Watermark with dynamic variables [BM]█ OVERVIEW
This indicator allows users to add highly customizable watermark messages to their charts. Perfect for branding, annotation, or displaying dynamic chart information, this script offers advanced customization options including dynamic variables, text formatting, and flexible positioning.
█ CONCEPTS
Watermarks are overlay messages on charts. This script introduces placeholders — special keywords wrapped in % signs — that dynamically replace themselves with chart-related data. These watermarks can enhance charts with context, timestamps, or branding.
█ FEATURES
Dynamic Variables : Replace placeholders with real-time data such as bar index, timestamps, and more.
Advanced Customization : Modify text size, color, background, and alignment.
Multiple Messages : Add up to four independent messages per group, with two groups supported (A and B).
Positioning Options : Place watermarks anywhere on the chart using predefined locations.
Timezone Support : Display timestamps in a preferred timezone with customizable formats.
█ INPUTS
The script offers comprehensive input options for customization. Each Watermark (A and B) contains identical inputs for configuration.
Watermark settings are divided into two levels:
Watermark-Level Settings
These settings apply to the entire watermark group (A/B):
Show Watermark: Toggle the visibility of the watermark group on the chart.
Position: Choose where the watermark group is displayed on the chart.
Reverse Line Order: Enable to reverse the order of the lines displayed in Watermark A.
Message-Level Settings
Each watermark contains up to four configurable messages. These messages can be independently customized with the following options:
Message Content: Enter the custom text to be displayed. You can include placeholders for dynamic data.
Text Size: Select from predefined sizes (Tiny, Small, Normal, Large, Huge) or specify a custom size.
Text Alignment and Colors:
- Adjust the alignment of the text (Left, Center, Right).
- Set text and background colors for better visibility.
Format Time: Enable time formatting for this watermark message and configure the format and timezone. The settings for each message include message content, text size, alignment, and more. Please refer to Formatting dates and times for more details on valid formatting tokens.
█ PLACEHOLDERS
Placeholders are special keywords surrounded by % signs, which the script dynamically replaces with specific chart-related data. These placeholders allow users to insert dynamic content, such as bar information or timestamps, into watermark messages.
Below is the complete list of currently available placeholders:
bar_index , barstate.isconfirmed , barstate.isfirst , barstate.ishistory , barstate.islast , barstate.islastconfirmedhistory , barstate.isnew , barstate.isrealtime , chart.is_heikinashi , chart.is_kagi , chart.is_linebreak , chart.is_pnf , chart.is_range , chart.is_renko , chart.is_standard , chart.left_visible_bar_time , chart.right_visible_bar_time , close , dayofmonth , dayofweek , dividends.future_amount , dividends.future_ex_date , dividends.future_pay_date , earnings.future_eps , earnings.future_period_end_time , earnings.future_revenue , earnings.future_time , high , hl2 , hlc3 , hlcc4 , hour , last_bar_index , last_bar_time , low , minute , month , ohlc4 , open , second , session.isfirstbar , session.isfirstbar_regular , session.islastbar , session.islastbar_regular , session.ismarket , session.ispostmarket , session.ispremarket , syminfo.basecurrency , syminfo.country , syminfo.currency , syminfo.description , syminfo.employees , syminfo.expiration_date , syminfo.industry , syminfo.main_tickerid , syminfo.mincontract , syminfo.minmove , syminfo.mintick , syminfo.pointvalue , syminfo.prefix , syminfo.pricescale , syminfo.recommendations_buy , syminfo.recommendations_buy_strong , syminfo.recommendations_date , syminfo.recommendations_hold , syminfo.recommendations_sell , syminfo.recommendations_sell_strong , syminfo.recommendations_total , syminfo.root , syminfo.sector , syminfo.session , syminfo.shareholders , syminfo.shares_outstanding_float , syminfo.shares_outstanding_total , syminfo.target_price_average , syminfo.target_price_date , syminfo.target_price_estimates , syminfo.target_price_high , syminfo.target_price_low , syminfo.target_price_median , syminfo.ticker , syminfo.tickerid , syminfo.timezone , syminfo.type , syminfo.volumetype , ta.accdist , ta.iii , ta.nvi , ta.obv , ta.pvi , ta.pvt , ta.tr , ta.vwap , ta.wad , ta.wvad , time , time_close , time_tradingday , timeframe.isdaily , timeframe.isdwm , timeframe.isintraday , timeframe.isminutes , timeframe.ismonthly , timeframe.isseconds , timeframe.isticks , timeframe.isweekly , timeframe.main_period , timeframe.multiplier , timeframe.period , timenow , volume , weekofyear , year
█ HOW TO USE
1 — Add the Script:
Apply "Watermark with dynamic variables " to your chart from the TradingView platform.
2 — Configure Inputs:
Open the script settings by clicking the gear icon next to the script's name.
Customize visibility, message content, and appearance for Watermark A and Watermark B.
3 — Utilize Placeholders:
Add placeholders like %bar_index% or %timenow% in the "Watermark - Message" fields to display dynamic data.
Empty lines in the message box are reflected on the chart, allowing you to shift text up or down.
Using in the message box translates to a new line on the chart.
4 — Preview Changes:
Adjust settings and view updates in real-time on your chart.
█ EXAMPLES
Branding
DodgyDD's charts
Debugging
█ LIMITATIONS
Only supports variables defined within the script.
Limited to four messages per watermark.
Visual alignment may vary across different chart resolutions or zoom levels.
Placeholder parsing relies on correct input formatting.
█ NOTES
This script is designed for users seeking enhanced chart annotation capabilities. It provides tools for dynamic, customizable watermarks but is not a replacement for chart objects like text labels or drawings. Please ensure placeholders are properly formatted for correct parsing.
Additionally, this script can be a valuable tool for Pine Script developers during debugging . By utilizing dynamic placeholders, developers can display real-time values of variables and chart data directly on their charts, enabling easier troubleshooting and code validation.
Statistics
Supertrend StatsSupertrend with Probabilistic Stats and MA Filter
Overview: The Supertrend with Probabilistic Stats and MA Filter is a comprehensive TradingView Pine Script indicator designed to enhance trading strategies by combining the trend-detection capabilities of the Supertrend indicator with the trend-confirmation strength of Moving Averages (MA). Additionally, it offers robust statistical tracking to provide traders with valuable insights into the performance and reliability of their trading signals.
Key Features:
Supertrend Indicator Integration:
Trend Detection: Utilizes the Supertrend algorithm to identify prevailing market trends.
Buy/Sell Signals: Generates clear buy and sell signals based on trend reversals.
Customizable Parameters: Allows adjustment of ATR period and multiplier to suit different trading styles and market conditions.
Visual Aids: Plots Supertrend lines on the chart and highlights trend areas for easy visualization.
Moving Average (MA) Filter:
Trend Confirmation: Filters buy signals to occur only when the open price is above the MA and sell signals only when the open price is below the MA.
Customizable MA Types: Supports various MA types, including SMA, EMA, SMMA (RMA), WMA, and VWMA.
Flexible Configuration: Offers options to enable/disable the MA filter, select MA type, set MA length, and adjust MA source and offset.
Statistical Tracking:
Trimmed Mean Calculation: Computes trimmed means for bullish and bearish movements, removing outliers to provide a more accurate average movement.
Success Rate Metrics: Calculates the success rates (%) for both bullish and bearish signals, indicating the percentage of signals that resulted in favorable price movements.
Candle Count Analysis: Tracks the average number of candles each bullish and bearish move lasts, offering insights into the duration of trends.
Data Visualization: Presents all statistical data in a neatly formatted table on the chart, allowing for quick reference and analysis.
Customizable Statistics Table:
Text Color Customization: Provides an option to change the table text color to match personal preferences or chart aesthetics, enhancing readability.
Comprehensive Metrics: Displays key statistics such as Bullish/Bearish Averages, Counts, Success Rates, and Average Candle Counts.
Optional Pinbar Filtering:
Signal Refinement: Adds an additional layer of signal confirmation by filtering buy and sell signals based on pinbar candlestick patterns.
Adjustable Thresholds: Allows customization of the pinbar wick threshold to fine-tune signal accuracy.
Visual Enhancements:
Markers: Optionally displays markers on the first and last candles of bullish and bearish moves for better trend identification.
Highlighter: Shades the chart background to indicate current trend direction, aiding in visual trend recognition.
How It Works:
Trend Identification with Supertrend:
The indicator calculates the Supertrend based on user-defined ATR periods and multipliers.
It plots the Supertrend lines and generates buy/sell signals when the price crosses these lines, indicating a potential trend reversal.
Filtering Signals with Moving Average:
When the MA filter is enabled, the indicator ensures that buy signals are only considered valid if the candle's open price is above the selected MA, and sell signals only if the open price is below the MA.
This additional confirmation aligns trades with the broader market trend, potentially increasing signal reliability.
Statistical Analysis:
Upon triggering a buy or sell signal, the indicator records the entry price and tracks the subsequent price movements.
It calculates trimmed means to assess average movements while excluding extreme outliers.
Success rates are computed by comparing the closing price against the entry price, indicating how often signals result in favorable outcomes.
The average number of candles per move provides insight into trend duration and volatility.
Visualization and Customization:
All statistical data is presented in a table on the chart, with customizable text colors for enhanced readability.
Optional pinbar filtering and visual markers further refine and illustrate trading signals, aiding in decision-making.
Benefits to Traders:
Enhanced Signal Reliability:
By combining Supertrend with an MA filter, the indicator ensures that only signals aligning with the broader market trend are considered, potentially reducing false signals.
Data-Driven Decision Making:
The comprehensive statistical tracking offers traders insights into the performance of their signals, enabling informed adjustments to their trading strategies based on empirical data.
Trend Confirmation and Alignment:
The MA filter acts as a trend confirmation tool, ensuring that trades are placed in the direction of the prevailing trend, which can enhance the probability of successful trades.
Performance Metrics at a Glance:
The statistics table provides all necessary performance metrics in a single view, allowing traders to quickly assess the effectiveness of their strategy without sifting through extensive data.
Customization and Flexibility:
With options to adjust MA types, lengths, and table text colors, traders can tailor the indicator to fit their specific preferences and trading environments.
Visual Clarity and Aids:
The plotted Supertrend lines, MA line, signal markers, and highlighter enhance visual clarity, making it easier to identify trends and potential trade opportunities on the chart.
Usage Instructions:
Adding the Indicator:
Copy the Script: Select and copy the entire Pine Script provided.
Open TradingView: Navigate to TradingView and open your desired asset's chart.
Access Pine Editor: Click on the Pine Editor tab at the bottom of the TradingView interface.
