FON60DK by leventsahThe strategy generates buy and sell signals using the Tillson T3 and TOTT (Twin Optimized Trend Tracker) indicators. Additionally, the Williams %R indicator is used to filter the signals. Below is an explanation of the main components of the code:
1. Input Parameters:
Tillson T3 and TOTT parameters: Separate parameters are defined for both buy (AL) and sell (SAT) conditions. These parameters control the sensitivity and behavior of the indicators.
Williams %R period: The period for the Williams %R indicator is set to determine overbought and oversold levels.
2. Tillson T3 Calculation:
The Tillson T3 indicator is a smoothed moving average that uses an exponential moving average (EMA) with additional smoothing. The formula calculates a weighted average of multiple EMAs to produce a smoother line.
The t3 function computes the Tillson T3 value based on the close price and the input parameters.
3. TOTT Calculation (Twin Optimized Trend Tracker):
The TOTT indicator is a trend-following tool that adjusts its sensitivity based on market conditions. It uses a combination of price action and a volatility coefficient to determine trend direction.
The Var_Func function calculates the TOTT value, which is then used to derive the OTT (Optimized Trend Tracker) levels for both buy and sell conditions.
4. Williams %R Calculation:
Williams %R is a momentum oscillator that measures overbought and oversold levels. It is calculated using the highest high and lowest low over a specified period.
5. Buy and Sell Conditions:
Buy Condition: A buy signal is generated when the Tillson T3 value crosses above the TOTT upper band (OTTup) and the Williams %R is above -20 (indicating an oversold condition).
Sell Condition: A sell signal is generated when the Tillson T3 value crosses below the TOTT lower band (OTTdnS) and the Williams %R is above -70 (used to close long positions).
6. Strategy Execution:
The strategy.entry function is used to open a long position when the buy condition is met.
The strategy.close function is used to close the long position when the sell condition is met.
7. Visualization:
The bars on the chart are colored green when a long position is open.
The Tillson T3, TOTT upper band (OTTup), and TOTT lower band (OTTdn) are plotted on the chart for both buy and sell conditions.
8. Plots:
The Tillson T3 values for buy and sell conditions are plotted in blue.
The TOTT upper and lower bands are plotted in green and red, respectively, for both buy and sell conditions.
Summary:
This strategy combines trend-following indicators (Tillson T3 and TOTT) with a momentum oscillator (Williams %R) to generate buy and sell signals. The use of separate parameters for buy and sell conditions allows for fine-tuning the strategy based on market behavior. The visual elements, such as colored bars and plotted indicators, help traders quickly identify signals and trends on the chart.
ค้นหาในสคริปต์สำหรับ "indicators"
Alboncalc: Top and Bottom Detector - Straight Line ContinuityDescription:
The "Alboncalc: Top and Bottom Detector - Straight Line Continuity" is an innovative indicator for identifying key price reversal points (tops and bottoms) with precision. Unlike traditional indicators that focus on abstract data representations like oscillators or momentum-based lines, this indicator directly overlays the price chart. It draws a continuous line connecting highs and lows (tops and bottoms), providing traders with a clear and immediate visual representation of market swings. The lines automatically adjust in real-time, maintaining a straight path during trend continuations and only shifting when a trend reversal is detected.
Originality and Usefulness:
This indicator stands out from other tools available on TradingView due to its unique ability to maintain a continuous line across price swings, preserving accuracy and visual clarity. Most traditional top-and-bottom detectors merely mark points or provide indicators that are disconnected from price action, making it harder for traders to spot patterns. This script takes a different approach by drawing lines directly on the price chart, offering greater precision and better trend visualization. This innovation is particularly useful for traders who rely on visual cues and price action analysis to make decisions. It simplifies the process of identifying reversal points and trends without needing to rely on lagging indicators.
How It Works:
This indicator detects tops and bottoms based on user-defined periods. When the highest point in a given period is detected, it marks it as a top, and similarly, when the lowest point is detected, it marks it as a bottom. As the price moves, the indicator adjusts the lines to connect consecutive tops and bottoms. If the trend continues in the same direction (e.g., an uptrend), the line remains straight and extends. If a reversal is detected, a new line is drawn to connect the previous bottom (or top) to the new reversal point, providing an accurate visual representation of market trends.
How to Use:
1. Load the Indicator: Add the "Alboncalc: Top and Bottom Detector - Straight Line Continuity" to your chart from the TradingView script library.
2. Customize Settings: Adjust the "Top Period" and "Bottom Period" inputs to fine-tune the sensitivity of top and bottom detection based on your preferred timeframe.
3. Observe Price Action: As the price moves, the indicator will draw lines directly over the price chart, connecting tops and bottoms.
4. Interpret the Lines: Use the continuous lines to identify ongoing trends and potential reversal points. The line remains straight during trend continuation, indicating sustained movement in one direction. A new line signifies a reversal in the trend.
This tool is ideal for traders using trend-following strategies, breakout detection, or those who prefer clean, visual price action analysis (Only Tops and Bottons).
Underlying Concepts:
The core of this indicator is based on the highest high and lowest low concept, which is common in technical analysis. The logic is simple:
- A top is detected when the price reaches a high point compared to a user-defined number of prior candles (i.e., the `top_period`).
- A bottom is detected when the price hits a low point compared to the prior candles (i.e., the `bottom_period`).
When the price continues in the same trend, the line is extended without a break. This behavior ensures that trends are represented in a clear and consistent manner, which helps traders better identify trend continuations and reversals.
Code Breakdown:
```pinescript
//@version=5
indicator("Top and Bottom Detector - Straight Line Continuity", overlay=true)
```
- This initializes the indicator and specifies that it will overlay directly on the price chart.
```pinescript
var int top_period = input.int(5, title="Top Period", minval=1)
var int bottom_period = input.int(5, title="Bottom Period", minval=1)
```
- These inputs allow the user to customize the number of candles used to identify tops and bottoms. A higher period results in fewer but more significant top/bottom detections, while a lower period increases sensitivity.
```pinescript
isTop = ta.highest(top_period) == high
isBottom = ta.lowest(bottom_period) == low
```
- These lines check if the current candle has the highest high or the lowest low in the defined period. If true, the current price is either a top or a bottom.
```pinescript
var line currentLine = na
var float last_price = na
var int last_index = na
var bool isUpTrend = na
```
- These variables store the current line being drawn (`currentLine`), the last detected price (`last_price`), and the direction of the trend (`isUpTrend`). `last_index` tracks where the last top or bottom was detected.
```pinescript
if (isTop or isBottom)
if (not na(last_price))
if ((isTop and isUpTrend) or (isBottom and not isUpTrend))
line.set_xy2(currentLine, bar_index, (isTop ? high : low))
else
currentLine := line.new(x1=last_index, y1=last_price, x2=bar_index, y2=(isTop ? high : low), color=color.yellow, width=2)
last_price := (isTop ? high : low)
last_index := bar_index
isUpTrend := isTop
```
- The `if` block handles the logic of drawing the line. If a top or bottom is detected, and the trend continues (either an uptrend for tops or a downtrend for bottoms), the current line is extended using `line.set_xy2`. If a reversal is detected, a new line is drawn using `line.new`.
- The `last_price` and `last_index` variables are updated after each detection, and the `isUpTrend` flag is set based on whether a top or bottom was found.
Conclusion:
This indicator offers a more precise and visually intuitive way of identifying tops and bottoms directly on the price chart, making it an essential tool for traders focused on price action. Its ability to draw continuous lines through ongoing trends and adjust only upon a reversal makes it superior in terms of visual clarity compared to most conventional indicators.
Confluence StrategyOverview of Confluence Strategy
The Confluence Strategy in trading refers to the combination of multiple technical indicators, support/resistance levels, and chart patterns to identify high-probability trading opportunities. The idea is that when several indicators agree on a price movement, the likelihood of that movement being successful increases.
Key Components
Technical Indicators:
Moving Averages (MA): Commonly used to determine the trend direction. Look for crossovers (e.g., the 50-day MA crossing above the 200-day MA).
Relative Strength Index (RSI): Helps identify overbought or oversold conditions. A reading above 70 may indicate overbought conditions, while below 30 suggests oversold.
MACD (Moving Average Convergence Divergence): Useful for spotting changes in momentum. Look for MACD crossovers and divergence from price.
Support and Resistance Levels:
Identify key levels where price has historically reversed. These can be drawn from previous highs/lows, Fibonacci retracement levels, or psychological price levels.
Chart Patterns:
Patterns like head and shoulders, double tops/bottoms, or flags can indicate potential reversals or continuations in price.
Strategy Implementation
Set Up Your Chart:
Add the desired indicators (e.g., MA, RSI, MACD) to your TradingView chart.
Mark significant support and resistance levels.
Identify Confluence Points:
Look for situations where multiple indicators align. For instance, if the price is near a support level, the RSI is below 30, and the MACD shows bullish divergence, this may signal a buying opportunity.
Entry and Exit Points:
Entry: Place a trade when your confluence conditions are met. Use limit orders for better prices.
Exit: Set profit targets based on resistance levels or use trailing stops. Consider the risk-reward ratio to ensure your trades are favorable.
Risk Management:
Always implement stop-loss orders to protect against unexpected market moves. Position size should reflect your risk tolerance.
Example of a Confluence Trade
Setup:
Price approaches a strong support level.
RSI shows oversold conditions (below 30).
The 50-day MA is about to cross above the 200-day MA (bullish crossover).
Action:
Enter a long position as the conditions align.
Set a stop loss just below the support level and a take profit at the next resistance level.
Conclusion
The Confluence Strategy can significantly enhance trading accuracy by ensuring that multiple indicators support a trade decision. Traders on TradingView can customize their indicators and charts to fit their personal trading styles, making it a flexible approach to technical analysis.
ICT Master Suite [Trading IQ]Hello Traders!
We’re excited to introduce the ICT Master Suite by TradingIQ, a new tool designed to bring together several ICT concepts and strategies in one place.
The Purpose Behind the ICT Master Suite
There are a few challenges traders often face when using ICT-related indicators:
Many available indicators focus on one or two ICT methods, which can limit traders who apply a broader range of ICT related techniques on their charts.
There aren't many indicators for ICT strategy models, and we couldn't find ICT indicators that allow for testing the strategy models and setting alerts.
Many ICT related concepts exist in the public domain as indicators, not strategies! This makes it difficult to verify that the ICT concept has some utility in the market you're trading and if it's worth trading - it's difficult to know if it's working!
Some users might not have enough chart space to apply numerous ICT related indicators, which can be restrictive for those wanting to use multiple ICT techniques simultaneously.
The ICT Master Suite is designed to offer a comprehensive option for traders who want to apply a variety of ICT methods. By combining several ICT techniques and strategy models into one indicator, it helps users maximize their chart space while accessing multiple tools in a single slot.
Additionally, the ICT Master Suite was developed as a strategy . This means users can backtest various ICT strategy models - including deep backtesting. A primary goal of this indicator is to let traders decide for themselves what markets to trade ICT concepts in and give them the capability to figure out if the strategy models are worth trading!
