Prometheus Fractal WaveThe Fractal Wave is an indicator that uses a fractal analysis to determine where reversals may happen. This is done through a Fractal process, making sure a price point is in a certain set and then getting a Distance metric.
Calculation:
A bullish Fractal is defined by the current bar’s high being less than the last bar’s high, and the last bar’s high being greater than the second to last bar’s high, and the last bar’s high being greater than the third to last bar’s high.
A bearish Fractal is defined by the current low being greater than the last bar’s low, and the last bar’s low being less than the second to last bar’s low, and the last bar’s low being less than the third to last bar’s low.
When there is that bullish or bearish fractal the value we store is either the last bar’s high or low respective to bullish or bearish fractal.
Once we have that value stored we either subtract the last bar’s low from the bullish Fractal value, and subtract the last bar’s high from the bearish Fractal value. Those are our Distances.
Code:
isBullishFractal() =>
high > high and high < high and high > high
isBearishFractal() =>
low < low and low > low and low < low
var float lastBullishFractal = na
var float lastBearishFractal = na
if isBullishFractal() and barstate.isconfirmed
lastBullishFractal := high
if isBearishFractal() and barstate.isconfirmed
lastBearishFractal := low
//------------------------------
//-------CACLULATION------------
//------------------------------
bullWaveDistance = na(lastBullishFractal) ? na : lastBullishFractal - low
bearWaveDistance = na(lastBearishFractal) ? na : high - lastBearishFractal
We then plot the bullish distance and the negative bearish distance.
The trade scenarios come from when one breaks the zero line and then goes back above or below. So if the last bullish distance was below 0 and is now above, or if the last negative bearish distance was above 0 and now below. We plot a green label below a candle for a bullish scenario, or a red label above a candle for a bearish one, you can turn them on or off.
Code:
plot(bullWaveDistance, color=color.green, title="Bull Wave Distance", linewidth=2)
plot(-bearWaveDistance, color=color.red, title="Bear Wave Distance", linewidth=2)
plot(0, "Zero Line", color=color.gray, display = display.pane)
bearish_reversal = plot_labels ? bullWaveDistance < 0 and bullWaveDistance > 0 : na
bullish_reversal = plot_labels ? -bearWaveDistance > 0 and -bearWaveDistance < 0 : na
plotshape(bullish_reversal, location=location.belowbar, color=color.green, style=shape.labelup, title="Bullish Fractal", text="↑", display = display.all - display.status_line, force_overlay = true)
plotshape(bearish_reversal, location=location.abovebar, color=color.red, style=shape.labeldown, title="Bearish Fractal", text="↓", display = display.all - display.status_line, force_overlay = true)
We can see in this daily NASDAQ:QQQ chart that the indicator gives us marks that can either be used as Reversal signals or as breathers in the trend.
Since it is designed to provide reversals, on something like Gold where the uptrend has been strong, the signals may be just short breathers, not full blown strong reversal signs.
The indicator works just as well intra day as it does on larger timeframes.
We encourage traders to not follow indicators blindly, none are 100% accurate. Please comment on any desired updates, all criticism is welcome!
Cerca negli script per "bar"
Wick/Tail Candle MeasurementsThis indicator runs on trading view. It was programmed with pine script v5.
Once the indicator is running you can scroll your chart to any year or date on the chart, then for the input select the date your interested in knowing the length of the tails and wicks from a bar and their lengths are measured in points.
To move the measurement, you can select the vertical bar built into the indicator AFTER clicking the green label and moving it around using the vertical bar *only*. You must click the vertical bar in the middle of the label to move the indicator calculation to another bar. You can also just select the date using the input as mentioned. This indicator calculates just one bar at a time.
measurements are from bar OPEN to bar HIGH for measured WICKS regardless of the bar being long or short and from bar OPEN to bar LOW for measured TAILS also regardless of the bar being long or short.
This indicator calculates tails and wicks including the bar body in the calculations. Basically showing you how much the market moved in a certain direction for the entire duration of that Doji candle.
Its designed to measure completed bars on the daily futures charts. (Dow Jones, ES&P500, Nasdaq, Russell 2000, etc) Although it may work well on other markets. The indicator could easily be tweaked in order to work well with other markets. It is not designed for forex markets currently.
Bull Bear Power With EMA FilterDescription of Indicator:
This Pine Script indicator colors price bars based on the open price in relation to custom moving averages (EMA/SMA), Bull/Bear Power (BBPower), and an optional VWAP filter. The bar colors help identify bullish and bearish conditions with added visual cues for price positioning relative to VWAP.
Key Features:
Customizable Moving Averages (EMA/SMA):
The user can select between EMA or SMA for both short-term and long-term moving averages.
Default moving averages are set to 5 (short-term) and 9 (long-term) but can be adjusted by the user.
Bullish Condition (Blue or Purple Bars):
A bar is colored blue if the following conditions are met:
The open price is above both the short-term and long-term moving averages.
The short-term moving average (MA 1) is above the long-term moving average (MA 2).
BBPower (open price minus the 13-period EMA) is positive, indicating bullish strength.
If the VWAP filter is enabled and the price opens below VWAP, the bullish bars will turn purple.
Bearish Condition (Yellow or Orange Bars):
A bar is colored yellow if the following conditions are met:
The open price is below both the short-term and long-term moving averages.
The short-term moving average (MA 1) is below the long-term moving average (MA 2).
BBPower is negative or zero, indicating bearish market conditions.
If the VWAP filter is enabled and the price opens above VWAP, the bearish bars will turn orange.
VWAP Filter (Optional):
An optional filter allows the user to add VWAP (Volume-Weighted Average Price) to the bar coloring logic.
When the VWAP filter is enabled, it provides additional information about price positioning relative to VWAP, turning bullish bars purple and bearish bars orange depending on whether the price opens above or below VWAP.
Usage:
Bullish Trend: Look for blue or purple bars to identify potential bullish momentum.
Bearish Trend: Look for yellow or orange bars to spot bearish conditions in the market.
The indicator allows users to customize the length and type of moving averages (EMA or SMA), as well as decide whether to apply the VWAP filter.
This indicator provides traders with clear visual signals to quickly assess the strength of bullish or bearish conditions based on the price's position relative to custom moving averages, BBPower, and VWAP, helping with trend identification and potential trade setups.
Uptrick: DPO Signal & Zone Indicator
## **Uptrick: DPO Signal & Zone Indicator**
### **Introduction:**
The **Uptrick: DPO Signal & Zone Indicator** is a sophisticated technical analysis tool tailored to provide insights into market momentum, identify potential trading signals, and recognize extreme market conditions. It leverages the Detrended Price Oscillator (DPO) to strip out long-term trends from price movements, allowing traders to focus on short-term fluctuations and cyclical behavior. The indicator integrates multiple components, including a Detrended Price Oscillator, a Signal Line, a Histogram, and customizable alert levels, to deliver a robust framework for market analysis and trading decision-making.
### **Detailed Breakdown:**
#### **1. Detrended Price Oscillator (DPO):**
- **Purpose and Functionality:**
- The DPO is designed to filter out long-term trends from the price data, isolating short-term price movements. This helps in understanding the cyclical patterns and momentum of an asset, allowing traders to detect periods of acceleration or deceleration that might be overlooked when focusing solely on long-term trends.
- **Calculation:**
- **Formula:** `dpo = close - ta.sma(close, smaLength)`
- **`close`:** The asset’s closing price for each period in the dataset.
- **`ta.sma(close, smaLength)`:** The Simple Moving Average (SMA) of the closing prices over a period defined by `smaLength`.
- The DPO is derived by subtracting the SMA value from the current closing price. This calculation reveals how much the current price deviates from the moving average, effectively detrending the price data.
- **Interpretation:**
- **Positive DPO Values:** Indicate that the current price is higher than the moving average, suggesting bullish market conditions and a potential upward trend.
- **Negative DPO Values:** Indicate that the current price is lower than the moving average, suggesting bearish market conditions and a potential downward trend.
- **Magnitude of DPO:** Reflects the strength of momentum. Larger positive or negative values suggest stronger momentum in the respective direction.
#### **2. Signal Line:**
- **Purpose and Functionality:**
- The Signal Line is a smoothed average of the DPO, intended to act as a reference point for generating trading signals. It helps to filter out short-term fluctuations and provides a clearer perspective on the prevailing trend.
- **Calculation:**
- **Formula:** `signalLine = ta.sma(dpo, signalLength)`
- **`ta.sma(dpo, signalLength)`:** The SMA of the DPO values over a period defined by `signalLength`.
- The Signal Line is calculated by applying a moving average to the DPO values. This smoothing process reduces noise and highlights the underlying trend direction.
- **Interpretation:**
- **DPO Crossing Above Signal Line:** Generates a buy signal, suggesting that short-term momentum is turning bullish relative to the longer-term trend.
- **DPO Crossing Below Signal Line:** Generates a sell signal, suggesting that short-term momentum is turning bearish relative to the longer-term trend.
- **Signal Line’s Role:** Provides a benchmark for assessing the strength of the DPO. The interaction between the DPO and the Signal Line offers actionable insights into potential entry or exit points.
#### **3. Histogram:**
- **Purpose and Functionality:**
- The Histogram visualizes the difference between the DPO and the Signal Line. It provides a graphical representation of momentum strength and direction, allowing traders to quickly gauge market conditions.