Paste and Add to Chart: Paste the script into the editor and click "Add to Chart" to apply the indicator.
Configuring Settings:
Supertrend Parameters: Adjust the ATR period and multiplier to suit your trading style and the asset's volatility.
MA Filter Settings:
Enable MA Filter: Toggle "Enable MA Filter?" to ON to activate the filter.
Select MA Type: Choose from SMA, EMA, SMMA (RMA), WMA, or VWMA.
Set MA Length: Define the period for the MA calculation.
MA Source and Offset: Choose the price source (default is close) and set any desired plot offset.
Statistical Tracking:
Trimmed Mean Percentage: Set the percentage to trim outliers in mean calculations.
Show Cross Markers: Toggle to display or hide markers on the first and last candles of bullish and bearish moves.
Table Customization:
Table Text Color: Select your preferred text color for the statistics table to match your chart's theme or enhance readability.
Pinbar Filtering (Optional):
Enable Pinbar Filtering: Toggle to refine signals based on pinbar patterns.
Set Pinbar Wick Threshold: Adjust the threshold to define the characteristics of a valid pinbar.
Interpreting the Indicators:
Buy/Sell Signals: Look for labeled "BUY" and "SELL" signals on the chart that align with Supertrend reversals and MA conditions.
Statistics Table: Refer to the table located at the bottom right of the chart to assess:
Bullish/Bearish Averages: Average price movements following signals.
Counts: Total number of bullish and bearish signals.
Success Rates (%): Percentage of signals that resulted in profitable trades.
Candle Averages: Average duration of bullish and bearish moves in terms of candle counts.
Markers and Highlighter: Utilize visual markers and shaded trend areas to better understand market trends and the context of each signal.
Making Informed Decisions:
Assess Signal Performance: Use the success rates and averages to evaluate the effectiveness of your current settings and make necessary adjustments.
Adjust Parameters: Modify Supertrend and MA parameters based on observed performance and changing market conditions to optimize signal accuracy.
Combine with Other Analysis: Integrate insights from this indicator with other technical analysis tools and fundamental factors to form a holistic trading strategy.
Conclusion: The Supertrend with Probabilistic Stats and MA Filter indicator offers a powerful combination of trend detection, signal filtering, and statistical analysis. By providing detailed performance metrics and ensuring that trades align with the broader market trend, this indicator empowers traders to make more informed, data-driven decisions. Whether you're a novice seeking clarity or an experienced trader aiming to refine your strategy, this tool serves as a valuable asset in your trading toolkit.
If you have any further questions or require additional customizations, feel free to reach out!
DCA Fundamentals 1.0DCA Fundamentals 1.0
Description:
DCA Fundamentals 1.0 is an invite-only indicator designed to help traders and investors make informed decisions by analyzing key fundamental metrics of a company. It aggregates essential financial data—such as book value, earnings per share, total equity, total debt, net income, and total revenue—to provide a comprehensive overview of the stock’s intrinsic value and risk profile. By examining factors like the debt-to-equity ratio and dynamically computing Buffet’s Limit, this tool assists in identifying whether a stock may be undervalued, fairly valued, or overvalued.
Key Features:
Intrinsic Value Calculation: Estimates a stock’s intrinsic worth using a weighted combination of book value per share and EPS.
Buffet’s Limit & Margin of Safety: Adjusts intrinsic value based on the company’s debt-to-equity ratio, providing a margin of safety percentage to gauge potential investment risk.
Debt Warning: Highlights when the debt-to-equity ratio exceeds 2, signaling possible financial instability.
Data Visualization: Displays equity, debt, net income, and revenue as area plots or histograms, helping users quickly assess financial health.
Investment Status: Classifies the stock as undervalued, fairly valued, or overvalued based on current price relative to intrinsic value and Buffet’s Limit.
Dividend-to-ROE Ratio: Offers insight into dividend payout sustainability relative to the company’s return on equity.
Instructions
Fallback Data Handling:
If any financial data is unavailable, fallback values are automatically used to ensure that key calculations remain meaningful and uninterrupted.
Intrinsics & Risk Assessment:
Intrinsic Value: Computed using book value and EPS to understand the stock’s core worth.
Buffet’s Limit: Adjusted from the intrinsic value based on the debt-to-equity ratio. The resulting margin of safety helps gauge the current price’s risk level.
Debt Warning:
Debt-to-Equity Ratio > 2: Triggers a red warning, advising caution due to potentially excessive debt.
Visual Indicators:
Intrinsically Undervalued (Green Area): When price is below intrinsic value, a green shaded area suggests the stock may be undervalued, potentially presenting a buying opportunity.
Debt vs. Equity (Area Plots):
Red Area: Represents debt. A larger red area signals relatively high debt levels.
Green Area: Represents equity. A larger green area suggests stronger financial health.
Revenue & Net Income (Histograms):
Green Bars: Positive or improving fundamentals.
Red Bars: Negative or declining performance.
Investment Status:
Undervalued (Green): Price below intrinsic value.
Fairly Valued (Yellow): Price between intrinsic value and Buffet’s Limit.
Overvalued (Red): Price above intrinsic value, implying increased downside risk.
Table Display:
A convenient table summarizes key metrics at a glance, including P/E ratio, Debt-to-Equity ratio, intrinsic value, margin of safety, net income, total revenue, and the Dividend-to-ROE Ratio.
Dividend-to-ROE Ratio:
This metric provides additional context on the company’s dividend policy relative to its return on equity, aiding in evaluating dividend sustainability.
Disclaimer
Important Disclaimer:
The DCA Fundamentals 1.0 indicator is provided solely for educational and informational purposes. It is not investment advice, a recommendation, or an endorsement of any security or strategy. All calculations are based on data provided by third parties, and their accuracy or completeness is not guaranteed.
Investing and trading involve significant risks. You may lose more than your initial investment. Historical performance or indicators cannot guarantee future results. Before making any investment decisions, you should conduct thorough research, consider consulting a qualified financial professional, and implement robust risk management strategies.
By using DCA Fundamentals 1.0, you acknowledge these risks and agree that neither the creator nor any affiliated parties are responsible for any losses incurred. Use this tool at your own discretion and risk.
MOEX Aerospace & Defense IndexMOEX Aerospace & Defense Index is a financial instrument that forms an objective assessment of the capitalization of the entire aerospace and military–industrial complex of Russia, the securities of issuers of which are represented on the public stock market.
The MOEX Aerospace & Defense Index includes shares of 14 issuers of the aerospace and defense industry complex of Russia in fair shares proportional to the occupied shares of the Russian and global arms markets, as well as involvement in the Russian state defense order during wartime. The Index includes issuers from such sectors of the economy as: military aviation industry, rocket and space industry, military automotive industry and wheel products, military electronics and communications, military metallurgy and metal products.
The Index includes:
RUS:AMEZ RUS:CHKZ RUS:CHMF RUS:DZRDP RUS:ELMT RUS:IRKT RUS:KMAZ RUS:NAUK RUS:NSVZ RUS:RKKE RUS:SVAV RUS:UNAC RUS:VSMO RUS:ZVEZ
Based on historical data for the period from 2014 (the year of the beginning of the geopolitical confrontation between Russia and Ukraine), the MOEX Aerospace & Defense Index demonstrates profitability 3-4 times higher than the Russian benchmark of the stock market - the Moscow Stock Exchange Index. It makes the financial instrument the most attractive in times of geopolitical crises or military confrontations.
MOEX Aerospace & Defense Index allows you to:
1. To invest in a balanced manner in the aerospace and defense industrial complex of Russia, which produces advanced military and aerospace weapons for the needs of the Russian army and for export, which occupies 25% of the global arms market. Investing in the aerospace and military-industrial complex of Russia is especially relevant in the context of a special military operation in Ukraine, the growth of geopolitical tensions and the resulting large budget incentives for this industry from the Government of the Russian Federation.
2. To neutralize fluctuations in low-liquid and highly volatile stocks of the aerospace and military-industrial complex of Russia. The aerospace and defense industries are characterized by high volatility in stock prices, which is why the creation of a balanced index can offset price fluctuations for low-liquid shares of companies included in the index and reduce the risk for investors.
3. Politically diversify the investment portfolio. The inclusion of the MOEX Aerospace & Defense Index in the investment portfolio can reduce the risks associated with price fluctuations during geopolitical crises or military confrontations.
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MOEX Aerospace & Defense Index – финансовый инструмент, формирующий объективную оценку капитализации всего аэрокосмического и оборонно-промышленного комплекса России, ценные бумаги эмитентов которого представлены на публичном рынке акций.
В MOEX Aerospace & Defense Index включены акции 14 эмитентов аэрокосмического и оборонно-промышленного комплекса России в справедливых долях, пропорциональных занимаемым долям российского и мирового рынка вооружений, а также задействования в российском гособоронзаказе в военное время. Индекс включает в себя эмитентов из таких секторов экономики, как: военная авиационная промышленность, ракетно-космическая промышленность, военная автомобильная промышленность и колесные изделия, военная электроника и связь, военная металлургия и изделия из металла.
В Индекс включены:
RUS:AMEZ RUS:CHKZ RUS:CHMF RUS:DZRDP RUS:ELMT RUS:IRKT RUS:KMAZ RUS:NAUK RUS:NSVZ RUS:RKKE RUS:SVAV RUS:UNAC RUS:VSMO RUS:ZVEZ
На исторических данных за период с 2014-го года (года начала геополитической конфронтации России и Украины) MOEX Aerospace & Defense Index демонстрирует доходность в 3-4 раза выше российского бенчмарка рынка акций – Индекса Мосбиржи, что делает финансовый инструмент наиболее привлекательным во времена геополитических кризисов или военных противостояний.
MOEX Aerospace & Defense Index позволяет:
1. Сбалансированно инвестировать в аэрокосмический и оборонно-промышленный комплекс России, производящий передовое военное и аэрокосмическое вооружение для нужд армии России и на экспорт, который занимает 25% мирового рынка вооружений. Инвестирование в аэрокосмический и оборонно-промышленный комплекс России особенно актуально в условиях специальной военной операции на Украине, росте геополитической напряженности и следующих из этого большого бюджетного стимулирования данной отрасли со стороны Правительства Российской Федерации.
2. Нивелировать колебания в низколиквидных и высоко волатильных акциях аэрокосмического и оборонно-промышленного комплекса России. Аэрокосмическая и оборонная отрасли характеризуются высокой волатильностью цен на акции, ввиду чего создание сбалансированного индекса может нивелировать колебания цен на низколиквидные акции компаний, входящих в индекс, и снизить риск для инвесторов.