What Makes the ICT Master Suite Different
There are many ICT-related indicators available on TradingView, each offering valuable insights. What the ICT Master Suite aims to do is bring together a wider selection of these techniques into one tool. This includes both key ICT methods and strategy models, allowing traders to test and activate strategies all within one indicator.
Features
The ICT Master Suite offers:
Multiple ICT strategy models, including the 2022 Strategy Model and Unicorn Model, which can be built, tested, and used for live trading.
Calculation and display of key price areas like Breaker Blocks, Rejection Blocks, Order Blocks, Fair Value Gaps, Equal Levels, and more.
The ability to set alerts based on these ICT strategies and key price areas.
A comprehensive, yet practical, all-inclusive ICT indicator for traders.
Customizable Timeframe - Calculate ICT concepts on off-chart timeframes
Unicorn Strategy Model
2022 Strategy Model
Liquidity Raid Strategy Model
OTE (Optimal Trade Entry) Strategy Model
Silver Bullet Strategy Model
Order blocks
Breaker blocks
Rejection blocks
FVG
Strong highs and lows
Displacements
Liquidity sweeps
Power of 3
ICT Macros
HTF previous bar high and low
Break of Structure indications
Market Structure Shift indications
Equal highs and lows
Swings highs and swing lows
Fibonacci TPs and SLs
Swing level TPs and SLs
Previous day high and low TPs and SLs
And much more! An ongoing project!
How To Use
Many traders will already be familiar with the ICT related concepts listed above, and will find using the ICT Master Suite quite intuitive!
Despite this, let's go over the features of the tool in-depth and how to use the tool!
The image above shows the ICT Master Suite with almost all techniques activated.
ICT 2022 Strategy Model
The ICT Master suite provides the ability to test, set alerts for, and live trade the ICT 2022 Strategy Model.
The image above shows an example of a long position being entered following a complete setup for the 2022 ICT model.
A liquidity sweep occurs prior to an upside breakout. During the upside breakout the model looks for the FVG that is nearest 50% of the setup range. A limit order is placed at this FVG for entry.
The target entry percentage for the range is customizable in the settings. For instance, you can select to enter at an FVG nearest 33% of the range, 20%, 66%, etc.
The profit target for the model generally uses the highest high of the range (100%) for longs and the lowest low of the range (100%) for shorts. Stop losses are generally set at 0% of the range.
The image above shows the short model in action!
Whether you decide to follow the 2022 model diligently or not, you can still set alerts when the entry condition is met.
ICT Unicorn Model
The image above shows an example of a long position being entered following a complete setup for the ICT Unicorn model.
A lower swing low followed by a higher swing high precedes the overlap of an FVG and breaker block formed during the sequence.
During the upside breakout the model looks for an FVG and breaker block that formed during the sequence and overlap each other. A limit order is placed at the nearest overlap point to current price.
The profit target for this example trade is set at the swing high and the stop loss at the swing low. However, both the profit target and stop loss for this model are configurable in the settings.
For Longs, the selectable profit targets are:
Swing High
Fib -0.5
Fib -1
Fib -2
For Longs, the selectable stop losses are:
Swing Low
Bottom of FVG or breaker block
The image above shows the short version of the Unicorn Model in action!
For Shorts, the selectable profit targets are:
Swing Low
Fib -0.5
Fib -1
Fib -2
For Shorts, the selectable stop losses are:
Swing High
Top of FVG or breaker block
The image above shows the profit target and stop loss options in the settings for the Unicorn Model.
Optimal Trade Entry (OTE) Model
The image above shows an example of a long position being entered following a complete setup for the OTE model.
Price retraces either 0.62, 0.705, or 0.79 of an upside move and a trade is entered.
The profit target for this example trade is set at the -0.5 fib level. This is also adjustable in the settings.
For Longs, the selectable profit targets are:
Swing High
Fib -0.5
Fib -1
Fib -2
The image above shows the short version of the OTE Model in action!
For Shorts, the selectable profit targets are:
Swing Low
Fib -0.5
Fib -1
Fib -2
Liquidity Raid Model
The image above shows an example of a long position being entered following a complete setup for the Liquidity Raid Modell.
The user must define the session in the settings (for this example it is 13:30-16:00 NY time).
During the session, the indicator will calculate the session high and session low. Following a “raid” of either the session high or session low (after the session has completed) the script will look for an entry at a recently formed breaker block.
If the session high is raided the script will look for short entries at a bearish breaker block. If the session low is raided the script will look for long entries at a bullish breaker block.
For Longs, the profit target options are:
Swing high
User inputted Lib level
For Longs, the stop loss options are:
Swing low
User inputted Lib level
Breaker block bottom
The image above shows the short version of the Liquidity Raid Model in action!
For Shorts, the profit target options are:
Swing Low
User inputted Lib level
For Shorts, the stop loss options are:
Swing High
User inputted Lib level
Breaker block top
Silver Bullet Model
The image above shows an example of a long position being entered following a complete setup for the Silver Bullet Modell.
During the session, the indicator will determine the higher timeframe bias. If the higher timeframe bias is bullish the strategy will look to enter long at an FVG that forms during the session. If the higher timeframe bias is bearish the indicator will look to enter short at an FVG that forms during the session.
For Longs, the profit target options are:
Nearest Swing High Above Entry
Previous Day High
For Longs, the stop loss options are:
Nearest Swing Low
Previous Day Low
The image above shows the short version of the Silver Bullet Model in action!
For Shorts, the profit target options are:
Nearest Swing Low Below Entry
Previous Day Low
For Shorts, the stop loss options are:
Nearest Swing High
Previous Day High
Order blocks
The image above shows indicator identifying and labeling order blocks.
The color of the order blocks, and how many should be shown, are configurable in the settings!
Breaker Blocks
The image above shows indicator identifying and labeling order blocks.
The color of the breaker blocks, and how many should be shown, are configurable in the settings!
Rejection Blocks
The image above shows indicator identifying and labeling rejection blocks.
The color of the rejection blocks, and how many should be shown, are configurable in the settings!
Fair Value Gaps
The image above shows indicator identifying and labeling fair value gaps.
The color of the fair value gaps, and how many should be shown, are configurable in the settings!
Additionally, you can select to only show fair values gaps that form after a liquidity sweep. Doing so reduces "noisy" FVGs and focuses on identifying FVGs that form after a significant trading event.
The image above shows the feature enabled. A fair value gap that occurred after a liquidity sweep is shown.
Market Structure
The image above shows the ICT Master Suite calculating market structure shots and break of structures!
The color of MSS and BoS, and whether they should be displayed, are configurable in the settings.
Displacements
The images above show indicator identifying and labeling displacements.
The color of the displacements, and how many should be shown, are configurable in the settings!
Equal Price Points
The image above shows the indicator identifying and labeling equal highs and equal lows.
The color of the equal levels, and how many should be shown, are configurable in the settings!
Previous Custom TF High/Low
The image above shows the ICT Master Suite calculating the high and low price for a user-defined timeframe. In this case the previous day’s high and low are calculated.
To illustrate the customizable timeframe function, the image above shows the indicator calculating the previous 4 hour high and low.
Liquidity Sweeps
The image above shows the indicator identifying a liquidity sweep prior to an upside breakout.
The image above shows the indicator identifying a liquidity sweep prior to a downside breakout.
The color and aggressiveness of liquidity sweep identification are adjustable in the settings!
Power Of Three
The image above shows the indicator calculating Po3 for two user-defined higher timeframes!
Macros
The image above shows the ICT Master Suite identifying the ICT macros!
ICT Macros are only displayable on the 5 minute timeframe or less.
Strategy Performance Table
In addition to a full-fledged TradingView backtest for any of the ICT strategy models the indicator offers, a quick-and-easy strategy table exists for the indicator!
The image above shows the strategy performance table in action.
Keep in mind that, because the ICT Master Suite is a strategy script, you can perform fully automatic backtests, deep backtests, easily add commission and portfolio balance and look at pertinent metrics for the ICT strategies you are testing!
Lite Mode
Traders who want the cleanest chart possible can toggle on “Lite Mode”!
In Lite Mode, any neon or “glow” like effects are removed and key levels are marked as strict border boxes. You can also select to remove box borders if that’s what you prefer!
Settings Used For Backtest
For the displayed backtest, a starting balance of $1000 USD was used. A commission of 0.02%, slippage of 2 ticks, a verify price for limit orders of 2 ticks, and 5% of capital investment per order.
A commission of 0.02% was used due to the backtested asset being a perpetual future contract for a crypto currency. The highest commission (lowest-tier VIP) for maker orders on many exchanges is 0.02%. All entered positions take place as maker orders and so do profit target exits. Stop orders exist as stop-market orders.
A slippage of 2 ticks was used to simulate more realistic stop-market orders. A verify limit order settings of 2 ticks was also used. Even though BTCUSDT.P on Binance is liquid, we just want the backtest to be on the safe side. Additionally, the backtest traded 100+ trades over the period. The higher the sample size the better; however, this example test can serve as a starting point for traders interested in ICT concepts.
Community Assistance And Feedback
Given the complexity and idiosyncratic applications of ICT concepts amongst its proponents, the ICT Master Suite’s built-in strategies and level identification methods might not align with everyone's interpretation.
That said, the best we can do is precisely define ICT strategy rules and concepts to a repeatable process, test, and apply them! Whether or not an ICT strategy is trading precisely how you would trade it, seeing the model in action, taking trades, and with performance statistics is immensely helpful in assessing predictive utility.
If you think we missed something, you notice a bug, have an idea for strategy model improvement, please let us know! The ICT Master Suite is an ongoing project that will, ideally, be shaped by the community.
A big thank you to the @PineCoders for their Time Library!
Thank you!
Mean Reversion Cloud (Ornstein-Uhlenbeck) // AlgoFyreThe Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator detects mean-reversion opportunities by applying the Ornstein-Uhlenbeck process. It calculates a dynamic mean using an Exponential Weighted Moving Average, surrounded by volatility bands, signaling potential buy/sell points when prices deviate.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Adaptive Mean Calculation
🔸Volatility-Based Cloud
🔸Speed of Reversion (θ)
🔶 FUNCTIONALITY
🔸Dynamic Mean and Volatility Bands
🞘 How it works
🞘 How to calculate
🞘 Code extract
🔸Visualization via Table and Plotshapes
🞘 Table Overview
🞘 Plotshapes Explanation
🞘 Code extract
🔶 INSTRUCTIONS
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
🞘 Understanding What to Look For on the Chart
🞘 Possible Entry Signals
🞘 Possible Take Profit Strategies
🞘 Possible Stop-Loss Levels
🞘 Additional Tips
🔸Customize settings
🔶 CONCLUSION
▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅
🔶 ORIGINALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) is a unique indicator that applies the Ornstein-Uhlenbeck stochastic process to identify mean-reverting behavior in asset prices. Unlike traditional moving average-based indicators, this model uses an Exponentially Weighted Moving Average (EWMA) to calculate the long-term mean, dynamically adjusting to recent price movements while still considering all historical data. It also incorporates volatility bands, providing a "cloud" that visually highlights overbought or oversold conditions. By calculating the speed of mean reversion (θ) through the autocorrelation of log returns, this indicator offers traders a more nuanced and mathematically robust tool for identifying mean-reversion opportunities. These innovations make it especially useful for markets that exhibit range-bound characteristics, offering timely buy and sell signals based on statistical deviations from the mean.
