Advanced Physics Financial Indicator Each component represents a scientific theory and is applied to the price data in a way that reflects key principles from that theory.
Detailed Explanation
1. Fractal Geometry - High/Low Signal
Concept: Fractal geometry studies self-similar patterns that repeat at different scales. In markets, fractals can be used to detect recurring patterns or turning points.
Implementation: The script detects pivot highs and lows using ta.pivothigh and ta.pivotlow, representing local turning points in price. The fractalSignal is set to 1 for a pivot high, -1 for a pivot low, and 0 if there is no signal. This logic reflects the cyclical, self-similar nature of price movements.
Practical Use: This signal is useful for identifying local tops and bottoms, allowing traders to spot potential reversals or consolidation points where fractal patterns emerge.
2. Quantum Mechanics - Probabilistic Monte Carlo Simulation
Concept: Quantum mechanics introduces uncertainty and probability into systems, much like how future price movements are inherently uncertain. Monte Carlo simulations are used to model a range of possible outcomes based on random inputs.
Implementation: In this script, we simulate 100 random outcomes by generating a random number between -1 and 1 for each iteration. These random values are stored in an array, and the average of these values is calculated to represent the Quantum Signal.
Practical Use: This probabilistic signal provides a sense of randomness and uncertainty in the market, reflecting the possibility of price movement in either direction. It simulates the market’s chaotic nature by considering multiple possible outcomes and their average.
3. Thermodynamics - Efficiency Ratio Signal
Concept: Thermodynamics deals with energy efficiency and entropy in systems. The efficiency ratio in financial terms can be used to measure how efficiently the price is moving relative to volatility.
Implementation: The Efficiency Ratio is calculated as the absolute price change over n periods divided by the sum of absolute changes for each period within n. This ratio shows how much of the price movement is directional versus random, mimicking the concept of efficiency in thermodynamic systems.
Practical Use: A high efficiency ratio suggests that the market is trending smoothly (high efficiency), while a low ratio indicates choppy, non-directional movement (low efficiency, or high entropy).
4. Chaos Theory - ATR Signal
Concept: Chaos theory studies how complex systems are highly sensitive to initial conditions, leading to unpredictable behavior. In markets, chaotic price movements can often be captured through volatility indicators.
Implementation: The script uses a very long ATR period (1000) to reflect slow-moving chaos over time. The Chaos Signal is computed by measuring the deviation of the current price from its long-term average (SMA), normalized by ATR. This captures price deviations over time, hinting at chaotic market behavior.
Practical Use: The signal measures how far the price deviates from its long-term average, which can signal the degree of chaos or extreme behavior in the market. High deviations indicate chaotic or volatile conditions, while low deviations suggest stability.
5. Network Theory - Correlation with BTC
Concept: Network theory studies how different components within a system are interconnected. In markets, assets are often correlated, meaning that price movements in one asset can influence or be influenced by another.
Implementation: This indicator calculates the correlation between the asset’s price and the price of Bitcoin (BTC) over 30 periods. The Network Signal shows how connected the asset is to BTC, reflecting broader market dynamics.
Practical Use: In a highly correlated market, BTC can act as a leading indicator for other assets. A strong correlation with BTC might suggest that the asset is likely to move in line with Bitcoin, while a weak or negative correlation might indicate that the asset is moving independently.
6. String Theory - RSI & MACD Interaction
Concept: String theory attempts to unify the fundamental forces of nature into a single framework. In trading, we can view the RSI and MACD as interacting forces that provide insights into momentum and trend.
Implementation: The script calculates the RSI and MACD and combines them into a single signal. The formula for String Signal is (RSI - 50) / 100 + (MACD Line - Signal Line) / 100, normalizing both indicators to a scale where their contributions are additive. The RSI represents momentum, and MACD shows trend direction and strength.
Practical Use: This signal helps in detecting moments where momentum (RSI) and trend strength (MACD) align, giving a clearer picture of the asset's direction and overbought/oversold conditions. It unifies these two indicators to create a more holistic view of market behavior.
7. Fluid Dynamics - On-Balance Volume (OBV) Signal
Concept: Fluid dynamics studies how fluids move and flow. In markets, volume can be seen as a "flow" that drives price movement, much like how fluid dynamics describe the flow of liquids.
Implementation: The script uses the OBV (On-Balance Volume) indicator to track the cumulative flow of volume based on price changes. The signal is further normalized by its moving average to smooth out fluctuations and make it more reflective of price pressure over time.
Practical Use: The Fluid Signal shows how the flow of volume is driving price action. If the OBV rises significantly, it suggests that there is strong buying pressure, while a falling OBV indicates selling pressure. It’s analogous to how pressure builds in a fluid system.
8. Final Signal - Combining All Physics-Based Indicators
Implementation: Each of the seven physics-inspired signals is combined into a single Final Signal by averaging their values. This approach blends different market insights from various scientific domains, creating a comprehensive view of the market’s condition.
Practical Use: The final signal gives you a holistic, multi-dimensional view of the market by merging different perspectives (fractal behavior, quantum probability, efficiency, chaos, correlation, momentum/trend, and volume flow). This approach helps traders understand the market's dynamics from multiple angles, offering deeper insights than any single indicator.
9. Color Coding Based on Signal Extremes
Concept: The color of the final signal plot dynamically reflects whether the market is in an extreme state.
Implementation: The signal color is determined using percentiles. If the Final Signal is in the top 55th percentile of its range, the signal is green (bullish). If it is between the 45th and 55th percentiles, it is orange (neutral). If it falls below the 45th percentile, it is red (bearish).
Practical Use: This visual representation helps traders quickly identify the strength of the signal. Bullish conditions (green), neutral conditions (orange), and bearish conditions (red) are clearly distinguished, simplifying decision-making.
在腳本中搜尋"indicators"
Buy and Sell Signals Based on SMI {K28}Buy/Sell Signals Based on SMI
This indicator provides buy and sell signals based on the Stochastic Momentum Index (SMI) to assist traders in identifying potential entry and exit points in the market. Here’s how to effectively use this indicator:
Usage Instructions:
Signal Interpretation:
No signal is 100% guaranteed
Green Labels: Indicate strong buy signals when the SMI crosses above its EMA, especially if the candle is green (closing price higher than opening price).
Red Labels: Indicate strong sell signals when the SMI crosses below its EMA.
Cautious Signals:
Blue Buy Labels: These buy signals appear when the SMI is in a cautious zone (between -20 and 20). They may not be as reliable, so confirm these signals with other indicators before acting.
Yellow Sell Labels: These buy signals appear when the SMI is in a cautious zone (between -20 and 20). They may not be as reliable, so confirm these signals with other indicators before acting.
Gray Buy and Sell Labels: Indicate potential false signals (when the SMI is overbought or oversold). Use other confirmation indicators to verify these signals.
Trade Strategy:
This indicator is designed for traders looking to make small, consistent profits. Focus on executing more trades rather than waiting for larger price movements.
Be mindful that the indicator may yield frequent signals, so it's essential to maintain discipline and only take trades that meet your criteria for confirmation.
Important Notes:
Caution with Signals: Always exercise caution when acting on blue or gray labels. These may indicate less reliable signals, so it's crucial to confirm with additional indicators.
No Perfect Indicator: Please remember that no trading indicator is perfect. Use this indicator at your own risk, and consider incorporating risk management strategies into your trading plan.
Conclusion:
By employing this SMI indicator, you can enhance your trading strategy focused on generating small, consistent profits through frequent trades. However, always verify signals and stay aware of market conditions to optimize your trading performance.
MTF RSI+CMO PROThis RSI+CMO script combines the Relative Strength Index (RSI) and Chande Momentum Oscillator (CMO), providing a powerful tool to help traders analyze price momentum and spot potential turning points in the market. Unlike using RSI alone, the CMO (especially with a 14-period length) moves faster and accentuates price pops and dips in the histogram, making price shifts more apparent.
Indicator Features:
➡️RSI and CMO Combined: This indicator allows traders to track both RSI and CMO values simultaneously, highlighting differences in their movement. RSI and CMO values are both plotted on the histogram, while CMO values are also drawn as a line moving through the histogram, giving a visual representation of their relationship. The often faster-moving CMO accentuates short-term price movements, helping traders spot subtle shifts in momentum that the RSI might smooth out.
➡️Multi-Time Frame Table: A real-time, multi-time frame table displays RSI and CMO values across various timeframes. This gives traders an overview of momentum across different intervals, making it easier to spot trends and divergences across short and long-term time frames.
➡️Momentum Chart Label: A chart label compares the current RSI and CMO values with values from 1 and 2 bars back, providing an additional metric to gauge momentum. This feature allows traders to easily see if momentum is increasing or decreasing in real-time.
➡️RSI/CMO Bullish and Bearish Signals: Colored arrow plot shapes (above the histogram) indicate when RSI and CMO values are signaling bullish or bearish conditions. For example, green arrows appear when RSI is above 65, while purple arrows show when RSI is below 30 and CMO is below -40, indicating strong bearish momentum.
➡️Divergences in Histogram: The histogram can make it easier for traders to spot divergences between price and momentum. For instance, if the price is making new highs but the RSI or CMO is not, a bearish divergence may be forming. Similarly, bullish divergences can be spotted when prices are making lower lows while RSI or CMO is rising.
➡️Alert System: Alerts are built into the indicator and will trigger when specific conditions are met, allowing traders to stay informed of potential entry or exit points based on RSI and CMO levels without constantly monitoring the chart. These are set manually. Look for the 3 dots in the indicator name.
How Traders Can Use the Indicator:
💥Identifying Momentum Shifts: The RSI+CMO combination is ideal for spotting momentum shifts in the market. Traders can monitor the histogram and the CMO line to determine if the market is gaining or losing strength.
💥Confirming Trade Entries/Exits: Use the real-time RSI and CMO values across multiple time frames to confirm trades. For instance, if the 1-hour RSI is above 70 but the 1-minute RSI is turning down, it could indicate short-term overbought conditions, signaling a potential exit or reversal.
💥Spotting Divergences: Divergences are critical for predicting potential reversals. The histogram can be used to spot divergences when RSI and CMO values deviate from price action, offering an early signal of market exhaustion.
💥Tracking Multi-Time Frame Trends: The multi-time frame table provides insight into the market’s overall trend across several timeframes, helping traders ensure their decisions align with both short and long-term trends.
RSI vs. CMO: Why Use Both?
While both RSI and CMO measure momentum, the CMO often moves faster with a value of 14 for example, reacting to price changes more quickly. This makes it particularly effective for detecting sharp price movements, while RSI helps smooth out price action. By using both, traders get a clearer picture of the market's momentum, particularly during volatile periods.
Confluence and Price Fluidity:
One of the powerful ways to enhance the effectiveness of this indicator is by using it in conjunction with other technical analysis tools to create confluence. Confluence occurs when multiple indicators or price action signals align, providing stronger confirmation for a trade decision. For example:
🎯Support and Resistance Levels: Traders can use RSI+CMO in combination with key support and resistance zones. If the price is nearing a support level and RSI+CMO values start to signal a bullish reversal, this alignment strengthens the case for entering a long position.
🎯Moving Averages: When the RSI+CMO signals a potential trend reversal and this is confirmed by a crossover in moving averages (such as a 50-day and 200-day moving average), traders gain additional confidence in the trade direction.
🎯Momentum Indicators: Traders can also look for momentum indicators like the MACD to confirm the strength of a trend or potential reversal. For instance, if the RSI+CMO values start to decrease rapidly while both the RSI+CMO also shows overbought conditions, this could provide stronger confirmation to exit a long trade or enter a short position.
🎯Candlestick Patterns: Price fluidity can be monitored using candlestick formations. For example, a bearish engulfing pattern with decreasing RSI+CMo values offers confluence, adding confidence to the signal to close or short the trade.