- **Calculation:**
- **Formula:** `histogram = dpo - signalLine`
- The Histogram is computed by subtracting the Signal Line value from the DPO value. Positive values indicate that the DPO is above the Signal Line, while negative values indicate that the DPO is below the Signal Line.
- **Interpretation:**
- **Color Coding:**
- **Green Bars:** Represent positive values, indicating bullish momentum.
- **Red Bars:** Represent negative values, indicating bearish momentum.
- **Width of Bars:** Indicates the strength of momentum. Wider bars signify stronger momentum, while narrower bars suggest weaker momentum.
- **Zero Line:** A horizontal gray line that separates positive and negative histogram values. Crosses of the histogram through this zero line can signal shifts in momentum direction.
#### **4. Alert Levels:**
- **Purpose and Functionality:**
- Alert levels define specific thresholds to identify extreme market conditions, such as overbought and oversold states. These levels help traders recognize potential reversal points and extreme market conditions.
- **Inputs:**
- **`alertLevel1`:** Defines the upper threshold for identifying overbought conditions.
- **Default Value:** 0.5
- **`alertLevel2`:** Defines the lower threshold for identifying oversold conditions.
- **Default Value:** -0.5
- **Interpretation:**
- **Overbought Condition:** When the DPO exceeds `alertLevel1`, indicating that the market may be overbought. This condition suggests that the asset could be due for a correction or reversal.
- **Oversold Condition:** When the DPO falls below `alertLevel2`, indicating that the market may be oversold. This condition suggests that the asset could be poised for a rebound or reversal.
#### **5. Visual Elements:**
- **DPO and Signal Line Plots:**
- **DPO Plot:**
- **Color:** Blue
- **Width:** 2 pixels
- **Purpose:** To visually represent the deviation of the current price from the moving average.
- **Signal Line Plot:**
- **Color:** Red
- **Width:** 1 pixel
- **Purpose:** To provide a smoothed reference for the DPO and generate trading signals.
- **Histogram Plot:**
- **Color Coding:**
- **Green:** For positive values, signaling bullish momentum.
- **Red:** For negative values, signaling bearish momentum.
- **Style:** Histogram bars are displayed with varying width to represent the strength of momentum.
- **Zero Line:** A gray horizontal line separating positive and negative histogram values.
- **Overbought/Oversold Zones:**
- **Background Colors:**
- **Green Shading:** Applied when the DPO exceeds `alertLevel1`, indicating an overbought condition.
- **Red Shading:** Applied when the DPO falls below `alertLevel2`, indicating an oversold condition.
- **Horizontal Lines:**
- **Dotted Green Line:** At `alertLevel1`, marking the upper alert threshold.
- **Dotted Red Line:** At `alertLevel2`, marking the lower alert threshold.
- **Purpose:** To provide clear visual cues for extreme market conditions, aiding in the identification of potential reversal points.
#### **6. Trading Signals and Alerts:**
- **Buy Signal:**
- **Trigger:** When the DPO crosses above the Signal Line.
- **Visual Representation:** A "BUY" label appears below the price bar in the specified buy color.
- **Purpose:** Indicates a potential buying opportunity as short-term momentum turns bullish.
- **Sell Signal:**
- **Trigger:** When the DPO crosses below the Signal Line.
- **Visual Representation:** A "SELL" label appears above the price bar in the specified sell color.
- **Purpose:** Indicates a potential selling opportunity as short-term momentum turns bearish.
- **Overbought/Oversold Alerts:**
- **Overbought Alert:** Triggered when the DPO crosses below `alertLevel1`.
- **Oversold Alert:** Triggered when the DPO crosses above `alertLevel2`.
- **Visual Representation:** Labels "OVERBOUGHT" and "OVERSOLD" appear with distinctive colors and sizes to highlight extreme conditions.
- **Purpose:** To signal potential reversal points and extreme market conditions that may lead to price corrections or trend reversals.
- **Alert Conditions:**
- **DPO Cross Above Signal Line:** Alerts traders when the DPO crosses above the Signal Line, generating a buy signal.
- **DPO Cross Below Signal Line:** Alerts traders when the DPO crosses below the Signal Line, generating a sell signal.
- **DPO Above Upper Alert Level:** Alerts when the DPO is above `alertLevel1`, indicating an overbought condition.
- **DPO Below Lower Alert Level:** Alerts when the DPO is below `alertLevel2`, indicating an oversold condition.
- **Purpose:** To provide real-time notifications of significant market events, enabling traders to make informed decisions promptly.
### **Practical Applications:**
#### **1. Trend Following Strategies:**
- **Objective:**
- To capture and ride the prevailing market trends by entering trades that align with the direction of the momentum.
- **How to Use:**
- Monitor buy and sell signals generated by the DPO crossing the Signal Line. A buy signal suggests a bullish trend and a potential long trade, while a sell signal suggests a bearish trend and a potential short trade.
- Use the Histogram to confirm the strength of the trend. Expanding green bars indicate strong bullish momentum, while expanding red bars indicate strong bearish momentum.
- **Advantages:**
- Helps traders stay aligned with the market trend, increasing the likelihood of capturing substantial price moves.
#### **2. Reversal Trading:**
- **Objective:**
- To identify potential market reversals
by detecting overbought and oversold conditions.
- **How to Use:**
- Look for overbought and oversold signals based on the DPO crossing `alertLevel1` and `alertLevel2`. These conditions suggest that the market may be due for a reversal.
- Confirm reversal signals with the Histogram. A decrease in histogram bars (from green to red or vice versa) may support the reversal hypothesis.
- **Advantages:**
- Provides early warnings of potential market reversals, allowing traders to position themselves before significant price changes occur.
#### **3. Momentum Analysis:**
- **Objective:**
- To gauge the strength and direction of market momentum for making informed trading decisions.
- **How to Use:**
- Analyze the Histogram to assess momentum strength. Positive and expanding histogram bars indicate increasing bullish momentum, while negative and expanding bars suggest increasing bearish momentum.
- Use momentum insights to validate or question existing trading positions and strategies.
- **Advantages:**
- Offers valuable information about the market's momentum, helping traders confirm the validity of trends and trading signals.
### **Customization and Flexibility:**
The **Uptrick: DPO Signal & Zone Indicator** offers extensive customization options to accommodate diverse trading preferences and market conditions:
- **SMA Length and Signal Line Length:**
- Adjust the `smaLength` and `signalLength` parameters to control the sensitivity and responsiveness of the DPO and Signal Line. Shorter lengths make the indicator more responsive to price changes, while longer lengths provide smoother, less volatile signals.
- **Alert Levels:**
- Modify `alertLevel1` and `alertLevel2` to fit varying market conditions and volatility. Setting these levels appropriately helps tailor the indicator to different asset classes and trading strategies.
- **Color and Shape Customization:**
- Customize the colors and sizes of buy/sell signals, histogram bars, and alert levels to enhance visual clarity and align with personal preferences. This customization helps ensure that the indicator integrates seamlessly with a trader's charting setup.
### **Conclusion:**
The **Uptrick: DPO Signal & Zone Indicator** is a multifaceted analytical tool that combines the power of the Detrended Price Oscillator with customizable visual elements and alert levels to deliver a comprehensive approach to market analysis. By offering insights into momentum strength, trend direction, and potential reversal points, this indicator equips traders with valuable information to make informed decisions and enhance their trading strategies. Its flexibility and customization options ensure that it can be adapted to various trading styles and market conditions, making it a versatile addition to any trader's toolkit.
Trend and RSI Bias FusionTrend and RSI Bias Fusion Indicator
This is my first ever indicator. I created this indicator for myself. I was inspired by the indicators created by Bjorgum, Duyck and QuantTherapy and decided to create multiple indicators that either work well combined with their indicators or something new that applies some of their indicator concepts. I decided to share this because I believe in learning and earing together as a community. I will later share the rest of the indicators I have created. This is my first time ever sharing any indicator so if you guys have any questions or suggestions write them.
Overview
The "Trend and RSI Bias Fusion" indicator is a versatile tool designed to help traders identify key market trends, potential reversals, momentum shifts, and RSI-based pullbacks. This indicator fuses trend analysis and RSI bias into a single, comprehensive visual, making it easier to make informed trading decisions across various timeframes and market conditions.
Features
Dual Timeframe Analysis: Combines trend analysis on a higher timeframe (e.g., Daily) with RSI analysis on a lower timeframe (e.g., 4-Hour), providing a more granular view of market conditions. You can, however, choose any timeframe you want for instance 12hr with trend and 2hr RSI analysis.
Trend and Momentum Visualization: The indicator uses Exponential Moving Averages (EMAs) to determine trend direction and colors the chart background to reflect bullish or bearish trends, along with momentum strength.
RSI Bias Detection: Automatically identifies overbought and oversold conditions using the RSI, providing a clear indication of potential market reversals or continuations.
Color-Coded Bars: Optionally color codes bars based on either trend direction or RSI bias, giving you a quick visual cue of the market's state.
Reversal Markers: Displays trend reversal markers on the chart when the short-term EMA crosses over or under the long-term EMA.
Calculation Details
Exponential Moving Averages (EMAs): The indicator calculates short-term and long-term EMAs using the closing prices.