🔸Adaptive Mean Calculation Traditional MA indicators use fixed lengths, which can lead to lagging signals or over-sensitivity in volatile markets. The Mean Reversion Cloud uses an Exponentially Weighted Moving Average (EWMA), which adapts to price movements by dynamically adjusting its calculation, offering a more responsive mean.
🔸Volatility-Based Cloud Unlike simple moving averages that only plot a single line, the Mean Reversion Cloud surrounds the dynamic mean with volatility bands. These bands, based on standard deviations, provide traders with a visual cue of when prices are statistically likely to revert, highlighting potential reversal zones.
🔸Speed of Reversion (θ) The indicator goes beyond price averages by calculating the speed at which the price reverts to the mean (θ), using the autocorrelation of log returns. This gives traders an additional tool for estimating the likelihood and timing of mean reversion, making the signals more reliable in practice.
🔶 FUNCTIONALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator is designed to detect potential mean-reversion opportunities in asset prices by applying the Ornstein-Uhlenbeck stochastic process. It calculates a dynamic mean through the Exponentially Weighted Moving Average (EWMA) and plots volatility bands based on the standard deviation of the asset's price over a specified period. These bands create a "cloud" that represents expected price fluctuations, helping traders to identify overbought or oversold conditions. By calculating the speed of reversion (θ) from the autocorrelation of log returns, the indicator offers a more refined way of assessing how quickly prices may revert to the mean. Additionally, the inclusion of volatility provides a comprehensive view of market conditions, allowing for more accurate buy and sell signals.
Let's dive into the details:
🔸Dynamic Mean and Volatility Bands The dynamic mean (μ) is calculated using the EWMA, giving more weight to recent prices but considering all historical data. This process closely resembles the Ornstein-Uhlenbeck (OU) process, which models the tendency of a stochastic variable (such as price) to revert to its mean over time. Volatility bands are plotted around the mean using standard deviation, forming the "cloud" that signals overbought or oversold conditions. The cloud adapts dynamically to price fluctuations and market volatility, making it a versatile tool for mean-reversion strategies. 🞘 How it works Step one: Calculate the dynamic mean (μ) The Ornstein-Uhlenbeck process describes how a variable, such as an asset's price, tends to revert to a long-term mean while subject to random fluctuations. In this indicator, the EWMA is used to compute the dynamic mean (μ), mimicking the mean-reverting behavior of the OU process. Use the EWMA formula to compute a weighted mean that adjusts to recent price movements. Assign exponentially decreasing weights to older data while giving more emphasis to current prices. Step two: Plot volatility bands Calculate the standard deviation of the price over a user-defined period to determine market volatility. Position the upper and lower bands around the mean by adding and subtracting a multiple of the standard deviation. 🞘 How to calculate Exponential Weighted Moving Average (EWMA)
The EWMA dynamically adjusts to recent price movements:
mu_t = lambda * mu_{t-1} + (1 - lambda) * P_t
Where mu_t is the mean at time t, lambda is the decay factor, and P_t is the price at time t. The higher the decay factor, the more weight is given to recent data.
Autocorrelation (ρ) and Standard Deviation (σ)
To measure mean reversion speed and volatility: rho = correlation(log(close), log(close ), length) Where rho is the autocorrelation of log returns over a specified period.
To calculate volatility:
sigma = stdev(close, length)
Where sigma is the standard deviation of the asset's closing price over a specified length.
Upper and Lower Bands
The upper and lower bands are calculated as follows:
upper_band = mu + (threshold * sigma)
lower_band = mu - (threshold * sigma)
Where threshold is a multiplier for the standard deviation, usually set to 2. These bands represent the range within which the price is expected to fluctuate, based on current volatility and the mean.
🞘 Code extract // Calculate Returns
returns = math.log(close / close )
// Calculate Long-Term Mean (μ) using EWMA over the entire dataset
var float ewma_mu = na // Initialize ewma_mu as 'na'
ewma_mu := na(ewma_mu ) ? close : decay_factor * ewma_mu + (1 - decay_factor) * close
mu = ewma_mu
// Calculate Autocorrelation at Lag 1
rho1 = ta.correlation(returns, returns , corr_length)
// Ensure rho1 is within valid range to avoid errors
rho1 := na(rho1) or rho1 <= 0 ? 0.0001 : rho1
// Calculate Speed of Mean Reversion (θ)
theta = -math.log(rho1)
// Calculate Volatility (σ)
sigma = ta.stdev(close, corr_length)
// Calculate Upper and Lower Bands
upper_band = mu + threshold * sigma
lower_band = mu - threshold * sigma
🔸Visualization via Table and Plotshapes
The table shows key statistics such as the current value of the dynamic mean (μ), the number of times the price has crossed the upper or lower bands, and the consecutive number of bars that the price has remained in an overbought or oversold state.
Plotshapes (diamonds) are used to signal buy and sell opportunities. A green diamond below the price suggests a buy signal when the price crosses below the lower band, and a red diamond above the price indicates a sell signal when the price crosses above the upper band.
The table and plotshapes provide a comprehensive visualization, combining both statistical and actionable information to aid decision-making.
🞘 Code extract // Reset consecutive_bars when price crosses the mean
var consecutive_bars = 0
if (close < mu and close >= mu) or (close > mu and close <= mu)
consecutive_bars := 0
else if math.abs(deviation) > 0
consecutive_bars := math.min(consecutive_bars + 1, dev_length)
transparency = math.max(0, math.min(100, 100 - (consecutive_bars * 100 / dev_length)))
🔶 INSTRUCTIONS
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator can be set up by adding it to your TradingView chart and configuring parameters such as the decay factor, autocorrelation length, and volatility threshold to suit current market conditions. Look for price crossovers and deviations from the calculated mean for potential entry signals. Use the upper and lower bands as dynamic support/resistance levels for setting take profit and stop-loss orders. Combining this indicator with additional trend-following or momentum-based indicators can improve signal accuracy. Adjust settings for better mean-reversion detection and risk management.
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
Adding the Indicator to the Chart:
Go to your TradingView chart.
Click on the "Indicators" button at the top.
Search for "Mean Reversion Cloud (Ornstein-Uhlenbeck)" in the indicators list.
Click on the indicator to add it to your chart.
Configuring the Indicator:
Open the indicator settings by clicking on the gear icon next to its name on the chart.
Decay Factor: Adjust the decay factor (λ) to control the responsiveness of the mean calculation. A higher value prioritizes recent data.
Autocorrelation Length: Set the autocorrelation length (θ) for calculating the speed of mean reversion. Longer lengths consider more historical data.
Threshold: Define the number of standard deviations for the upper and lower bands to determine how far price must deviate to trigger a signal.
Chart Setup:
Select the appropriate timeframe (e.g., 1-hour, daily) based on your trading strategy.
Consider using other indicators such as RSI or MACD to confirm buy and sell signals.
🞘 Understanding What to Look For on the Chart
Indicator Behavior:
Observe how the price interacts with the dynamic mean and volatility bands. The price staying within the bands suggests mean-reverting behavior, while crossing the bands signals potential entry points.
The indicator calculates overbought/oversold conditions based on deviation from the mean, highlighted by color-coded cloud areas on the chart.
Crossovers and Deviation:
Look for crossovers between the price and the mean (μ) or the bands. A bullish crossover occurs when the price crosses below the lower band, signaling a potential buying opportunity.
A bearish crossover occurs when the price crosses above the upper band, suggesting a potential sell signal.
Deviations from the mean indicate market extremes. A large deviation indicates that the price is far from the mean, suggesting a potential reversal.
Slope and Direction:
Pay attention to the slope of the mean (μ). A rising slope suggests bullish market conditions, while a declining slope signals a bearish market.
The steepness of the slope can indicate the strength of the mean-reversion trend.
🞘 Possible Entry Signals
Bullish Entry:
Crossover Entry: Enter a long position when the price crosses below the lower band with a positive deviation from the mean.
Confirmation Entry: Use additional indicators like RSI (above 50) or increasing volume to confirm the bullish signal.
Bearish Entry:
Crossover Entry: Enter a short position when the price crosses above the upper band with a negative deviation from the mean.
Confirmation Entry: Look for RSI (below 50) or decreasing volume to confirm the bearish signal.
Deviation Confirmation:
Enter trades when the deviation from the mean is significant, indicating that the price has strayed far from its expected value and is likely to revert.
🞘 Possible Take Profit Strategies
Static Take Profit Levels:
Set predefined take profit levels based on historical volatility, using the upper and lower bands as guides.
Place take profit orders near recent support/resistance levels, ensuring you're capitalizing on the mean-reversion behavior.
Trailing Stop Loss:
Use a trailing stop based on a percentage of the price deviation from the mean to lock in profits as the trend progresses.
Adjust the trailing stop dynamically along the calculated bands to protect profits as the price returns to the mean.
Deviation-Based Exits:
Exit when the deviation from the mean starts to decrease, signaling that the price is returning to its equilibrium.
🞘 Possible Stop-Loss Levels
Initial Stop Loss:
Place an initial stop loss outside the lower band (for long positions) or above the upper band (for short positions) to protect against excessive deviations.
Use a volatility-based buffer to avoid getting stopped out during normal price fluctuations.
Dynamic Stop Loss:
Move the stop loss closer to the mean as the price converges back towards equilibrium, reducing risk.
Adjust the stop loss dynamically along the bands to account for sudden market movements.
🞘 Additional Tips
Combine with Other Indicators:
Enhance your strategy by combining the Mean Reversion Cloud with momentum indicators like MACD, RSI, or Bollinger Bands to confirm market conditions.
Backtesting and Practice:
Backtest the indicator on historical data to understand how it performs in various market environments.
Practice using the indicator on a demo account before implementing it in live trading.
Market Awareness:
Keep an eye on market news and events that might cause extreme price movements. The indicator reacts to price data and might not account for news-driven events that can cause large deviations.
🔸Customize settings 🞘 Decay Factor (λ): Defines the weight assigned to recent price data in the calculation of the mean. A value closer to 1 places more emphasis on recent prices, while lower values create a smoother, more lagging mean.
🞘 Autocorrelation Length (θ): Sets the period for calculating the speed of mean reversion and volatility. Longer lengths capture more historical data, providing smoother calculations, while shorter lengths make the indicator more responsive.
🞘 Threshold (σ): Specifies the number of standard deviations used to create the upper and lower bands. Higher thresholds widen the bands, producing fewer signals, while lower thresholds tighten the bands for more frequent signals.
🞘 Max Gradient Length (γ): Determines the maximum number of consecutive bars for calculating the deviation gradient. This setting impacts the transparency of the plotted bands based on the length of deviation from the mean.
🔶 CONCLUSION
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator offers a sophisticated approach to identifying mean-reversion opportunities by applying the Ornstein-Uhlenbeck stochastic process. This dynamic indicator calculates a responsive mean using an Exponentially Weighted Moving Average (EWMA) and plots volatility-based bands to highlight overbought and oversold conditions. By incorporating advanced statistical measures like autocorrelation and standard deviation, traders can better assess market extremes and potential reversals. The indicator’s ability to adapt to price behavior makes it a versatile tool for traders focused on both short-term price deviations and longer-term mean-reversion strategies. With its unique blend of statistical rigor and visual clarity, the Mean Reversion Cloud provides an invaluable tool for understanding and capitalizing on market inefficiencies.