By combining the MTF RSI+CMO PRO with other tools, traders ensure that they are not relying on a single indicator. This layered approach can reduce the likelihood of false signals and improve overall trading accuracy.
Chandelier Exit Pro w/ExtensionsChandelier Exit Pro w/Extensions
The Chandelier Exit Pro w/Extensions indicator is designed to assist traders in managing risk and identifying trend reversals. The strategy is based on the Chandelier Exit concept, originally created by Charles Le Beau. It uses the Average True Range (ATR) to calculate dynamic stop levels that adjust based on market volatility. This script not only implements the standard Chandelier Exit, but also introduces extension levels and alerts to enhance decision-making.
Key Features:
➡️Dynamic Stop Levels: The indicator calculates stop levels for both long and short positions based on an ATR multiple. This allows traders to determine exit points by monitoring when the price crosses above or below these levels. These levels adapt in real-time based on price volatility, making them a versatile tool for trend-following strategies.
➡️Extension Levels: In addition to the primary stop levels, the script includes extension levels for more advanced stop-loss management. Traders can view active and extension levels separately, providing more flexibility in their exit strategies.
➡️Labels and Visual Cues: The indicator provides dynamic labels that automatically update and follow the plotted stop levels. Labels include the ATR multiplier value (e.g., "2.5" or "2.5ext"), clearly showing the significance of each level. When price crosses below or above a level, the corresponding label is highlighted, aiding traders in quickly identifying the most relevant stop level.
➡️Bar Confirmation and Alerts: The script includes an "await bar confirmation" option to ensure that the stop levels and alerts only trigger after the bar has closed. Alerts are customizable and will notify traders when price crosses critical levels, helping to make timely decisions without the need to constantly monitor charts.
➡️Multiple ATR Levels for Enhanced Precision: The indicator supports up to four different ATR levels, each with customizable multipliers. This allows traders to set different thresholds for exits based on varying degrees of volatility. For example, Level 1 (2.5x ATR) might represent a tighter stop, while Level 4 (10x ATR) could serve as a wider stop for long-term positions.
➡️Calc_bars_count: Improves efficiency of the indicator by reducing the on-chart calculations in to the past. This input can be found at the bottom of the INPUTS tab.
How it Helps Traders:
💥Trend Identification: By using the Chandelier Exit levels, traders can identify when the trend is likely to reverse. When the price crosses below the stop level in a long trade or above the stop level in a short trade, it signals a potential exit point.
💥Volatility-based Adjustments: Unlike static stop-loss methods, the ATR-based stop levels dynamically adjust based on the market’s volatility. This means tighter stops during low volatility periods and wider stops during high volatility periods, reducing the chance of being stopped out prematurely.
💥Risk Management: The dynamic stop levels and extension levels provide a structured way to manage risk. Traders can set tighter stops for short-term trades and wider stops for longer-term trades. The script's visual labels make it easy to track these levels in real-time.
💥Automation with Alerts: The built-in alert system ensures that traders are notified when key levels are crossed. This helps to avoid emotional decision-making and allows for better execution of trading strategies.
Confluence and Price Fluidity:
One of the powerful ways to enhance the effectiveness of the Chandelier Exit indicator is by using it in conjunction with other technical analysis tools to create confluence. Confluence occurs when multiple indicators or price action signals align, providing stronger confirmation for a trade decision. For example:
🎯Support and Resistance Levels: Traders can use the Chandelier Exit levels in combination with key support and resistance zones. If the price is nearing a support level and the Chandelier Exit signals a bullish reversal, this alignment strengthens the case for entering a long position.
🎯Moving Averages: When the Chandelier Exit signals a trend reversal and this is confirmed by a crossover in moving averages (such as a 50-day and 200-day moving average), traders gain additional confidence in the trade direction.
🎯Momentum Indicators: Traders can also look for momentum indicators like RSI or MACD to confirm the strength of a trend or potential reversal. For instance, if the Chandelier Exit triggers a short signal and the RSI also shows overbought conditions, this could provide stronger confirmation to exit a long trade or enter a short position.
🎯Candlestick Patterns: Price fluidity can be monitored using candlestick formations. For example, a bearish engulfing pattern near a Chandelier Exit resistance level offers confluence, adding confidence to the signal to close or short the trade.
By combining the Chandelier Exit with other tools, traders ensure that they are not relying on a single indicator. This layered approach can reduce the likelihood of false signals and improve overall trading accuracy.
Practical Use Case:
Imagine a trader enters a long position, and the price moves favorably. Using the Chandelier Exit, the trader sets the initial stop level at 2.5x ATR below the highest close. As the price continues to rise, the stop level follows the price, locking in profits. If the market suddenly turns, the price crossing below the stop level signals an exit, helping the trader preserve gains. With extension levels, the trader can further refine exits, adjusting based on their risk tolerance and market conditions.
Good luck and I hope that you can find a place in your tool bag to use this dynamic indicator 🙏
Fibonacci & Bollinger Bands StrategyThis strategy combines Bollinger Bands and Fibonacci retracement/extension levels to identify potential entry and exit points in the market. Here’s a breakdown of each component and how the strategy works:
1. Bollinger Bands:
Bollinger Bands consist of a simple moving average (SMA) and two standard deviations (upper and lower bands) plotted above and below the SMA. The bands expand and contract based on market volatility.
Purpose in Strategy:
The lower band represents an area where the market might be oversold.
The upper band represents an area where the market might be overbought.
The price crossing these bands suggests overextended market conditions, which can be used to identify potential reversals.
2. Fibonacci Retracement and Extension Levels:
Fibonacci retracement levels are horizontal lines that indicate where price might find support or resistance as it retraces some of its previous movement. Common retracement levels are 61.8% and 78.6%.
Fibonacci extension levels are used to project areas where the price might extend after completing a retracement. These levels can help determine potential targets after a significant price movement.
Purpose in Strategy:
The strategy calculates the most recent swing high (fibHigh) and swing low (fibLow) over a lookback period. It then plots Fibonacci retracement and extension levels based on this range.
The Fibonacci levels are used as key support and resistance areas. The price approaching or touching these levels signals potential turning points in the market.
3. Entry Criteria:
A long position (buy) is triggered when:
The price crosses below the lower Bollinger Band, indicating an oversold condition.
The price is near or above a Fibonacci extension level (calculated based on the most recent price swing).
This suggests that the price is potentially reaching a strong support area, where a reversal is likely.
4. Exit Criteria:
The long position is closed (exit trade) when either:
The price touches or crosses the upper Bollinger Band, signaling an overbought condition.
The price reaches a Fibonacci retracement level or exceeds the recent swing high (fibHigh), indicating a potential exhaustion point or a reversal area.
5. General Strategy Logic:
The strategy takes advantage of market volatility (captured by the Bollinger Bands) and key support/resistance levels (determined by Fibonacci retracement and extension levels).
By combining these two techniques, the strategy identifies potential entry points at oversold levels with the expectation that the market will retrace or reverse upward, especially when near key Fibonacci extension levels.
Exit points are identified by potential overbought levels (Bollinger upper band) or key Fibonacci retracement levels, where the price might reverse downward.
6. Conditions to Execute the Strategy:
The Fibonacci levels are only calculated once the price has made a significant movement, establishing a recent high and low over a 50-bar period (which you can adjust). This ensures the Fibonacci levels are based on meaningful swings.
The entry and exit signals are filtered using both Bollinger Bands and Fibonacci levels to ensure that trades are not taken solely based on one indicator, thus reducing false signals.
Key Features of the Strategy:
Trend-following with reversal: It tries to catch reversals when the price hits extreme levels (Bollinger Bands) while respecting important Fibonacci levels.
Dynamic market adaptation: The strategy adapts to market conditions as it recalculates Fibonacci levels based on recent price swings and adjusts the Bollinger Bands for market volatility.
Confirmation through multiple indicators: It uses both the volatility-based signals from Bollinger Bands and the price structure from Fibonacci levels to confirm trade entries and exits.
Summary of the Strategy:
The strategy looks to buy low and sell high based on oversold/overbought signals from Bollinger Bands and Fibonacci levels that indicate key support and resistance zones.
By combining these two technical indicators, the strategy aims to reduce risk and increase accuracy by only entering trades when both indicators suggest favorable conditions.
Volatility Gaussian Bands [BigBeluga]The Volatility Gaussian Bands indicator is a cutting-edge tool designed to analyze market trends and volatility with high precision. By applying a Gaussian filter to smooth price data and implementing dynamic bands based on market volatility, this indicator provides clear signals for trend direction, strength, and potential reversals. With updated volatility calculations, it enhances the accuracy of trend detection, making it a powerful addition to any trader's toolkit.
⮁ KEY FEATURES & USAGE
● Gaussian Filter Trend Bands:
The Gaussian Filter forms the foundation of this indicator by smoothing price data to reveal the underlying trend. The trend is visualized through upper and lower bands that adjust dynamically based on market volatility. These bands provide clear visual cues for traders: a crossover above the upper band indicates a potential uptrend, while a cross below the lower band signals a potential downtrend. This feature allows traders to identify trends with greater accuracy and act accordingly.
● Dynamic Trend Strength Gauges:
The indicator includes trend strength gauges positioned at the top and bottom of the chart. These gauges dynamically measure the strength of the uptrend and downtrend, based on the middle Gaussian line. Even if the trend is downward, a rising midline will cause the upward trend strength gauge to show an increase, offering a nuanced view of the market’s momentum.
Weakening of the trend:
● Fast Trend Change Indicators:
Triangles with a "+" symbol appear on the chart to signal rapid changes in trend direction. These indicators are particularly useful when the trend changes swiftly while the midline continues to grow in its previous direction. For instance, during a downtrend, if the trend suddenly shifts upward while the midline is still declining, a triangle with a "+" will indicate this quick reversal. This feature is crucial for traders looking to capitalize on rapid market movements.
● Retest Signals:
Retest signals, displayed as triangles, highlight potential areas where the price may retest the Gaussian line during a trend. These signals provide an additional layer of analysis, helping traders confirm trend continuations or identify possible reversals. The retest signals can be customized based on the trader’s preferences.
⮁ CUSTOMIZATION
● Length Adjustment:
The length of the Gaussian filter can be customized to control the sensitivity of trend detection. Shorter lengths make the indicator more responsive, while longer lengths offer a smoother, more stable trend line.
● Volatility Calculation Mode:
Traders can select from different modes (AVG, MEDIAN, MODE) to calculate the Gaussian filter, allowing for flexibility in how trends are detected and analyzed.
● Retest Signals Toggle:
Enable or disable the retest signals based on your trading strategy. This toggle allows traders to choose whether they want these additional signals to appear on the chart, providing more control over the information displayed during their analysis.
⮁ CONCLUSION
The Volatility Gaussian Bands indicator is a versatile and powerful tool for traders focused on trend and volatility analysis. By combining Gaussian-filtered trend lines with dynamic volatility bands, trend strength gauges, and rapid trend change indicators, this tool provides a comprehensive view of market conditions. Whether you are following established trends or looking to catch early reversals, the Volatility Gaussian Bands offers the precision and adaptability needed to enhance your trading strategy.
Universal Ratio Trend Matrix [InvestorUnknown]The Universal Ratio Trend Matrix is designed for trend analysis on asset/asset ratios, supporting up to 40 different assets. Its primary purpose is to help identify which assets are outperforming others within a selection, providing a broad overview of market trends through a matrix of ratios. The indicator automatically expands the matrix based on the number of assets chosen, simplifying the process of comparing multiple assets in terms of performance.
Key features include the ability to choose from a narrow selection of indicators to perform the ratio trend analysis, allowing users to apply well-defined metrics to their comparison.