The crossover between these EMAs is used to determine the trend direction:
Short-Term EMA: Typically a 14-period EMA.
Long-Term EMA: Typically a 50-period EMA.
Momentum: Calculated using the RSI and then centered around zero by subtracting 50. This allows the indicator to distinguish between positive and negative momentum.
RSI Bias: The RSI is calculated on a lower timeframe to detect overbought (above 60) and oversold (below 40) conditions, which are used to determine the bias:
RSI Above 60: Indicates potential overbought conditions (bearish bias).
RSI Below 40: Indicates potential oversold conditions (bullish bias).
How to Use the Indicator
Select Your Timeframes: Choose your preferred trend timeframe (e.g., Daily) and RSI timeframe (e.g., 4-2 Hour) in the indicator settings. These should match your trading strategy and the asset class you're analyzing.
Interpret Trend and Momentum
Background Color: The background color reflects the current trend direction:
Green/Lime: Uptrend, with lime indicating positive momentum.
Red/Maroon: Downtrend, with maroon indicating positive momentum within a downtrend.
Momentum Histogram: The histogram plot shows momentum, color-coded by the trend. A histogram above zero with green/lime indicates bullish momentum, while below zero with red/maroon indicates bearish momentum.
Image above: Both RSI and Trend are set to daily, uses RSI bar color
Read RSI Bias:
The RSI bias line helps identify the current market state relative to overbought or oversold levels. The RSI value is plotted on the chart, with lines at 60 and 40 to mark these levels.
When the RSI crosses above 60, it suggests a bearish bias; crossing below 40 suggests a bullish bias.
Use Reversal Markers: The indicator places small circles on the chart at points where the short-term EMA crosses the long-term EMA, signaling potential trend reversals.
Bar Color Customization:
You can choose to color the bars based on either the trend or the RSI bias in the indicator settings. In the Images below I have changed the colors to fit my personal style , Blue for uptrend and Pink for downtrend:
Trend-Based: Bars will reflect the trend direction (green for uptrend or in this case blue, red for downtrend or in this case pink).
RSI-Based: Bars will reflect RSI conditions (yellow for overbought, maroon for oversold).
Image above: RSI is set to 4hr and Trend is set to daily, uses RSI bar color
Image above: RSI is set to 4hr and Trend is set to daily, uses Trend bar color
Image above: Both RSI and Trend are set to daily, uses RSI bar color
Image above: Both RSI and Trend are set to daily, uses Trend bar color
Image above: Both RSI and Trend are set to daily, without bar color
Image above: Both RSI and Trend are set to daily, how it looks on a clean chart
Example Use Case Swing Traders:
For instance, if you're trading a 4-hour chart of USDCHF:
Set the trend timeframe to Daily and the RSI timeframe to 4-Hour.
Watch for background color shifts and reversal markers to determine trend direction.
Use RSI bias to time your entries and exits, especially around overbought/oversold levels.
Enable bar coloring to quickly see when conditions favor either trend continuation or reversal.
This indicator is particularly effective for swing traders and those who want to align their trades with higher timeframe trends while using momentum and RSI for entry and exit signals.
For Day Traders
Timeframe Selection:
Trend Timeframe: Set to a higher intraday timeframe such as the 1 or 2 Hour chart.
RSI Timeframe: Set to a shorter timeframe like 15-10 Minutes or 5-Minutes to capture finer details of intraday momentum shifts.
Using the Indicator:
Trend Identification: Day traders can use the background color to quickly identify whether the market is in a bullish or bearish trend on the 1-Hour chart. A green background suggests looking for long opportunities, while a red background suggests short opportunities.
Momentum Analysis: The histogram can help day traders gauge the strength of the current trend. For example, if the histogram is green and above zero, the trader may consider buying pullbacks within the trend.
RSI Bias: Monitor RSI levels on the lower timeframe (e.g., 15-Minutes). If the RSI crosses below 40, it indicates an oversold condition, potentially signaling a buying opportunity, especially if it aligns with a bullish trend on the higher timeframe.
Trade Execution:
Look for entries when the RSI shows a reversal or pullback in the direction of the higher timeframe trend.
Use the trend reversal markers to confirm potential intraday reversals, adding extra confidence to trade setups.
For Scalpers
Timeframe Selection:
Trend Timeframe: Set to a short intraday timeframe like 15-Minutes or 5-Minutes.
RSI Timeframe: Use an even shorter timeframe, such as 1-Minute, to capture rapid price movements.
Final Notes:
The "Trend and RSI Bias Fusion" indicator is a powerful tool that combines trend analysis, momentum assessment, and RSI insights into one cohesive package. By integrating these different aspects, the indicator helps traders navigate complex market environments with greater clarity and confidence. Customize the settings to fit your specific trading style and market and use it to stay ahead of market trends and potential reversals.
My Scripts/Indicators/Ideas /Systems that I share are only for educational purposes!
Uptrick: RSI Histogram
1. **Introduction to the RSI and Moving Averages**
2. **Detailed Breakdown of the Uptrick: RSI Histogram**
3. **Calculation and Formula**
4. **Visual Representation**
5. **Customization and User Settings**
6. **Trading Strategies and Applications**
7. **Risk Management**
8. **Case Studies and Examples**
9. **Comparison with Other Indicators**
10. **Advanced Usage and Tips**
---
## 1. Introduction to the RSI and Moving Averages
### **1.1 Relative Strength Index (RSI)**
The Relative Strength Index (RSI) is a momentum oscillator developed by J. Welles Wilder and introduced in his 1978 book "New Concepts in Technical Trading Systems." It is widely used in technical analysis to measure the speed and change of price movements.
**Purpose of RSI:**
- **Identify Overbought/Oversold Conditions:** RSI values range from 0 to 100. Traditionally, values above 70 are considered overbought, while values below 30 are considered oversold. These thresholds help traders identify potential reversal points in the market.
- **Trend Strength Measurement:** RSI also indicates the strength of a trend. High RSI values suggest strong bullish momentum, while low values indicate bearish momentum.
**Calculation of RSI:**
1. **Calculate the Average Gain and Loss:** Over a specified period (e.g., 14 days), calculate the average gain and loss.
2. **Compute the Relative Strength (RS):** RS is the ratio of average gain to average loss.
3. **RSI Formula:** RSI = 100 - (100 / (1 + RS))
### **1.2 Moving Averages (MA)**
Moving Averages are used to smooth out price data and identify trends by filtering out short-term fluctuations. Two common types are:
**Simple Moving Average (SMA):** The average of prices over a specified number of periods.
**Exponential Moving Average (EMA):** A type of moving average that gives more weight to recent prices, making it more responsive to recent price changes.
**Smoothed Moving Average (SMA):** Used to reduce the impact of volatility and provide a clearer view of the underlying trend. The RMA, or Running Moving Average, used in the USH script is similar to an EMA but based on the average of RSI values.
## 2. Detailed Breakdown of the Uptrick: RSI Histogram
### **2.1 Indicator Overview**
The Uptrick: RSI Histogram (USH) is a technical analysis tool that combines the RSI with a moving average to create a histogram that reflects momentum and trend strength.
**Key Components:**
- **RSI Calculation:** Determines the relative strength of price movements.
- **Moving Average Application:** Smooths the RSI values to provide a clearer trend indication.
- **Histogram Plotting:** Visualizes the deviation of the smoothed RSI from a neutral level.
### **2.2 Indicator Purpose**
The primary purpose of the USH is to provide a clear visual representation of the market's momentum and trend strength. It helps traders identify:
- **Bullish and Bearish Trends:** By showing how far the smoothed RSI is from the neutral 50 level.
- **Potential Reversal Points:** By highlighting changes in momentum.
### **2.3 Indicator Design**
**RSI Moving Average (RSI MA):** The RSI MA is a smoothed version of the RSI, calculated using a running moving average. This smooths out short-term fluctuations and provides a clearer indication of the underlying trend.
**Histogram Calculation:**
- **Neutral Level:** The histogram is plotted relative to the neutral level of 50. This level represents a balanced market where neither bulls nor bears have dominance.
- **Histogram Values:** The histogram bars show the difference between the RSI MA and the neutral level. Positive values indicate bullish momentum, while negative values indicate bearish momentum.
## 3. Calculation and Formula
### **3.1 RSI Calculation**
The RSI calculation involves:
1. **Average Gain and Loss:** Calculated over the specified length (e.g., 14 periods).
2. **Relative Strength (RS):** RS = Average Gain / Average Loss.
3. **RSI Formula:** RSI = 100 - (100 / (1 + RS)).
### **3.2 Moving Average Calculation**
For the USH indicator, the RSI is smoothed using a running moving average (RMA). The RMA formula is similar to that of the EMA but is based on averaging RSI values over the specified length.
### **3.3 Histogram Calculation**
The histogram value is calculated as:
- **Histogram Value = RSI MA - 50**
**Plotting the Histogram:**
- **Positive Histogram Values:** Indicate that the RSI MA is above the neutral level, suggesting bullish momentum.
- **Negative Histogram Values:** Indicate that the RSI MA is below the neutral level, suggesting bearish momentum.
## 4. Visual Representation
### **4.1 Histogram Bars**
The histogram is plotted as bars on the chart:
- **Bullish Bars:** Colored green when the RSI MA is above 50.
- **Bearish Bars:** Colored red when the RSI MA is below 50.