Relative Rating Index (RRI)The technical rating is one of the most perfect indicators. The reason is that this indicator is based on a majority vote of multiple indicators. It is logical that the judgment based on a majority vote of multiple indicators would not be inferior to the judgment made using only a single indicator. However, just as any indicator has its shortcomings, the technical rating also has weaknesses. The most significant issue is that it primarily provides only a momentary evaluation of the current situation.
Let's consider this in more detail. In the technical rating, an evaluation of 1.0 by the majority vote of indicators is considered a strong buy. However, in the market, there are naturally varying levels of strength. For example, would a market that only once reached an evaluation of 1.0 within a given period be considered the same as a market that consistently maintains an evaluation of 1.0? The latter clearly shows a stronger trend, but the technical rating does not provide an objective criterion for such differentiation. While it is possible to check the histogram to see how long the buy or sell rating has continued, there is no objective standard for judgment.
The indicator I have created this time compensates for this weakness by using the concept of RSI. As is well known, RSI is an indicator that shows the momentum of the market. RSI typically calculates the strength of the price increase during a 14-period by dividing the total upward movement by the total movement range. Similarly, I thought that if we divide the positive evaluations of the technical rating during a given period by the total evaluations, we could calculate the "momentum of the technical rating," which shows how often positive ratings have appeared during that period.
Below is the calculation formula.
1. Setting the Evaluation Period
Decide the period to calculate (e.g., 14 periods). This is denoted as `n`.
2. Total Positive Ratings of the Technical Rating
Calculate the total number of times the technical rating is evaluated as "strong buy" or "buy" during each period. This is called `positive_sum`.
3. Total Ratings
Count the total number of ratings (including buy, sell, and neutral) during the period. This is called `total_sum`.
4. Calculating the Upward Strength
Divide `positive_sum` by `total_sum` to calculate the ratio of positive ratings in the technical rating. This is called the "ratio of positive ratings."
The ratio of positive ratings, denoted as `P`, is calculated as follows:
P = positive_sum / total_sum
5. Calculating RRI
Following the calculation method of RSI, RRI is calculated by the following formula:
RRI = 100 - (100 / (1 + (P / (1 - P))))
As you can see, the calculation method is similar to that of RSI. Therefore, initially, I intended to name this indicator the Technical Rating RSI. However, RSI calculates based on the difference between the previous bar's price and the current bar's price, whereas this indicator calculates by summing the values of the technical ratings themselves. In the case of prices, if the difference between bars is zero, it indicates a flat market, but in the case of technical rating values, if 1.0 continues for two consecutive periods, it signifies an extremely strong buy rather than a flat market. For this reason, I decided that the calculation method could no longer be considered the same as the traditional RSI, and to avoid confusion, I chose to give this new indicator the name "Relative Rating Index" (RRI), as it provides a new type of numerical evaluation.
The information provided by this indicator is simple. When RRI exceeds 50, it means that more than 50% of the technical rating evaluations during the set period (I recommend 50 periods, but please determine the optimal value based on your timeframe) are buy evaluations. However, since there may be many false signals around exactly 50, I define it as buy-dominant when the value exceeds 60 and sell-dominant when it falls below 40. Additionally, if the graph itself is rising, it indicates that the buying momentum is strengthening, and if it is falling, it indicates that the selling momentum is increasing.
Furthermore, there are lines drawn at 90 and 10, but please note that unlike RSI, these do not indicate overbought or oversold conditions. When RRI exceeds 90, it means that over 90% of the technical rating evaluations during the specified period are buy evaluations, indicating an ongoing extremely strong buy trend. Until the RRI graph turns downward and falls below 90, it should rather be considered a buying opportunity.
With this new indicator, the technical rating becomes an indicator with depth, providing evaluations not only for the moment but over a specified period. I hope you find it helpful in your market analysis.
Tare's Multi-Timeframe Market Heatmap
Tare's Multi-Timeframe Market Heatmap is a powerful tool designed to help traders quickly gauge market sentiment across multiple timeframes using a combination of RSI (Relative Strength Index) and MACD (Moving Average Convergence Divergence) indicators. This indicator analyzes four customizable timeframes to determine whether the market is bullish or bearish, providing a visual heatmap to indicate the overall market direction and strength.
Key Features:
Multi-Timeframe Analysis: The indicator allows you to select up to four different timeframes (e.g., 5 minutes, 15 minutes, 30 minutes, 1 hour) to analyze the market's behavior comprehensively.
RSI and MACD Integration: By combining RSI and MACD indicators, the heatmap provides a more robust analysis, taking into account both momentum (RSI) and trend (MACD) indicators. This dual approach helps in identifying stronger and more reliable signals.
Visual Heatmap: The indicator plots a histogram that changes color and intensity based on the combined bullish or bearish strength across the selected timeframes:
Green: Indicates bullish strength, with a darker shade representing stronger bullish signals across multiple timeframes.
Red: Indicates bearish strength, with a darker shade representing stronger bearish signals across multiple timeframes.
Customizable Settings: You can customize the length settings for RSI and MACD, including the RSI period, MACD fast and slow lengths, and signal length, allowing for tailored analysis based on your trading strategy.
Signal Exposure for Other Strategies: The indicator exposes both bullish and bearish signals, which can be used as inputs for other custom strategies within TradingView. This feature allows seamless integration and enhances the versatility of your trading approach.
How to Use:
Adjust the timeframes and indicator settings in the indicator's input menu to match your trading style.
Observe the color and intensity of the histogram to understand the current market sentiment across the selected timeframes.
Utilize the exposed signals (bullish and bearish) in conjunction with other strategies or indicators for a more comprehensive trading system.
Tare's Multi-Timeframe Market Heatmap provides traders with a clear, concise, and customizable overview of market conditions, making it an essential tool for multi-timeframe analysis and decision-making.
Market Breadth - AsymmetrikMarket Breadth - Asymmetrik User Manual
Overview
The Market Breadth - Asymmetrik is a script designed to provide insights into the overall market condition by plotting three key indicators based on stocks within the S&P 500 index. It helps traders assess market momentum and strength through visual cues and is especially useful for understanding the proportion of stocks trading above their respective moving averages.
Features
1. Market Breadth Indicators:
- Breadth 20D (green line): Represents the percentage of stocks in the S&P 500 that are above their 20-day moving average.
- Breadth 50D (yellow line): Represents the percentage of stocks in the S&P 500 that are above their 50-day moving average.
- Breadth 100D (red line): Represents the percentage of stocks in the S&P 500 that are above their 100-day moving average.
2. Horizontal Lines for Context:
- Green line at 10%
- Lighter green line at 20%
- Grey line at 50%
- Light red line at 80%
- Dark red line at 90%
3. Background Color Alerts:
- Green background when all three indicators are under 20%, indicating a potential oversold market condition.
- Red background when all three indicators are over 80%, indicating a potential overbought market condition.
Interpreting the Indicator
- Market Breadth Lines: Observe the plotted lines to assess the percentage of stocks above their moving averages.
- Horizontal Lines: Use the horizontal lines to quickly identify important threshold levels.
- Background Colors: Pay attention to background colors for quick insights:
- Green: All indicators suggest a potentially oversold market condition (below 20).
- Red: All indicators suggest a potentially overbought market condition (above 80).
Troubleshooting
- If the indicator does not appear as expected, please contact me.
- This indicator works only on daily and weekly timeframes.
Conclusion
This Market Breadth Indicator offers a visual representation of market momentum and strength through three key indicators, helping you identify potential buying and selling zones.
Uptrick: Bullish/Bearish Signal DetectorDetailed Explanation of the "Uptrick: Bullish/Bearish Signal Detector" Script
The "Uptrick: Bullish/Bearish Signal Detector" script is a sophisticated tool designed for the TradingView platform, leveraging Pine Script version 5. This script is crafted to enhance traders' ability to identify bullish (buy) and bearish (sell) signals directly on their trading charts. By combining the power of the MACD (Moving Average Convergence Divergence) and RSI (Relative Strength Index) indicators, this script provides a unique and efficient method for detecting potential trading opportunities. Below is an in-depth exploration of its purpose, features, and functionality.
Purpose
The primary purpose of this script is to assist traders in identifying potential entry and exit points in the market by signaling bullish and bearish conditions. This automated detection helps traders make more informed decisions without the need to manually analyze complex indicators. By overlaying signals directly on the price chart, the script allows for quick visual identification of market trends and reversals.
Uniqueness
What sets this script apart is its dual use of MACD and RSI indicators. While many trading strategies might rely on a single indicator, combining MACD and RSI enhances the reliability of the signals by filtering out false positives. The script not only identifies trends but also adds a layer of confirmation through the RSI, which measures the speed and change of price movements.
Inputs and Features
Customizable Label Appearance:
The script allows users to customize the appearance of the labels that indicate bullish and bearish signals. Users can set their preferred colors for the labels and the text, ensuring that the signals are easily distinguishable and aesthetically pleasing on their charts.
MACD Calculation:
The script calculates the MACD line and signal line using user-defined input values for the fast length, slow length, and signal length. The MACD histogram, which is the difference between the MACD line and the signal line, is used to determine the momentum of the market.
RSI Calculation:
The RSI is calculated using a user-defined input length. The RSI helps in identifying overbought or oversold conditions, which are crucial for confirming the strength of the trend detected by the MACD.
Bullish and Bearish Conditions:
The script defines bullish conditions as those where the MACD histogram is positive and the RSI is above 50. Bearish conditions are defined where the MACD histogram is negative and the RSI is below 50. This combination of conditions ensures that signals are generated based on both momentum and relative strength, reducing the likelihood of false signals.
Label Plotting:
The script plots labels on the chart to indicate bullish and bearish signals. When a bullish condition is met, and the previous signal was not bullish, a "LONG" label is plotted. Similarly, when a bearish condition is met, and the previous signal was not bearish, a "SHORT" label is plotted. This feature helps in clearly marking the points of interest for traders, making it easier to spot potential trades.
Tracking Previous Signals:
To avoid repetitive signals, the script keeps track of the last signal. If the last signal was bullish, it avoids plotting another bullish signal immediately. The same logic applies to bearish signals. This tracking ensures that signals are spaced out and only significant changes in market conditions are highlighted.
How It Works
The script operates in a loop, processing each bar (or candlestick) on the chart as new data comes in. It calculates the MACD and RSI values for each bar and checks if the current conditions meet the criteria for a bullish or bearish signal. If a signal is detected and it is different from the last signal, a label is plotted on the chart at the current bar's price level. This real-time processing allows traders to see the signals as they form, providing timely insights into market movements.
Practical Application
For practical use, a trader would add this script to their TradingView chart. They can customize the input parameters for the MACD and RSI calculations to fit their trading strategy or preferred settings. Once added, the script will automatically analyze the price data and start plotting "LONG" and "SHORT" labels based on the detected signals. Traders can then use these labels to make decisions on entering or exiting trades, adjusting their strategy as necessary based on the signals provided.