Drawback: Due to the computational intensity involved in calculating ratios across many assets, the indicator has a limitation related to loading speed. TradingView has time limits for calculations, and for users on the basic (free) plan, this could result in frequent errors due to exceeded time limits. To use the indicator effectively, users with any paid plans should run it on timeframes higher than 8h (the lowest timeframe on which it managed to load with 40 assets), as lower timeframes may not reliably load.
Indicators:
RSI_raw: Simple function to calculate the Relative Strength Index (RSI) of a source (asset price).
RSI_sma: Calculates RSI followed by a Simple Moving Average (SMA).
RSI_ema: Calculates RSI followed by an Exponential Moving Average (EMA).
CCI: Calculates the Commodity Channel Index (CCI).
Fisher: Implements the Fisher Transform to normalize prices.
Utility Functions:
f_remove_exchange_name: Strips the exchange name from asset tickers (e.g., "INDEX:BTCUSD" to "BTCUSD").
f_remove_exchange_name(simple string name) =>
string parts = str.split(name, ":")
string result = array.size(parts) > 1 ? array.get(parts, 1) : name
result
f_get_price: Retrieves the closing price of a given asset ticker using request.security().
f_constant_src: Checks if the source data is constant by comparing multiple consecutive values.
Inputs:
General settings allow users to select the number of tickers for analysis (used_assets) and choose the trend indicator (RSI, CCI, Fisher, etc.).
Table settings customize how trend scores are displayed in terms of text size, header visibility, highlighting options, and top-performing asset identification.
The script includes inputs for up to 40 assets, allowing the user to select various cryptocurrencies (e.g., BTCUSD, ETHUSD, SOLUSD) or other assets for trend analysis.
Price Arrays:
Price values for each asset are stored in variables (price_a1 to price_a40) initialized as na. These prices are updated only for the number of assets specified by the user (used_assets).
Trend scores for each asset are stored in separate arrays
// declare price variables as "na"
var float price_a1 = na, var float price_a2 = na, var float price_a3 = na, var float price_a4 = na, var float price_a5 = na
var float price_a6 = na, var float price_a7 = na, var float price_a8 = na, var float price_a9 = na, var float price_a10 = na
var float price_a11 = na, var float price_a12 = na, var float price_a13 = na, var float price_a14 = na, var float price_a15 = na
var float price_a16 = na, var float price_a17 = na, var float price_a18 = na, var float price_a19 = na, var float price_a20 = na
var float price_a21 = na, var float price_a22 = na, var float price_a23 = na, var float price_a24 = na, var float price_a25 = na
var float price_a26 = na, var float price_a27 = na, var float price_a28 = na, var float price_a29 = na, var float price_a30 = na
var float price_a31 = na, var float price_a32 = na, var float price_a33 = na, var float price_a34 = na, var float price_a35 = na
var float price_a36 = na, var float price_a37 = na, var float price_a38 = na, var float price_a39 = na, var float price_a40 = na
// create "empty" arrays to store trend scores
var a1_array = array.new_int(40, 0), var a2_array = array.new_int(40, 0), var a3_array = array.new_int(40, 0), var a4_array = array.new_int(40, 0)
var a5_array = array.new_int(40, 0), var a6_array = array.new_int(40, 0), var a7_array = array.new_int(40, 0), var a8_array = array.new_int(40, 0)
var a9_array = array.new_int(40, 0), var a10_array = array.new_int(40, 0), var a11_array = array.new_int(40, 0), var a12_array = array.new_int(40, 0)
var a13_array = array.new_int(40, 0), var a14_array = array.new_int(40, 0), var a15_array = array.new_int(40, 0), var a16_array = array.new_int(40, 0)
var a17_array = array.new_int(40, 0), var a18_array = array.new_int(40, 0), var a19_array = array.new_int(40, 0), var a20_array = array.new_int(40, 0)
var a21_array = array.new_int(40, 0), var a22_array = array.new_int(40, 0), var a23_array = array.new_int(40, 0), var a24_array = array.new_int(40, 0)
var a25_array = array.new_int(40, 0), var a26_array = array.new_int(40, 0), var a27_array = array.new_int(40, 0), var a28_array = array.new_int(40, 0)
var a29_array = array.new_int(40, 0), var a30_array = array.new_int(40, 0), var a31_array = array.new_int(40, 0), var a32_array = array.new_int(40, 0)
var a33_array = array.new_int(40, 0), var a34_array = array.new_int(40, 0), var a35_array = array.new_int(40, 0), var a36_array = array.new_int(40, 0)
var a37_array = array.new_int(40, 0), var a38_array = array.new_int(40, 0), var a39_array = array.new_int(40, 0), var a40_array = array.new_int(40, 0)
f_get_price(simple string ticker) =>
request.security(ticker, "", close)
// Prices for each USED asset
f_get_asset_price(asset_number, ticker) =>
if (used_assets >= asset_number)
f_get_price(ticker)
else
na
// overwrite empty variables with the prices if "used_assets" is greater or equal to the asset number
if barstate.isconfirmed // use barstate.isconfirmed to avoid "na prices" and calculation errors that result in empty cells in the table
price_a1 := f_get_asset_price(1, asset1), price_a2 := f_get_asset_price(2, asset2), price_a3 := f_get_asset_price(3, asset3), price_a4 := f_get_asset_price(4, asset4)
price_a5 := f_get_asset_price(5, asset5), price_a6 := f_get_asset_price(6, asset6), price_a7 := f_get_asset_price(7, asset7), price_a8 := f_get_asset_price(8, asset8)
price_a9 := f_get_asset_price(9, asset9), price_a10 := f_get_asset_price(10, asset10), price_a11 := f_get_asset_price(11, asset11), price_a12 := f_get_asset_price(12, asset12)
price_a13 := f_get_asset_price(13, asset13), price_a14 := f_get_asset_price(14, asset14), price_a15 := f_get_asset_price(15, asset15), price_a16 := f_get_asset_price(16, asset16)
price_a17 := f_get_asset_price(17, asset17), price_a18 := f_get_asset_price(18, asset18), price_a19 := f_get_asset_price(19, asset19), price_a20 := f_get_asset_price(20, asset20)
price_a21 := f_get_asset_price(21, asset21), price_a22 := f_get_asset_price(22, asset22), price_a23 := f_get_asset_price(23, asset23), price_a24 := f_get_asset_price(24, asset24)
price_a25 := f_get_asset_price(25, asset25), price_a26 := f_get_asset_price(26, asset26), price_a27 := f_get_asset_price(27, asset27), price_a28 := f_get_asset_price(28, asset28)
price_a29 := f_get_asset_price(29, asset29), price_a30 := f_get_asset_price(30, asset30), price_a31 := f_get_asset_price(31, asset31), price_a32 := f_get_asset_price(32, asset32)
price_a33 := f_get_asset_price(33, asset33), price_a34 := f_get_asset_price(34, asset34), price_a35 := f_get_asset_price(35, asset35), price_a36 := f_get_asset_price(36, asset36)
price_a37 := f_get_asset_price(37, asset37), price_a38 := f_get_asset_price(38, asset38), price_a39 := f_get_asset_price(39, asset39), price_a40 := f_get_asset_price(40, asset40)
Universal Indicator Calculation (f_calc_score):
This function allows switching between different trend indicators (RSI, CCI, Fisher) for flexibility.
It uses a switch-case structure to calculate the indicator score, where a positive trend is denoted by 1 and a negative trend by 0. Each indicator has its own logic to determine whether the asset is trending up or down.
// use switch to allow "universality" in indicator selection
f_calc_score(source, trend_indicator, int_1, int_2) =>
int score = na
if (not f_constant_src(source)) and source > 0.0 // Skip if you are using the same assets for ratio (for example BTC/BTC)
x = switch trend_indicator
"RSI (Raw)" => RSI_raw(source, int_1)
"RSI (SMA)" => RSI_sma(source, int_1, int_2)
"RSI (EMA)" => RSI_ema(source, int_1, int_2)
"CCI" => CCI(source, int_1)
"Fisher" => Fisher(source, int_1)
y = switch trend_indicator
"RSI (Raw)" => x > 50 ? 1 : 0
"RSI (SMA)" => x > 50 ? 1 : 0
"RSI (EMA)" => x > 50 ? 1 : 0
"CCI" => x > 0 ? 1 : 0
"Fisher" => x > x ? 1 : 0
score := y
else
score := 0
score
Array Setting Function (f_array_set):
This function populates an array with scores calculated for each asset based on a base price (p_base) divided by the prices of the individual assets.
It processes multiple assets (up to 40), calling the f_calc_score function for each.
// function to set values into the arrays
f_array_set(a_array, p_base) =>
array.set(a_array, 0, f_calc_score(p_base / price_a1, trend_indicator, int_1, int_2))
array.set(a_array, 1, f_calc_score(p_base / price_a2, trend_indicator, int_1, int_2))
array.set(a_array, 2, f_calc_score(p_base / price_a3, trend_indicator, int_1, int_2))
array.set(a_array, 3, f_calc_score(p_base / price_a4, trend_indicator, int_1, int_2))
array.set(a_array, 4, f_calc_score(p_base / price_a5, trend_indicator, int_1, int_2))
array.set(a_array, 5, f_calc_score(p_base / price_a6, trend_indicator, int_1, int_2))
array.set(a_array, 6, f_calc_score(p_base / price_a7, trend_indicator, int_1, int_2))
array.set(a_array, 7, f_calc_score(p_base / price_a8, trend_indicator, int_1, int_2))
array.set(a_array, 8, f_calc_score(p_base / price_a9, trend_indicator, int_1, int_2))
array.set(a_array, 9, f_calc_score(p_base / price_a10, trend_indicator, int_1, int_2))
array.set(a_array, 10, f_calc_score(p_base / price_a11, trend_indicator, int_1, int_2))
array.set(a_array, 11, f_calc_score(p_base / price_a12, trend_indicator, int_1, int_2))
array.set(a_array, 12, f_calc_score(p_base / price_a13, trend_indicator, int_1, int_2))
array.set(a_array, 13, f_calc_score(p_base / price_a14, trend_indicator, int_1, int_2))
array.set(a_array, 14, f_calc_score(p_base / price_a15, trend_indicator, int_1, int_2))
array.set(a_array, 15, f_calc_score(p_base / price_a16, trend_indicator, int_1, int_2))
array.set(a_array, 16, f_calc_score(p_base / price_a17, trend_indicator, int_1, int_2))
array.set(a_array, 17, f_calc_score(p_base / price_a18, trend_indicator, int_1, int_2))
array.set(a_array, 18, f_calc_score(p_base / price_a19, trend_indicator, int_1, int_2))
array.set(a_array, 19, f_calc_score(p_base / price_a20, trend_indicator, int_1, int_2))
array.set(a_array, 20, f_calc_score(p_base / price_a21, trend_indicator, int_1, int_2))
array.set(a_array, 21, f_calc_score(p_base / price_a22, trend_indicator, int_1, int_2))
array.set(a_array, 22, f_calc_score(p_base / price_a23, trend_indicator, int_1, int_2))
array.set(a_array, 23, f_calc_score(p_base / price_a24, trend_indicator, int_1, int_2))
array.set(a_array, 24, f_calc_score(p_base / price_a25, trend_indicator, int_1, int_2))
array.set(a_array, 25, f_calc_score(p_base / price_a26, trend_indicator, int_1, int_2))
array.set(a_array, 26, f_calc_score(p_base / price_a27, trend_indicator, int_1, int_2))
array.set(a_array, 27, f_calc_score(p_base / price_a28, trend_indicator, int_1, int_2))
array.set(a_array, 28, f_calc_score(p_base / price_a29, trend_indicator, int_1, int_2))
array.set(a_array, 29, f_calc_score(p_base / price_a30, trend_indicator, int_1, int_2))
array.set(a_array, 30, f_calc_score(p_base / price_a31, trend_indicator, int_1, int_2))
array.set(a_array, 31, f_calc_score(p_base / price_a32, trend_indicator, int_1, int_2))
array.set(a_array, 32, f_calc_score(p_base / price_a33, trend_indicator, int_1, int_2))
array.set(a_array, 33, f_calc_score(p_base / price_a34, trend_indicator, int_1, int_2))
array.set(a_array, 34, f_calc_score(p_base / price_a35, trend_indicator, int_1, int_2))
array.set(a_array, 35, f_calc_score(p_base / price_a36, trend_indicator, int_1, int_2))
array.set(a_array, 36, f_calc_score(p_base / price_a37, trend_indicator, int_1, int_2))
array.set(a_array, 37, f_calc_score(p_base / price_a38, trend_indicator, int_1, int_2))
array.set(a_array, 38, f_calc_score(p_base / price_a39, trend_indicator, int_1, int_2))
array.set(a_array, 39, f_calc_score(p_base / price_a40, trend_indicator, int_1, int_2))
a_array
Conditional Array Setting (f_arrayset):
This function checks if the number of used assets is greater than or equal to a specified number before populating the arrays.