### **4.2 Customization Options**
Traders can customize:
- **RSI Length:** Adjust the length of the RSI calculation to match their trading style.
- **Bull and Bear Colors:** Choose colors for histogram bars to enhance visual clarity.
### **4.3 Interpretation**
**Bullish Signal:** A histogram bar that moves from red to green indicates a potential shift to a bullish trend.
**Bearish Signal:** A histogram bar that moves from green to red indicates a potential shift to a bearish trend.
## 5. Customization and User Settings
### **5.1 Adjusting RSI Length**
The length parameter determines the number of periods over which the RSI is calculated and smoothed. Shorter lengths make the RSI more sensitive to price changes, while longer lengths provide a smoother view of trends.
### **5.2 Color Settings**
Traders can adjust:
- **Bull Color:** Color of histogram bars indicating bullish momentum.
- **Bear Color:** Color of histogram bars indicating bearish momentum.
**Customization Benefits:**
- **Visual Clarity:** Traders can choose colors that stand out against their chart’s background.
- **Personal Preference:** Adjust settings to match individual trading styles and preferences.
## 6. Trading Strategies and Applications
### **6.1 Trend Following**
**Identifying Entry Points:**
- **Bullish Entry:** When the histogram changes from red to green, it signals a potential entry point for long positions.
- **Bearish Entry:** When the histogram changes from green to red, it signals a potential entry point for short positions.
**Trend Confirmation:** The histogram helps confirm the strength of a trend. Strong, consistent green bars indicate robust bullish momentum, while strong, consistent red bars indicate robust bearish momentum.
### **6.2 Swing Trading**
**Momentum Analysis:**
- **Entry Signals:** Look for significant shifts in the histogram to time entries. A shift from bearish to bullish (red to green) indicates potential for upward movement.
- **Exit Signals:** A shift from bullish to bearish (green to red) suggests a potential weakening of the trend, signaling an exit or reversal point.
### **6.3 Range Trading**
**Market Conditions:**
- **Consolidation:** The histogram close to zero suggests a range-bound market. Traders can use this information to identify support and resistance levels.
- **Breakout Potential:** A significant move away from the neutral level may indicate a potential breakout from the range.
### **6.4 Risk Management**
**Stop-Loss Placement:**
- **Bullish Positions:** Place stop-loss orders below recent support levels when the histogram is green.
- **Bearish Positions:** Place stop-loss orders above recent resistance levels when the histogram is red.
**Position Sizing:** Adjust position sizes based on the strength of the histogram signals. Strong trends (indicated by larger histogram bars) may warrant larger positions, while weaker signals suggest smaller positions.
## 7. Risk Management
### **7.1 Importance of Risk Management**
Effective risk management is crucial for long-term trading success. It involves protecting capital, managing losses, and optimizing trade setups.
### **7.2 Using USH for Risk Management**
**Stop-Loss and Take-Profit Levels:**
- **Stop-Loss Orders:** Use the histogram to set stop-loss levels based on trend strength. For instance, place stops below support levels in bullish trends and above resistance levels in bearish trends.
- **Take-Profit Targets:** Adjust take-profit levels based on histogram changes. For example, lock in profits as the histogram starts to shift from green to red.
**Position Sizing:**
- **Trend Strength:** Scale position sizes based on the strength of histogram signals. Larger histogram bars indicate stronger trends, which may justify larger positions.
- **Volatility:** Consider market volatility and adjust position sizes to mitigate risk.
## 8. Case Studies and Examples
### **8.1 Example 1: Bullish Trend**
**Scenario:** A trader notices a transition from red to green histogram bars.
**Analysis:**
- **Entry Point:** The transition indicates a potential bullish trend. The trader decides to enter a long position.
- **Stop-Loss:** Set stop-loss below recent support levels.
- **Take-Profit:** Consider taking profits as the histogram moves back towards zero or turns red.
**Outcome:** The bullish trend continues, and the histogram remains green, providing a profitable trade setup.
### **8.2 Example 2: Bearish Trend**
**Scenario:** A trader observes a transition from green to red histogram bars.
**Analysis:**
- **Entry Point:** The transition suggests a potential
bearish trend. The trader decides to enter a short position.
- **Stop-Loss:** Set stop-loss above recent resistance levels.
- **Take-Profit:** Consider taking profits as the histogram approaches zero or shifts to green.
**Outcome:** The bearish trend continues, and the histogram remains red, resulting in a successful trade.
## 9. Comparison with Other Indicators
### **9.1 RSI vs. USH**
**RSI:** Measures momentum and identifies overbought/oversold conditions.
**USH:** Builds on RSI by incorporating a moving average and histogram to provide a clearer view of trend strength and momentum.
### **9.2 RSI vs. MACD**
**MACD (Moving Average Convergence Divergence):** A trend-following momentum indicator that uses moving averages to identify changes in trend direction.
**Comparison:**
- **USH:** Provides a smoothed RSI perspective and visual histogram for trend strength.
- **MACD:** Offers signals based on the convergence and divergence of moving averages.
### **9.3 RSI vs. Stochastic Oscillator**
**Stochastic Oscillator:** Measures the level of the closing price relative to the high-low range over a specified period.
**Comparison:**
- **USH:** Focuses on smoothed RSI values and histogram representation.
- **Stochastic Oscillator:** Provides overbought/oversold signals and potential reversals based on price levels.
## 10. Advanced Usage and Tips
### **10.1 Combining Indicators**
**Multi-Indicator Strategies:** Combine the USH with other technical indicators (e.g., Moving Averages, Bollinger Bands) for a comprehensive trading strategy.
**Confirmation Signals:** Use the USH to confirm signals from other indicators. For instance, a bullish histogram combined with a moving average crossover may provide a stronger buy signal.
### **10.2 Customization Tips**
**Adjust RSI Length:** Experiment with different RSI lengths to match various market conditions and trading styles.
**Color Preferences:** Choose histogram colors that enhance visibility and align with personal preferences.
### **10.3 Continuous Learning**
**Backtesting:** Regularly backtest the USH with historical data to refine strategies and improve accuracy.
**Education:** Stay updated with trading education and adapt strategies based on market changes and personal experiences.
4C Data Table SuiteOverview
The 4C Data Table Suite is a versatile TradingView indicator, designed for traders who focus on the critical role of prior bar levels in their strategy. By highlighting the high and low points of previous bars, this tool aids in pinpointing crucial support and resistance zones, which often act as psychological triggers for market participants. The unique feature of this indicator is its dual-color coding: it colors bars green for bullish closes and red for bearish closes in the Prior Bar Table, and for the current timeframe, it highlights whether the current price is above (green) or below (red) these levels.
Concepts
1 — This sophisticated indicator is not just about visual cues; it provides a rich tableau of data including:
• Current timeframe countdown to bar close
• GMT-adjusted clock for precise trade timing
• Real-time updates on market internals and volatility measures such as the NYSE TICK and ATR
2 — The configuration is highly customizable, allowing users to:
• Adjust the table's positioning and text size
• Choose color settings for text, background, and borders to suit their visual preference
• Toggle the display of various data components based on their trading needs
3 — The Prior Bar Table is particularly valuable for:
• Traders using prior bar levels as triggers for trade entries and exits
• Quick visual assessment of market sentiment
• Real-time decision-making supported by dynamic color coding based on current price movements relative to prior highs and lows
Features
• Prior Bar Levels Table: Visualizes high and low levels of prior bars with intuitive color coding, aiding traders in assessing market trends.
• Dynamic Color Coding: Updates colors based on the closure of the previous bar and current price positions relative to the past bar's highs and lows.
• Comprehensive Market Data: Includes a suite of essential data such as market internals, the prior bar’s range, and the latest price information.
• Customizable Visuals: Offers extensive options for customizing the appearance and data presented, ensuring the tool fits seamlessly into any trader's strategy.
How to Use
1. To add the indicator, search for "4C Data Table Suite" under indicators on TradingView and apply it to your chart.
2. Navigate to the indicator settings to customize the display properties, including table position, color schemes, and which data points to display.
3. Utilize the toggles within the "Prior Bar Levels Table" settings to adjust the visibility and behavior of the table, depending on your trading approach.
Limitations
• This indicator is optimized for use on time-based charts and may not perform as intended on tick charts or non-standard timeframes.
• The color coding is based on the closure of bars, which may not always reflect intrabar movements, potentially affecting real-time decision-making in highly volatile markets.
Notes
• Ensure your TradingView interface is set to the correct timezone settings to align the GMT clock accurately.
• The dynamic color updates are designed to provide at-a-glance insights but should be used in conjunction with other analysis tools for best results.
*If you find that the indicator is blocking some of the candles at the bottom of the screen, go to (Chart) Settings, Canvas, and then adjust the Bottom/Top margin by increasing the % Amount.
Thanks
Special thanks to the TradingView community and developers whose feedback and insights have helped refine the functionalities of the 4C Data Table Suite. Your collaborative spirit is what makes continuous improvement possible.
Brooks Always In [KintsugiTrading]Brooks Always In
Overview:
The "Brooks Always In Indicator" by KintsugiTrading is a tool designed for traders who follow price action methodologies inspired by Al Brooks. This indicator identifies key bar patterns and breakouts, plots an Exponential Moving Average (EMA), and highlights consecutive bullish and bearish bars. It is intended to assist traders in making informed decisions based on price action dynamics.
