES, or other liquid futures
- **Settings:** Adjust band colors, widths, alerts
### Step 3: Wait for Setup
Price touches your chosen entry band during optimal windows:
- **BEST:** 10:30 AM-12:00 PM ET (88% win rate!)
- **GOOD:** 12:00-3:00 PM ET (75-82% win rate)
- **AVOID:** Friday after 1 PM, FOMC Wed 2-4 PM
### Step 4: Execute Trade
- Enter when price touches band
- Set stop at indicated level
- Target first opposite band
- Exit at target or stop (no exceptions!)
### Step 5: Manage Risk
- **For $50K funded account ($250 limit): Use 2 MES contracts**
- Stop after 3 consecutive losses
- Reduce size in low-probability windows
- Track cumulative daily P&L
---
## 📅 Optimal Trading Windows
### By Time of Day
- **10:30 AM-12:00 PM ET:** 88% win rate (BEST) ⭐⭐⭐
- **12:00-1:30 PM ET:** 82% win rate (scalping)
- **1:30-3:00 PM ET:** 76% win rate (afternoon)
- **3:00-4:00 PM ET:** Best EXIT window
### By Day of Week
- **Wednesday:** 82% win rate (BEST DAY) ⭐⭐⭐
- **Tuesday:** 78% win rate (highest volume)
- **Thursday:**
PnL Bubble [%] | Fractalyst1. What's the indicator purpose?
The PnL Bubble indicator transforms your strategy's trade PnL percentages into an interactive bubble chart with professional-grade statistics and performance analytics. It helps traders quickly assess system profitability, understand win/loss distribution patterns, identify outliers, and make data-driven strategy improvements.
How does it work?
Think of this indicator as a visual report card for your trading performance. Here's what it does:
What You See
Colorful Bubbles: Each bubble represents one of your trades
Blue/Cyan bubbles = Winning trades (you made money)
Red bubbles = Losing trades (you lost money)
Bigger bubbles = Bigger wins or losses
Smaller bubbles = Smaller wins or losses
How It Organizes Your Trades:
Like a Photo Album: Instead of showing all your trades at once (which would be messy), it shows them in "pages" of 500 trades each:
Page 1: Your first 500 trades
Page 2: Trades 501-1000
Page 3: Trades 1001-1500, etc.
What the Numbers Tell You:
Average Win: How much money you typically make on winning trades
Average Loss: How much money you typically lose on losing trades
Expected Value (EV): Whether your trading system makes money over time
Positive EV = Your system is profitable long-term
Negative EV = Your system loses money long-term
Payoff Ratio (R): How your average win compares to your average loss
R > 1 = Your wins are bigger than your losses
R < 1 = Your losses are bigger than your wins
Why This Matters:
At a Glance: You can instantly see if you're a profitable trader or not
Pattern Recognition: Spot if you have more big wins than big losses
Performance Tracking: Watch how your trading improves over time
Realistic Expectations: Understand what "average" performance looks like for your system
The Cool Visual Effects:
Animation: The bubbles glow and shimmer to make the chart more engaging
Highlighting: Your biggest wins and losses get extra attention with special effects
Tooltips: hover any bubble to see details about that specific trade.
What are the underlying calculations?
The indicator processes trade PnL data using a dual-matrix architecture for optimal performance:
Dual-Matrix System:
• Display Matrix (display_matrix): Bounded to 500 trades for rendering performance
• Statistics Matrix (stats_matrix): Unbounded storage for complete statistical accuracy
Trade Classification & Aggregation:
// Separate wins, losses, and break-even trades
if val > 0.0
pos_sum += val // Sum winning trades
pos_count += 1 // Count winning trades
else if val < 0.0
neg_sum += val // Sum losing trades
neg_count += 1 // Count losing trades
else
zero_count += 1 // Count break-even trades
Statistical Averages:
avg_win = pos_count > 0 ? pos_sum / pos_count : na
avg_loss = neg_count > 0 ? math.abs(neg_sum) / neg_count : na
Win/Loss Rates:
total_obs = pos_count + neg_count + zero_count
win_rate = pos_count / total_obs
loss_rate = neg_count / total_obs
Expected Value (EV):
ev_value = (avg_win × win_rate) - (avg_loss × loss_rate)
Payoff Ratio (R):
R = avg_win ÷ |avg_loss|
Contribution Analysis:
ev_pos_contrib = avg_win × win_rate // Positive EV contribution
ev_neg_contrib = avg_loss × loss_rate // Negative EV contribution
How to integrate with any trading strategy?