// only set values into arrays for USED assets
f_arrayset(asset_number, a_array, p_base) =>
if (used_assets >= asset_number)
f_array_set(a_array, p_base)
else
na
Main Logic
The main logic initializes arrays to store scores for each asset. Each array corresponds to one asset's performance score.
Setting Trend Values: The code calls f_arrayset for each asset, populating the respective arrays with calculated scores based on the asset prices.
Combining Arrays: A combined_array is created to hold all the scores from individual asset arrays. This array facilitates further analysis, allowing for an overview of the performance scores of all assets at once.
// create a combined array (work-around since pinescript doesn't support having array of arrays)
var combined_array = array.new_int(40 * 40, 0)
if barstate.islast
for i = 0 to 39
array.set(combined_array, i, array.get(a1_array, i))
array.set(combined_array, i + (40 * 1), array.get(a2_array, i))
array.set(combined_array, i + (40 * 2), array.get(a3_array, i))
array.set(combined_array, i + (40 * 3), array.get(a4_array, i))
array.set(combined_array, i + (40 * 4), array.get(a5_array, i))
array.set(combined_array, i + (40 * 5), array.get(a6_array, i))
array.set(combined_array, i + (40 * 6), array.get(a7_array, i))
array.set(combined_array, i + (40 * 7), array.get(a8_array, i))
array.set(combined_array, i + (40 * 8), array.get(a9_array, i))
array.set(combined_array, i + (40 * 9), array.get(a10_array, i))
array.set(combined_array, i + (40 * 10), array.get(a11_array, i))
array.set(combined_array, i + (40 * 11), array.get(a12_array, i))
array.set(combined_array, i + (40 * 12), array.get(a13_array, i))
array.set(combined_array, i + (40 * 13), array.get(a14_array, i))
array.set(combined_array, i + (40 * 14), array.get(a15_array, i))
array.set(combined_array, i + (40 * 15), array.get(a16_array, i))
array.set(combined_array, i + (40 * 16), array.get(a17_array, i))
array.set(combined_array, i + (40 * 17), array.get(a18_array, i))
array.set(combined_array, i + (40 * 18), array.get(a19_array, i))
array.set(combined_array, i + (40 * 19), array.get(a20_array, i))
array.set(combined_array, i + (40 * 20), array.get(a21_array, i))
array.set(combined_array, i + (40 * 21), array.get(a22_array, i))
array.set(combined_array, i + (40 * 22), array.get(a23_array, i))
array.set(combined_array, i + (40 * 23), array.get(a24_array, i))
array.set(combined_array, i + (40 * 24), array.get(a25_array, i))
array.set(combined_array, i + (40 * 25), array.get(a26_array, i))
array.set(combined_array, i + (40 * 26), array.get(a27_array, i))
array.set(combined_array, i + (40 * 27), array.get(a28_array, i))
array.set(combined_array, i + (40 * 28), array.get(a29_array, i))
array.set(combined_array, i + (40 * 29), array.get(a30_array, i))
array.set(combined_array, i + (40 * 30), array.get(a31_array, i))
array.set(combined_array, i + (40 * 31), array.get(a32_array, i))
array.set(combined_array, i + (40 * 32), array.get(a33_array, i))
array.set(combined_array, i + (40 * 33), array.get(a34_array, i))
array.set(combined_array, i + (40 * 34), array.get(a35_array, i))
array.set(combined_array, i + (40 * 35), array.get(a36_array, i))
array.set(combined_array, i + (40 * 36), array.get(a37_array, i))
array.set(combined_array, i + (40 * 37), array.get(a38_array, i))
array.set(combined_array, i + (40 * 38), array.get(a39_array, i))
array.set(combined_array, i + (40 * 39), array.get(a40_array, i))
Calculating Sums: A separate array_sums is created to store the total score for each asset by summing the values of their respective score arrays. This allows for easy comparison of overall performance.
Ranking Assets: The final part of the code ranks the assets based on their total scores stored in array_sums. It assigns a rank to each asset, where the asset with the highest score receives the highest rank.
// create array for asset RANK based on array.sum
var ranks = array.new_int(used_assets, 0)
// for loop that calculates the rank of each asset
if barstate.islast
for i = 0 to (used_assets - 1)
int rank = 1
for x = 0 to (used_assets - 1)
if i != x
if array.get(array_sums, i) < array.get(array_sums, x)
rank := rank + 1
array.set(ranks, i, rank)
Dynamic Table Creation
Initialization: The table is initialized with a base structure that includes headers for asset names, scores, and ranks. The headers are set to remain constant, ensuring clarity for users as they interpret the displayed data.
Data Population: As scores are calculated for each asset, the corresponding values are dynamically inserted into the table. This is achieved through a loop that iterates over the scores and ranks stored in the combined_array and array_sums, respectively.
Automatic Extending Mechanism
Variable Asset Count: The code checks the number of assets defined by the user. Instead of hardcoding the number of rows in the table, it uses a variable to determine the extent of the data that needs to be displayed. This allows the table to expand or contract based on the number of assets being analyzed.
Dynamic Row Generation: Within the loop that populates the table, the code appends new rows for each asset based on the current asset count. The structure of each row includes the asset name, its score, and its rank, ensuring that the table remains consistent regardless of how many assets are involved.
// Automatically extending table based on the number of used assets
var table table = table.new(position.bottom_center, 50, 50, color.new(color.black, 100), color.white, 3, color.white, 1)
if barstate.islast
if not hide_head
table.cell(table, 0, 0, "Universal Ratio Trend Matrix", text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.merge_cells(table, 0, 0, used_assets + 3, 0)
if not hide_inps
table.cell(table, 0, 1,
text = "Inputs: You are using " + str.tostring(trend_indicator) + ", which takes: " + str.tostring(f_get_input(trend_indicator)),
text_color = color.white, text_size = fontSize), table.merge_cells(table, 0, 1, used_assets + 3, 1)
table.cell(table, 0, 2, "Assets", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, 2, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.cell(table, 0, x + 3, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = f_asset_col(array.get(ranks, x)), text_size = fontSize)
for r = 0 to (used_assets - 1)
for c = 0 to (used_assets - 1)
table.cell(table, c + 1, r + 3, text = str.tostring(array.get(combined_array, c + (r * 40))),
text_color = hl_type == "Text" ? f_get_col(array.get(combined_array, c + (r * 40))) : color.white, text_size = fontSize,
bgcolor = hl_type == "Background" ? f_get_col(array.get(combined_array, c + (r * 40))) : na)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, x + 3, "", bgcolor = #010c3b)
table.cell(table, used_assets + 1, 2, "", bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 1, x + 3, "==>", text_color = color.white)
table.cell(table, used_assets + 2, 2, "SUM", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
table.cell(table, used_assets + 3, 2, "RANK", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 2, x + 3,
text = str.tostring(array.get(array_sums, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_sum(array.get(array_sums, x), array.get(ranks, x)))
table.cell(table, used_assets + 3, x + 3,
text = str.tostring(array.get(ranks, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_rank(array.get(ranks, x)))
Crypto Volatility Bitcoin Correlation Strategy Description:
The Crypto Volatility Bitcoin Correlation Strategy is designed to leverage market volatility specifically in Bitcoin (BTC) using a combination of volatility indicators and trend-following techniques. This strategy utilizes the VIXFix (a volatility indicator adapted for crypto markets) and the BVOL7D (Bitcoin 7-Day Volatility Index from BitMEX) to identify periods of high volatility, while confirming trends with the Exponential Moving Average (EMA). These components work together to offer a comprehensive system that traders can use to enter positions when volatility and trends are aligned in their favor.
Key Features:
VIXFix (Volatility Index for Crypto Markets): This indicator measures the highest price of Bitcoin over a set period and compares it with the current low price to gauge market volatility. A rise in VIXFix indicates increasing market volatility, signaling that large price movements could occur.
BVOL7D (Bitcoin 7-Day Volatility Index): This volatility index, provided by BitMEX, measures the volatility of Bitcoin over the past 7 days. It helps traders monitor the recent volatility trend in the market, particularly useful when making short-term trading decisions.
Exponential Moving Average (EMA): The 50-period EMA acts as a trend indicator. When the price is above the EMA, it suggests the market is in an uptrend, and when the price is below the EMA, it suggests a downtrend.
How It Works:
Long Entry: A long position is triggered when both the VIXFix and BVOL7D indicators are rising, signaling increased volatility, and the price is above the 50-period EMA, confirming that the market is trending upward.
Exit: The strategy exits the position when the price crosses below the 50-period EMA, which signals a potential weakening of the uptrend and a decrease in volatility.
This strategy ensures that traders only enter positions when the volatility aligns with a clear trend, minimizing the risk of entering trades during periods of market uncertainty.
Testing and Timeframe:
This strategy has been tested on Bitcoin using the daily timeframe, which provides a longer-term perspective on market trends and volatility. However, users can adjust the timeframe according to their trading preferences. It is crucial to note that this strategy does not include comprehensive risk management, aside from the exit condition when the price crosses below the EMA. Users are strongly advised to implement their own risk management techniques, such as setting appropriate stop-loss levels, to safeguard their positions during high volatility periods.
Utility:
The Crypto Volatility Bitcoin Correlation Strategy is particularly well-suited for traders who aim to capitalize on the high volatility often seen in the Bitcoin market. By combining volatility measurements (VIXFix and BVOL7D) with a trend-following mechanism (EMA), this strategy helps identify optimal moments for entering and exiting trades. This approach ensures that traders participate in potentially profitable market moves while minimizing exposure during times of uncertainty.
Use Cases:
Volatility-Based Entries: Traders looking to take advantage of market volatility spikes will find this strategy useful for timing entry points during market swings.
Trend Confirmation: By using the EMA as a confirmation tool, traders can avoid entering trades that go against the trend, which can result in significant losses during volatile market conditions.
Risk Management: While the strategy exits when price falls below the EMA, it is important to recognize that this is not a full risk management system. Traders should use caution and integrate additional risk measures, such as stop-losses and position sizing, to better manage potential losses.
How to Use:
Step 1: Monitor the VIXFix and BVOL7D indicators. When both are rising and the Bitcoin price is above the EMA, the strategy will trigger a long entry, indicating that the market is experiencing increased volatility with a confirmed uptrend.
Step 2: Exit the position when the price drops below the 50-period EMA, signaling that the trend may be reversing or weakening, reducing the likelihood of continued upward price movement.
This strategy is open-source and is intended to help traders navigate volatile market conditions, particularly in Bitcoin, using proven indicators for volatility and trend confirmation.
Risk Disclaimer:
This strategy has been tested on the daily timeframe of Bitcoin, but users should be aware that it does not include built-in risk management except for the below-EMA exit condition. Users should be extremely cautious when using this strategy and are encouraged to implement their own risk management, such as using stop-losses, position sizing, and setting appropriate limits. Trading involves significant risk, and this strategy does not guarantee profits or prevent losses. Past performance is not indicative of future results. Always test any strategy in a demo environment before applying it to live markets.