Conclusion
The "Uptrick: Bullish/Bearish Signal Detector" script is a powerful tool for any trader looking to leverage technical indicators for better trading decisions. By combining MACD and RSI, it offers a robust method for detecting market trends and potential reversals. The customizable features and real-time signal plotting make it a versatile and user-friendly addition to any trading toolkit. This script not only simplifies the process of technical analysis but also enhances the accuracy of trading signals, thereby potentially increasing the trader's success rate in the market.
Dominance DashboardThe "Dominance Dashboard" is a TradingView indicator designed to provide traders with a clear and concise visual representation of Bitcoin Dominance (BTC.D). This script helps traders monitor BTC.D in real-time, offering insights into market dynamics and potential trading signals based on the changes in Bitcoin's market dominance.
Key Features
Real-Time BTC Dominance: Displays the current Bitcoin Dominance percentage.
Historical Comparison: Shows the previous day's Bitcoin Dominance for comparison.
Visual Indicators: Utilizes color-coded arrows to indicate changes in Bitcoin Dominance (increase or decrease).
Customizable Table Settings: Users can customize the position, size, and colors of the displayed table to suit their preferences.
How It Works
Data Retrieval: The script uses request.security to fetch the latest and previous day's BTC.D data.
Comparison Mechanism: It compares the current BTC Dominance with the previous day's value to determine whether BTC.D has increased, decreased, or remained unchanged.
Dynamic Table Display: Based on user preferences, the script dynamically adjusts the table's position, text size, and colors.
Using the Indicator
Table Settings:
Show Header: Toggle the visibility of the table header.
Position: Choose from various predefined positions (e.g., Top Right, Bottom Right) to place the dashboard on your chart.
Text Size: Select the text size (Tiny, Small, Normal, Large) for better readability.
Text and Border Colors: Customize the text and border colors to match your chart's theme.
Timeframe Settings:
Show Current BTC Dominance: Enable or disable the display of the current BTC Dominance percentage.
Underlying Concepts
BTC Dominance: Bitcoin Dominance (BTC.D) measures Bitcoin's market capitalization relative to the total market capitalization of all cryptocurrencies. Monitoring BTC.D helps traders understand the flow of capital in and out of Bitcoin compared to other cryptocurrencies.
Visual Change Indicators: The script uses simple arrow symbols (🔼 for an increase and 🔽 for a decrease) to provide quick visual cues about the direction of change in BTC Dominance. This feature helps traders quickly assess market conditions without deep analysis.
Practical Use Cases
Market Sentiment Analysis: A rising BTC Dominance could indicate a market shift towards Bitcoin, potentially signaling a bearish sentiment for altcoins. Conversely, a falling BTC Dominance might suggest increased interest in altcoins.
Trend Confirmation: Traders can use BTC Dominance trends to confirm their trading strategies. For example, if a trader is bullish on Bitcoin, an increasing BTC Dominance might reinforce their position.
Portfolio Management: By monitoring BTC Dominance, traders can make informed decisions about portfolio rebalancing between Bitcoin and other cryptocurrencies.
Wall Street Cheat Sheet IndicatorThe Wall Street Cheat Sheet Indicator is a unique tool designed to help traders identify the psychological stages of the market cycle based on the well-known Wall Street Cheat Sheet. This indicator integrates moving averages and RSI to dynamically label market stages, providing clear visual cues on the chart.
Key Features:
Dynamic Stage Identification: The indicator automatically detects and labels market stages such as Disbelief, Hope, Optimism, Belief, Thrill, Euphoria, Complacency, Anxiety, Denial, Panic, Capitulation, Anger, and Depression. These stages are derived from the emotional phases of market participants, helping traders anticipate market movements.
Technical Indicators: The script uses two key technical indicators:
200-day Simple Moving Average (SMA): Helps identify long-term market trends.
50-day Simple Moving Average (SMA): Aids in recognizing medium-term trends.
Relative Strength Index (RSI): Assesses the momentum and potential reversal points based on overbought and oversold conditions.
Clear Visual Labels: The current market stage is displayed directly on the chart, making it easy to spot trends and potential turning points.
Usefulness:
This indicator is not just a simple mashup of existing tools. It uniquely combines the concept of market psychology with practical technical analysis tools (moving averages and RSI). By labeling the psychological stages of the market cycle, it provides traders with a deeper understanding of market sentiment and potential future movements.
How It Works:
Disbelief: Detected when the price is below the 200-day SMA and RSI is in the oversold territory, indicating a potential bottom.
Hope: Triggered when the price crosses above the 50-day SMA, with RSI starting to rise but still below 50, suggesting an early uptrend.
Optimism: Occurs when the price is above the 50-day SMA and RSI is between 50 and 70, indicating a strengthening trend.
Belief: When the price is well above the 50-day SMA and RSI is between 70 and 80, showing strong bullish momentum.
Thrill and Euphoria: Identified when RSI exceeds 80, indicating overbought conditions and potential for a peak.
Complacency to Depression: These stages are identified based on price corrections and drops relative to moving averages and declining RSI values.
Best Practices:
High-Time Frame Focus: This indicator works best on high-time frame charts, specifically the 1-week Bitcoin (BTCUSDT) chart. The longer time frame provides a clearer picture of the overall market cycle and reduces noise.
Trend Confirmation: Use in conjunction with other technical analysis tools such as trendlines, Fibonacci retracement levels, and support/resistance zones for more robust trading strategies.
How to Use:
Add the Indicator: Apply the Wall Street Cheat Sheet Indicator to your TradingView chart.
Analyze Market Stages: Observe the dynamic labels indicating the current stage of the market cycle.
Make Informed Decisions: Use the insights from the indicator to time your entries and exits, aligning your trades with the market sentiment.
This indicator is a valuable tool for traders looking to understand market psychology and make informed trading decisions based on the stages of the market cycle.
MTF Williams Vix Market Bottoms [CryptoSea]MTF Williams Vix Fix Indicator is a dynamic tool tailored for traders looking to capture market extremes with high precision. This multi-timeframe indicator leverages the concept of the Williams Vix Fix to spot potential reversals before they occur.
Key Features
Multi-Timeframe Analysis: Provides simultaneous visibility across multiple timeframes, enabling traders to assess market conditions comprehensively from a single chart.
Advanced Volatility Detection: Utilizes a modified Vix Fix formula to highlight extreme price deviations, which often precede significant market reversals.
Customizable Settings: Offers extensive input options to tweak the lookback periods, percentile thresholds, and visibility settings, aligning with various trading strategies.
Visual Band Indicators: Features upper bands and range highs that signal potential overbought and oversold conditions, enhancing trading decision-making.
Below, you can see how the indicator performs across different timeframes, providing valuable insights into market behavior.
How it Works
Vix Fix Calculation: Determines the worst-case 'panic' sell-offs in price as a percentage of the high, capturing the emotional extremes of the market.
Statistical Bands: Employs Bollinger bands over the Vix Fix values to define normal and extreme volatility conditions.
Color-Coded Indicators: Uses color differentiation to instantly highlight when readings surpass critical upper band or range high thresholds, signaling key trading opportunities.
For instance, in the analysis provided below, notice how the indicator flags significant market moves, allowing traders to anticipate potential entry or exit points.
Application
Risk Management: Aids in identifying extreme market conditions where prices may revert, helping in effective position sizing and risk management.
Strategic Planning: Enhances strategic trading plans by identifying not only when but also where market extremes may occur, considering multiple timeframes.
Customization: Adapts seamlessly to different market environments with adjustable settings for volatility thresholds and visual display preferences.
The MTF Williams Vix Fix Indicator by is an essential tool for traders aiming to leverage market volatility for optimal entry and exit, ensuring they are well-equipped to handle market extremes with confidence.
Market Cipher B by WeloTradesMarket Cipher B by WeloTrades: Detailed Script Description
//Overview//
"Market Cipher B by WeloTrades" is an advanced trading tool that combines multiple technical indicators to provide a comprehensive market analysis framework. By integrating WaveTrend, RSI, and MoneyFlow indicators, this script helps traders to better identify market trends, potential reversals, and trading opportunities. The script is designed to offer a holistic view of the market by combining the strengths of these individual indicators.
//Key Features and Originality//
WaveTrend Analysis:
WaveTrend Channel (WT1 and WT2): The core of this script is the WaveTrend indicator, which uses the smoothed average of typical price to identify overbought and oversold conditions. WT1 and WT2 are calculated to track market momentum and cyclical price movements.
Major Divergences (🐮/🐻): The script detects and highlights major bullish and bearish divergences automatically, providing traders with visual cues for potential reversals. This helps in making informed decisions based on divergence patterns.
Relative Strength Index (RSI):
RSI Levels: RSI is used to measure the speed and change of price movements, with specific levels indicating overbought and oversold conditions.
Customizable Levels: Users can configure the overbought and oversold thresholds, allowing for a tailored analysis based on individual trading strategies.
MoneyFlow Indicator:
Fast and Slow MoneyFlow: This indicator tracks the flow of capital into and out of the market, offering insights into the underlying market strength. It includes configurable periods and multipliers for both fast and slow MoneyFlow.
Vertical Positioning: The script allows users to adjust the vertical position of MoneyFlow plots to maintain a clear and uncluttered chart.
Stochastic RSI:
Stochastic RSI Levels: This combines the RSI and Stochastic indicators to provide a momentum oscillator that is sensitive to price changes. It is used to identify overbought and oversold conditions within a specified period.
Customizable Levels: Traders can set specific levels for more precise analysis.
//How It Works//
The script integrates these indicators through advanced algorithms, creating a synergistic effect that enhances market analysis. Here’s a detailed explanation of the underlying concepts and calculations:
WaveTrend Indicator:
Calculation: WaveTrend is based on the typical price (average of high, low, and close) smoothed over a specified channel length. WT1 and WT2 are derived from this typical price and further smoothed using the Average Channel Length. The difference between WT1 and WT2 indicates momentum, helping to identify cyclical market trends.
RSI (Relative Strength Index):
Calculation: RSI calculates the average gains and losses over a specified period to measure the speed and change of price movements. It oscillates between 0 and 100, with levels set to identify overbought (>70) and oversold (<30) conditions.
MoneyFlow Indicator:
Calculation: MoneyFlow is derived by multiplying price changes by volume and smoothing the results over specified periods. Fast MoneyFlow reacts quickly to price changes, while Slow MoneyFlow offers a broader view of capital movement trends.
Stochastic RSI:
Calculation: Stochastic RSI is computed by applying the Stochastic formula to RSI values, which highlights the RSI’s relative position within its range over a given period. This helps in identifying momentum shifts more precisely.
//How to Use the Script//
Display Settings:
Users can enable or disable various components like WaveTrend OB & OS levels, MoneyFlow plots, and divergence alerts through checkboxes.
Example: Turn on "Show Major Divergence" to see major bullish and bearish divergence signals directly on the chart.
Adjust Channel Settings:
Customize the data source, channel length, and smoothing periods in the "WaveTrend Channel SETTINGS" group.
Example: Set the "Channel Length" to 10 for a more responsive WaveTrend line or adjust the "Average Channel Length" to 21 for smoother trends.
Set Overbought & Oversold Levels:
Configure levels for WaveTrend, RSI, and Stochastic RSI in their respective settings groups.