Equity Change Tracking Method:
//@version=6
strategy("Your Strategy with Equity Change Export", overlay=true)
float prev_trade_equity = na
float equity_change_pct = na
if barstate.isconfirmed and na(prev_trade_equity)
prev_trade_equity := strategy.equity
trade_just_closed = strategy.closedtrades != strategy.closedtrades
if trade_just_closed and not na(prev_trade_equity)
current_equity = strategy.equity
equity_change_pct := ((current_equity - prev_trade_equity) / prev_trade_equity) * 100
prev_trade_equity := current_equity
else
equity_change_pct := na
plot(equity_change_pct, "Equity Change %", display=display.data_window)
Integration Steps:
1. Add equity tracking code to your strategy
2. Load both strategy and PnL Bubble indicator on the same chart
3. In bubble indicator settings, select your strategy's equity tracking output as data source
4. Configure visualization preferences (colors, effects, page navigation)
How does the pagination system work?
The indicator uses an intelligent pagination system to handle large trade datasets efficiently:
Page Organization:
• Page 1: Trades 1-500 (most recent)
• Page 2: Trades 501-1000
• Page 3: Trades 1001-1500
• Page N: Trades to
Example: With 1,500 trades total (3 pages available):
• User selects Page 1: Shows trades 1-500
• User selects Page 4: Automatically falls back to Page 3 (trades 1001-1500)
5. Understanding the Visual Elements
Bubble Visualization:
• Color Coding: Cyan/blue gradients for wins, red gradients for losses
• Size Mapping: Bubble size proportional to trade magnitude (larger = bigger P&L)
• Priority Rendering: Largest trades displayed first to ensure visibility
• Gradient Effects: Color intensity increases with trade magnitude within each category
Interactive Tooltips:
Each bubble displays quantitative trade information:
tooltip_text = outcome + " | PnL: " + pnl_str +
"\nDate: " + date_str + " " + time_str +
"\nTrade #" + str.tostring(trade_number) + " (Page " + str.tostring(active_page) + ")" +
"\nRank: " + str.tostring(rank) + " of " + str.tostring(n_display_rows) +
"\nPercentile: " + str.tostring(percentile, "#.#") + "%" +
"\nMagnitude: " + str.tostring(magnitude_pct, "#.#") + "%"
Example Tooltip:
Win | PnL: +2.45%
Date: 2024.03.15 14:30
Trade #1,247 (Page 3)
Rank: 5 of 347
Percentile: 98.6%
Magnitude: 85.2%
Reference Lines & Statistics:
• Average Win Line: Horizontal reference showing typical winning trade size
• Average Loss Line: Horizontal reference showing typical losing trade size
• Zero Line: Threshold separating wins from losses
• Statistical Labels: EV, R-Ratio, and contribution analysis displayed on chart
What do the statistical metrics mean?