Uptrick: Market MoodsThe "Uptrick: Market Moods" indicator is an advanced technical analysis tool designed for the TradingView platform. It combines three powerful indicators—Relative Strength Index (RSI), Average True Range (ATR), and Bollinger Bands—into one cohesive framework, aimed at helping traders better understand and interpret market sentiment. By capturing shifts in the emotional climate of the market, it provides a holistic view of market conditions, which can range from calm to stressed or even highly excited. This multi-dimensional analysis tool stands apart from traditional single-indicator approaches by offering a more complete picture of market dynamics, making it a valuable resource for traders looking to anticipate and react to changes in market behavior.
The RSI in the "Uptrick: Market Moods" indicator is used to measure momentum. RSI is an essential component of many technical analysis strategies, and in this tool, it is used to identify potential market extremes. When RSI values are high, they indicate an overbought condition, meaning the market may be approaching a peak. Conversely, low RSI values suggest an oversold condition, signaling that the market could be nearing a bottom. These extremes provide crucial clues about shifts in market sentiment, helping traders gauge whether the current emotional state of the market is likely to result in a reversal. This understanding is pivotal in predicting whether the market is transitioning from calm to stressed or from excited to overbought.
The Average True Range adds another layer to this analysis by offering insights into market volatility. Volatility is a key factor in understanding the mood of the market, as periods of high volatility often reflect high levels of excitement or stress, while low volatility typically indicates a calm, steady market. ATR is calculated based on the range of price movements over a given period, and the higher the value, the more volatile the market is. The "Uptrick: Market Moods" indicator uses ATR to dynamically gauge volatility levels, helping traders understand whether the market is currently moving in a way that aligns with its emotional mood. For example, an increase in ATR accompanied by an RSI value that indicates overbought conditions could suggest that the market is in a highly excited state, with the potential for either strong momentum continuation or a sharp reversal.
Bollinger Bands complement these tools by providing visual cues about price volatility and the range within which the market is likely to move. Bollinger Bands plot two standard deviations away from a simple moving average of the price. This banding technique helps traders visualize how far the price is likely to deviate from its average over a certain period. The "Uptrick: Market Moods" indicator uses Bollinger Bands to establish price boundaries and identify breakout conditions. When prices break above the upper band or below the lower band, it often signals that the market is either highly stressed or excited. This breakout condition serves as a visual representation of the market mood, alerting traders to moments when prices are moving beyond typical ranges and when significant emotional shifts are occurring in the market.
Technically, the "Uptrick: Market Moods" indicator has been developed using TradingView’s Pine Script language, a highly efficient language for building custom indicators. It employs functions like ta.rsi, ta.atr, and ta.sma to perform the necessary calculations. The use of these built-in functions ensures that the calculations are both accurate and efficient, allowing the indicator to operate in real-time without lagging, even in volatile market conditions. The ta.rsi function is used to compute the Relative Strength Index, while ta.atr calculates the Average True Range, and ta.sma is used to smooth out price data for the Bollinger Bands. These functions are applied dynamically within the script, allowing the "Uptrick: Market Moods" indicator to respond to changes in market conditions in real time.
The user interface of the "Uptrick: Market Moods" indicator is designed to provide a visually intuitive experience. The market mood is color-coded on the chart, making it easy for traders to identify whether the market is calm, stressed, or excited at a glance. This feature is especially useful for traders who need to make quick decisions in fast-moving markets. Additionally, the indicator includes an interactive table that updates in real-time, showing the most recent mood state and its frequency. This provides valuable statistical insights into market behavior over specific time frames, helping traders track the dominant emotional state of the market. Whether the market is in a prolonged calm state or rapidly transitioning through moods, this real-time feedback offers actionable data that can help traders adjust their strategies accordingly.
The RSI component of the "Uptrick: Market Moods" indicator helps detect the speed and direction of price movements, offering insight into whether the market is approaching extreme conditions. By providing signals based on overbought and oversold levels, the RSI helps traders decide whether to enter or exit positions. The ATR element acts as a volatility gauge, dynamically adjusting traders’ expectations in response to changes in market volatility. Meanwhile, the Bollinger Bands help identify trends and potential breakout conditions, serving as an additional confirmation tool that highlights when the price has moved beyond normal boundaries, indicating heightened market excitement or stress.
Despite the robust capabilities of the "Uptrick: Market Moods" indicator, it does have limitations. In markets affected by sudden shifts, such as those driven by major news events or external economic factors, the indicator’s performance may not always be reliable. These external factors can cause rapid mood swings that are difficult for any technical analysis tool to fully anticipate. Additionally, the indicator’s complexity may pose a learning curve for novice traders, particularly those who are unfamiliar with the concepts of RSI, ATR, and Bollinger Bands. However, with practice, traders can become proficient in using the tool to its full potential, leveraging the insights it provides to better navigate market shifts.
For traders seeking a deeper understanding of market sentiment, the "Uptrick: Market Moods" indicator is an invaluable resource. It is recommended for those dealing with medium to high volatility instruments, where understanding emotional shifts can offer a strategic advantage. While it can be used on its own, integrating it with other forms of analysis, such as fundamental analysis and additional technical indicators, can enhance its effectiveness. By confirming signals with other tools, traders can reduce the likelihood of false signals and improve their overall trading strategy.
To further enhance the accuracy of the "Uptrick: Market Moods" indicator, it can be integrated with volume-based tools like Volume Profile or On-Balance Volume (OBV). This combination allows traders to confirm the moods identified by the indicator with volume data, providing additional confirmation of market sentiment. For example, when the market is in an excited mood, an increase in trading volume could reinforce the reliability of that signal. Conversely, if the market is stressed but volume remains low, traders may want to proceed with caution. Using multiple indicators together creates a more comprehensive trading approach, helping traders better manage risk and make informed decisions based on multiple data points.
In conclusion, the "Uptrick: Market Moods" indicator is a powerful and unique addition to the suite of technical analysis tools available on TradingView. It provides traders with a multi-dimensional view of market sentiment by combining the analytical strengths of RSI, ATR, and Bollinger Bands into a single tool. Its ability to capture and interpret the emotional mood of the market makes it an essential tool for traders seeking to gain an edge in understanding market behavior. While the indicator has certain limitations, particularly in rapidly shifting markets, its ability to provide real-time insights into market sentiment is a valuable asset for traders of all experience levels. Used in conjunction with other tools and sound trading practices, the "Uptrick: Market Moods" indicator offers a comprehensive solution for navigating the complexities of financial markets.
Multi-Sector Trend AnalysisThis script, titled "Multi-Sector Trend Analysis: Track Sector Momentum and Trends," is designed to assist traders and investors in monitoring multiple sectors of the stock market simultaneously. It leverages technical analysis by incorporating trend detection and momentum indicators like moving averages and the Relative Strength Index (RSI) to offer insights into the price action of various market sectors.
Core Features:
1. Sector-Based Analysis: The script covers 20 major sectors from the NSE (National Stock Exchange) such as Auto, Banking, Energy, FMCG, IT, Pharma, and others. Users can customize which sectors they wish to analyze using the available input fields.
Technical Indicators: The script uses two core technical indicators to detect trends and momentum:
2. Moving Averages: The script calculates both fast and slow exponential moving averages (EMAs). These are critical for identifying short- and long-term price trends and crossovers, helping detect shifts in momentum.
3. Relative Strength Index (RSI): A well-known momentum indicator that shows whether a stock is overbought or oversold. This script uses a 14-period RSI to gauge the strength of each sector.
4. Trend Detection: The script identifies whether the current market trend is "Up" or "Down" based on the relationship between the fast and slow EMAs (i.e., whether the fast EMA is above or below the slow EMA). It highlights this trend visually in a table format, allowing quick and easy trend recognition.
5. Gain/Loss Tracking: This feature calculates the percentage gain or loss since the last EMA crossover (a key point in trend change), giving users a sense of how much the price has moved since the trend shifted.
6. Customizable Table for Display: The script displays the analyzed data in a table format, where users can view each sector's:
Symbol
Trend (Up or Down)
RSI Value
Gain/Loss Since the Last EMA Crossover
This table is customizable in terms of size and color theme (dark or light), providing flexibility in presentation for different charting styles.
How It Works:
Sector Selection: Users can input up to 20 different sector symbols for analysis.
Moving Averages: Users can define the period lengths for both the fast and slow EMAs to suit their trading strategies.
Table Options: Choose between different table sizes and opt for a dark theme to enhance the visual appearance on charts.
How to Use:
Select the symbols (sectors) that you want to track. The script includes pre-configured symbols for major sectors on the NSE, but you can modify these to suit your needs.
Adjust the fast and slow EMA lengths to your preference. A common setting would be 3 for the fast EMA and 4 for the slow EMA, but more conservative traders might opt for higher values.
Customize the table size and theme based on your preference, whether you want a compact table or a larger one for easier readability.
Why Use This Script:
This script is ideal for traders looking to:
Monitor multiple market sectors simultaneously.
Identify key trends across sectors quickly.
Understand momentum and detect potential reversals through RSI and EMA crossovers.
Stay informed on sector performance using a clear visual table that tracks gains or losses.
By using this script, traders can gain better insights into sector-based trading strategies, improve their sector rotation tactics, and stay informed about the broader market environment. It provides a powerful yet easy-to-use tool for both beginner and advanced traders.
Trend Fusion: ADX&EMA+Ichimoku (Custom)SAME AS THE ORIGINAL (WITHOUT BOTTOM PART)
Trend Fusion: ADX & EMA+Ichimoku is an innovative indicator designed to provide traders with comprehensive insights into market trends. Combining the power of the Average Directional Index (ADX) with Exponential Moving Averages (EMA) and the Ichimoku Cloud, this indicator offers a sophisticated approach to trend analysis.
This indicator stands out for its unique integration of multiple trend-following indicators, offering traders a holistic view of market dynamics. Unlike traditional trend indicators that focus solely on price movements, Trend Fusion incorporates the ADX, EMA, and Ichimoku Cloud to provide a more nuanced understanding of trend strength and direction. By combining these indicators, traders can make more informed decisions and enhance their trading strategies.
How it works:
Trend Fusion generates buy and sell signals based on the convergence of these indicators. A combination of strong ADX readings, EMA crossovers, and alignment with the Ichimoku Cloud confirms trend direction and provides entry and exit points for traders.
Average Directional Index (ADX): Measures the strength of the prevailing trend by analyzing price movements. A rising ADX indicates a strengthening trend, while a falling ADX suggests weakening momentum.
Exponential Moving Averages (EMA): Detects potential trend reversals through crossover signals. A bullish crossover (fast EMA crossing above slow EMA) suggests an uptrend, while a bearish crossover indicates a downtrend.
Ichimoku Cloud: Provides support and resistance levels along with trend direction. Price movements above the cloud indicate bullish sentiment, while movements below the cloud suggest bearish sentiment.
How to useColour codes:
Green Candles: Represent a strong uptrend, indicating robust buying momentum. The intensity of green color deepens with increasing trend strength.
Red Candles: Indicate a strong downtrend, signaling significant selling pressure in the market. The intensity of red color deepens with increasing trend strength.
Yellow Candles: Suggest a weak trend, characterized by indecision and lack of clear direction. The intensity of yellow color varies based on the strength of the trend, with lighter shades indicating weaker trends and darker shades suggesting slightly stronger trends.
Trend Strength: Monitor the ADX to gauge the strength of the prevailing trend. Higher ADX values indicate stronger trends, while lower values suggest weaker trends.
Trend Direction: Confirm trend direction using EMA crossovers and Ichimoku Cloud signals. Look for bullish crossovers and price movements above the cloud for uptrends, and bearish crossovers and movements below the cloud for downtrends.