Features:
Consecutive Bar Patterns:
Identifies and highlights consecutive bullish and bearish bars.
Differentiates between bars that are above/below the EMA and those that are not.
Customizable EMA:
Option to display an Exponential Moving Average (EMA) with user-defined length and offset.
The EMA can be smoothed using various methods such as SMA, EMA, SMMA (RMA), WMA, and VWMA.
Breakout Patterns:
Recognizes bullish and bearish breakout bars and outside bars.
Tracks inside bars and prior bar conditions to better understand the market context.
Customizable Display:
Users can display or hide the EMA, consecutive bar patterns, and consecutive bars relative to the moving average.
How to Use:
Customize Settings:
First, I like to navigate to the top right corner of the chart (bolt icon), and change both the bull and bear body color to match the background (white/black) - this helps the user visualize the indicator far better.
Next, Toggle to display EMA, consecutive bar patterns, and consecutive bars relative to the moving average using the provided input options.
Adjust the EMA length, source, and offset as per your trading strategy.
Select the smoothing method and length for the EMA if desired.
Analyze Key Patterns:
Observe the highlighted bars on the chart to identify consecutive bullish and bearish patterns.
Use the plotted EMA to gauge the general trend and analyze the relationship between price bars and the moving average.
Informed Decision Making:
Utilize the identified bar patterns and breakouts to make informed trading decisions, such as identifying potential entry and exit points based on price action dynamics.
Good luck with your trading!
QuasimodoThis indicator helps traders spot certain patterns on a price chart that might indicate a change in price direction. These patterns are known as "engulfing patterns."
How It Works1.
Bullish Engulfing Patterns:- The current bar (or candle) closes higher than it opens (it's a green or white candle).- The previous bar closed lower than it opened (it was a red or black candle).- The current bar's high is higher than the previous bar's high, and its low is lower than the previous bar's low.- There's another variation where both the current and previous bars are green, but the current bar is still higher and lower than the previous one.
2. Bearish Engulfing Patterns:- The current bar closes lower than it opens (it's a red or black candle).- The previous bar closed higher than it opened (it was a green or white candle).- The current bar's low is lower than the previous bar's low, and its high is higher than the previous bar's high.- There's another variation where both the current and previous bars are red, but the current bar is still higher and lower than the previous one.
What It Shows-
When the indicator spots one of these patterns, it colors the previous candle:-
Yellow for a bullish pattern (price might go up).-
Pink for a bearish pattern (price might go down).
Alerts- The indicator can also send an alert to let you know when it finds one of these patterns, so you don't miss it.
Adaptive Trend Classification: Moving Averages [InvestorUnknown]Adaptive Trend Classification: Moving Averages
Overview
The Adaptive Trend Classification (ATC) Moving Averages indicator is a robust and adaptable investing tool designed to provide dynamic signals based on various types of moving averages and their lengths. This indicator incorporates multiple layers of adaptability to enhance its effectiveness in various market conditions.
Key Features
Adaptability of Moving Average Types and Lengths: The indicator utilizes different types of moving averages (EMA, HMA, WMA, DEMA, LSMA, KAMA) with customizable lengths to adjust to market conditions.
Dynamic Weighting Based on Performance: ] Weights are assigned to each moving average based on the equity they generate, with considerations for a cutout period and decay rate to manage (reduce) the influence of past performances.
Exponential Growth Adjustment: The influence of recent performance is enhanced through an adjustable exponential growth factor, ensuring that more recent data has a greater impact on the signal.
Calibration Mode: Allows users to fine-tune the indicator settings for specific signal periods and backtesting, ensuring optimized performance.
Visualization Options: Multiple customization options for plotting moving averages, color bars, and signal arrows, enhancing the clarity of the visual output.
Alerts: Configurable alert settings to notify users based on specific moving average crossovers or the average signal.
User Inputs
Adaptability Settings
λ (Lambda): Specifies the growth rate for exponential growth calculations.
Decay (%): Determines the rate of depreciation applied to the equity over time.
CutOut Period: Sets the period after which equity calculations start, allowing for a focus on specific time ranges.
Robustness Lengths: Defines the range of robustness for equity calculation with options for Narrow, Medium, or Wide adjustments.
Long/Short Threshold: Sets thresholds for long and short signals.
Calculation Source: The data source used for calculations (e.g., close price).
Moving Averages Settings
Lengths and Weights: Allows customization of lengths and initial weights for each moving average type (EMA, HMA, WMA, DEMA, LSMA, KAMA).
Calibration Mode
Calibration Mode: Enables calibration for fine-tuning inputs.
Calibrate: Specifies which moving average type to calibrate.
Strategy View: Shifts entries and exits by one bar for non-repainting backtesting.
Calculation Logic
Rate of Change (R): Calculates the rate of change in the price.
Set of Moving Averages: Generates multiple moving averages with different lengths for each type.
diflen(length) =>
int L1 = na, int L_1 = na
int L2 = na, int L_2 = na
int L3 = na, int L_3 = na
int L4 = na, int L_4 = na
if robustness == "Narrow"
L1 := length + 1, L_1 := length - 1
L2 := length + 2, L_2 := length - 2
L3 := length + 3, L_3 := length - 3
L4 := length + 4, L_4 := length - 4
else if robustness == "Medium"
L1 := length + 1, L_1 := length - 1
L2 := length + 2, L_2 := length - 2
L3 := length + 4, L_3 := length - 4
L4 := length + 6, L_4 := length - 6
else
L1 := length + 1, L_1 := length - 1
L2 := length + 3, L_2 := length - 3
L3 := length + 5, L_3 := length - 5
L4 := length + 7, L_4 := length - 7
// Function to calculate different types of moving averages
ma_calculation(source, length, ma_type) =>
if ma_type == "EMA"
ta.ema(source, length)
else if ma_type == "HMA"
ta.sma(source, length)
else if ma_type == "WMA"
ta.wma(source, length)
else if ma_type == "DEMA"
ta.dema(source, length)
else if ma_type == "LSMA"
lsma(source,length)
else if ma_type == "KAMA"
kama(source, length)
else
na
// Function to create a set of moving averages with different lengths
SetOfMovingAverages(length, source, ma_type) =>
= diflen(length)
MA = ma_calculation(source, length, ma_type)
MA1 = ma_calculation(source, L1, ma_type)
MA2 = ma_calculation(source, L2, ma_type)
MA3 = ma_calculation(source, L3, ma_type)
MA4 = ma_calculation(source, L4, ma_type)
MA_1 = ma_calculation(source, L_1, ma_type)
MA_2 = ma_calculation(source, L_2, ma_type)
MA_3 = ma_calculation(source, L_3, ma_type)
MA_4 = ma_calculation(source, L_4, ma_type)
Exponential Growth Factor: Computes an exponential growth factor based on the current bar index and growth rate.
// The function `e(L)` calculates an exponential growth factor based on the current bar index and a given growth rate `L`.
e(L) =>
// Calculate the number of bars elapsed.
// If the `bar_index` is 0 (i.e., the very first bar), set `bars` to 1 to avoid division by zero.
bars = bar_index == 0 ? 1 : bar_index
// Define the cuttime time using the `cutout` parameter, which specifies how many bars will be cut out off the time series.
cuttime = time
// Initialize the exponential growth factor `x` to 1.0.
x = 1.0
// Check if `cuttime` is not `na` and the current time is greater than or equal to `cuttime`.
if not na(cuttime) and time >= cuttime
// Use the mathematical constant `e` raised to the power of `L * (bar_index - cutout)`.
// This represents exponential growth over the number of bars since the `cutout`.
x := math.pow(math.e, L * (bar_index - cutout))
x
Equity Calculation: Calculates the equity based on starting equity, signals, and the rate of change, incorporating a natural decay rate.
pine code
// This function calculates the equity based on the starting equity, signals, and rate of change (R).
eq(starting_equity, sig, R) =>
cuttime = time
if not na(cuttime) and time >= cuttime
// Calculate the rate of return `r` by multiplying the rate of change `R` with the exponential growth factor `e(La)`.
r = R * e(La)
// Calculate the depreciation factor `d` as 1 minus the depreciation rate `De`.
d = 1 - De
var float a = 0.0
// If the previous signal `sig ` is positive, set `a` to `r`.
if (sig > 0)
a := r
// If the previous signal `sig ` is negative, set `a` to `-r`.
else if (sig < 0)
a := -r
// Declare the variable `e` to store equity and initialize it to `na`.
var float e = na
// If `e ` (the previous equity value) is not available (first calculation):
if na(e )
e := starting_equity
else
// Update `e` based on the previous equity value, depreciation factor `d`, and adjustment factor `a`.
e := (e * d) * (1 + a)
// Ensure `e` does not drop below 0.25.
if (e < 0.25)
e := 0.25
e
else
na
Signal Generation: Generates signals based on crossovers and computes a weighted signal from multiple moving averages.
Main Calculations
The indicator calculates different moving averages (EMA, HMA, WMA, DEMA, LSMA, KAMA) and their respective signals, applies exponential growth and decay factors to compute equities, and then derives a final signal by averaging weighted signals from all moving averages.
Visualization and Alerts
The final signal, along with additional visual aids like color bars and arrows, is plotted on the chart. Users can also set up alerts based on specific conditions to receive notifications for potential trading opportunities.