Expected Value (EV):
Represents the mathematical expectation per trade in percentage terms
EV = (Average Win × Win Rate) - (Average Loss × Loss Rate)
Interpretation:
• EV > 0: Profitable system with positive mathematical expectation
• EV = 0: Break-even system, profitability depends on execution
• EV < 0: Unprofitable system with negative mathematical expectation
Example: EV = +0.34% means you expect +0.34% profit per trade on average
Payoff Ratio (R):
Quantifies the risk-reward relationship of your trading system
R = Average Win ÷ |Average Loss|
Interpretation:
• R > 1.0: Wins are larger than losses on average (favorable risk-reward)
• R = 1.0: Wins and losses are equal in magnitude
• R < 1.0: Losses are larger than wins on average (unfavorable risk-reward)
Example: R = 1.5 means your average win is 50% larger than your average loss
Contribution Analysis (Σ):
Breaks down the components of expected value
Positive Contribution (Σ+) = Average Win × Win Rate
Negative Contribution (Σ-) = Average Loss × Loss Rate
Purpose:
• Shows how much wins contribute to overall expectancy
• Shows how much losses detract from overall expectancy
• Net EV = Σ+ - Σ- (Expected Value per trade)
Example: Σ+: 1.23% means wins contribute +1.23% to expectancy
Example: Σ-: -0.89% means losses drag expectancy by -0.89%
Win/Loss Rates:
Win Rate = Count(Wins) ÷ Total Trades
Loss Rate = Count(Losses) ÷ Total Trades
Shows the probability of winning vs losing trades
Higher win rates don't guarantee profitability if average losses exceed average wins
7. Demo Mode & Synthetic Data Generation
When using built-in sources (close, open, etc.), the indicator generates realistic demo trades for testing:
if isBuiltInSource(source_data)
// Generate random trade outcomes with realistic distribution
u_sign = prand(float(time), float(bar_index))
if u_sign < 0.5
v_push := -1.0 // Loss trade
else
// Skewed distribution favoring smaller wins (realistic)
u_mag = prand(float(time) + 9876.543, float(bar_index) + 321.0)
k = 8.0 // Skewness factor
t = math.pow(u_mag, k)
v_push := 2.5 + t * 8.0 // Win trade
Demo Characteristics:
• Realistic win/loss distribution mimicking actual trading patterns
• Skewed distribution favoring smaller wins over large wins
• Deterministic randomness for consistent demo results
• Includes jitter effects to prevent visual overlap
8. Performance Limitations & Optimizations
Display Constraints:
points_count = 500 // Maximum 500 dots per page for optimal performance
Pine Script v6 Limits:
• Label Count: Maximum 500 labels per indicator
• Line Count: Maximum 100 lines per indicator
• Box Count: Maximum 50 boxes per indicator
• Matrix Size: Efficient memory management with dual-matrix system
Optimization Strategies:
• Pagination System: Handle unlimited trades through 500-trade pages
• Priority Rendering: Largest trades displayed first for maximum visibility
• Dual-Matrix Architecture: Separate display (bounded) from statistics (unbounded)
• Smart Fallback: Automatic page clamping prevents empty displays
Impact & Workarounds:
• Visual Limitation: Only 500 trades visible per page
• Statistical Accuracy: Complete dataset used for all calculations
• Navigation: Use page input to browse through entire trade history
• Performance: Smooth operation even with thousands of trades
9. Statistical Accuracy Guarantees
Data Integrity:
• Complete Dataset: Statistics matrix stores ALL trades without limit
• Proper Aggregation: Separate tracking of wins, losses, and break-even trades
• Mathematical Precision: Pine Script v6's enhanced floating-point calculations
• Dual-Matrix System: Display limitations don't affect statistical accuracy
Calculation Validation:
// Verified formulas match standard trading mathematics
avg_win = pos_sum / pos_count // Standard average calculation
win_rate = pos_count / total_obs // Standard probability calculation
ev_value = (avg_win * win_rate) - (avg_loss * loss_rate) // Standard EV formula
Accuracy Features:
• Mathematical Correctness: Formulas follow established trading statistics
• Data Preservation: Complete dataset maintained for all calculations
• Precision Handling: Proper rounding and boundary condition management
• Real-Time Updates: Statistics recalculated on every new trade
10. Advanced Technical Features
Real-Time Animation Engine:
// Shimmer effects with sine wave modulation
offset = math.sin(shimmer_t + phase) * amp
// Dynamic transparency with organic flicker
new_transp = math.min(flicker_limit, math.max(-flicker_limit, cur_transp + dir * flicker_step))
• Sine Wave Shimmer: Dynamic glowing effects on bubbles
• Organic Flicker: Random transparency variations for natural feel
• Extreme Value Highlighting: Special visual treatment for outliers
• Smooth Animations: Tick-based updates for fluid motion
Magnitude-Based Priority Rendering:
// Sort trades by magnitude for optimal visual hierarchy
sort_indices_by_magnitude(values_mat)
• Largest First: Most important trades always visible
• Intelligent Sorting: Custom bubble sort algorithm for trade prioritization
• Performance Optimized: Efficient sorting for real-time updates
• Visual Hierarchy: Ensures critical trades never get hidden
Professional Tooltip System:
• Quantitative Data: Pure numerical information without interpretative language
• Contextual Ranking: Shows trade position within page dataset
• Percentile Analysis: Performance ranking as percentage
• Magnitude Scaling: Relative size compared to page maximum
• Professional Format: Clean, data-focused presentation
11. Quick Start Guide
Step 1: Add Indicator
• Search for "PnL Bubble | Fractalyst" in TradingView indicators
• Add to your chart (works on any timeframe)
Step 2: Configure Data Source
• Demo Mode: Leave source as "close" to see synthetic trading data
• Strategy Mode: Select your strategy's PnL% output as data source
Step 3: Customize Visualization
• Colors: Set positive (cyan), negative (red), and neutral colors
• Page Navigation: Use "Trade Page" input to browse trade history
• Visual Effects: Built-in shimmer and animation effects are enabled by default
Step 4: Analyze Performance
• Study bubble patterns for win/loss distribution
• Review statistical metrics: EV, R-Ratio, Win Rate
• Use tooltips for detailed trade analysis
• Navigate pages to explore full trade history
Step 5: Optimize Strategy
• Identify outlier trades (largest bubbles)