Entry and Exit Signals: Enter trades when all components align, signaling a strong trend. Use EMA crossovers and cloud confirmations to identify potential entry points, and consider exiting trades when these signals reverse.
The ADX calculation and signal logic are based on the ADX script by PineCoders, with modifications to integrate it into this indicator.
The EMA crossover logic is adapted from the GDAX EMA Cross script by stefano98.
The Ichimoku Cloud calculation and plotting are adapted from the Ichimoku Cloud script by lonesometheblue.
Trading involves risk, and past performance is not indicative of future results. It is recommended to use this indicator alongside other technical analysis tools and risk management strategies.
FiboTrace.V33FiboTrace.V33 - Advanced Fibonacci Retracement Indicator is a powerful and visually intuitive Fibonacci retracement indicator designed to help traders identify key support and resistance levels across multiple timeframes. Whether you’re a day trader, swing trader, or long-term investor, FiboTrace.V33 provides the essential tools needed to spot potential price reversals and continuations with precision.
Key Features:
• Dynamic Fibonacci Levels: Automatically plots the most relevant Fibonacci retracement levels based on recent swing highs and lows, ensuring you always have the most accurate and up-to-date levels on your chart.
• Gradient Color Zones: Easily distinguish between different Fibonacci levels with visually appealing gradient color fills. These zones help you quickly identify key areas of price interaction, making your analysis more efficient.
• Customizable Levels: Tailor FiboTrace.V33 to your trading style by adjusting the Fibonacci levels and colors to match your preferences. This flexibility allows you to focus on the levels most relevant to your strategy.
• Multi-Timeframe Versatility: Works seamlessly across all timeframes, from 1-minute charts for day traders to weekly and monthly charts for long-term investors. The indicator adapts to your trading horizon, providing reliable signals in any market environment.
• Confluence Alerts: Receive alerts when price enters zones where multiple Fibonacci levels overlap, indicating strong support or resistance. This feature helps you catch high-probability trade setups without constantly monitoring the charts.
How to Use:
• Identify Entry and Exit Points: Use the plotted Fibonacci levels to determine potential entry and exit points. Price retracements to key Fibonacci levels can signal opportunities to enter trades in the direction of the prevailing trend.
• Spot Reversals and Continuations: Watch for price action around the gradient color zones. A bounce off a Fibonacci level may indicate a trend continuation, while a break could signal a potential reversal.
• Combine with Other Indicators: For best results, consider using FiboTrace.V33 in conjunction with other technical indicators, such as moving averages, RSI, or MACD, to confirm signals and enhance your trading strategy.
Timeframe Recommendations:
• Shorter Timeframes (1-minute to 1-hour): Ideal for quick, intraday trades, though signals might be more prone to noise due to rapid market fluctuations.
• Medium Timeframes (4-hour to daily): Perfect for swing trading, offering more reliable Fibonacci levels that capture broader market trends.
• Longer Timeframes (weekly to monthly): Best for long-term investors, where Fibonacci levels act as strong support and resistance based on significant market moves.
• General Tip: Fibonacci retracement levels are more reliable on higher timeframes, but combining them with other indicators like moving averages or RSI can enhance signal accuracy across any timeframe.
Why FiboTrace.V33?
FiboTrace.V33 is more than just a Fibonacci retracement tool—it’s an essential part of any trader’s toolkit. Its intuitive design and advanced features help you stay ahead of the market, making it easier to identify high-probability trading opportunities and manage risk effectively.
TradeTracker v33 - Interactive Journal [AR33_]TradeTracker v33 - Interactive Journal is a unique tool designed to enhance your trading experience by integrating an interactive journal directly onto your charts. Unlike traditional trading journals that require manual entries outside of TradingView, this script allows traders to document, track, and review their trades in real-time, right where the action happens.
What sets TradeTracker v33 apart from existing tools is its seamless blend of note-taking, task management, and performance tracking—all within a single, intuitive interface. With features like customizable checklists, due dates, and color-coded status indicators, this script provides a powerful and practical solution for traders who want to stay organized and disciplined.
2. Description
. TradeTracker v33 - Interactive Journal is designed to keep traders on track by allowing them to record trade-related notes, set tasks, and mark progress directly on their charts.
Here’s how it works:
• Purpose: The script serves as an all-in-one journal and task manager, helping traders document their trading strategies, track ongoing tasks, and review completed actions. It’s particularly useful for maintaining discipline and ensuring that every trade is executed according to a well-thought-out plan.
• How It Works:
• Interactive Notes and Tasks: Users can create and manage notes and tasks directly on their charts. Each note can be customized with a title, description, due date, and completion status.
• Status Indicators: Tasks are color-coded based on their status—green for completed, red for overdue, and default colors for pending tasks—allowing traders to quickly assess their progress.
• Dynamic Display: Notes are displayed in a clean, organized table on the chart, making it easy to review multiple tasks without cluttering the trading interface.
• Usage:
• Adding Notes: Simply fill in the note title, content, and optional due date within the script’s input settings, and the note will appear on your chart.
• Tracking Progress: Mark tasks as completed with a simple toggle, and the script will update their status in real-time.
• Customizing Your Workflow: Adjust the position, size, and visibility of notes to fit your trading style, ensuring that your journal supports rather than distracts from your trading activities.
3. Chart Presentation
To provide a clear and focused user experience, TradeTracker v33 - Interactive Journal is designed to be the sole feature on your chart when published. This ensures that users can easily identify and interact with their notes and tasks without any unnecessary distractions.
• Clean and Focused Display: The chart will exclusively display the interactive journal, showcasing how tasks and notes appear and update in real-time as you manage them.
• Useful Annotations: Annotations such as checkboxes and status indicators are clearly explained within the script’s description and are vital to understanding the functionality of the tool.
• Minimal Distractions: Only elements directly related to the script’s functionality are included on the chart, ensuring that users can easily follow along and implement the script in their own trading setup.
Volume-Price PercentileDescription:
The "Volume-Price Percentile Live" indicator is designed to provide real-time analysis of the relationship between volume percentiles and price percentiles on any given timeframe. This tool helps traders assess market activity by comparing how current volume levels rank relative to historical volume data and how current price movements (specifically high-low ranges) rank relative to historical price data. The indicator visualizes the ratio of volume percentile to price percentile as a histogram, allowing traders to gauge the relative strength of volume against price movements in real time.
Functionality:
Volume Percentile: Calculates the percentile rank of the current volume within a user-defined rolling period (default is 30 bars). This percentile indicates where the current volume stands in comparison to historical volumes over the specified period.
Price Percentile: Calculates the percentile rank of the current candle's high-low difference within a user-defined rolling period (default is 30 bars). This percentile reflects the current price movement's strength relative to past movements over the specified period.
Percentile Ratio (VP Ratio): The indicator plots the ratio of the volume percentile to the price percentile. This ratio helps identify periods when volume is significantly higher or lower relative to price movement, providing insights into potential market imbalances or strength.
Real-Time Data: By fetching data from a lower timeframe (e.g., 1-minute), the indicator updates continuously within the current timeframe, offering live, intra-candle updates. This ensures that traders can see the histogram change in real-time as new data becomes available, without waiting for the current candle to close.
How to Use:
Adding the Indicator: To use this indicator, add it to your chart on TradingView by selecting it from the Indicators list once it is published publicly.
Setting Parameters:
Volume Period Length: This input sets the rolling window length for calculating the volume percentile (default is 30). You can adjust it based on the desired sensitivity or historical period relevance.
Candle Period Length: This input sets the rolling window length for calculating the price percentile based on the high-low difference of candles (default is 30). Adjust this to match your trading style or analysis period.
Interpreting the Histogram:
The histogram represents the volume percentile divided by the price percentile.
Above 1: A value greater than 1 indicates that volume is relatively strong compared to price movement, which may suggest high activity or potential accumulation/distribution phases.
Below 1: A value less than 1 suggests that price movement is relatively stronger than volume, indicating potential weakness in volume relative to price moves.
Near 1: Values close to 1 suggest a balanced relationship between volume and price movement.
Application: Use this indicator to identify potential breakout or breakdown scenarios, assess the strength of price movements, and confirm trends. When volume percentile consistently leads price percentile, it might signal sustained interest and support for the current price trend. Conversely, if volume percentile lags significantly, it might warn of potential trend weakness.
Best Practices:
Multiple Timeframe Analysis: While the indicator provides real-time updates on any timeframe, consider using it alongside higher timeframe analysis to confirm trends and volume behavior across different periods.
Customization: Adjust the period lengths based on the asset’s typical volume and price behavior, as well as your trading strategy (e.g., short-term scalping vs. long-term trend following).
Complement with Other Indicators: Use this indicator in conjunction with other volume-based tools, trend indicators, or momentum oscillators to gain a comprehensive view of market dynamics.
Color Library // AlgoFyreAlgoFyre's Color Library is a Pine Script v5 library offering fire-themed palettes with light and dark modes. It helps traders easily integrate consistent, visually appealing color schemes into TradingView indicators. Used in AlgoFyre's Scripts and Indicators.
Color Overview
The AlgoFyreColorLibrary includes a comprehensive set of colors inspired by fire, ranging from light reds and oranges to dark browns and deep reds. The library is divided into two main themes:
Light Theme:
Includes lighter shades of red, orange, and brown, suitable for use on light backgrounds.
Dark Theme:
Includes darker shades of red, orange, and brown, suitable for use on dark backgrounds.
How to Use
To use the AlgoFyreColorLibrary in your Pine Script indicators and strategies, follow these steps:
Import the Library:
Import the AlgoFyreColorLibrary at the beginning of your script.
Call the getColor Function:
Use the getColor function to retrieve the desired color with the specified transparency.
Example
//@version=5
import "AF-L-0001" as af
// Example usage of getColor function
color primaryColor = af.getColor("af_primary1", 50)
color signalColor = af.getColor("af_green", 80)
// Plotting example
plot(close, color=primaryColor, linewidth=2, title="Primary Color Line")
plot(close, color=signalColor, linewidth=2, title="Signal Color Line")
The library AF-L-0001 is imported with the alias af.
The getColor function is used to get colors with specified transparency.
The colors are then used to plot lines on the chart.
Pulse Oscillator [UAlgo]The "Pulse Oscillator " is a trading tool designed to capture market momentum and trend changes by combining the strengths of multiple well-known technical indicators. By integrating the RSI (Relative Strength Index), CCI (Commodity Channel Index), and Stochastic Oscillator, this indicator provides traders with a comprehensive view of market conditions, offering both trend filtering and precise buy/sell signals. The oscillator is customizable, allowing users to fine-tune its parameters to match different trading strategies and timeframes. With its built-in smoothing techniques and level adjustments, the Pulse Oscillator aims to be a reliable tool for both trend-following and counter-trend trading strategies.
🔶 Key Features
Multi-Indicator Integration: Combines RSI, CCI, and Stochastic Oscillator to create a weighted momentum oscillator.
Why Use Multi-Indicator Integration?
Script uses Multi-Indicator Integration to combine the strengths of different technical indicators—such as RSI, CCI, and Stochastic Oscillator—into a single tool. This approach helps to reduce the weaknesses of individual indicators, providing a more comprehensive and reliable analysis of market conditions. By integrating multiple indicators, we can generate more accurate signals, filter out noise, and enhance our trading decisions.
Customizable Parameters: Allows users to adjust weights, periods, and smoothing techniques, providing flexibility to adapt the indicator to various market conditions.
Trend Filtering Option: An optional trend filter is available to enhance the accuracy of buy and sell signals, reducing the risk of false signals in choppy markets.
Dynamic Levels: The indicator dynamically calculates multiple levels of support and resistance, adjusting to market conditions with customizable decay factors and offsets.