Repainting
The indicator does support intra-bar changes of signal but will not repaint once the bar is closed, if you want to get alerts only for signals after bar close, turn on “Strategy View” while setting up the alert.
Conclusion
The Adaptive Trend Classification: Moving Averages Indicator is a sophisticated tool for investors, offering extensive customization and adaptability to changing market conditions. By integrating multiple moving averages and leveraging dynamic weighting based on performance, it aims to provide reliable and timely investing signals.
HTF Descending TriangleHTF Descending Triangle aims at detecting descending triangles using higher time frame data, without repainting nor misalignment issues.
Descending triangles are defined by a falling upper trend line and an horizontal lower trend line. It is a chart pattern used in technical analysis to predict the continuation of a downtrend.
This indicator can be useful if you, like me, believe that higher time frames can offer a broader perspective and provide clearer signals, smoothing out market noise and showing longer-term trends.
You can change the indicator settings as you see fit to tighten or loosen the detection, and achieve the best results for your use case.
Features
It draws the detected descending triangle on the chart.
It supports alerting when a detection occurs.
It allows for setting the higher time frame to run the detection on.
It allows for setting the minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
It allows for setting a low factor detection criteria to apply on higher time frame bars low as a proportion of the distance between the reference bar low and open/close.
It allows for turning on an adjustment of the triangle using highest/lowest values within valid higher time frame bars.
Settings
Higher Time Frame dropdown: Selects higher time frame to run the detection on. It must be higher than, and a multiple of, the chart's timeframe.
Valid Bars Minimum field: Sets minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
Low Factor checkbox: Turns on/off low factor detection criteria.
Low Factor field: Sets low factor to apply on higher time frame bars low as a proportion of the distance between the reference bar low and open/close.
Adjust Triangle checkbox: Turns on/off triangle adjustment using highest/lowest values within valid higher time frame bars.
Detection Algorithm Notes
The detection algorithm recursively selects a higher time frame bar as reference. Then it looks at the consecutive higher time frame bars (as per the requested number of minimum valid bars) as follows:
High must be lower than previous bar.
Open/close min value must be higher than reference bar low.
When low factor criteria is turned on, low must be lower than reference bar open/close min value minus low factor proportion of the distance between reference bar low and open/close min value.
HTF Ascending TriangleHTF Ascending Triangle aims at detecting ascending triangles using higher time frame data, without repainting nor misalignment issues.
Ascending triangles are defined by an horizontal upper trend line and a rising lower trend line. It is a chart pattern used in technical analysis to predict the continuation of an uptrend.
This indicator can be useful if you, like me, believe that higher time frames can offer a broader perspective and provide clearer signals, smoothing out market noise and showing longer-term trends.
You can change the indicator settings as you see fit to tighten or loosen the detection, and achieve the best results for your use case.
Features
It draws the detected ascending triangle on the chart.
It supports alerting when a detection occurs.
It allows for setting the higher time frame to run the detection on.
It allows for setting the minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
It allows for setting a high factor detection criteria to apply on higher time frame bars high as a proportion of the distance between the reference bar high and open/close.
It allows for turning on an adjustment of the triangle using highest/lowest values within valid higher time frame bars.
Settings
Higher Time Frame dropdown: Selects higher time frame to run the detection on. It must be higher than, and a multiple of, the chart's timeframe.
Valid Bars Minimum field: Sets minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
High Factor checkbox: Turns on/off high factor detection criteria.
High Factor field: Sets high factor to apply on higher time frame bars high as a proportion of the distance between the reference bar high and close/open.
Adjust Triangle checkbox: Turns on/off triangle adjustment using highest/lowest values within valid higher time frame bars.
Detection Algorithm Notes
The detection algorithm recursively selects a higher time frame bar as reference. Then it looks at the consecutive higher time frame bars (as per the requested number of minimum valid bars) as follows:
Low must be higher than previous bar.
Open/close max value must be lower than reference bar high.
When high factor criteria is turned on, high must be higher than reference bar open/close max value plus high factor proportion of the distance between reference bar high and open/close max value.
regressionsLibrary "regressions"
This library computes least square regression models for polynomials of any form for a given data set of x and y values.
fit(X, y, reg_type, degrees)
Takes a list of X and y values and the degrees of the polynomial and returns a least square regression for the given polynomial on the dataset.
Parameters:
X (array) : (float ) X inputs for regression fit.
y (array) : (float ) y outputs for regression fit.
reg_type (string) : (string) The type of regression. If passing value for degrees use reg.type_custom
degrees (array) : (int ) The degrees of the polynomial which will be fit to the data. ex: passing array.from(0, 3) would be a polynomial of form c1x^0 + c2x^3 where c2 and c1 will be coefficients of the best fitting polynomial.
Returns: (regression) returns a regression with the best fitting coefficients for the selecected polynomial
regress(reg, x)
Regress one x input.
Parameters:
reg (regression) : (regression) The fitted regression which the y_pred will be calulated with.
x (float) : (float) The input value cooresponding to the y_pred.
Returns: (float) The best fit y value for the given x input and regression.
predict(reg, X)
Predict a new set of X values with a fitted regression. -1 is one bar ahead of the realtime
Parameters:
reg (regression) : (regression) The fitted regression which the y_pred will be calulated with.
X (array)
Returns: (float ) The best fit y values for the given x input and regression.
generate_points(reg, x, y, left_index, right_index)
Takes a regression object and creates chart points which can be used for plotting visuals like lines and labels.
Parameters:
reg (regression) : (regression) Regression which has been fitted to a data set.
x (array) : (float ) x values which coorispond to passed y values
y (array) : (float ) y values which coorispond to passed x values
left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value.
right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value.
Returns: (chart.point ) Returns an array of chart points
plot_reg(reg, x, y, left_index, right_index, curved, close, line_color, line_width)
Simple plotting function for regression for more custom plotting use generate_points() to create points then create your own plotting function.
Parameters:
reg (regression) : (regression) Regression which has been fitted to a data set.
x (array)
y (array)
left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value.
right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value.
curved (bool) : (bool) If the polyline is curved or not.
close (bool) : (bool) If true the polyline will be closed.
line_color (color) : (color) The color of the line.
line_width (int) : (int) The width of the line.
Returns: (polyline) The polyline for the regression.
series_to_list(src, left_index, right_index)
Convert a series to a list. Creates a list of all the cooresponding source values
from left_index to right_index. This should be called at the highest scope for consistency.
Parameters:
src (float) : (float ) The source the list will be comprised of.
left_index (int) : (float ) The left most bar (farthest back historical bar) which the cooresponding source value will be taken for.
right_index (int) : (float ) The right most bar closest to the realtime bar which the cooresponding source value will be taken for.
Returns: (float ) An array of size left_index-right_index
range_list(start, stop, step)
Creates an from the start value to the stop value.
Parameters:
start (int) : (float ) The true y values.
stop (int) : (float ) The predicted y values.
step (int) : (int) Positive integer. The spacing between the values. ex: start=1, stop=6, step=2:
Returns: (float ) An array of size stop-start
regression
Fields:
coeffs (array__float)
degrees (array__float)
type_linear (series__string)
type_quadratic (series__string)
type_cubic (series__string)
type_custom (series__string)
_squared_error (series__float)
X (array__float)
Volume Delta Candles [LuxAlgo]Volume Delta Candles provides insights about Intrabar trading activity in an easy-to-interpret manner. Lower timeframe or real-time data is used for displaying Volume Delta percentage against the total volume as a coloured bar part.
The script also highlights the intrabar price with the maximum trading activity, as well as complementary information.
🔶 USAGE
The tool focuses on intrabar volume to provide more information about the trading activity associated with a candle, without having to use an external volume indicator.
Each indicator components is further explained below:
🔹 Volume Delta
The volume delta is obtained by the difference between buy volume and sell volume, where buy volume is the volume associated with a bullish intrabar candle, and sell volume with a bearish intrabar candle.
Positive volume delta is displayed with a green candle area, while negative delta is displayed with a red candle area.
🔹 Bar Coloring
The script displays VD as a percentage of the whole, or from the candle half, depending on the setting ' Display '.
Bars can be coloured as follows:
Full (100%) when Display is set at ' Full Bar '
Half (50% or 100% of half a bar) when Display is set at ' Half Bar '
A negative VD (more bearish than bullish volume) will fill the bar from the top (or centre) of the bar towards the bottom, and a positive VD will fill a bar from the bottom (or centre) of the bar towards the top.
A negative VD on a green candle will show a red-coloured VD against a green-bordered candle. On the other hand, a positive VD on a red candle will show a green-coloured VD against a red-bordered candle.
Colours for VD sentiment opposite to the candle sentiment can be set differently if desired.
🔹 Highest Volume Price Level
The script displays a white (black on light mode) line highlighting the intrabar price level with the highest volume.
When ' Show Previous Max Volume Price ' is checked, a white (black on light mode) dot is displayed 1 bar to the right.
🔶 DETAILS
🔹 Tick/LTF data
The above example used Lower TimeFrame (LTF) data.
The following example uses real-time tick data ( Settings -> Data From )
Both options, LTF or tick data, will show a vertical dotted line where the data starts.