3. Политически диверсифицировать инвестиционный портфель. Включение MOEX Aerospace & Defense Index в инвестиционный портфель может снизить риски, связанные с колебаниями цен во время геополитических кризисов или военных противостояний.
Up and Downwhat is "Up and Down"?
It is an indicator designed to show you in detail on the chart and warn you when there is an increase or decrease in the market at a level that you consider important.
what it does?
When the price difference between a top and bottom is greater than the level you selected (the default input is 10 percent), it indicates this along with the percentage value on the chart. Then, it indicates the start and end points with lines so that you can see the change from where to where. It shows the price's current percentage distance from the last bottom or top in the upper right corner.
it also colors the candles so you can better understand how fast the price is moving. The greener the candles, the stronger the rise, and conversely, the greater the decline, the redder the candles. Of course, if you set an alarm, it will tell you in which trading pair, in which time period, at what percentage and in which direction there is a movement.
how it does it?
It uses a moving average with a short length to find bottoms and tops. It then measures the distance from the last peak to the bottom and expresses it as a percentage. It uses momentum using the moving average as a source to paint the candles. To compress this momentum between the values 255 and 0, I used a formula that I also used in my limited fisher transform work (because the inputs in the color.rgb function take values between 0 and 255). It was a bit challenging to use the lines correctly, but with the "ta.valuewhen" function and a little experimenting, they were I made sure they were drawn correctly.
how to use it?
It is quite simple to use. First, select the minimum interval you want to receive alarms. If you make this value too high, you will not receive any alarms; if you make it too low, you will receive too many alarms. Choose the range that will benefit you most for the trading pair you are using. Then all you have to do is set an alarm. When you set an alarm, leave the note section blank and the indicator will send you the necessary information.
Mastering ATR for Smart Stop Loss and Take Profit PlacementUsing the ATR indicator to set Stop Loss and Take Profit levels provides a dynamic and flexible way to manage risk based on the volatility of the market. This method ensures that your SL and TP are always in tune with current market conditions, preventing unnecessary stop-outs while maximizing the potential for profit. The table in the script makes it easy to view your calculated levels directly on the chart, improving your trading efficiency.
If you're looking for a more automated way to manage your trades, integrating ATR-based SL and TP can be a powerful tool in your strategy.
Happy Trading!
Sangana beta tableIdeal to use this indicator in Monthly timeframe.
This indicator shows three values on a table.
First column is stocks list from a particular sector(sector selection from settings)
Second column is beta of stock. Beta can be used to check how correlated(multiplied by how volatile) the stock is with respect to market S&P500 or Nifty500.
Third column is average percentage of a stock price movement in a month from low price to high price. This is just calculated on the price. If one enters at the low of that monthly candle and exits at the high of that monthly candle, they can expect to gain that much percentage on an average that is shown in this column.
How to use this indicator : Bigger returns on a stock is expected if it swings good amount of percentage from low to high on a regular basis. Either short term or long term, investing in the stocks which high average percentage from low to high, yields better returns. However downside also gives bigger losses if stock is going down. Stay in high volatile stocks, if one is sure of upside movement.
Sorting of beta column or percentage column can be chosen on settings. Sorting is always down high to low.
This indicator is tracks stocks in S&P500 or Nifty500.
Crypto Market Cap Momentum Analyzer (AiBitcoinTrend)The Crypto Market Cap Momentum Analyzer (AiBitcoinTrend) is a robust tool designed to uncover trading opportunities by blending market cap analysis and momentum dynamics. Inspired by research-backed quantitative strategies, this indicator helps traders identify trend-following and mean-reversion setups in the cryptocurrency market by evaluating recent performance and market cap size.
This indicator classifies cryptocurrencies into market cap quintiles and ranks them based on their 2-week momentum. It then suggests potential trades—whether to go long, anticipate reversals, or simply hold—based on the crypto's market cap group and momentum trends.
👽 How the Indicator Works
👾 Market Cap Classification
The indicator categorizes cryptocurrencies into one of five market cap groups based on user-defined inputs:
Large Cap: Highest market cap tier
Upper Mid Cap: Second highest group
Mid Cap: Middle-tier market caps
Lower Mid Cap: Slightly below the mid-tier
Small Cap: Lowest market cap tier
This classification dynamically adjusts based on the provided market cap data, ensuring that you’re always working with a representative market structure.
👾 Momentum Calculation
By default, the indicator uses a 2-week momentum measure (e.g., a 14-day lookback when set to daily). It compares a cryptocurrency’s current price to its price 14 bars ago, thereby quantifying its short-term performance. Users can adjust the momentum period and rebalance period to capture shorter or longer-term trends depending on their trading style.
👾 Dynamic Ranking and Trade Suggestions
After assigning cryptos to size quintiles, the indicator sorts them by their momentum within each quintile. This two-step process results in:
Long Trade: For smaller market cap groups (Small, Lower Mid, Mid Cap) that have low (bottom-quintile) momentum, anticipating a trend continuation or breakout.
Reversal Trade: For the largest market cap group (Large Cap) that shows low momentum, expecting a mean-reversion back to equilibrium.
Hold: In scenarios where the coin’s momentum doesn’t present a strong contrarian or trend-following signal.
👽 Applications
👾 Trend-Following in Smaller Caps: Identify small or mid-cap cryptos with low momentum that might be poised for a breakout or sustained trend.
👾 Mean-Reversion in Large Caps: Pinpoint large-cap cryptocurrencies experiencing a temporary lull in performance, potentially ripe for a rebound.
👽 Why It Works in Crypto
The cryptocurrency market is heavily driven by retail investor sentiment and volatility. Research shows that:
Small-Cap Cryptos: Tend to experience higher volatility and speculative trends, making them ideal for momentum trades.
Large-Cap Cryptos: Exhibit more predictable behavior, making them suitable for mean-reversion strategies when momentum is low.
This indicator captures these dynamics to give traders a strategic edge in identifying both momentum and reversal opportunities.
👽 Indicator Settings
👾 Rebalance Period: The frequency at which momentum and trade suggestions are recalculated (Daily, Weekly, Monthly).
Shorter Periods (Daily): Fast updates, suitable for short-term trades, but more noise.
Longer Periods (Weekly/Monthly): Smoother signals, ideal for swing trading and more stable trends.
👾 Momentum Period: The lookback period for momentum calculation (default is 14 bars).
Shorter Periods: More responsive but prone to noise.
Longer Periods : Reflects broader trends, reducing sensitivity to short-term fluctuations.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
Quantum ChronoRenko Dynamics Edge - Traditional### **Quantum ChronoRenko Dynamics Edge - Traditional**
**Description:**
The **Quantum ChronoRenko Dynamics Edge - Traditional** is an advanced Renko-based indicator designed for precision trading. It leverages the power of Renko charts to detect price movements, highlight critical trading signals, and dynamically track profit and risk levels. This indicator is built with modern trading strategies in mind, offering robust tools for all traders, from beginners to professionals.
**Key Features:**
1. **Renko-Based Signal Generation**:
- Detects **Buy Signals** when the price closes above the Renko high level.
- Detects **Sell Signals** when the price closes below the Renko low level.
- Ensures signals are non-repainting and confirmed on bar closures.
2. **Take Profit (TP) and Stop Loss (SL) Tracking**:
- Automatically calculates and plots TP and SL levels for every signal.
- Dynamic levels are displayed directly on the chart for better decision-making.
3. **Advanced Signal Management**:
- Prevents duplicate signals within the same Renko range.
- Resets signal conditions when a new Renko range is formed.
4. **Visual Enhancements**:
- Renko high and low levels are plotted with customizable colors and styles.
- TP and SL levels are marked with distinct cross shapes for clarity.
- Optional fill between Renko levels to highlight price ranges.
5. **Real-Time Alerts**:
- Generates alerts for Buy and Sell signals when a candle closes above or below the Renko levels.
- Alerts are designed to help traders react quickly to opportunities.
6. **Comprehensive Statistics**:
- Tracks the number of Buy/Sell signals.
- Calculates the number of TP and SL hits for each signal type.
- Displays detailed percentages and totals in an easy-to-read table.
**Key Benefits**:
- **Non-Repainting Logic**: Ensures stable and reliable signals based on confirmed price movements.
- **Customizability**: Flexible settings for Renko brick size, TP/SL values, and visual enhancements.
- **Professional-Level Insights**: Provides detailed statistics for tracking strategy performance.
**Use Cases**:
- Perfect for intraday and swing traders who rely on Renko charts for clear trend signals.
- Suitable for identifying key breakout opportunities and managing trades with precise TP/SL levels.
Example Usage:
For daily scalping, set the following parameters:
Brick Size: 3
Time Frame: 10 Minutes
This setup provides clean trend signals and dynamic TP/SL tracking for short-term trades.
**Why "Traditional"?**
This version uses the **Traditional Renko method**, ensuring consistent price-based calculations that align with professional trading strategies.
---
**Disclaimer**:
This indicator is a tool to aid trading decisions but does not guarantee profits. Always use proper risk management.
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CandleCandle: A Comprehensive Pine Script™ Library for Candlestick Analysis
Overview
The Candle library, developed in Pine Script™, provides traders and developers with a robust toolkit for analyzing candlestick data. By offering easy access to fundamental candlestick components like open, high, low, and close prices, along with advanced derived metrics such as body-to-wick ratios, percentage calculations, and volatility analysis, this library enables detailed insights into market behavior.
This library is ideal for creating custom indicators, trading strategies, and backtesting frameworks, making it a powerful resource for any Pine Script™ developer.
Key Features
1. Core Candlestick Data
• Open : Access the opening price of the current candle.
• High : Retrieve the highest price.
• Low : Retrieve the lowest price.
• Close : Access the closing price.
2. Candle Metrics
• Full Size : Calculates the total range of the candle (high - low).
• Body Size : Computes the size of the candle’s body (open - close).
• Wick Size : Provides the combined size of the upper and lower wicks.
3. Wick and Body Ratios
• Upper Wick Size and Lower Wick Size .
• Body-to-Wick Ratio and Wick-to-Body Ratio .
4. Percentage Calculations
• Upper Wick Percentage : The proportion of the upper wick size relative to the full candle size.
• Lower Wick Percentage : The proportion of the lower wick size relative to the full candle size.
• Body Percentage and Wick Percentage relative to the candle’s range.
5. Candle Direction Analysis
• Determines if a candle is "Bullish" or "Bearish" based on its closing and opening prices.
6. Price Metrics
• Average Price : The mean of the open, high, low, and close prices.
• Midpoint Price : The midpoint between the high and low prices.
7. Volatility Measurement
• Calculates the standard deviation of the OHLC prices, providing a volatility metric for the current candle.