Visual Clarity: The indicator visually represents different levels and trends with color-coded plots and fills, making it easier for traders to interpret market conditions at a glance.
Alerts: Configurable alerts for buy and sell signals, as well as trend changes, enabling traders to stay informed of key market movements without constant monitoring.
🔶 Interpreting the Indicator
Buy Signal: A buy signal is generated when the Slow Line crosses under the Fast Line during an uptrend or when the trend filter is disabled. This indicates a potential bullish reversal or continuation of an upward trend.
Sell Signal: A sell signal occurs when the Slow Line crosses above the Fast Line during a downtrend or when the trend filter is disabled, signaling a potential bearish reversal or continuation of a downward trend.
Trend Change: The indicator detects trend changes when the Fast Line shifts from increasing to decreasing or vice versa, providing early warning of possible market reversals.
Dynamic Levels: The indicator calculates upper and lower levels based on the Fast Line's values. These levels can be used to identify overbought or oversold conditions and potential areas of support or resistance.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
AB_Bnf_Selling_5minThe Mathematical Level Reversal Strategy is designed to identify potential reversal points in the market using mathematical levels combined with price action on a 5-minute chart. This strategy is particularly effective for intraday traders who seek to capitalize on precise entry and exit points based on calculated levels rather than traditional indicators like moving averages or Bollinger Bands.
Creators' Mathematical Levels Explanation
Mathematical levels are predetermined price points calculated based on various factors such as previous high/low points, Fibonacci retracements, or other arithmetic calculations. These levels are used to anticipate areas where the price might reverse or experience significant support or resistance.
higher threshold: A predefined level where the price is expected to experience resistance, leading to a potential reversal downward.
Lower Threshold: A predefined level where the price might find support, leading to a potential upward reversal.
In this strategy, we focus on price movements around the upper mathematical level, where prices are likely to reverse downwards.
Strategy Logic
Setup:
The strategy is applied on a 5-minute chart.
Mathematical levels are calculated based on your preferred method, such as Fibonacci levels, pivot points, or custom calculations. For this strategy, let's assume we are using a specific predefined upper level.
Sell Signal Criteria:
A 5-minute candle must cross above the predefined upper mathematical level or close entirely above it (open and close both above the level).
The following candle must break below the low of the candle that crossed the upper level and close below that low. This confirms a bearish reversal.
Once these conditions are met, a sell signal is triggered.
Stop Loss:
The stop loss is placed at the high of the candle that crossed above the upper mathematical level.
This level represents the point where the trade setup would be invalidated.
Take Profit:
Target 1: The first take profit is set at a level that offers a 1:5 risk-to-reward ratio.
Target 2: An alternative take profit level is set at a 1:3 risk-to-reward ratio, providing flexibility based on market conditions.
Trade Management:
Once a trade is initiated, no new trades will be taken until the current trade hits either the stop loss or the first take profit level. This prevents overlapping signals and helps in managing risk effectively.
Originality and Usefulness
This strategy offers a unique approach by using mathematical levels instead of traditional indicators. It provides traders with a clear framework for identifying and executing high-probability reversal trades, particularly in intraday markets.
Originality:
The strategy's originality lies in its reliance on mathematical levels combined with a multi-candle confirmation pattern. This approach reduces the chances of false signals and offers a robust method for identifying potential reversals.
Usefulness:
The strategy is particularly useful for traders who prefer a more quantitative approach, relying on calculated price levels rather than indicators. The clear rules for entry, stop loss, and take profit make it easier to execute consistently.
The inclusion of both 1:5 and 1:3 risk-to-reward targets allows for flexibility depending on market conditions, ensuring that traders can adapt to varying levels of volatility.
Chart Signals and Examples
To demonstrate the effectiveness of this strategy, let's look at a few hypothetical examples on a 5-minute chart:
Example 1: Clear Reversal Signal
The price steadily rises and crosses above the predefined upper mathematical level. The next candle breaks below the low of this candle and closes lower, triggering a sell signal.
A red dotted line is drawn at the stop loss level (the high of the candle that crossed the upper level).
Two green dashed lines are drawn to indicate the first and second take profit levels.
Example 2: No Signal Due to Ongoing Trade
After an initial sell signal is triggered, the price fluctuates but does not hit either the stop loss or the first take profit target. During this period, the strategy refrains from issuing any new signals, adhering to the trade management rule.
Example 3: Trade Reaches Target 1
In another scenario, the price moves sharply in favor of the trade after the signal is triggered. The first take profit level is hit, securing a profit. The trade is then considered closed, and the strategy is ready to issue a new signal when conditions are met.
DSL Oscillator [BigBeluga]DSL Oscillator BigBeluga
The DSL (Discontinued Signal Lines) Oscillator is an advanced technical analysis tool that combines elements of the Relative Strength Index (RSI), Discontinued Signal Lines, and Zero-Lag Exponential Moving Average (ZLEMA). This versatile indicator is designed to help traders identify trend direction, momentum, and potential reversal points in the market.
What are Discontinued Signal Lines (DSL)?
Discontinued Signal Lines are an extension of the traditional signal line concept used in many indicators. While a standard signal line compares an indicator's value to its smoothed (slightly lagging) state, DSL takes this idea further by using multiple adaptive lines that respond to the indicator's current value. This approach provides a more nuanced view of the indicator's state and momentum, making it easier to determine trends and desired states of the indicator.
🔵 KEY FEATURES
● Discontinued Signal Lines (DSL)
Uses multiple adaptive lines that respond to the indicator's value
Provides a more nuanced view of the indicator's state and momentum
Helps determine trends and desired states of the indicator more effectively
Available in "Fast" and "Slow" modes for different responsiveness
Acts as dynamic support and resistance levels for the oscillator
● DSL Oscillator
Based on a combination of RSI and Discontinued Signal Lines
// Discontinued Signal Lines
dsl_lines(src, length)=>
UP = 0.
DN = 0.
UP := (src > ta.sma(src, length)) ? nz(UP ) + dsl_mode / length * (src - nz(UP )) : nz(UP )
DN := (src < ta.sma(src, length)) ? nz(DN ) + dsl_mode / length * (src - nz(DN )) : nz(DN )
Smoothed using Zero-Lag Exponential Moving Average for reduced lag
// Zero-Lag Exponential Moving Average function
zlema(src, length) =>
lag = math.floor((length - 1) / 2)
ema_data = 2 * src - src
ema2 = ta.ema(ema_data, length)
ema2
Oscillates between 0 and 100
Color-coded for easy interpretation of market conditions
● Signal Generation
Generates buy signals when the oscillator crosses above the lower DSL line below 50
Generates sell signals when the oscillator crosses below the upper DSL line above 50
Signals are visualized on both the oscillator and the main chart
● Visual Cues
Background color changes on signal occurrences for easy identification
Candles on the main chart are colored based on the latest signal
Oscillator line color changes based on its position relative to the DSL lines
🔵 HOW TO USE
● Trend Identification
Use the color and position of the DSL Oscillator relative to its Discontinued Signal Lines to determine the overall market trend
● Entry Signals
Look for buy signals (green circles) when the oscillator crosses above the lower DSL line
Look for sell signals (blue circles) when the oscillator crosses below the upper DSL line
Confirm signals with the triangles on the main chart and background color changes
● Exit Signals
Consider exiting long positions on exit signals and short positions on Entery signals
Watch for the oscillator crossing back between the DSL lines as a potential early exit signal
● Momentum Analysis
Strong momentum is indicated when the oscillator moves rapidly towards extremes and away from the DSL lines
Weakening momentum can be spotted when the oscillator struggles to reach new highs or lows, or starts converging with the DSL lines
The space between the DSL lines can indicate potential momentum strength - wider gaps suggest stronger trends
● Confirmation
Use the DSL lines as dynamic support/resistance levels for the oscillator
Look for convergence between oscillator signals and price action on the main chart
Combine signals with other technical indicators or chart patterns for stronger confirmation
🔵 CUSTOMIZATION
The DSL Oscillator offers several customization options:
Adjust the main calculation length for the DSL lines
Choose between "Fast" and "Slow" modes for the DSL lines calculation
By fine-tuning these settings, traders can adapt the DSL Oscillator to various market conditions and personal trading strategies.
The DSL Oscillator provides a multi-faceted approach to market analysis, combining trend identification, momentum assessment, and signal generation in one comprehensive tool. Its dynamic nature and visual cues make it suitable for both novice and experienced traders across various timeframes and markets. The integration of RSI, Discontinued Signal Lines, and ZLEMA offers traders a sophisticated yet intuitive tool to inform their trading decisions.
The use of Discontinued Signal Lines sets this oscillator apart from traditional indicators by providing a more adaptive and nuanced view of market conditions. This can potentially lead to more accurate trend identification and signal generation, especially in markets with varying volatility.
Traders can use the DSL Oscillator to identify trends, spot potential reversals, and gauge market momentum. The combination of the oscillator, dynamic signal lines, and clear visual signals provides a holistic view of market conditions. As with all technical indicators, it's recommended to use the DSL Oscillator in conjunction with other forms of analysis and within the context of a well-defined trading strategy.
Volume Insignts AnalyzerDescription:
The Volume Insight Analyzer is an advanced Pine Script designed for traders who want a comprehensive view of volume dynamics on their charts. This script combines multiple volume-based indicators to help identify key trading opportunities, including significant volume days, volume dry-ups, and pocket pivots.
Key Features:
VDU (Volume Dry-Up) Detection: Automatically identifies and marks days when the volume is significantly below its moving average, helping to spot potential breakout or breakdown points. Customizable volume thresholds allow for tailored analysis based on your trading strategy. The Volume Dry-Up label appears when the volume is substantially below its average level and the price is near a key moving average. This condition indicates a period of equilibrium between supply and demand, suggesting a potential low-risk entry point for traders.
Pocket Pivot Analysis using 5 and 10 Length Pocket Pivots: Highlights days with exceptionally high volume compared to recent history, indicating potential pocket pivots. Visual markers on the chart and volume bars color-coded for 5 and 10-day lengths. Pocket pivot points are identified when the volume on a given day exceeds the maximum volume observed over the past several days. Specifically, a 5-day pocket pivot point is marked when today's volume surpasses the highest selling volume of the last 5 days. A cluster of 5-day pocket pivot points within a base is a strong indicator of stock strength. Similarly, a 10-day pocket pivot point following a Volume Dry-Up (VDU) suggests a potential entry opportunity. Moreover, a pre-existing cluster of 5-day pocket pivot points before a 10-day pocket pivot point provides greater conviction in the trade.
Volume Moving Averages: Set different lengths for primary and secondary moving averages to track volume trends over daily, weekly, and monthly timeframes. Options to display moving average lines on the volume chart.
Volume Visualization:
a. Major and Minor Volume Bars: Option to display bars that are either above or below average volume levels. Adjustable settings to show or hide these bars based on user preference.
b. Volume Bar Coloring: Volume bars are color-coded based on significant volume thresholds, including green for bullish signals, red for bearish signals, and orange for volume dry-ups.
Volume Metrics Table: A customizable table that displays real-time volume metrics including Relative Volume (RVOL), Turnover, and the number of high volume days. The table can be oriented horizontally or vertically and styled according to your theme preferences.
Visual Indicators:
a) Volume Dry-Up (VDU) Labels: Clearly marked VDU events with textual annotations on the chart.
b) Bullish and Bearish Arrows: Arrows indicating potential bullish or bearish closes based on volume analysis, enhancing decision-making.
Customization Options:
a) Dark and Light Theme Support: Toggle between dark and light themes to match your chart settings.
b) Adjustable Parameters: Easily configure input settings such as volume thresholds, MA lengths, and table display options to fit your trading style.
How to Use:
Set Parameters: Adjust the script settings such as volume thresholds, moving average lengths, and display preferences according to your analysis needs.
Analyze Volume Patterns: Use the indicators and visual markers provided by the script to identify significant volume patterns and potential trading signals.