Example: Set the WaveTrend Overbought Level to 60 and Oversold Level to -60 to define critical thresholds.
Money Flow Settings:
Adjust the periods and multipliers for Fast and Slow MoneyFlow indicators, and set their vertical positions for better visualization.
Example: Set the Fast Money Flow Period to 9 and Slow Money Flow Period to 12 to capture both short-term and long-term capital movements.
//Justification for Combining Indicators//
Enhanced Market Analysis:
Combining WaveTrend, RSI, and MoneyFlow provides a more comprehensive view of market conditions. Each indicator brings a unique perspective, making the analysis more robust.
WaveTrend identifies cyclical trends, RSI measures momentum, and MoneyFlow tracks capital movement. Together, they provide a multi-dimensional analysis of the market.
Improved Decision-Making:
By integrating these indicators, the script helps traders make more informed decisions. For example, a bullish divergence detected by WaveTrend might be validated by an RSI moving out of oversold territory and supported by increasing MoneyFlow.
Customization and Flexibility:
The script offers extensive customization options, allowing traders to tailor it to their specific needs and strategies. This flexibility makes it suitable for different trading styles and timeframes.
//Conclusion//
The indicator stands out due to its innovative combination of WaveTrend, RSI, and MoneyFlow indicators, offering a well-rounded tool for market analysis. By understanding how each component works and how they complement each other, traders can leverage this script to enhance their market analysis and trading strategies, making more informed and confident decisions.
Remember to always backtest the indicator first before implying it to your strategy.
Kalman Filtered RSI Oscillator [BackQuant]Kalman Filtered RSI Oscillator
The Kalman Filtered RSI Oscillator is BackQuants new free indicator designed for traders seeking an advanced, empirical approach to trend detection and momentum analysis. By integrating the robustness of a Kalman filter with the adaptability of the Relative Strength Index (RSI), this tool offers a sophisticated method to capture market dynamics. This indicator is crafted to provide a clearer, more responsive insight into price trends and momentum shifts, enabling traders to make informed decisions in fast-moving markets.
Core Principles
Kalman Filter Dynamics:
At its core, the Kalman Filtered RSI Oscillator leverages the Kalman filter, renowned for its efficiency in predicting the state of linear dynamic systems amidst uncertainties. By applying it to the RSI calculation, the tool adeptly filters out market noise, offering a smoothed price source that forms the basis for more accurate momentum analysis. The inclusion of customizable parameters like process noise, measurement noise, and filter order allows traders to fine-tune the filter’s sensitivity to market changes, making it a versatile tool for various trading environments.
RSI Adaptation:
The RSI is a widely used momentum oscillator that measures the speed and change of price movements. By integrating the RSI with the Kalman filter, the oscillator not only identifies the prevailing trend but also provides a smoothed representation of momentum. This synergy enhances the indicator's ability to signal potential reversals and trend continuations with a higher degree of reliability.
Advanced Smoothing Techniques:
The indicator further offers an optional smoothing feature for the RSI, employing a selection of moving averages (HMA, THMA, EHMA, SMA, EMA, WMA, TEMA, VWMA) for traders seeking to reduce volatility and refine signal clarity. This advanced smoothing mechanism is pivotal for traders looking to mitigate the effects of short-term price fluctuations on the RSI's accuracy.
Empirical Significance:
Empirically, the Kalman Filtered RSI Oscillator stands out for its dynamic adjustment to market conditions. Unlike static indicators, the Kalman filter continuously updates its estimates based on incoming price data, making it inherently more responsive to new market information. This dynamic adaptation, combined with the RSI's momentum analysis, offers a powerful approach to understanding market trends and momentum with a depth not available in traditional indicators.
Trend Identification and Momentum Analysis:
Traders can use the Kalman Filtered RSI Oscillator to identify strong trends and momentum shifts. The color-coded RSI columns provide immediate visual cues on the market's direction and strength, aiding in quick decision-making.
Optimal for Various Market Conditions:
The flexibility in tuning the Kalman filter parameters makes this indicator suitable for a wide range of assets and market conditions, from volatile to stable markets. Traders can adjust the settings based on empirical testing to find the optimal configuration for their trading strategy.
Complementary to Other Analytical Tools:
While powerful on its own, the Kalman Filtered RSI Oscillator is best used in conjunction with other analytical tools and indicators. Combining it with volume analysis, price action patterns, or other trend-following indicators can provide a comprehensive view of the market, allowing for more nuanced and informed trading decisions.
The Kalman Filtered RSI Oscillator is a groundbreaking tool that marries empirical precision with advanced trend analysis techniques. Its innovative use of the Kalman filter to enhance the RSI's performance offers traders an unparalleled ability to navigate the complexities of modern financial markets. Whether you're a novice looking to refine your trading approach or a seasoned professional seeking advanced analytical tools, the Kalman Filtered RSI Oscillator represents a significant step forward in technical analysis capabilities.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Machine Learning: Multiple Logistic Regression
Multiple Logistic Regression Indicator
The Logistic Regression Indicator for TradingView is a versatile tool that employs multiple logistic regression based on various technical indicators to generate potential buy and sell signals. By utilizing key indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend, the indicator aims to provide a systematic approach to decision-making in financial markets.
How It Works:
Technical Indicators:
The script uses multiple technical indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend as input variables for the logistic regression model.
These indicators are normalized to create categorical variables, providing a consistent scale for the model.
Logistic Regression:
The logistic regression function is applied to the normalized input variables (x1 to x6) with user-defined coefficients (b0 to b6).
The logistic regression model predicts the probability of a binary outcome, with values closer to 1 indicating a bullish signal and values closer to 0 indicating a bearish signal.
Loss Function (Cross-Entropy Loss):
The cross-entropy loss function is calculated to quantify the difference between the predicted probability and the actual outcome.
The goal is to minimize this loss, which essentially measures the model's accuracy.
// Error Function (cross-entropy loss)
loss(y, p) =>
-y * math.log(p) - (1 - y) * math.log(1 - p)
// y - depended variable
// p - multiple logistic regression
Gradient Descent:
Gradient descent is an optimization algorithm used to minimize the loss function by adjusting the weights of the logistic regression model.
The script iteratively updates the weights (b1 to b6) based on the negative gradient of the loss function with respect to each weight.
// Adjusting model weights using gradient descent
b1 -= lr * (p + loss) * x1
b2 -= lr * (p + loss) * x2
b3 -= lr * (p + loss) * x3
b4 -= lr * (p + loss) * x4
b5 -= lr * (p + loss) * x5
b6 -= lr * (p + loss) * x6
// lr - learning rate or step of learning
// p - multiple logistic regression
// x_n - variables
Learning Rate:
The learning rate (lr) determines the step size in the weight adjustment process. It prevents the algorithm from overshooting the minimum of the loss function.
Users can set the learning rate to control the speed and stability of the optimization process.
Visualization:
The script visualizes the output of the logistic regression model by coloring the SMA.
Arrows are plotted at crossover and crossunder points, indicating potential buy and sell signals.
Lables are showing logistic regression values from 1 to 0 above and below bars
Table Display:
A table is displayed on the chart, providing real-time information about the input variables, their values, and the learned coefficients.
This allows traders to monitor the model's interpretation of the technical indicators and observe how the coefficients change over time.
How to Use:
Parameter Adjustment:
Users can adjust the length of technical indicators (rsi_length, cci_length, etc.) and the Z score length based on their preference and market characteristics.
Set the initial values for the regression coefficients (b0 to b6) and the learning rate (lr) according to your trading strategy.
Signal Interpretation:
Buy signals are indicated by an upward arrow (▲), and sell signals are indicated by a downward arrow (▼).
The color-coded SMA provides a visual representation of the logistic regression output by color.
Table Information:
Monitor the table for real-time information on the input variables, their values, and the learned coefficients.
Keep an eye on the learning rate to ensure a balance between model adjustment speed and stability.
Backtesting and Validation:
Before using the script in live trading, conduct thorough backtesting to evaluate its performance under different market conditions.
Validate the model against historical data to ensure its reliability.
Alxuse Supertrend 4EMA Buy and Sell for tutorialAll abilities of Supertrend, moreover :
Drawing 4 EMA band & the ability to change values, change colors, turn on/off show.
Sends Signal Sell and Buy in multi timeframe.
The ability used in the alert section and create customized alerts.
To receive valid alerts the replay section , the timeframe of the chart must be the same as the timeframe of the indicator.
Supertrend with a simple EMA Filter can improve the performance of the signals during a strong trend.
For detecting the continuation of the downward and upward trend we can use 4 EMA colors.
In the upward trend , the EMA lines are in order of green, blue, red, yellow from bottom to top.
In the downward trend, the EMA lines are in order of yellow, red, blue, green from bottom to top.
How it works:
x1 = MA1 < MA2 and MA2 < MA3 and MA3 < MA4 and ta.crossunder(MA3, MA4)
x2 = MA1 < MA2 and MA2 < MA3 and MA3 < MA4 and ta.crossunder(MA2, MA3)
x3 = MA1 < MA2 and MA2 < MA3 and MA3 < MA4 and ta.crossunder(MA1, MA2)
y1 = MA4 < MA3 and MA3 < MA2 and MA2 < MA1 and ta.crossover(MA3, MA4)
y2 = MA4 < MA3 and MA3 < MA2 and MA2 < MA1 and ta.crossover(MA2, MA3)
y3 = MA4 < MA3 and MA3 < MA2 and MA2 < MA1 and ta.crossover(MA1, MA2)
Red triangle = x1 or x2 or x3
Green triangle = y1 or y2 or y3
Long = BUY signal and followed by a Green triangle
Exit Long = SELL signal
Short = SELL signal and followed by a Red triangle
Exit Short = BUY signal
It is also possible to get help from the Stochastic RSI and MACD indicators for confirmation.
For receiving a signal with these two conditions or more conditions, i am making a video tutorial that I will release soon.
Supertrend
Definition
Supertrend is a trend-following indicator based on Average True Range (ATR). The calculation of its single line combines trend detection and volatility. It can be used to detect changes in trend direction and to position stops.
The basics
The Supertrend is a trend-following indicator. It is overlaid on the main chart and their plots indicate the current trend. A Supertrend can be used with varying periods (daily, weekly, intraday etc.) and on varying instruments.
The Supertrend has several inputs that you can adjust to match your trading strategy. Adjusting these settings allows you to make the indicator more or less sensitive to price changes.
For the Supertrend inputs, you can adjust atrLength and multiplier:
the atrLength setting is the lookback length for the ATR calculation;
multiplier is what the ATR is multiplied by to offset the bands from price.
When the price falls below the indicator curve, it turns red and indicates a downtrend. Conversely, when the price rises above the curve, the indicator turns green and indicates an uptrend. After each close above or below Supertrend, a new trend appears.
Summary
The Supertrend helps you make the right trading decisions. However, there are times when it generates false signals. Therefore, it is best to use the right combination of several indicators. Like any other indicator, Supertrend works best when used with other indicators such as MACD, Parabolic SAR, or RSI.