Code Architecture
Example Functionality
The library employs a modular structure, exporting various functions that can be used independently or in combination. For instance:
// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © DevArjun
//@version=6
indicator("Candle Data", overlay = true)
import DevArjun/Candle/1 as Candle
// Body Size %
bodySize = Candle.BodySize()
// Determining the candle direction
candleDirection = Candle.CandleDirection()
// Calculating the volatility of the current candle
volatility = Candle.Volatility()
// Plotting the metrics (for demonstration)
plot(bodySize, title="Body Size", color=color.blue)
label.new(bar_index, high, candleDirection, style=label.style_circle)
Scalability
The modularity of the Candle library allows seamless integration into more extensive trading systems. Functions can be mixed and matched to suit specific analytical or strategic needs.
Use Cases
Trading Strategies
Developers can use the library to create strategies based on candle properties such as:
• Identifying long-bodied candles (momentum signals).
• Detecting wicks as potential reversal zones.
• Filtering trades based on candle ratios.
Visualization
Plotting components like body size, wick size, and directional labels helps visualize market behavior and identify patterns.
Backtesting
By incorporating volatility and ratio metrics, traders can design and test strategies on historical data, ensuring robust performance before live trading.
Education
This library is a great tool for teaching candlestick analysis and how each component contributes to market behavior.
Portfolio Highlights
Project Objective
To create a Pine Script™ library that simplifies candlestick analysis by providing comprehensive metrics and insights, empowering traders and developers with advanced tools for market analysis.
Development Challenges and Solutions
• Challenge : Achieving high precision in calculating ratios and percentages.
• Solution : Implemented robust mathematical operations and safeguarded against division-by-zero errors.
• Challenge : Ensuring modularity and scalability.
• Solution : Designed functions as independent modules, allowing flexible integration.
Impact
• Efficiency : The library reduces the time required to calculate complex candlestick metrics.
• Versatility : Supports various trading styles, from scalping to swing trading.
• Clarity : Clean code and detailed documentation ensure usability for developers of all levels.
Conclusion
The Candle library exemplifies the power of Pine Script™ in simplifying and enhancing candlestick analysis. By including this project in your portfolio, you showcase your expertise in:
• Financial data analysis.
• Pine Script™ development.
• Creating tools that solve real-world trading challenges.
This project demonstrates both technical proficiency and a keen understanding of market analysis, making it an excellent addition to your professional portfolio.
Library "Candle"
A comprehensive library to access and analyze the basic components of a candlestick, including open, high, low, close prices, and various derived metrics such as full size, body size, wick sizes, ratios, percentages, and additional analysis metrics.
Open()
Open
@description Returns the opening price of the current candle.
Returns: float - The opening price of the current candle.
High()
High
@description Returns the highest price of the current candle.
Returns: float - The highest price of the current candle.
Low()
Low
@description Returns the lowest price of the current candle.
Returns: float - The lowest price of the current candle.
Close()
Close
@description Returns the closing price of the current candle.
Returns: float - The closing price of the current candle.
FullSize()
FullSize
@description Returns the full size (range) of the current candle (high - low).
Returns: float - The full size of the current candle.
BodySize()
BodySize
@description Returns the body size of the current candle (open - close).
Returns: float - The body size of the current candle.
WickSize()
WickSize
@description Returns the size of the wicks of the current candle (full size - body size).
Returns: float - The size of the wicks of the current candle.
UpperWickSize()
UpperWickSize
@description Returns the size of the upper wick of the current candle.
Returns: float - The size of the upper wick of the current candle.
LowerWickSize()
LowerWickSize
@description Returns the size of the lower wick of the current candle.
Returns: float - The size of the lower wick of the current candle.
BodyToWickRatio()
BodyToWickRatio
@description Returns the ratio of the body size to the wick size of the current candle.
Returns: float - The body to wick ratio of the current candle.
UpperWickPercentage()
UpperWickPercentage
@description Returns the percentage of the upper wick size relative to the full size of the current candle.
Returns: float - The percentage of the upper wick size relative to the full size of the current candle.
LowerWickPercentage()
LowerWickPercentage
@description Returns the percentage of the lower wick size relative to the full size of the current candle.
Returns: float - The percentage of the lower wick size relative to the full size of the current candle.
WickToBodyRatio()
WickToBodyRatio
@description Returns the ratio of the wick size to the body size of the current candle.
Returns: float - The wick to body ratio of the current candle.
BodyPercentage()
BodyPercentage
@description Returns the percentage of the body size relative to the full size of the current candle.
Returns: float - The percentage of the body size relative to the full size of the current candle.
WickPercentage()
WickPercentage
@description Returns the percentage of the wick size relative to the full size of the current candle.
Returns: float - The percentage of the wick size relative to the full size of the current candle.
CandleDirection()
CandleDirection
@description Returns the direction of the current candle.
Returns: string - "Bullish" if the candle is bullish, "Bearish" if the candle is bearish.
AveragePrice()
AveragePrice
@description Returns the average price of the current candle (mean of open, high, low, and close).
Returns: float - The average price of the current candle.
MidpointPrice()
MidpointPrice
@description Returns the midpoint price of the current candle (mean of high and low).
Returns: float - The midpoint price of the current candle.
Volatility()
Volatility
@description Returns the standard deviation of the OHLC prices of the current candle.
Returns: float - The volatility of the current candle.
Median Deviation Suite [InvestorUnknown]The Median Deviation Suite uses a median-based baseline derived from a Double Exponential Moving Average (DEMA) and layers multiple deviation measures around it. By comparing price to these deviation-based ranges, it attempts to identify trends and potential turning points in the market. The indicator also incorporates several deviation types—Average Absolute Deviation (AAD), Median Absolute Deviation (MAD), Standard Deviation (STDEV), and Average True Range (ATR)—allowing traders to visualize different forms of volatility and dispersion. Users should calibrate the settings to suit their specific trading approach, as the default values are not optimized.
Core Components
Median of a DEMA:
The foundation of the indicator is a Median applied to the 7-day DEMA (Double Exponential Moving Average). DEMA aims to reduce lag compared to simple or exponential moving averages. By then taking a median over median_len periods of the DEMA values, the indicator creates a robust and stable central tendency line.
float dema = ta.dema(src, 7)
float median = ta.median(dema, median_len)
Multiple Deviation Measures:
Around this median, the indicator calculates several measures of dispersion:
ATR (Average True Range): A popular volatility measure.
STDEV (Standard Deviation): Measures the spread of price data from its mean.
MAD (Median Absolute Deviation): A robust measure of variability less influenced by outliers.
AAD (Average Absolute Deviation): Similar to MAD, but uses the mean absolute deviation instead of median.
Average of Deviations (avg_dev): The average of the above four measures (ATR, STDEV, MAD, AAD), providing a combined sense of volatility.
Each measure is multiplied by a user-defined multiplier (dev_mul) to scale the width of the bands.
aad = f_aad(src, dev_len, median) * dev_mul
mad = f_mad(src, dev_len, median) * dev_mul
stdev = ta.stdev(src, dev_len) * dev_mul
atr = ta.atr(dev_len) * dev_mul
avg_dev = math.avg(aad, mad, stdev, atr)
Deviation-Based Bands:
The indicator creates multiple upper and lower lines based on each deviation type. For example, using MAD:
float mad_p = median + mad // already multiplied by dev_mul
float mad_m = median - mad
Similar calculations are done for AAD, STDEV, ATR, and the average of these deviations. The indicator then determines the overall upper and lower boundaries by combining these lines:
float upper = f_max4(aad_p, mad_p, stdev_p, atr_p)
float lower = f_min4(aad_m, mad_m, stdev_m, atr_m)
float upper2 = f_min4(aad_p, mad_p, stdev_p, atr_p)
float lower2 = f_max4(aad_m, mad_m, stdev_m, atr_m)
This creates a layered structure of volatility envelopes. Traders can observe which layers price interacts with to gauge trend strength.
Determining Trend
The indicator generates trend signals by assessing where price stands relative to these deviation-based lines. It assigns a trend score by summing individual signals from each deviation measure. For instance, if price crosses above the MAD-based upper line, it contributes a bullish point; crossing below an ATR-based lower line contributes a bearish point.
When the aggregated trend score crosses above zero, it suggests a shift towards a bullish environment; crossing below zero indicates a bearish bias.
// Define Trend scores
var int aad_t = 0
if ta.crossover(src, aad_p)
aad_t := 1
if ta.crossunder(src, aad_m)
aad_t := -1
var int mad_t = 0
if ta.crossover(src, mad_p)
mad_t := 1
if ta.crossunder(src, mad_m)
mad_t := -1
var int stdev_t = 0
if ta.crossover(src, stdev_p)
stdev_t := 1
if ta.crossunder(src, stdev_m)
stdev_t := -1
var int atr_t = 0
if ta.crossover(src, atr_p)
atr_t := 1
if ta.crossunder(src, atr_m)
atr_t := -1
var int adev_t = 0
if ta.crossover(src, adev_p)
adev_t := 1
if ta.crossunder(src, adev_m)
adev_t := -1
int upper_t = src > upper ? 3 : 0
int lower_t = src < lower ? 0 : -3
int upper2_t = src > upper2 ? 1 : 0
int lower2_t = src < lower2 ? 0 : -1
float trend = aad_t + mad_t + stdev_t + atr_t + adev_t + upper_t + lower_t + upper2_t + lower2_t
var float sig = 0
if ta.crossover(trend, 0)
sig := 1
else if ta.crossunder(trend, 0)
sig := -1
Practical Usage and Calibration
Default settings are not optimized: The given parameters serve as a starting point for demonstration. Users should adjust:
median_len: Affects how smooth and lagging the median of the DEMA is.
dev_len and dev_mul: Influence the sensitivity of the deviation measures. Larger multipliers widen the bands, potentially reducing false signals but introducing more lag. Smaller multipliers tighten the bands, producing quicker signals but potentially more whipsaws.
This flexibility allows the trader to tailor the indicator for various markets (stocks, forex, crypto) and time frames.
Backtesting and Performance Metrics
The code integrates with a backtesting library that allows traders to:
Evaluate the strategy historically
Compare the indicator’s signals with a simple buy-and-hold approach
Generate performance metrics (e.g., mean returns, Sharpe Ratio, Sortino Ratio) to assess historical effectiveness.
Disclaimer
No guaranteed results: Historical performance does not guarantee future outcomes. Market conditions can vary widely.
User responsibility: Traders should combine this indicator with other forms of analysis, appropriate risk management, and careful calibration of parameters.