Monitor Metrics: Refer to the volume metrics table for a quick overview of key volume-related statistics and trends.
Make Informed Decisions: Utilize the visual cues and volume data provided by the script to enhance your trading strategy and make more informed decisions.
Disclaimer:
This script is for informational purposes only and should not be considered as trading advice. Use it in conjunction with other analysis tools and consult with a financial advisor if needed. Trading involves risk, and past performance does not guarantee future results.
CNN Fear and Greed IndexThe “CNN Fear and Greed Index” indicator in this context is designed to gauge market sentiment based on a combination of several fundamental indicators. Here’s a breakdown of how this indicator works and what it represents:
Components of the Indicator:
1. Stock Price Momentum:
• Calculates the momentum of the S&P 500 index relative to its 125-day moving average. Momentum is essentially the rate of acceleration or deceleration of price movements over time.
2. Stock Price Strength:
• Measures the breadth of the market by comparing the number of stocks hitting 52-week highs versus lows. This provides insights into the overall strength or weakness of the market trend.
3. Stock Price Breadth:
• Evaluates the volume of shares trading on the rise versus the falling volume. Higher volume on rising days suggests positive market breadth, while higher volume on declining days indicates negative breadth.
4. Put and Call Options Ratio (Put/Call Ratio):
• This ratio indicates the sentiment of investors in the options market. A higher put/call ratio typically signals increased bearish sentiment (more puts relative to calls) and vice versa.
5. Market Volatility (VIX):
• Also known as the “fear gauge,” the VIX measures the expected volatility in the market over the next 30 days. Higher VIX values indicate higher expected volatility and often correlate with increased fear or uncertainty in the market.
6. Safe Haven Demand:
• Compares the returns of stocks (represented by S&P 500) versus safer investments like 10-year Treasury bonds. Higher returns on bonds relative to stocks suggest a flight to safety or risk aversion.
7. Junk Bond Demand:
• Measures the spread between yields on high-yield (junk) bonds and investment-grade bonds. Widening spreads may indicate increasing risk aversion as investors demand higher yields for riskier bonds.
Normalization and Weighting:
• Normalization: Each component is normalized to a scale of 0 to 100 using a function that adjusts the range based on historical highs and lows of the respective indicator.
• Weighting: The user can adjust the relative importance (weight) of each component using input parameters. This customization allows for different interpretations of market sentiment based on which factors are considered more influential.
Fear and Greed Index Calculation:
• The Fear and Greed Index is calculated as a weighted average of all normalized components. This index provides a single numerical value that summarizes the overall sentiment of the market based on the selected indicators.
Usage:
• Visualization: The indicator plots the Fear and Greed Index and its components on the chart. This allows traders and analysts to visually assess the sentiment trends over time.
• Analysis: Changes in the Fear and Greed Index can signal shifts in market sentiment. For example, a rising index may indicate increasing greed and potential overbought conditions, while a falling index may suggest increasing fear and potential oversold conditions.
• Customization: Traders can customize the indicator by adjusting the weights assigned to each component based on their trading strategies and market insights.
By integrating multiple fundamental indicators into a single index, the “CNN Fear and Greed Index” provides a comprehensive snapshot of market sentiment, helping traders make informed decisions about market entry, exit, and risk management strategies.
Super IndicatorOverview of the Combined Indicator
This combined indicator leverages three major technical analysis tools:
Bollinger Bands
Linear Regression Channels
Scalping Strategy Indicators (RSI, MACD, SMA)
Each of these tools provides unique insights into market conditions, and their integration offers a comprehensive view of price movements, trends, and potential trading signals.
1. Bollinger Bands
Purpose:
Bollinger Bands are used to measure market volatility and identify overbought or oversold conditions.
Components:
Basis (Middle Band): Typically a 20-period Simple Moving Average (SMA).
Upper Band: Basis + (2 * Standard Deviation).
Lower Band: Basis - (2 * Standard Deviation).
Why They Complement:
Bollinger Bands expand and contract based on market volatility. When the bands are narrow, it indicates low volatility and potential for a significant move. Wide bands indicate high volatility. This helps traders gauge the strength of market moves and potential reversals.
2. Linear Regression Channels
Purpose:
Linear Regression Channels identify the overall trend direction and measure deviation from the mean price over a specific period.
Components:
Middle Line (Linear Regression Line): The line of best fit through the price data over a specified period.
Upper and Lower Lines: Channels created by adding/subtracting a multiple of the standard deviation or another deviation measure from the regression line.
Why They Complement:
Linear Regression Channels provide a clear visual representation of the trend direction and the range within which prices typically fluctuate. This can help traders identify trend continuations and reversals, making it easier to spot entry and exit points.
3. Scalping Strategy Indicators
Purpose:
The RSI, MACD, and SMA are used to generate short-term buy and sell signals, which are essential for scalping strategies aimed at capturing quick profits from small price movements.
Components:
RSI (Relative Strength Index): Measures the speed and change of price movements, typically over 14 periods. It helps identify overbought and oversold conditions.
MACD (Moving Average Convergence Divergence): Consists of the MACD line, Signal line, and histogram. It helps identify changes in the strength, direction, momentum, and duration of a trend.
SMA (Simple Moving Average): The average price over a specified period, used to smooth out price data and identify trends.
Why They Complement:
These indicators provide short-term signals that can confirm or refute the signals given by Bollinger Bands and Linear Regression Channels. For example, a buy signal might be more reliable if the price is near the lower Bollinger Band and the MACD crosses above its signal line.
How They Work Together
Scenario 1: Confirming Trend Continuations
Bollinger Bands: Price staying near the upper band suggests a strong uptrend.
Linear Regression Channels: Price staying above the middle line confirms the uptrend.
5-Minute Scalping Strategy: RSI not in overbought territory, and MACD showing bullish momentum confirms continuation.
Scenario 2: Identifying Reversals
Bollinger Bands: Price touching or moving outside the lower band suggests oversold conditions.
Linear Regression Channels: Price at the lower channel line indicates potential support.
5-Minute Scalping Strategy: RSI in oversold territory, and MACD showing a bullish crossover indicates a reversal.
Scenario 3: Volatility Breakouts
Bollinger Bands: Bands contracting indicates low volatility and potential breakout.
Linear Regression Channels: Price moving away from the middle line signals potential breakout direction.
Scalping Strategy: MACD and RSI confirming the breakout direction for entry.
Input Parameters:
Define settings for Bollinger Bands, Linear Regression Channels, and the scalping strategy.
Allow users to customize lengths, multipliers, and colors.
Bollinger Bands Calculation:
Calculate the basis (SMA) and standard deviation.
Derive the upper and lower bands from the basis and standard deviation.
Linear Regression Channel Calculation:
Compute the slope, average, and intercept of the linear regression line.
Calculate deviations to plot upper and lower channel lines.
5-Minute Scalping Strategy:
Calculate RSI, MACD, and SMA for short-term trend analysis.
Define buy and sell conditions based on these indicators.
Plotting and Alerts:
Plot Bollinger Bands and Linear Regression Channels on the chart.
Plot buy and sell signals with shapes.
Set alerts for key conditions like exiting the regression channel bounds and trend switches.
Conclusion
By combining Bollinger Bands, Linear Regression Channels, and a 5-minute scalping strategy, this indicator offers a robust tool for traders. Bollinger Bands provide volatility insights, Linear Regression Channels highlight trend direction and potential reversals, and the scalping strategy offers precise entry and exit points. Together, these tools can enhance a trader's ability to make informed decisions in various market conditions.
Pivot Points - [RealFact]Description:
The Pivot Points indicator is a powerful tool for identifying potential support and resistance levels based on previous price action. It calculates key pivot levels (P), along with support (S1, S2) and resistance (R1, R2) levels, which are used to forecast potential turning points in the market.
Key Features:
Pivot Calculation: Based on the previous period's high, low, and close prices.
Support and Resistance Levels: Three support (S1, S2) and three resistance (R1, R2) levels.
Customizable Timeframes: Applicable to various timeframes including daily, weekly, and monthly charts.
Visual Representation: Levels are clearly plotted on the chart, making it easy to identify key areas.
Trading Strategies: Useful for breakout, reversal, and trend-following strategies.
How to Use:
Identify Key Levels: Use the pivot point (P) to determine the general market trend.
Support and Resistance: Look for price reactions at S1, S2, R1 and R2 to find potential entry and exit points.
Combine with Other Indicators: Enhance analysis by combining with other technical indicators such as Moving Averages, RSI, or MACD.
Formula:
Pivot Point (P) = (High + Low + Close) / 3
Support 1 (S1) = 2P - High
Resistance 1 (R1) = 2P - Low
Support 2 (S2) = P - (High - Low)
Resistance 2 (R2) = P + (High - Low)
Best Practices:
Confirm with Volume: Look for volume confirmation when price approaches pivot levels.
Avoid False Breakouts: Be cautious of false breakouts and use other indicators to confirm price moves.
Persistent Homology Based Trend Strength OscillatorPersistent Homology Based Trend Strength Oscillator
The Persistent Homology Based Trend Strength Oscillator is a unique and powerful tool designed to measure the persistence of market trends over a specified rolling window. By applying the principles of persistent homology, this indicator provides traders with valuable insights into the strength and stability of uptrends and downtrends, helping to inform better trading decisions.
What Makes This Indicator Original?
This indicator's originality lies in its application of persistent homology , a method from topological data analysis, to financial markets. Persistent homology examines the shape and features of data across multiple scales, identifying patterns that persist as the scale changes. By adapting this concept, the oscillator tracks the persistence of uptrends and downtrends in price data, offering a novel approach to trend analysis.
Concepts Underlying the Calculations:
Persistent Homology: This method identifies features such as clusters, holes, and voids that persist as the scale changes. In the context of this indicator, it tracks the duration and stability of price trends.
Rolling Window Analysis: The oscillator uses a specified window size to calculate the average length of uptrends and downtrends, providing a dynamic view of trend persistence over time.
Threshold-Based Trend Identification: It differentiates between uptrends and downtrends based on specified thresholds for price changes, ensuring precision in trend detection.
How It Works:
The oscillator monitors consecutive changes in closing prices to identify uptrends and downtrends.
An uptrend is detected when the closing price increase exceeds a specified positive threshold.
A downtrend is detected when the closing price decrease exceeds a specified negative threshold.
The lengths of these trends are recorded and averaged over the chosen window size.
The Trend Persistence Index is calculated as the difference between the average uptrend length and the average downtrend length, providing a measure of trend persistence.
How Traders Can Use It:
Identify Trend Strength: The Trend Persistence Index offers a clear measure of the strength and stability of uptrends and downtrends. A higher value indicates stronger and more persistent uptrends, while a lower value suggests stronger and more persistent downtrends.
Spot Trend Reversals: Significant shifts in the Trend Persistence Index can signal potential trend reversals. For instance, a transition from positive to negative values might indicate a shift from an uptrend to a downtrend.
Confirm Trends: Use the Trend Persistence Index alongside other technical indicators to confirm the strength and duration of trends, enhancing the accuracy of your trading signals.
Manage Risk: Understanding trend persistence can help traders manage risk by identifying periods of high trend stability versus periods of potential volatility. This can be crucial for timing entries and exits.
Example Usage:
Default Settings: Start with the default settings to get a feel for the oscillator’s behavior. Observe how the Trend Persistence Index reacts to different market conditions.
Adjust Thresholds: Fine-tune the positive and negative thresholds based on the asset's volatility to improve trend detection accuracy.
Combine with Other Indicators: Use the Persistent Homology Based Trend Strength Oscillator in conjunction with other technical indicators such as moving averages, RSI, or MACD for a comprehensive analysis.
Backtesting: Conduct backtesting to see how the oscillator would have performed in past market conditions, helping you to refine your trading strategy.