Exponential Moving Average
Definition
The Exponential Moving Average (EMA) is a specific type of moving average that points towards the importance of the most recent data and information from the market. The Exponential Moving Average is just like it’s name says - it’s exponential, weighting the most recent prices more than the less recent prices. The EMA can be compared and contrasted with the simple moving average.
Similar to other moving averages, the EMA is a technical indicator that produces buy and sell signals based on data that shows evidence of divergence and crossovers from general and historical averages. Additionally, the EMA tries to amplify the importance that the most recent data points play in a calculation.
It is common to use more than one EMA length at once, to provide more in-depth and focused data. For example, by choosing 10-day and 200-day moving averages, a trader is able to determine more from the results in a long-term trade, than a trader who is only analyzing one EMA length.
It’s best to use the EMA when for trending markets, as it shows uptrends and downtrends when a market is strong and weak, respectively. An experienced trader will know to look both at the line the EMA projects, as well as the rate of change that comes from each bar as it moves to the next data point. Analyzing these points and data streams correctly will help the trader determine when they should buy, sell, or switch investments from bearish to bullish or vice versa.
Short-term averages, on the other hand, is a different story when analyzing Exponential Moving Average data. It is most common for traders to quote and utilize 12- and 26-day EMAs in the short-term. This is because they are used to create specific indicators. Look into Moving Average Convergence Divergence (MACD) for more information. Similarly, the 50- and 200-day moving averages are most common for analyzing long-term trends.
Moving averages can be very useful for traders using technical analysis for profit. It is important to identify and realize, however, their shortcomings, as all moving averages tend to suffer from recurring lag. It is difficult to modify the moving average to work in your favor at times, often having the preferred time to enter or exit the market pass before the moving average even shows changes in the trend or price movement for that matter.
All of this is true, however, the EMA strives to make this easier for traders. The EMA is unique because it places more emphasis on the most recent data. Therefore, price movement and trend reversals or changes are closely monitored, allowing for the EMA to react quicker than other moving averages.
Limitations
Although using the Exponential Moving Average has a lot of advantages when analyzing market trends, it is also uncertain whether or not the use of most recent data points truly affects technical and market analysis. In addition, the EMA relies on historical data as its basis for operating and because news, events, and other information can change rapidly the indicator can misinterpret this information by weighting the current prices higher than when the event actually occurred.
Summary
The Exponential Moving Average (EMA) is a moving average and technical indicator that reflects and projects the most recent data and information from the market to a trader and relies on a base of historical data. It is one of many different types of moving averages and has an easily calculable formula.
The added features to the indicator are made for training, it is advisable to use it with caution in tradings.
DCA Liquidation Calculation [ChartPrime]The DCA Liquidation Calculator is a powerful table indicator designed for both manual and bot-assisted traders who practice Dollar Cost Averaging (DCA). Its primary objective is to help traders avoid getting liquidated and make informed decisions when managing their positions. This comprehensive table indicator provides essential information to DCA traders, enabling them to plan their trades effectively and mitigate potential risks of liquidation.
Key Features:
Liquidation Price Awareness: The DCA Liquidation Calculator calculates and displays the liquidation price for each trade within your position. This critical information empowers traders to set appropriate stop-loss levels and avoid being liquidated in adverse market conditions, especially in leveraged trading scenarios.
DCA Recommendations: Whether you are executing DCA manually or using a trading bot, the DCA Liquidation Calculator offers valuable guidance. It suggests optimal entry prices and provides insights into the percentage deviation from the current market price, helping traders make well-timed and well-informed DCA decisions.
Position Sizing: Proper position sizing is essential for risk management. The DCA Liquidation Calculator helps traders determine the percentage of capital to allocate to each trade based on the provided insights. By using the recommended position sizing, traders can protect their capital and potentially maximize profits.
Profit and Loss Visualization: Gain real-time visibility into your Profit and Loss (PnL) with the DCA Liquidation Calculator. This feature allows you to monitor your trades' performance, enabling you to adapt your strategies as needed and make data-driven decisions.
Margin Call Indicators: Anticipating potential margin calls is crucial for maintaining a healthy trading account. The DCA Liquidation Calculator's smart analysis helps you identify and manage potential margin call situations, reducing the risk of account liquidation.
Capital Requirements: Before entering a trade, it's vital to know the required capital. The DCA Liquidation Calculator provides you with this information, ensuring you are adequately prepared to execute your trades without overextending your resources.
Maximum Trade Limit: Considering your available capital, the DCA Liquidation Calculator helps you determine the maximum number of trades you can enter. This feature ensures you maintain a disciplined and sustainable trading approach aligned with your financial capabilities.
Color-Coded Risk Indicators:
Green Liquidation Price Cell: Indicates that the position is considered safe from liquidation at the given parameters.
Yellow Liquidation Price Cell: Warns traders of potential liquidation risk. Exercise caution and monitor the trade closely to avoid undesirable outcomes.
Purple Liquidation Price Cell: Shows the liquidation price, but it does not necessarily indicate an imminent liquidation. Use this information to make prudent risk management decisions.
Red Row: Signals that the trade cannot be executed due to insufficient capital. Consider alternative strategies or ensure adequate capitalization before proceeding.
Settings explained:
In conclusion, the DCA Liquidation Calculator equips traders with essential tools to make well-calculated decisions, minimize liquidation risks, and optimize their Dollar Cost Averaging strategy. By offering comprehensive insights into your trading position, this indicator empowers you to navigate the markets with confidence and increase your potential for successful and sustainable trading.
Divergence Strength OscillatorDetects divergence before it has formed a valid divergent pivot, across multiple indicators. After publishing my Strength of Divergence Across Multiple Indicators script, it seemed there were a lot of people who wanted to see the divergence signals before the divergent pivots were actually confirmed. Everyone complains about indicators repainting, yet in the next breath they complain about not wanting to wait for a signal to be confirmed before it appears on their chart! No matter how many times you ask, you can't have your cake and eat it too.
While this isn't exactly cake, it's as close as you're gonna get. This oscillator will calculate the strength of divergence as it forms on any bar that could potentially be a pivot point (e.g. for a pivot low, the preceding bars must be higher than it) and track the net (bullish - bearish) value.
For example:
PLEASE NOTE that this is not intended to be a "Buy" or "Sell" signal, and it would be foolish to use it as such. The purpose of this script is to show you potential divergences as early as possible, so that you have more time to plan and evaluate confluent signals, etc.
The Divergence Strength Calculation:
The total divergence strength value is the sum of the divergence strengths of all indicators for which divergence was detected at a given bar. Each indicator's individual divergence strength is comprised of two basic components: (1) |ΔPrice| - the magnitude of the change in price over the divergence period (pivot-to-pivot), and (2) |ΔIndicator| - the magnitude of the change in indicator value over the divergence period.
Because different indicators' scales and volatility can vary greatly, the Δ values are expressed in terms of standard deviation to ensure that the values are meaningful and equitable across all indicators and assets/instruments/currency pairs, etc:
|ΔIndicator| = |indicator_value_1 - indicator_value_2| / 2 * StDev(indicator_series,100)
Based on work for my Strength of Divergence Across Multiple Indicators script:
Coral Trend Pullback Strategy (TradeIQ)Description:
Strategy is taken from the TradeIQ YouTube video called "I Finally Found 80% Win Rate Trading Strategy For Crypto".
Check out the full video for further details/clarification on strategy entry/exit conditions.
The default settings are exactly as TradeIQ described in his video.
However I found some better results by some tweaking settings, increasing R:R ratio and by turning off confirmation indicators.
This would suggest that perhaps the current confirmation indicators are not the best options. I'm happy to try add some other optional confirmation indicators if they look to be more effective.
Recommended timeframe: 1H
Strategy incorporates the following features:
Risk management:
Configurable X% loss per stop loss
Configurable R:R ratio
Trade entry:
Based on strategy conditions below
Trade exit:
Based on strategy conditions below
Backtesting:
Configurable backtesting range by date
Trade drawings:
Each entry condition indicator can be turned on and off
TP/SL boxes drawn for all trades. Can be turned on and off
Trade exit information labels. Can be turned on and off
NOTE: Trade drawings will only be applicable when using overlay strategies
Alerting:
Alerts on LONG and SHORT trade entries
Debugging:
Includes section with useful debugging techniques
Strategy conditions
Trade entry:
LONG
C1: Coral Trend is bullish
C2: At least 1 candle where low is above Coral Trend since last cross above Coral Trend
C3: Pullback happens and price closes below Coral Trend
C4: Coral Trend colour remains bullish for duration of pullback
C5: After valid pullback, price then closes above Coral Trend
C6: Optional confirmation indicators (choose either C6.1 or C6.2 or NONE):
C6.1: ADX and DI (Single indicator)
C6.1.1: Green line is above red line
C6.1.2: Blue line > 20
C6.1.3: Blue trending up over last 1 candle
C6.2: Absolute Strengeh Histogram + HawkEye Volume Indicator (Two indicators combined)
C6.2.1: Absolute Strengeh Histogram colour is blue
C6.2.2: HawkEye Volume Indicator colour is green
SHORT
C1: Coral Trend is bearish
C2: At least 1 candle where high is below Coral Trend since last cross below Coral Trend
C3: Pullback happens and price closes above Coral Trend
C4: Coral Trend colour remains bearish for duration of pullback
C5: After valid pullback, price then closes below Coral Trend
C6: Optional confirmation indicators (choose either C6.1 or C6.2 or NONE):
C6.1: ADX and DI (Single indicator)
C6.1.1: Red line is above green line
C6.1.2: Blue line > 20
C6.1.3: Blue trending up over last 1 candle
C6.2: Absolute Strengeh Histogram + HawkEye Volume Indicator (Two indicators combined)
C6.2.1: Absolute Strengeh Histogram colour is red
C6.2.2: HawkEye Volume Indicator colour is red
NOTE: All the optional confirmation indicators cannot be overlayed with Coral Trend so feel free to add each separately to the chart for visual purposes
Trade exit:
Stop Loss: Calculated by recent swing low over previous X candles (configurable with "Local High/Low Lookback")
Take Profit: Calculated from R:R multiplier * Stop Loss size
Credits
Strategy origin: TradeIQ's YouTube video called "I Finally Found 80% Win Rate Trading Strategy For Crypto"
It combines the following indicators for trade entry conditions:
Coral Trend Indicator by @LazyBear (Main indicator)
Absolute Strength Histogram | jh by @jiehonglim (Optional confirmation indicator)
Indicator: HawkEye Volume Indicator by @LazyBear (Optional confirmation indicator)
ADX and DI by @BeikabuOyaji (Optional confirmation indicator)
Money Flow LineWhat is this? The Money Flow Line (MFL) indicator is at its core a more even-tempered version of the Price-Volume-Trend (PVT). The primary difference is the usage of `hlc3` ((high + low + close) / 3) rather than `close` to use the "typical price" that it critical to the calculation of the Money Flow Index (MFI). Other similar indicators include the Accumulation Distribution Line (ADL) and the On Balance Volume (OBV) indicators. The purpose of all of these indicators is to attempt to measure the strength of the money flow by combining price and volume into a rolling measurement that can be compared over time to look for confirmations and divergences.
The indicator also includes an optional averaging (smoothing) line that can be enabled in the display settings. Enabling this smoothing line with a desired period allows for simpler trend comparisons and also allows the user to view how far the line has diverged from the mean. This creates an indicator very similar to Elder's Force Index (EFI), which is also a `close * volume` style indicator.