R-based Strategy Template [Daveatt]Have you ever wondered how to properly track your trading performance based on risk rather than just profits?
This template solves that problem by implementing R-multiple tracking directly in TradingView's strategy tester.
This script is a tool that you must update with your own trading entry logic.
Quick notes
Before we dive in, I want to be clear: this is a template focused on R-multiple calculation and visualization.
I'm using a basic RSI strategy with dummy values just to demonstrate how the R tracking works. The actual trading signals aren't important here - you should replace them with your own strategy logic.
R multiple logic
Let's talk about what R-multiple means in practice.
Think of R as your initial risk per trade.
For instance, if you have a $10,000 account and you're risking 1% per trade, your 1R would be $100.
A trade that makes twice your risk would be +2R ($200), while hitting your stop loss would be -1R (-$100).
This way of measuring makes it much easier to evaluate your strategy's performance regardless of account size.
Whenever the SL is hit, we lose -1R
Proof showing the strategy tester whenever the SL is hit: i.imgur.com
The magic happens in how we calculate position sizes.
The script automatically determines the right position size to risk exactly your specified percentage on each trade.
This is done through a simple but powerful calculation:
risk_amount = (strategy.equity * (risk_per_trade_percent / 100))
sl_distance = math.abs(entry_price - sl_price)
position_size = risk_amount / (sl_distance * syminfo.pointvalue)
Limitations with lower timeframe gaps
This ensures that if your stop loss gets hit, you'll lose exactly the amount you intended to risk. No more, no less.
Well, could be more or less actually ... let's assume you're trading futures on a 15-minute chart but in the 1-minute chart there is a gap ... then your 15 minute SL won't get filled and you'll likely to not lose exactly -1R
This is annoying but it can't be fixed - and that's how trading works anyway.
Features
The template gives you flexibility in how you set your stop losses. You can use fixed points, ATR-based stops, percentage-based stops, or even tick-based stops.
Regardless of which method you choose, the position sizing will automatically adjust to maintain your desired risk per trade.
To help you track performance, I've added a comprehensive statistics table in the top right corner of your chart.
It shows you everything you need to know about your strategy's performance in terms of R-multiples: how many R you've won or lost, your win rate, average R per trade, and even your longest winning and losing streaks.
Happy trading!
And remember, measuring your performance in R-multiples is one of the most classical ways to evaluate and improve your trading strategies.
Daveatt
Spread Analysis (COIN/BTC)The Spread Analysis (COIN/BTC) indicator calculates the Z-score of the price ratio between Coinbase stock ( NASDAQ:COIN ) and Bitcoin ( CRYPTOCAP:BTC ). It helps identify overbought or oversold conditions based on deviations from the historical mean of their price relationship.
Key Features:
Z-Score Calculation:
• Tracks the relative price ratio of NASDAQ:COIN to $BTC.
• Compares the current ratio to its historical average, highlighting extreme overvaluation or undervaluation.
• Buy and Sell Signals:
• Buy Signal: Triggered when the Z-score is less than -2, indicating NASDAQ:COIN may be undervalued relative to $BTC.
• Sell Signal: Triggered when the Z-score exceeds 2, suggesting NASDAQ:COIN may be overvalued relative to $BTC.
• Dynamic Z-Score Visualization:
• Blue line plots the Z-score over time.
• Dashed lines at +2 and -2 mark overbought and oversold thresholds.
• Green and red triangles highlight actionable buy and sell signals.
Use Case:
This indicator is ideal for identifying relative valuation opportunities between NASDAQ:COIN and $BTC. Use it to exploit divergences in their historical relationship and anticipate potential reversions to the mean.
Limitations:
• Best suited for range-bound markets; may produce false signals in strongly trending conditions.
• Assumes a consistent correlation between NASDAQ:COIN and CRYPTOCAP:BTC , which may break during independent price drivers like news or earnings.
Market MonitorOverview
The Market Monitor Indicator provides a customisable view of dynamic percentage changes across selected indices or sectors, calculated by comparing current and previous closing prices over the chosen timeframe.
Key Features
Choose up to 20 predefined indices or your own selected indices/stocks.
Use checkboxes to show or hide individual entries.
Monitor returns over daily, weekly, monthly, quarterly, half-yearly, or yearly timeframes
Sort by returns (descending) to quickly identify top-performing indices or alphabetically for an organised and systematic review.
Customisation
Switch between Light Mode (Blue or Green themes) and Dark Mode for visual clarity.
Adjust the table’s size, position, and location.
Customise the table title to your own choice e.g. Sectoral, Broad, Portfolio etc.
Use Cases
Use multiple instances of the script with varying timeframes to study sectoral rotation and trends.
Customise the stocks to see your portfolio returns for the day or over the past week, or longer.
TradingCharts SCTR [Bginvestor]This indicator is replicating Tradingcharts, SCTR plot. If you know, you know.
Brief description: The StockCharts Technical Rank (SCTR), conceived by technical analyst John Murphy, emerges as a pivotal tool in evaluating a stock’s technical prowess. This numerical system, colloquially known as “scooter,” gauges a stock’s strength within various groups, employing six key technical indicators across different time frames.
How to use it:
Long-term indicators (30% weight each)
-Percent above/below the 200-day exponential moving average (EMA)
-125-day rate-of-change (ROC)
Medium-term indicators (15% weight each)
-percent above/below 50-day EMA
-20-day rate-of-change
Short-term indicators (5% weight each)
-Three-day slope of percentage price oscillator histogram divided by three
-Relative strength index
How to use SCTR:
Investors select a specific group for analysis, and the SCTR assigns rankings within that group. A score of 99.99 denotes robust technical performance, while zero signals pronounced underperformance. Traders leverage this data for strategic decision-making, identifying stocks with increasing SCTR for potential buying or spotting weak stocks for potential shorting.
Credit: I've made some modifications, but credit goes to GodziBear for back engineering the averaging / scaling of the equations.
Note: Not a perfect match to TradingCharts, but very, very close.
Sector Relative Strength [Afnan]This indicator calculates and displays the relative strength (RS) of multiple sectors against a chosen benchmark. It allows you to quickly compare the performance of various sectors within any global stock market. While the default settings are configured for the Indian stock market , this tool is not limited to it; you can use it for any market by selecting the appropriate benchmark and sector indices.
📊 Key Features ⚙️
Customizable Benchmark: Select any symbol as your benchmark for relative strength calculation. The default benchmark is set to `NSE:CNX100`. This allows for global market analysis by selecting the appropriate benchmark index of any country.
Multiple Sectors: Analyze up to 23 different sector indices. The default settings include major NSE sector indices. This can be customized to any market by using the relevant sector indices of that country.
Individual Sector Control: Toggle the visibility of each sector's RS on the chart.
Color-Coded Plots: Each sector's RS is plotted with a distinct color for easy identification.
Adjustable Lookback Period: Customize the lookback period for RS calculation.
Interactive Table: A sortable table displays the current RS values for all visible sectors, allowing for quick ranking.
Table Customization: Adjust the table's position, text size, and visibility.
Zero Line: A horizontal line at zero provides a reference point for RS values.
🧭 How to Use 🗺️
Add the indicator to your TradingView chart.
Select your desired benchmark symbol. The default is `NSE:CNX100`. For example, use SPY for the US market, or DAX for the German market.
Adjust the lookback period as needed.
Enable/disable the sector indices you want to analyze. The default includes major NSE sector indices like `NSE:CNXIT`, `NSE:CNXAUTO`, etc.
Customize the table's appearance as needed.
Observe the RS plots and the table to identify sectors with relative strength or weakness.
📝 Note 💡
This indicator is designed for sectorial analysis. You can use it with any market by selecting the appropriate benchmark and sector indices.
The default settings are configured for the Indian stock market with `NSE:CNX100` as the benchmark and major NSE sector indices pre-selected.
The relative strength calculation is based on the price change of the sector index compared to the benchmark over the lookback period.
Positive RS values indicate relative outperformance, while negative values indicate relative underperformance.
👨💻 Developer 🛠️
Afnan Tajuddin
Scatter Plot with Symbol or Data Source InputsDescription of setting items
Use Symbol for X Data?
Type: Checkbox (input.bool)
Explanation: Selects whether the data used for the X axis is obtained from a “symbol” or a “data source”.
If true: data for the X axis will be taken from a symbol (e.g. stock ticker).
If false: X axis data will be taken from the specified data source (e.g., closing price or volume).
Use Symbol for Y Data?
type: checkbox (input.bool)
Explanation: Selects whether the data used for the Y axis is retrieved from a “symbol” or a “data source”.
If true: Y-axis data is obtained from symbols.
If false: Data for the Y axis is obtained from the specified data source.
Select Ticker Symbol for X Data
type: symbol input (input.symbol)
description: selects the symbol to be used for the X axis (default is “AAPL”).
If “Use Symbol for X Data?” is set to true, this symbol will be used as the data for the X axis.
Select Ticker Symbol for Y Data
Type: Symbol input (input.symbol)
description: selects the symbol to be used for the Y axis (default is “GOOG”).
If “Use Symbol for Y Data?” is set to true, this symbol will be used as the data for the Y axis.
X Data Source
type: data source input (input.source)
description: specifies the data source to be used for the X axis.
Default is “close” (closing price).
Other possible values include open, high, low, volume, etc.
Y Data Source
Type: data source input (input.source)
Description: Specifies the data source to be used for the Y axis.
Default is “volume” (volume).
Other possible values include open, high, low, close, etc.
X Offset
type: integer input (input.int)
description: sets the offset value of the X axis.
This shifts the position of the X axis on the grid. The range is from -500 to 500.
Y Offset
Type: Integer input (constant)
description: offset value for y-axis.
Defaults to 0, but can be changed to adjust the Y axis position.
grid_width
type: integer input (input.int)
description: sets the width of the grid.
The default is 200. Increasing the value results in a finer grid.
grid_height
type: integer input (input.int)
description: sets the height of the grid.
Defaults to 200. Increasing the value results in a finer grid.
Frequency of updates
type: integer input (input.int)
description: set frequency of updates.
The higher the frequency of updates, the more bars will be used to calculate minimum and maximum values.
X Tick Interval
type: integer input (input.int)
description: sets the tick interval for the X axis.
The default is 10. To increase the number of ticks, decrease the value.
Y Tick Interval
Box border color
type: select color (input.color)
description: select color for grid box border
Default is blue.
Explanation of usage
To use symbol data: Set Use Symbol for X Data?
When “Use Symbol for X Data?” and “Use Symbol for Y Data?” are set to true, the data of the specified symbol is displayed on each axis. For example, you can use “AAPL” (Apple's stock price data) for the X axis and “GOOG” (Google's stock price data) for the Y axis.