🔹 LTF settings
When ' Data from ' LTF is chosen and ' Auto ' enabled, the LTF will be the nearest possible x times smaller TF than the current TF. When 'Premium' is disabled, the minimum TF will always be 1 minute to ensure TradingView plans lower than Premium don't get an error.
Examples with current Daily TF (when Premium is enabled):
500 : 3 minute LTF
1500 (default): 1 minute LTF
5000: 30 seconds LTF (1 minute if Premium is disabled)
🔹 Notes
Different LTFs give different data, which means different results; this doesn't mean it isn't correct; they are just different data sets.
(LTF is displayed at the top right corner)
To ensure maximum visibility of values, we recommend using Bars from the Bar's style menu.
🔶 SETTINGS
Data from: Lower TimeFrame or real-time Tick data
Resolution: LTF setting
Auto + multiple: Adjusts the initial set resolution
Premium: Enable when your TradingView plan is Premium or higher
🔹 Intrabar Data
Colours
Display: Full/Half bar
Show previous max volume price: White/black dot, showing previous highest volume price level
🔹 Table
Show TF: Show LTF at the top right corner
Colour + table text size
🔹 Details
Show details: label with 'Volume', 'Delta' (VD) and '%'
See USAGE for more information
Statistics • Chi Square • P-value • SignificanceThe Statistics • Chi Square • P-value • Significance publication aims to provide a tool for combining different conditions and checking whether the outcome is significant using the Chi-Square Test and P-value.
🔶 USAGE
The basic principle is to compare two or more groups and check the results of a query test, such as asking men and women whether they want to see a romantic or non-romantic movie.
–––––––––––––––––––––––––––––––––––––––––––––
| | ROMANTIC | NON-ROMANTIC | ⬅︎ MOVIE |
–––––––––––––––––––––––––––––––––––––––––––––
| MEN | 2 | 8 | 10 |
–––––––––––––––––––––––––––––––––––––––––––––
| WOMEN | 7 | 3 | 10 |
–––––––––––––––––––––––––––––––––––––––––––––
|⬆︎ SEX | 10 | 10 | 20 |
–––––––––––––––––––––––––––––––––––––––––––––
We calculate the Chi-Square Formula, which is:
Χ² = Σ ( (Observed Value − Expected Value)² / Expected Value )
In this publication, this is:
chiSquare = 0.
for i = 0 to rows -1
for j = 0 to colums -1
observedValue = aBin.get(i).aFloat.get(j)
expectedValue = math.max(1e-12, aBin.get(i).aFloat.get(colums) * aBin.get(rows).aFloat.get(j) / sumT) //Division by 0 protection
chiSquare += math.pow(observedValue - expectedValue, 2) / expectedValue
Together with the 'Degree of Freedom', which is (rows − 1) × (columns − 1) , the P-value can be calculated.
In this case it is P-value: 0.02462
A P-value lower than 0.05 is considered to be significant. Statistically, women tend to choose a romantic movie more, while men prefer a non-romantic one.
Users have the option to choose a P-value, calculated from a standard table or through a math.ucla.edu - Javascript-based function (see references below).
Note that the population (10 men + 10 women = 20) is small, something to consider.
Either way, this principle is applied in the script, where conditions can be chosen like rsi, close, high, ...
🔹 CONDITION
Conditions are added to the left column ('CONDITION')
For example, previous rsi values (rsi ) between 0-100, divided in separate groups
🔹 CLOSE
Then, the movement of the last close is evaluated
UP when close is higher then previous close (close )
DOWN when close is lower then previous close
EQUAL when close is equal then previous close
It is also possible to use only 2 columns by adding EQUAL to UP or DOWN
UP
DOWN/EQUAL
or
UP/EQUAL
DOWN
In other words, when previous rsi value was between 80 and 90, this resulted in:
19 times a current close higher than previous close
14 times a current close lower than previous close
0 times a current close equal than previous close
However, the P-value tells us it is not statistical significant.
NOTE: Always keep in mind that past behaviour gives no certainty about future behaviour.
A vertical line is drawn at the beginning of the chosen population (max 4990)
Here, the results seem significant.
🔹 GROUPS
It is important to ensure that the groups are formed correctly. All possibilities should be present, and conditions should only be part of 1 group.
In the example above, the two top situations are acceptable; close against close can only be higher, lower or equal.
The two examples at the bottom, however, are very poorly constructed.
Several conditions can be placed in more than 1 group, and some conditions are not integrated into a group. Even if the results are significant, they are useless because of the group formation.
A population count is added as an aid to spot errors in group formation.
In this example, there is a discrepancy between the population and total count due to the absence of a condition.
The results when rsi was between 5-25 are not included, resulting in unreliable results.
🔹 PRACTICAL EXAMPLES
In this example, we have specific groups where the condition only applies to that group.
For example, the condition rsi > 55 and rsi <= 65 isn't true in another group.
Also, every possible rsi value (0 - 100) is present in 1 of the groups.
rsi > 15 and rsi <= 25 28 times UP, 19 times DOWN and 2 times EQUAL. P-value: 0.01171
When looking in detail and examining the area 15-25 RSI, we see this:
The population is now not representative (only checking for RSI between 15-25; all other RSI values are not included), so we can ignore the P-value in this case. It is merely to check in detail. In this case, the RSI values 23 and 24 seem promising.
NOTE: We should check what the close price did without any condition.
If, for example, the close price had risen 100 times out of 100, this would make things very relative.
In this case (at least two conditions need to be present), we set 1 condition at 'always true' and another at 'always false' so we'll get only the close values without any condition:
Changing the population or the conditions will change the P-value.
In the following example, the outcome is evaluated when:
close value from 1 bar back is higher than the close value from 2 bars back
close value from 1 bar back is lower/equal than the close value from 2 bars back
Or:
close value from 1 bar back is higher than the close value from 2 bars back
close value from 1 bar back is equal than the close value from 2 bars back
close value from 1 bar back is lower than the close value from 2 bars back
In both examples, all possibilities of close against close are included in the calculations. close can only by higher, equal or lower than close
Both examples have the results without a condition included (5 = 5 and 5 < 5) so one can compare the direction of current close.
🔶 NOTES
• Always keep in mind that:
Past behaviour gives no certainty about future behaviour.
Everything depends on time, cycles, events, fundamentals, technicals, ...
• This test only works for categorical data (data in categories), such as Gender {Men, Women} or color {Red, Yellow, Green, Blue} etc., but not numerical data such as height or weight. One might argue that such tests shouldn't use rsi, close, ... values.
• Consider what you're measuring
For example rsi of the current bar will always lead to a close higher than the previous close, since this is inherent to the rsi calculations.
• Be careful; often, there are na -values at the beginning of the series, which are not included in the calculations!
• Always keep in mind considering what the close price did without any condition
• The numbers must be large enough. Each entry must be five or more. In other words, it is vital to make the 'population' large enough.
• The code can be developed further, for example, by splitting UP, DOWN in close UP 1-2%, close UP 2-3%, close UP 3-4%, ...
• rsi can be supplemented with stochRSI, MFI, sma, ema, ...
🔶 SETTINGS
🔹 Population
• Choose the population size; in other words, how many bars you want to go back to. If fewer bars are available than set, this will be automatically adjusted.
🔹 Inputs
At least two conditions need to be chosen.
• Users can add up to 11 conditions, where each condition can contain two different conditions.
🔹 RSI
• Length
🔹 Levels
• Set the used levels as desired.
🔹 Levels
• P-value: P-value retrieved using a standard table method or a function.
• Used function, derived from Chi-Square Distribution Function; JavaScript
LogGamma(Z) =>
S = 1
+ 76.18009173 / Z
- 86.50532033 / (Z+1)
+ 24.01409822 / (Z+2)
- 1.231739516 / (Z+3)
+ 0.00120858003 / (Z+4)
- 0.00000536382 / (Z+5)
(Z-.5) * math.log(Z+4.5) - (Z+4.5) + math.log(S * 2.50662827465)
Gcf(float X, A) => // Good for X > A +1
A0=0., B0=1., A1=1., B1=X, AOLD=0., N=0
while (math.abs((A1-AOLD)/A1) > .00001)
AOLD := A1
N += 1
A0 := A1+(N-A)*A0
B0 := B1+(N-A)*B0
A1 := X*A0+N*A1
B1 := X*B0+N*B1
A0 := A0/B1
B0 := B0/B1
A1 := A1/B1
B1 := 1
Prob = math.exp(A * math.log(X) - X - LogGamma(A)) * A1
1 - Prob
Gser(X, A) => // Good for X < A +1
T9 = 1. / A
G = T9
I = 1
while (T9 > G* 0.00001)
T9 := T9 * X / (A + I)
G := G + T9
I += 1
G *= math.exp(A * math.log(X) - X - LogGamma(A))
Gammacdf(x, a) =>
GI = 0.
if (x<=0)
GI := 0
else if (x
Chisqcdf = Gammacdf(Z/2, DF/2)
Chisqcdf := math.round(Chisqcdf * 100000) / 100000
pValue = 1 - Chisqcdf
🔶 REFERENCES
mathsisfun.com, Chi-Square Test
Chi-Square Distribution Function
Xen's Flag Pattern Scalper1. Input Parameters:
FlagLength: Determines the length of the flag pattern.
TakeProfit1Ratio, takeProfit2Ratio, takeProfit3Ratio: Define the ratios for calculating
the take-profit levels relative to the entry price.