Why is this important? After an extreme movement or volume spike the MFI will "snap back" sharply as that bar eventually exits the set period. This produces a result that is meaningless and skews the indicator away from the market structure. Because of this behavior, range clamping, and the loss of comparative history I prefer to shy away from oscillator style indicators. The Money Flow Line instead gives you all of the history so you may compare and see the broader trend without sharp snaps in history based on an arbitrary period setting.
Why is this better? This produces a no-lag indicator that isn't subject to the harsh skewing produced by they Money Flow Index's period calculation. It doesn't lose history like MFI or EFI, is clear about the trend direction, and prefers a "typical price" (averaging the entire range of each bar) rather than whatever happens to be the closing price for a given bar.
How can I use it? The indicator is attempting to measure supply and demand in the markets. No indicator is perfect, but we can use all of the information we have available to make our best predictions. There are only 3 pieces of data the market gives us:
1. Price (action)
2. Volume
3. Time
The Money Flow Line combines all of these data points into a readable rolling data set that attempts to show subtle balance of power shifts based on changes in volume and "smart money" (or "big money") stepping in and out of the picture. Much like PVT, we look for the same things:
- Trend Identification: an up or down trend appears in the MFL
- Confirmations: the MFL agrees with price action in direction and magnitude
- Divergence: the MFL disagrees with price action, indicating a reversal may be coming soon
When applying the smoothing line we can also look for similar things we would with EFI. The primary case would be to look for the MFL to jump very far away from the mean (a high magnitude movement) which indicates that price may be reverting towards the mean soon (a "mean reversion"). On the other hand, it may indicate strength in the current price direction. All of these predictions depend heavily on price action and market structure. Good luck!
Adaptive Jurik Filter MACD [Loxx]Adaptive Jurik Filter MACD uses Jurik Volty and Adaptive Double Jurik Filter Moving Average (AJFMA) to derive Jurik Filter smoothed volatility.
What is MACD?
Moving average convergence divergence (MACD) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average (EMA) from the 12-period EMA.
The result of that calculation is the MACD line. A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals. Traders may buy the security when the MACD crosses above its signal line and sell—or short—the security when the MACD crosses below the signal line. Moving average convergence divergence (MACD) indicators can be interpreted in several ways, but the more common methods are crossovers, divergences, and rapid rises/falls.
What is Jurik Volty?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
That's why investors, banks and institutions worldwide ask for the Jurik Research Moving Average ( JMA ). You may apply it just as you would any other popular moving average. However, JMA's improved timing and smoothness will astound you.
What is adaptive Jurik volatility?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
Included
- Change colors of oscillators and bars
CHOPORSI
CHOPORSI is a multiindicator.
This indicator help You to recognize potential in or out singal.
Base singals are from Choppines, RSI, AND DMI indicators.
It is a combination of 3 separate indicators like choppines RSI and DMI.
Then our new indicator see like bellow on next image.
Yellow line is sum of CHOP index and RSI , in this case we can say its a CHOPORSI Index.
Green line is DMI- line , this show us strength of sell position on the market.
We schould use other signals, like LSMA 50/100 to improve trend changing. Like on next picture.
Now how this indicator works?
Yellow line is the sum oF Chop and RSI value - 50.
Max and minimum value of CHOP and RSI are the same from 0 to 100.
We have sum of them.
Our minimum signal is 0+0-50=-50
maximum signal is 100+100-50= 150
Most times if both of tem are on top level ( then we have 150) the trend is chanhing from bullish to bearish.
The same way if the RSI ist on 0 and chop is over 50 ( then we have index 0 ) wee changing the tren from bearish to bullish.
Off course it not every time. We see other signals, to take our risk self not sugested by some art of indicators.
But if we are abowe topline, witch is set to 85 we can sey, we have have oversold signal.
Underline 30 isour potentialy buy signal.
Midrange 50 is mostly trand changin line.
This valu of top, mid bottom line you can change on the setting.
Every Coin have another level of this lines, and need to be checked individual to the coin.
Standard, settings are set fo timeframe : 12 min. 24 min, 1H and 4 H >
Blue crosses signalize possibilities trend changing.
This picture shou us how this indicator works.
Buy long signal : If yellow line is mostly at the bottom and green mostly on the top.
Sell long signal l. Yellow -top , green -bottom.
The Green line is from Directional Movement Index and is - DI line. Its show us selling trend. even higher position then mor sell of .
Standard value of CHOPPINES is 14 , works fin on 1H and abowe also wit the value of 28
Standard value for RSI AND -DI unchanging 14.
I tjink this is a simplu helpfull indycator.
WARNING!!! IF YOU AT THIS POINT CANT UNDERSUD THIS INDICATOR, PLEASE DONT USE THEM .
Signal, schould be confirmed with other indicators like MA, EMA even better with LSMA .
Please try it an make only paper trading, to undertand how its realy works.
Thank You!
Synthetic Price Action GeneratorNOTICE:
First thing you need to know, it "DOES NOT" reflect the price of the ticker you will load it on. THIS IS NOT AN INDICATOR FOR TRADING! It's a developer tool solely generating random values that look exactly like the fractals we observe every single day. This script's generated candles are as fake as the never ending garbage news cycles we are often force fed and expected to believe by using carefully scripted narratives peddled as hypnotic truth to psychologically and emotionally influence you to the point of control by coercion and subjugation. I wanted to make the script's synthetic nature very clear using that analogy, it's dynamically artificial. Do not accidentally become disillusioned by this scripts values, make trading decisions from it, and lastly don't become victim to predatory media magic ministry parrots with pretty, handsome smiles, compelling you to board their ferris wheel of fear. Now, on to the good stuff...
BACKSTORY:
Occasionally I find myself in situations where I have to build analyzers in Pine to actually build novel quantitative analytic indicators and tools worthy of future use. These analyzers certainly don't exist on this platform, but usually are required to engineer and tweak algorithms of the highest quality with the finest computational caliber. I have numerous other synthesizers to publish besides this one.
For many reasons, I needed a synthetic environment to utilize the analyzers I built in Pine, to even pursue building some exotic indicators and algorithms. Pine doesn't allow sourcing of tuples. Not to mention, I required numerous Pine advancements to make long held dreams into tangible realities. Many Pine upgrades have arrived and MANY, MANY more are in need of implementation for all. Now that I have this, intending to use it in the future often when in need, you can now use it too. I do anticipate some skilled Pine poets will employ this intended handy utility to design and/or improved indicators for trading.
ORIGIN:
This was inspired by the brilliance from the world renowned ALGOmist John F. Ehlers, but it's taken on a completely alien form from its original DNA. Browsing on the internet for something else, I came across an article with a small code snippet, and I remembered an old wish of mine. I have long known that by flipping back and forth on specific tickers and timeframes in my Watchlist is not the most efficient way to evaluate indicators in multiple theatres of price action. I realized, I always wanted to possess and use this sort of tool, so... I put it into Pine form, but now have decided to inject it with Pine Script steroids. The outcome is highly mutable candle formations in a reusable mutagenic package, observable above and masquerading as genuine looking price candles.
OVERVIEW:
I guess you could call it a price action synthesizer, but I entitled it "Synthetic Price Action Generator" for those who may be searching for such a thing. You may find this more useful on the All or 5Y charts initially to witness indication from beginning (barstate.isfirst === barindex==0) to end (last_bar_index), but you may also use keyboard shortcuts + + to view the earliest plottable bars on any timeframe. I often use that keyboard shortcut to qualify an indicator through the entirety of it's runtime.
A lot can go wrong unexpectedly with indicator initialization, and you will never know it if you don't inspect it. Many recursively endowed Infinite Impulse Response (IIR) Filters can initialize with unintended results that minutely ring in slightly erroneous fashion for the entire runtime, beginning to end, causing deviations from "what should of been..." values with false signals. Looking closely at spg(), you will recognize that 3 EMAs are employed to manage and maintain randomness of CLOSE, HIGH, and LOW. In fact, any indicator's barindex==0 initialization can be inspected with the keyboard shortcuts above. If you see anything obviously strange in an authors indicator, please contact the developer if possible and respectfully notify them.
PURPOSE:
The primary intended application of this script, is to offer developers from advanced to even novice skill levels assistance with building next generation indicators. Mostly, it's purpose is for testing and troubleshooting indicators AND evaluating how they perform in a "manageable" randomized environment. Some times indicators flake out on rare but problematic price fluctuations, and this may help you with finding your issues/errata sooner than later. While the candles upon initial loading look pristine, by tweaking it to the minval/maxval parameters limits OR beyond with a few code modifications, you can generate unusual volatility, for instance... huge wicks. Limits of minval= and maxval= of are by default set to a comfort zone of operation. Massive wicks or candle bodies will undoubtedly affect your indication and often render them useless on tickers that exhibit that behavior, like WGMCF intraday currently.
Copy/paste boundaries are provided for relevant insertion into another script. Paste placement should happen at the very top of a script. Note that by overwriting the close, open, high, etc... values, your compiler will give you generous warnings of "variable shadowing" in abundance, but this is an expected part of applying it to your novel script, no worries. plotcandle() can be copied over too and enabled/disabled in Settings->Style. Always remember to fully remove this scripts' code and those assignments properly before actual trading use of your script occurs, AND specifically when publishing. The entirety of this provided code should never, never exist in a published indicator.
OTHER INTENTIONS:
Even though these are 100% synthetic generated price points, you will notice ALL of the fractal pseudo-patterns that commonly exist in the markets, are naturally occurring with this generator too. You can also swiftly immerse yourself in pattern recognition exercises with increased efficiency in real time by clicking any SPAG Setting in focus and then using the up/down arrow keys. I hope I explained potential uses adequately...
On a personal note, the existence of fractal symmetry often makes me wonder, do we truly live in a totality chaotic universe or is it ordered mathematically for some outcomes to a certain extent. I think both. My observations, it's a pre-deterministic reality completely influenced by infinitesimal amounts of sentient free will with unimaginable existing and emerging quantities. Some how an unknown mysterious mechanism governing the totality of universal physics and mathematics counts this 100.0% flawlessly and perpetually. Anyways, you can't change the past that long existed before your birth or even yesterday, but you can choose to dream, create, and forge the future into your desires and hopes. As always, shite always happens when your not looking for it. What you choose to do after stepping in it unintentionally... is totally up to you. :) Maybe this tool and tips provided will aid you in not stepping in an algo cachucha up to your ankles somehow.
SCRIPTING LESSONS PORTRAYED IN THIS SCRIPT:
Pine etiquette and code cleanliness
Overwrite capabilities of built-in Pine variables for testing indicators
Various techniques to organize Settings panel while providing ease of adjustment utility
Use of tooltip= to provide users adequate valuable information. Most people want to trade with indicators, not blindly make adjustments to them without any knowledge of their intended operation/effects
When available time provides itself, I will consider your inquiries, thoughts, and concepts presented below in the comments section, should you have any questions or comments regarding this indicator. When my indicators achieve more prevalent use by TV members , I may implement more ideas when they present themselves as worthy additions. Have a profitable future everyone!