To set the symbol, select the desired ticker in Select Ticker Symbol for X Data and Select Ticker Symbol for Y Data.
To use a data source: select the
You can set Use Symbol for X Data? and Use Symbol for Y Data? to false and use the data source specified in X Data Source or Y Data Source instead (e.g., closing price or volume).
Change Grid Size:.
Set the width and height of the grid with grid_width and grid_height. Larger values allow for more detailed scatter plots.
Set Tick Intervals: Set the X Tick Interval and Y Tick Interval.
Adjust X Tick Interval and Y Tick Interval to change the tick spacing on the X and Y axes.
Data Range Adjustment: Adjust the Frequency of updates to change the frequency of updates.
The Frequency of updates can be changed to control how often the data range is updated. The higher this value, the more historical data is considered and displayed.
Box Color.
Box Border Color allows you to change the color of the box border.
This script is useful for visualizing different symbols and data sources, especially to show the relationship between financial data.
Caution.
Some data may exceed the memory size, but the scale is the same, so you will know most of the locations.
*I made it myself because I could not find anything to draw a scatter plot. You can also compare more than 3 pieces of data by displaying more than one scatter plot. Here is how to do it. Set X or Y as the reference data. Set the data you want to compare to the one that is not the standard. Next, set the same indicator and set the reference to another set of data you wish to compare. Now you can compare the three sets of data. It is effective to change the color of the display box to prevent the user from not knowing which is which. Thus, you should be able to compare more than 3 pieces of data, so give it a try.
Employee Portfolio Generator [By MUQWISHI]▋ INTRODUCTION :
The “Employee Portfolio Generator” simplifies the process of building a long-term investment portfolio tailored for employees seeking to build wealth through investments rather than traditional bank savings. The tool empowers employees to set up recurring deposits at customizable intervals, enabling to make additional purchases in a list of preferred holdings, with the ability to define the purchasing investment weight for each security. The tool serves as a comprehensive solution for tracking portfolio performance, conducting research, and analyzing specific aspects of portfolio investments. The output includes an index value, a table of holdings, and chart plots, providing a deeper understanding of the portfolio's historical movements.
_______________________
▋ OVERVIEW:
● Scenario (The chart above can be taken as an example) :
Let say, in 2010, a newly employed individual committed to saving $1,000 each month. Rather than relying on a traditional savings account, chose to invest the majority of monthly savings in stable well-established stocks. Allocating 30% of monthly saving to AMEX:SPY and another 30% to NASDAQ:QQQ , recognizing these as reliable options for steady growth. Additionally, there was an admired toward innovative business models of NASDAQ:AAPL , NASDAQ:MSFT , NASDAQ:AMZN , and NASDAQ:EBAY , leading to invest 10% in each of those companies. By the end of 2024, after 15 years, the total monthly deposits amounted to $179,000, which would have been the result of traditional saving alone. However, by sticking into long term invest, the value of the portfolio assets grew, reaching nearly $900,000.
_______________________
▋ OUTPUTS:
The table can be displayed in three formats:
1. Portfolio Index Title: displays the index name at the top, and at the bottom, it shows the index value, along with the chart timeframe, e.g., daily change in points and percentage.
2. Specifications: displays the essential information on portfolio performance, including the investment date range, total deposits, free cash, returns, and assets.
3. Holdings: a list of the holding securities inside a table that contains the ticker, last price, entry price, return percentage of the portfolio's total deposits, and latest weighted percentage of the portfolio. Additionally, a tooltip appears when the user passes the cursor over a ticker's cell, showing brief information about the company, such as the company's name, exchange market, country, sector, and industry.
4. Indication of New Deposit: An indication of a new deposit added to the portfolio for additional purchasing.
5. Chart: The portfolio's historical movements can be visualized in a plot, displayed as a bar chart, candlestick chart, or line chart, depending on the preferred format, as shown below.
_______________________
▋ INDICATOR SETTINGS:
Section(1): Table Settings
(1) Naming the index.
(2) Table location on the chart and cell size.
(3) Sorting Holdings Table. By securities’ {Return(%) Portfolio, Weight(%) Portfolio, or Ticker Alphabetical} order.
(4) Choose the type of index: {Assets, Return, or Return (%)}, and the plot type for the portfolio index: {Candle, Bar, or Line}.
(5) Positive/Negative colors.
(6) Table Colors (Title, Cell, and Text).
(7) To show/hide any of selected indicator’s components.
Section(2): Recurring Deposit Settings
(1) From DateTime of starting the investment.
(2) To DateTime of ending the investment
(3) The amount of recurring deposit into portfolio and currency.
(4) The frequency of recurring deposits into the portfolio {Weekly, 2-Weeks, Monthly, Quarterly, Yearly}
(5) The Depositing Model:
● Fixed: The amount for recurring deposits remains constant throughout the entire investment period.
● Increased %: The recurring deposit amount increases at the selected frequency and percentage throughout the entire investment period.
(5B) If the user selects “ Depositing Model: Increased % ”, specify the growth model (linear or exponential) and define the rate of increase.
Section(3): Portfolio Holdings
(1) Enable a ticker in the investment portfolio.
(2) The selected deposit frequency weight for a ticker. For example, if the monthly deposit is $1,000 and the selected weight for XYZ stock is 30%, $300 will be used to purchase shares of XYZ stock.
(3) Select up to 6 tickers that the investor is interested in for long-term investment.
Please let me know if you have any questions
Calculate Order Entry Units based on set Dollar ValuesFUNCTIONS
- Calculate UNITS quantity based on user's input dollar values.
- Show Units in table
USAGE
- Enter 6 usual order $ values
- Use units value in order entry
Tradingview doesn't have order entry in dollar value for most connections/exchanges so it's really tedious to calculate Units some other way every time.
This gives you the Units based on your most used order value sizes in a quick way.
Possible future updates
- Allow user settings for number of values to display
- Allow user option to set titles for each row
Note:
Tradingview really need to get off their butts and give us a real DOM panel and working dollar value order entry for all exchanges among other order entry panel updates.
I hope everyone is suggesting this to them.
DynamicPeriodPublicDynamic Period Calculation Library
This library provides tools for adaptive period determination, useful for creating indicators or strategies that automatically adjust to market conditions.
Overview
The Dynamic Period Library calculates adaptive periods based on pivot points, enabling the creation of responsive indicators and strategies that adjust to market volatility.
Key Features
Dynamic Periods: Computes periods using distances between pivot highs and lows.
Customizable Parameters: Users can adjust detection settings and period constraints.
Robust Handling: Includes fallback mechanisms for cases with insufficient pivot data.
Use Cases
Adaptive Indicators: Build tools that respond to market volatility by adjusting their periods dynamically.
Dynamic Strategies: Enhance trading strategies by integrating pivot-based period adjustments.
Function: `dynamic_period`
Description
Calculates a dynamic period based on the average distances between pivot highs and lows.
Parameters
`left` (default: 5): Number of left-hand bars for pivot detection.
`right` (default: 5): Number of right-hand bars for pivot detection.
`numPivots` (default: 5): Minimum pivots required for calculation.
`minPeriod` (default: 2): Minimum allowed period.
`maxPeriod` (default: 50): Maximum allowed period.
`defaultPeriod` (default: 14): Fallback period if no pivots are found.
Returns
A dynamic period calculated based on pivot distances, constrained by `minPeriod` and `maxPeriod`.
Example
//@version=6
import CrimsonVault/DynamicPeriodPublic/1
left = input.int(5, "Left bars", minval = 1)
right = input.int(5, "Right bars", minval = 1)
numPivots = input.int(5, "Number of Pivots", minval = 2)
period = DynamicPeriodPublic.dynamic_period(left, right, numPivots)
plot(period, title = "Dynamic Period", color = color.blue)
Implementation Notes
Pivot Detection: Requires sufficient historical data to identify pivots accurately.
Edge Cases: Ensures a default period is applied when pivots are insufficient.
Constraints: Limits period values to a user-defined range for stability.
Market Anomaly Detector (MAD)Market Anomaly Detector (MAD) Indicator - Detailed Description:
The Market Anomaly Detector (MAD) Indicator is a unique tool designed to identify potential market anomalies by combining several price action-based and momentum indicators. This indicator is especially useful for traders who seek to identify significant market shifts and anomalies before they become visible in conventional technical indicators.
Key Features of the MAD Indicator:
1. Z-Score Threshold for Anomaly Detection:
• The Z-Score measures how far a current price is from its average over a defined period, normalized by standard deviation. This allows the MAD indicator to detect outliers or anomalies in price movements.
• By adjusting the Z-Score Threshold, traders can tune the sensitivity of the indicator to capture only the most significant price deviations, filtering out noise and reducing false signals.
2. Volume and Liquidity Filter:
• Volume is a key indicator of market participation and sentiment. The MAD Indicator uses a volume multiplier to assess when price movements are supported by sufficient trading volume.
• A volume spike is identified when the current volume exceeds the average volume by a certain multiplier. This ensures that only high-confidence signals are generated, particularly useful for spotting trend reversals and breakout opportunities.
3. Signal Cooldown Period:
• To prevent overfitting and reduce false signals, a signal cooldown period is implemented. Once a buy or sell signal is triggered, the indicator waits for a specified number of bars (e.g., 5) before triggering another signal, even if the price action meets the criteria for a new signal. This helps maintain a cleaner trading environment and avoids confusion when the market is volatile.
4. Upper and Lower Bands for Trend Confirmation:
• The MAD Indicator uses bands based on the mean price and standard deviation, similar to Bollinger Bands. These upper and lower bands help to define the expected price range for a given period, indicating overbought or oversold conditions.
• The combination of Z-Score, volume, and band analysis helps pinpoint when the price breaks out of expected ranges, providing early warning signs for potential market shifts.
5. Trend Confirmation from Higher Timeframes:
• The MAD Indicator includes a multi-timeframe approach to trend confirmation, using the 50-period EMA on a higher timeframe (e.g., 1-hour chart). This ensures that signals are aligned with the overall market trend, enhancing the reliability of buy and sell signals.
How It Works:
• The MAD Indicator continuously monitors price action, volume, and statistical anomalies, using the Z-Score to determine when the price is significantly deviating from its historical average.
• When the price breaks above the upper band and a bullish anomaly is detected, a buy signal is generated. (Green Background)
• Similarly, when the price breaks below the lower band and a bearish anomaly is detected, a sell signal is triggered. (Red Background
• By filtering signals based on volume and using the cooldown period, the MAD Indicator ensures that only high-quality trades are signaled.