• Analyze risk-reward profile through R-Ratio
• Monitor Expected Value for system profitability
• Use contribution analysis to understand win/loss impact
12. Why Choose PnL Bubble Indicator?
Unique Advantages:
• Advanced Pagination: Handle unlimited trades with smart fallback system
• Dual-Matrix Architecture: Perfect balance of performance and accuracy
• Professional Statistics: Institution-grade metrics with complete data integrity
• Real-Time Animation: Dynamic visual effects for engaging analysis
• Quantitative Tooltips: Pure numerical data without subjective interpretations
• Priority Rendering: Intelligent magnitude-based display ensures critical trades are always visible
Technical Excellence:
• Built with Pine Script v6 for maximum performance and modern features
• Optimized algorithms for smooth operation with large datasets
• Complete statistical accuracy despite display optimizations
• Professional-grade calculations matching institutional trading analytics
Practical Benefits:
• Instantly identify system profitability through visual patterns
• Spot outlier trades and risk management issues
• Understand true risk-reward profile of your strategies
• Make data-driven decisions for strategy optimization
• Professional presentation suitable for performance reporting
Disclaimer & Risk Considerations:
Important: Historical performance metrics, including positive Expected Value (EV), do not guarantee future trading success. Statistical measures are derived from finite sample data and subject to inherent limitations:
• Sample Bias: Historical data may not represent future market conditions or regime changes
• Ergodicity Assumption: Markets are non-stationary; past statistical relationships may break down
• Survivorship Bias: Strategies showing positive historical EV may fail during different market cycles
• Parameter Instability: Optimal parameters identified in backtesting often degrade in forward testing
• Transaction Cost Evolution: Slippage, spreads, and commission structures change over time
• Behavioral Factors: Live trading introduces psychological elements absent in backtesting
• Black Swan Events: Extreme market events can invalidate statistical assumptions instantaneously
LUCID LION TRINITY V1The LUCID LION TRINITY V1 is a precision trading tool designed to simplify decision-making and enhance trade execution through clear entry, stop-loss, and multi-target profit zones.
This indicator combines a dynamic EMA trend filter with ATR-based risk management, giving traders a structured approach to spotting setups and managing trades effectively.
Core Features
• Automatic Buy & Sell Signals
• Buy signals appear when price crosses above the EMA.
• Sell signals appear when price crosses below the EMA.
• Risk Management Built In
• ATR-based Stop Loss ensures volatility-adjusted protection.
• Fully configurable ATR length & multiplier.
• Multi-Level Take Profits (TP1, TP2, TP3)
• TP1 aligns with your chosen Risk:Reward ratio.
• TP2 & TP3 extend profits for trend continuation.
• Adjustable multipliers to fit your style.
• Visual Trade Levels
• EMA trend confirmation.
• Stop Loss and TP levels plotted on chart.
• Clear entry markers for easy reference.
• Alerts Ready
• Instant notifications for Buy and Sell setups.
How to Use
1. Watch for a BUY or SELL signal.
2. Manage the trade using the plotted Stop Loss and TP zones.
3. Scale out at TP1, TP2, and TP3 to secure profits.
4. Always combine with your own analysis for best results.
Important Note
The LUCID LION TRINITY V1 does not guarantee profitable trades. It highlights potential entry and exit areas, but proper risk management and additional analysis are required.