How to Use the MAD Indicator:
• Buy Signal: Occurs when the price breaks above the upper band and there is a significant deviation from the mean (bullish anomaly).
• Sell Signal: Occurs when the price breaks below the lower band and there is a significant deviation from the mean (bearish anomaly).
• Volume Confirmation: Ensure that the buy/sell signals are supported by a volume spike, indicating strong market participation.
• Signal Cooldown Period: After a signal is triggered, the indicator waits for the cooldown period to avoid triggering multiple signals in quick succession.
Why It’s Worth Paying For:
The MAD Indicator combines advanced statistical analysis (Z-Score), price action, and volume analysis to identify market anomalies and breakouts before they are visible on standard indicators. By leveraging the power of mean reversion and statistical anomalies, this tool provides traders with high-confidence signals that can lead to profitable trades, especially in volatile markets. The integration of a multi-timeframe trend filter ensures that signals are aligned with the overall market trend, reducing the likelihood of false breakouts.
This indicator is ideal for trend-following traders looking for high-probability entries and mean-reversion traders aiming to capture price deviations. The signal cooldown period and volume filter provide an additional layer of precision, ensuring that you only act on the strongest market signals.
Quantify [Entry Model] | FractalystWhat’s the indicator’s purpose and functionality?
Quantify is a machine learning entry model designed to help traders identify high-probability setups to refine their strategies.
➙ Simply pick your bias, select your entry timeframes, and let Quantify handle the rest for you.
Can the indicator be applied to any market approach/trading strategy?
Absolutely, all trading strategies share one fundamental element: Directional Bias
Once you’ve determined the market bias using your own personal approach, whether it’s through technical analysis or fundamental analysis, select the trend direction in the Quantify user inputs.
The algorithm will then adjust its calculations to provide optimal entry levels aligned with your chosen bias. This involves analyzing historical patterns to identify setups with the highest potential expected values, ensuring your setups are aligned with the selected direction.
Can the indicator be used for different timeframes or trading styles?
Yes, regardless of the timeframe you’d like to take your entries, the indicator adapts to your trading style.
Whether you’re a swing trader, scalper, or even a position trader, the algorithm dynamically evaluates market conditions across your chosen timeframe.
How can this indicator help me to refine my trading strategy?
1. Focus on Positive Expected Value
• The indicator evaluates every setup to ensure it has a positive expected value, helping you focus only on trades that statistically favor long-term profitability.
2. Adapt to Market Conditions
• By analyzing real-time market behavior and historical patterns, the algorithm adjusts its calculations to match current conditions, keeping your strategy relevant and adaptable.
3. Eliminate Emotional Bias
• With clear probabilities, expected values, and data-driven insights, the indicator removes guesswork and helps you avoid emotional decisions that can damage your edge.
4. Optimize Entry Levels
• The indicator identifies optimal entry levels based on your selected bias and timeframes, improving robustness in your trades.
5. Enhance Risk Management
• Using tools like the Kelly Criterion, the indicator suggests optimal position sizes and risk levels, ensuring that your strategy maintains consistency and discipline.
6. Avoid Overtrading
• By highlighting only high-potential setups, the indicator keeps you focused on quality over quantity, helping you refine your strategy and avoid unnecessary losses.
How can I get started to use the indicator for my entries?
1. Set Your Market Bias
• Determine whether the market trend is Bullish or Bearish using your own approach.
• Select the corresponding bias in the indicator’s user inputs to align it with your analysis.
2. Choose Your Entry Timeframes
• Specify the timeframes you want to focus on for trade entries.
• The indicator will dynamically analyze these timeframes to provide optimal setups.
3. Let the Algorithm Analyze
• Quantify evaluates historical data and real-time price action to calculate probabilities and expected values.
• It highlights setups with the highest potential based on your selected bias and timeframes.
4. Refine Your Entries
• Use the insights provided—entry levels, probabilities, and risk calculations—to align your trades with a math-driven edge.
• Avoid overtrading by focusing only on setups with positive expected value.
5. Adapt to Market Conditions
• The indicator continuously adapts to real-time market behavior, ensuring its recommendations stay relevant and precise as conditions change.
How does the indicator calculate the current range?
The indicator calculates the current range by analyzing swing points from the very first bar on your charts to the latest available bar it identifies external liquidity levels, also known as BSLQ (buy-side liquidity levels) and SSLQ (sell-side liquidity levels).
What's the purpose of these levels? What are the underlying calculations?
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Pivot levels.
3. Identifying Discount and Premium Zones.
4. Importance of Risk-Reward in Premium and Discount Ranges
How does the script calculate probabilities?
The script calculates the probability of each liquidity level individually. Here's the breakdown:
1. Upon the formation of a new range, the script waits for the price to reach and tap into pivot level level. Status: "■" - Inactive
2. Once pivot level is tapped into, the pivot status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
4. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
What does the multi-timeframe functionality offer?
You can incorporate up to 4 higher timeframe probabilities directly into the table.
This feature allows you to analyze the probabilities of buyside and sellside liquidity across multiple timeframes, without the need to manually switch between them.
By viewing these higher timeframe probabilities in one place, traders can spot larger market trends and refine their entries and exits with a better understanding of the overall market context.
What are the multi-timeframe underlying calculations?
The script uses the same calculations (mentioned above) and uses security function to request the data such as price levels, bar time, probabilities and booleans from the user-input timeframe.
How does the Indicator Identifies Positive Expected Values?
Quantify instantly calculates whether a trade setup has the potential to generate positive expected value (EV).
To determine a positive EV setup, the indicator uses the formula:
EV = ( P(Win) × R(Win) ) − ( P(Loss) × R(Loss))
where:
- P(Win) is the probability of a winning trade.
- R(Win) is the reward or return for a winning trade, determined by the current risk-to-reward ratio (RR).
- P(Loss) is the probability of a losing trade.
- R(Loss) is the loss incurred per losing trade, typically assumed to be -1.
By calculating these values based on historical data and the current trading setup, the indicator helps you understand whether your trade has a positive expected value.
How can I know that the setup I'm going to trade with has a positive EV?
If the indicator detects that the adjusted pivot and buy/sell side probabilities have generated positive expected value (EV) in historical data, the risk-to-reward (RR) label within the range box will be colored blue and red .
If the setup does not produce positive EV, the RR label will appear gray.
This indicates that even the risk-to-reward ratio is greater than 1:1, the setup is not likely to yield a positive EV because, according to historical data, the number of losses outweighs the number of wins relative to the RR gain per winning trade.
What is the confidence level in the indicator, and how is it determined?
The confidence level in the indicator reflects the reliability of the probabilities calculated based on historical data. It is determined by the sample size of the probabilities used in the calculations. A larger sample size generally increases the confidence level, indicating that the probabilities are more reliable and consistent with past performance.
How does the confidence level affect the risk-to-reward (RR) label?
The confidence level (★) is visually represented alongside the probability label. A higher confidence level indicates that the probabilities used to determine the RR label are based on a larger and more reliable sample size.
How can traders use the confidence level to make better trading decisions?
Traders can use the confidence level to gauge the reliability of the probabilities and expected value (EV) calculations provided by the indicator. A confidence level above 95% is considered statistically significant and indicates that the historical data supporting the probabilities is robust. This high confidence level suggests that the probabilities are reliable and that the indicator’s recommendations are more likely to be accurate.
In data science and statistics, a confidence level above 95% generally means that there is less than a 5% chance that the observed results are due to random variation. This threshold is widely accepted in research and industry as a marker of statistical significance. Studies such as those published in the Journal of Statistical Software and the American Statistical Association support this threshold, emphasizing that a confidence level above 95% provides a strong assurance of data reliability and validity.
Conversely, a confidence level below 95% indicates that the sample size may be insufficient and that the data might be less reliable. In such cases, traders should approach the indicator’s recommendations with caution and consider additional factors or further analysis before making trading decisions.
How does the sample size affect the confidence level, and how does it relate to my TradingView plan?
The sample size for calculating the confidence level is directly influenced by the amount of historical data available on your charts. A larger sample size typically leads to more reliable probabilities and higher confidence levels.
Here’s how the TradingView plans affect your data access:
Essential Plan
The Essential Plan provides basic data access with a limited amount of historical data. This can lead to smaller sample sizes and lower confidence levels, which may weaken the robustness of your probability calculations. Suitable for casual traders who do not require extensive historical analysis.
Plus Plan
The Plus Plan offers more historical data than the Essential Plan, allowing for larger sample sizes and more accurate confidence levels. This enhancement improves the reliability of indicator calculations. This plan is ideal for more active traders looking to refine their strategies with better data.
Premium Plan
The Premium Plan grants access to extensive historical data, enabling the largest sample sizes and the highest confidence levels. This plan provides the most reliable data for accurate calculations, with up to 20,000 historical bars available for analysis. It is designed for serious traders who need comprehensive data for in-depth market analysis.
PRO+ Plans
The PRO+ Plans offer the most extensive historical data, allowing for the largest sample sizes and the highest confidence levels. These plans are tailored for professional traders who require advanced features and significant historical data to support their trading strategies effectively.
For many traders, the Premium Plan offers a good balance of affordability and sufficient sample size for accurate confidence levels.
What is the HTF probability table and how does it work?
The HTF (Higher Time Frame) probability table is a feature that allows you to view buy and sellside probabilities and their status from timeframes higher than your current chart timeframe.
Here’s how it works:
Data Request: The table requests and retrieves data from user-defined higher timeframes (HTFs) that you select.
Probability Display: It displays the buy and sellside probabilities for each of these HTFs, providing insights into the likelihood of price movements based on higher timeframe data.
Detailed Tooltips: The table includes detailed tooltips for each timeframe, offering additional context and explanations to help you understand the data better.
What do the different colors in the HTF probability table indicate?
The colors in the HTF probability table provide visual cues about the expected value (EV) of trading setups based on higher timeframe probabilities:
Blue: Suggests that entering a long position from the HTF user-defined pivot point, targeting buyside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Red: Indicates that entering a short position from the HTF user-defined pivot point, targeting sellside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Gray: Shows that neither long nor short trades from the HTF user-defined pivot point are expected to generate positive EV, suggesting that trading these setups may not be favorable.
What machine learning techniques are used in Quantify?
Quantify offers two main machine learning approaches:
1. Adaptive Learning (Fixed Sample Size): The algorithm learns from the entire dataset without resampling, maintaining a stable model that adapts to the latest market conditions.
2. Bootstrap Resampling: This method creates multiple subsets of the historical data, allowing the model to train on varying sample sizes. This technique enhances the robustness of predictions by ensuring that the model is not overfitting to a single dataset.