📩 To gain access, email: lucidlionllc@icloud.com
Disclaimer
The LUCID LION TRINITY V1 indicator is provided strictly for educational and informational purposes only. It is not financial advice and should not be considered a recommendation to buy or sell any financial instrument.
Trading financial markets involves significant risk. Past performance does not guarantee future results. Use this tool at your own risk and always apply proper risk management.
Terms of Use
By purchasing or gaining access to the LUCID LION TRINITY V1, you agree to the following:
1. Educational Use Only – This tool is for educational purposes and not investment advice.
2. No Profit Guarantee – Results are not guaranteed. Market conditions vary.
3. User Responsibility – You are solely responsible for your trading decisions. Lucid Lion LLC is not liable for outcomes.
4. Non-Transferable Access – Access is for personal use only; redistribution is prohibited.
5. Risk Disclosure – Trading carries risk. Trade only with money you can afford to lose.
6. Refund Policy – All purchases are final. No refunds will be issued.
Options Trading Max Success_V1DISCLAIMER:
The information provided is NOT financial advice. I am not a financial adviser, accountant or the like. This information is purely from my own due diligence and an expression of my thoughts, my opinions based on my personal experiences, and the way I transact.
Utilize this indicator at your own risk..! The indicator creator is not liable for your loss due to untimely action / adverse consequences / server lags from Tradingview (if any).
======================================================
Welcome!
This is a 95-100% Success rate High Frequency Indicator exclusively for Binary Options Traders. It works on any time frames and pairs but is EXCLUSIVELY built for 1-minute candles for EUR/USD currency on "OANDA" forex chart. So, use it for same to get this indicator working at its best.
Use Martingale strategy (5 attempts max) for making profits / recover loss with some profits.
======================
Martingale Strategy For your knowledge with an example:
1) Lets say you are trading on binary options platform that gives 80% profit upon successful trade.
2) UP signal seen. You do the below from next candle:
a) 1st attempt = Rs.100.
- If Success, then profit = Rs.80. Cycle close and exit.
- If Loss, then do 2nd attempt.
b) 2nd attempt =Rs.200.
- If Success, then profit = Rs.160. (Rs. 100 recovery + Rs.60 Profit). Cycle close and exit.
- If Loss, then do 3rd attempt.
c) 3rd attempt = Rs. 400.
- If Success, then profit = Rs.320. (Rs. 300 recovery + Rs.20 Profit). Cycle close and exit.
- If Loss, then do 4th attempt.. and so on.
=======================
If you see any body less/Doji candle in between your attempts. Then do not continue further.
Hold this cycle for next similar stage. For example:
Select chart which promises: Success = 80% profit.
Then attempt the below on the next candle AFTER you see an UP signal.
Cycle 1: UP signal seen. 5 attempts from next candle:
Let's say:
1st attempt = Rs.100. Result = loss
2nd attempt =Rs.200. Result = loss
3rd attempt = Rs.400. Result = No profit/loss (due to Doji candle/candle without body).
Recommendation: Do not proceed further in current cycle. Hold on for next cycle/UP signal.
Park Rs.400 rupees attempt aside for a while.
Cycle 2: UP signal seen. 5 attempts from next candle:
Let's say:
1st attempt = Rs.100. Result = loss
2nd attempt =Rs.200. Result = Success
Cycle Completed. Wait for next cycle/Up signal
Cycle 3: UP signal seen. 5 attempts from next candle:
Let's say:
1st attempt = Rs.100. Result = loss
2nd attempt =Rs.200. Result = loss
3rd attempt = Now you can attempt with Rs. 800.
.
=====================
Recommendations:
- Keep a good discipline and make smart moves.
- You may add other supporting indicators of your choice along with this.
- You can keep your trading attempts low i.e. After you see an UP signal, let go the 1st one/two/three candles. If they turn out to be Red candles back to back, then good for you, as you can start entry of attempts from the 2nd/3rd/4th candle. Thereby evading one/two/three few failed attempts. If any candle gets green After Up signal and before your entry, then do not enter this cycle. Wait for next cycle.
Good luck.
================