How does machine learning affect the expected value calculations in Quantify?
Machine learning plays a key role in improving the accuracy of expected value (EV) calculations. By analyzing historical price action, liquidity hits, and market bias patterns, the model continuously adjusts its understanding of risk and reward, allowing the expected value to reflect the most likely market movements. This results in more precise EV predictions, helping traders focus on setups that maximize profitability.
What is the Kelly Criterion, and how does it work in Quantify?
The Kelly Criterion is a mathematical formula used to determine the optimal position size for each trade, maximizing long-term growth while minimizing the risk of large drawdowns. It calculates the percentage of your portfolio to risk on a trade based on the probability of winning and the expected payoff.
Quantify integrates this with user-defined inputs to dynamically calculate the most effective position size in percentage, aligning with the trader’s risk tolerance and desired exposure.
How does Quantify use the Kelly Criterion in practice?
Quantify uses the Kelly Criterion to optimize position sizing based on the following factors:
1. Confidence Level: The model assesses the confidence level in the trade setup based on historical data and sample size. A higher confidence level increases the suggested position size because the trade has a higher probability of success.
2. Max Allowed Drawdown (User-Defined): Traders can set their preferred maximum allowed drawdown, which dictates how much loss is acceptable before reducing position size or stopping trading. Quantify uses this input to ensure that risk exposure aligns with the trader’s risk tolerance.
3. Probabilities: Quantify calculates the probabilities of success for each trade setup. The higher the probability of a successful trade (based on historical price action and liquidity levels), the larger the position size suggested by the Kelly Criterion.
What is a trailing stoploss, and how does it work in Quantify?
A trailing stoploss is a dynamic risk management tool that moves with the price as the market trend continues in the trader’s favor. Unlike a fixed take profit, which stays at a set level, the trailing stoploss automatically adjusts itself as the market moves, locking in profits as the price advances.
In Quantify, the trailing stoploss is enhanced by incorporating market structure liquidity levels (explain above). This ensures that the stoploss adjusts intelligently based on key price levels, allowing the trader to stay in the trade as long as the trend remains intact, while also protecting profits if the market reverses.
Why would a trader prefer a trailing stoploss based on liquidity levels instead of a fixed take-profit level?
Traders who use trailing stoplosses based on liquidity levels prefer this method because:
1. Market-Driven Flexibility: The stoploss follows the market structure rather than being static at a pre-defined level. This means the stoploss is less likely to be hit by small market fluctuations or false reversals. The stoploss remains adaptive, moving as the market moves.
2. Riding the Trend: Traders can capture more profit during a sustained trend because the trailing stop will adjust only when the trend starts to reverse significantly, based on key liquidity levels. This allows them to hold positions longer without prematurely locking in profits.
3. Avoiding Premature Exits: Fixed stoploss levels may exit a trade too early in volatile markets, while liquidity-based trailing stoploss levels respect the natural flow of price action, preventing the trader from exiting too soon during pullbacks or minor retracements.
🎲 Becoming the House: Gaining an Edge Over the Market
In American roulette, the casino has a 5.26% edge due to the presence of the 0 and 00 pockets. On even-money bets, players face a 47.37% chance of winning, while true 50/50 odds would require a 50% chance. This edge—the gap between the payout odds and the true probabilities—ensures that, statistically, the casino will always win over time, even if individual players win occasionally.
From a Trader’s Perspective
In trading, your edge comes from identifying and executing setups with a positive expected value (EV). For example:
• If you identify a setup with a 55.48% chance of winning and a 1:1 risk-to-reward (RR) ratio, your trade has a statistical advantage over a neutral (50/50) probability.
This edge works in your favor when applied consistently across a series of trades, just as the casino’s edge ensures profitability across thousands of spins.
🎰 Applying the Concept to Trading
Like casinos leverage their mathematical edge in games of chance, you can achieve long-term success in trading by focusing on setups with positive EV and managing your trades systematically. Here’s how:
1. Probability Advantage: Prioritize trades where the probability of success (win rate) exceeds the breakeven rate for your chosen risk-to-reward ratio.
• Example: With a 1:1 RR, you need a win rate above 50% to achieve positive EV.
2. Risk-to-Reward Ratio (RR): Even with a win rate below 50%, you can gain an edge by increasing your RR (e.g., a 40% win rate with a 2:1 RR still has positive EV).
3. Consistency and Discipline: Just as casinos profit by sticking to their mathematical advantage over thousands of spins, traders must rely on their edge across many trades, avoiding emotional decisions or overleveraging.
By targeting favorable probabilities and managing trades effectively, you “become the house” in your trading. This approach allows you to leverage statistical advantages to enhance your overall performance and achieve sustainable profitability.
What Makes the Quantify Indicator Original?
1. Data-Driven Edge
Unlike traditional indicators that rely on static formulas, Quantify leverages probability-based analysis and machine learning. It calculates expected value (EV) and confidence levels to help traders identify setups with a true statistical edge.
2. Integration of Market Structure
Quantify uses market structure liquidity levels to dynamically adapt. It identifies key zones like swing highs/lows and liquidity traps, enabling users to align entries and exits with where the market is most likely to react. This bridges the gap between price action analysis and quantitative trading.
3. Sophisticated Risk Management
The Kelly Criterion implementation is unique. Quantify allows traders to input their maximum allowed drawdown, dynamically adjusting risk exposure to maintain optimal position sizing. This ensures risk is scientifically controlled while maximizing potential growth.
4. Multi-Timeframe and Liquidity-Based Trailing Stops
The indicator doesn’t just suggest fixed profit-taking levels. It offers market structure-based trailing stop-loss functionality, letting traders ride trends as long as liquidity and probabilities favor the position, which is rare in most tools.
5. Customizable Bias and Adaptive Learning
• Directional Bias: Traders can set a bullish or bearish bias, and the indicator recalculates probabilities to align with the trader’s market outlook.
• Adaptive Learning: The machine learning model adapts to changes in data (via resampling or bootstrap methods), ensuring that predictions stay relevant in evolving markets.
6. Positive EV Focus
The focus on positive EV setups differentiates it from reactive indicators. It shifts trading from chasing signals to acting on setups that statistically favor profitability, akin to how professional quant funds operate.
7. User Empowerment
Through features like customizable timeframes, real-time probability updates, and visualization tools, Quantify empowers users to make data-informed decisions.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Salience Theory Crypto Returns (AiBitcoinTrend)The Salience Theory Crypto Returns Indicator is a sophisticated tool rooted in behavioral finance, designed to identify trading opportunities in the cryptocurrency market. Based on research by Bordalo et al. (2012) and extended by Cai and Zhao (2022), it leverages salience theory—the tendency of investors, particularly retail traders, to overemphasize standout returns.
In the crypto market, dominated by sentiment-driven retail investors, salience effects are amplified. Attention disproportionately focused on certain cryptocurrencies often leads to temporary price surges, followed by reversals as the market stabilizes. This indicator quantifies these effects using a relative return salience measure, enabling traders to capitalize on price reversals and trends, offering a clear edge in navigating the volatile crypto landscape.
👽 How the Indicator Works
Salience Measure Calculation :
👾 The indicator calculates how much each cryptocurrency's return deviates from the average return of all cryptos over the selected ranking period (e.g., 21 days).
👾 This deviation is the salience measure.
👾 The more a return stands out (salient outcome), the higher the salience measure.
Ranking:
👾 Cryptos are ranked in ascending order based on their salience measures.
👾 Rank 1 (lowest salience) means the crypto is closer to the average return and is more predictable.
👾 Higher ranks indicate greater deviation and unpredictability.
Color Interpretation:
👾 Green: Low salience (closer to average) – Trending or Predictable.
👾 Red/Orange: High salience (far from average) – Overpriced/Unpredictable.
👾 Text Gradient (Teal to Light Blue): Helps visualize potential opportunities for mean reversion trades (i.e., cryptos that may return to equilibrium).
👽 Core Features
Salience Measure Calculation
The indicator calculates the salience measure for each cryptocurrency by evaluating how much its return deviates from the average market return over a user-defined ranking period. This measure helps identify which assets are trending predictably and which are likely to experience a reversal.
Dynamic Ranking System
Cryptocurrencies are dynamically ranked based on their salience measures. The ranking helps differentiate between:
Low Salience Cryptos (Green): These are trending or predictable assets.
High Salience Cryptos (Red): These are overpriced or deviating significantly from the average, signaling potential reversals.
👽 Deep Dive into the Core Mathematics
Salience Theory in Action
Salience theory explains how investors, particularly in the crypto market, tend to prefer assets with standout returns (salient outcomes). This behavior often leads to overpricing of assets with high positive returns and underpricing of those with standout negative returns. The indicator captures these deviations to anticipate mean reversions or trend continuations.
Salience Measure Calculation
// Calculate the average return
avgReturn = array.avg(returns)
// Calculate salience measure for each symbol
salienceMeasures = array.new_float()
for i = 0 to array.size(returns) - 1
ret = array.get(returns, i)
salienceMeasure = math.abs(ret - avgReturn) / (math.abs(ret) + math.abs(avgReturn) + 0.1)
array.push(salienceMeasures, salienceMeasure)
Dynamic Ranking
Cryptos are ranked in ascending order based on their salience measures:
Low Ranks: Cryptos with low salience (predictable, trending).
High Ranks: Cryptos with high salience (unpredictable, likely to revert).
👽 Applications
👾 Trend Identification
Identify cryptocurrencies that are currently trending with low salience measures (green). These assets are likely to continue their current direction, making them good candidates for trend-following strategies.
👾 Mean Reversion Trading
Cryptos with high salience measures (red to light blue) may be poised for a mean reversion. These assets are likely to correct back towards the market average.
👾 Reversal Signals
Anticipate potential reversals by focusing on high-ranked cryptos (red). These assets exhibit significant deviation and are prone to price corrections.
👽 Why It Works in Crypto
The cryptocurrency market is dominated by retail investors prone to sentiment-driven behavior. This leads to exaggerated price movements, making the salience effect a powerful predictor of reversals.
👽 Indicator Settings
👾 Ranking Period : Number of bars used to calculate the average return and salience measure.
Higher Values: Smooth out short-term volatility.
Lower Values: Make the ranking more sensitive to recent price movements.
👾 Number of Quantiles : Divide ranked assets into quantile groups (e.g., quintiles).
Higher Values: More detailed segmentation (deciles, percentiles).
Lower Values: Broader grouping (quintiles, quartiles).
👾 Portfolio Percentage : Percentage of the portfolio allocated to each selected asset.
Enter a percentage (e.g., 20 for 20%), automatically converted to a decimal (e.g., 0.20).
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