RiskRewardRatio: Specifies the risk-reward ratio for calculating the stop-loss level
relative to the entry price.
2 Flag Conditions:
BullishFlag: Checks if the current bar meets the conditions for a bullish flag pattern. It
evaluates to true if the low of the current bar is lower than the low flagLength bars
ago, and the close of the current bar is higher than the high flagLength bars ago.
BearishFlag: Checks if the current bar meets the conditions for a bearish flag pattern. It evaluates to true if the high of the current bar is higher than the high flagLength bars
ago, and the close of the current bar is lower than the low flagLength bars ago.
3. Entry Price:
EntryPrice: Calculates the entry price based on whether a bullish or bearish flag
pattern is identified. For a bullish flag, the entry price is set to the low of the current bar.
For a bearish flag, the entry price is set to the high of the current bar.
4. Stop Loss:
StopLoss: Determines the stop-loss level based on the entry price and the specified
riskRewardRatio . For a bullish flag, the stop-loss level is calculated by subtracting the
difference between the high and low of the current bar multiplied by the riskRewardRatio from the low of the current bar. For a bearish flag, the stop-loss level
is calculated similarly but added to the high of the current bar.
5. Take Profit Levels:
Three take-profit levels ( takeProfit1, takeProfit2, takeProfit3 ) are calculated based on
the entry price, stop-loss level, and specified take-profit ratios ( takeProfit1Ratio,
takeProfit2Ratio, takeProfit3Ratio ).
6. Plotting Signals and Levels:
Bullish and bearish flag patterns are plotted using triangle shapes ( shape.triangleup for
bullish and shape.triangledown for bearish) above or below the bars, respectively.
Entry, stop-loss, and take-profit levels are plotted using horizontal lines ( line.new )
with different colors and styles. Entry and stop-loss levels are labeled with "Entry" and "SL",
respectively, while take-profit levels are labeled with "TP 1", "TP 2", and "TP 3".
The colors for bullish flags are white for entry, red for stop-loss, and green for take-profit levels. For bearish flags, the colors are the same, but the labels are plotted above the bars.
7. Label Placement:
Labels for entry, stop-loss, and take-profit levels are placed a distance of 4 bars to the right
of the entry price using bar_index + 4 .
This indicator is intended to help traders identify flag patterns on price charts and visualize potential entry, stop-loss, and take-profit levels associated with these patterns.
Please use risk management and when TP1 is hit, move stoploss to breakeven .
ottlibLibrary "ottlib"
█ OVERVIEW
This library contains functions for the calculation of the OTT (Optimized Trend Tracker) and its variants, originally created by Anıl Özekşi (Anil_Ozeksi). Special thanks to him for the concept and to Kıvanç Özbilgiç (KivancOzbilgic) and dg_factor (dg_factor) for adapting them to Pine Script.
█ WHAT IS "OTT"
The OTT (Optimized Trend Tracker) is a highly customizable and very effective trend-following indicator that relies on moving averages and a trailing stop at its core. Moving averages help reduce noise by smoothing out sudden price movements in the markets, while trailing stops assist in detecting trend reversals with precision. Initially developed as a noise-free trailing stop, the current variants of OTT range from rapid trend reversal detection to long-term trend confirmation, thanks to its extensive customizability.
It's well-known variants are:
OTT (Optimized Trend Tracker).
TOTT (Twin OTT).
OTT Channels.
RISOTTO (RSI OTT).
SOTT (Stochastic OTT).
HOTT & LOTT (Highest & Lowest OTT)
ROTT (Relative OTT)
FT (Original name is Fırsatçı Trend in Turkish which translates to Opportunist Trend)
█ LIBRARY FEATURES
This library has been prepared in accordance with the style, coding, and annotation standards of Pine Script version 5. As a result, explanations and examples will appear when users hover over functions or enter function parameters in the editor.
█ USAGE
Usage of this library is very simple. Just import it to your script with the code below and use its functions.
import ismailcarlik/ottlib/1 as ottlib
█ FUNCTIONS
• f_vidya(source, length, cmoLength)
Short Definition: Chande's Variable Index Dynamic Average (VIDYA).
Details: This function computes Chande's Variable Index Dynamic Average (VIDYA), which serves as the original moving average for OTT. The 'length' parameter determines the number of bars used to calculate the average of the given source. Lower values result in less smoothing of prices, while higher values lead to greater smoothing. While primarily used internally in this library, it has been made available for users who wish to utilize it as a moving average or use in custom OTT implementations.
Parameters:
source (float) : (series float) Series of values to process.
length (simple int) : (simple int) Number of bars to lookback.
cmoLength (simple int) : (simple int) Number of bars to lookback for calculating CMO. Default value is `9`.
Returns: (float) Calculated average of `source` for `length` bars back.
Example:
vidyaValue = ottlib.f_vidya(source = close, length = 20)
plot(vidyaValue, color = color.blue)
• f_mostTrail(source, multiplier)
Short Definition: Calculates trailing stop value.
Details: This function calculates the trailing stop value for a given source and the percentage. The 'multiplier' parameter defines the percentage of the trailing stop. Lower values are beneficial for catching short-term reversals, while higher values aid in identifying long-term trends. Although only used once internally in this library, it has been made available for users who wish to utilize it as a traditional trailing stop or use in custom OTT implementations.
Parameters:
source (float) : (series int/float) Series of values to process.
multiplier (simple float) : (simple float) Percent of trailing stop.
Returns: (float) Calculated value of trailing stop.
Example:
emaValue = ta.ema(source = close, length = 14)
mostValue = ottlib.f_mostTrail(source = emaValue, multiplier = 2.0)
plot(mostValue, color = emaValue >= mostValue ? color.green : color.red)
• f_ottTrail(source, multiplier)
Short Definition: Calculates OTT-specific trailing stop value.
Details: This function calculates the trailing stop value for a given source in the manner used in OTT. Unlike a traditional trailing stop, this function modifies the traditional trailing stop value from two bars prior by adjusting it further with half the specified percentage. The 'multiplier' parameter defines the percentage of the trailing stop. Lower values are beneficial for catching short-term reversals, while higher values aid in identifying long-term trends. Although primarily used internally in this library, it has been made available for users who wish to utilize it as a trailing stop or use in custom OTT implementations.
Parameters:
source (float) : (series int/float) Series of values to process.
multiplier (simple float) : (simple float) Percent of trailing stop.
Returns: (float) Calculated value of OTT-specific trailing stop.
Example:
vidyaValue = ottlib.f_vidya(source = close, length = 20)
ottValue = ottlib.f_ottTrail(source = vidyaValue, multiplier = 1.5)
plot(ottValue, color = vidyaValue >= ottValue ? color.green : color.red)
• ott(source, length, multiplier)
Short Definition: Calculates OTT (Optimized Trend Tracker).
Details: The OTT consists of two lines. The first, known as the "Support Line", is the VIDYA of the given source. The second, called the "OTT Line", is the trailing stop based on the Support Line. The market is considered to be in an uptrend when the Support Line is above the OTT Line, and in a downtrend when it is below.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `2`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `1.4`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `ottLine`.
Example:
= ottlib.ott(source = close, length = 2, multiplier = 1.4)
longCondition = ta.crossover(supportLine, ottLine)
shortCondition = ta.crossunder(supportLine, ottLine)
• tott(source, length, multiplier, bandsMultiplier)
Short Definition: Calculates TOTT (Twin OTT).
Details: TOTT consists of three lines: the "Support Line," which is the VIDYA of the given source; the "Upper Line," a trailing stop of the Support Line adjusted with an added multiplier; and the "Lower Line," another trailing stop of the Support Line, adjusted with a reduced multiplier. The market is considered in an uptrend if the Support Line is above the Upper Line and in a downtrend if it is below the Lower Line.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `40`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.6`.
bandsMultiplier (simple float) : Multiplier for bands. Default value is `0.0006`.
Returns: ( [ float, float, float ]) Tuple of `supportLine`, `upperLine` and `lowerLine`.
Example:
= ottlib.tott(source = close, length = 40, multiplier = 0.6, bandsMultiplier = 0.0006)
longCondition = ta.crossover(supportLine, upperLine)
shortCondition = ta.crossunder(supportLine, lowerLine)
• ott_channel(source, length, multiplier, ulMultiplier, llMultiplier)
Short Definition: Calculates OTT Channels.
Details: OTT Channels comprise nine lines. The central line, known as the "Mid Line," is the OTT of the given source's VIDYA. The remaining lines are positioned above and below the Mid Line, shifted by specified multipliers.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`
length (simple int) : (simple int) Number of bars to lookback. Default value is `2`
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `1.4`
ulMultiplier (simple float) : (simple float) Multiplier for upper line. Default value is `0.01`
llMultiplier (simple float) : (simple float) Multiplier for lower line. Default value is `0.01`
Returns: ( [ float, float, float, float, float, float, float, float, float ]) Tuple of `ul4`, `ul3`, `ul2`, `ul1`, `midLine`, `ll1`, `ll2`, `ll3`, `ll4`.
Example:
= ottlib.ott_channel(source = close, length = 2, multiplier = 1.4, ulMultiplier = 0.01, llMultiplier = 0.01)
• risotto(source, length, rsiLength, multiplier)
Short Definition: Calculates RISOTTO (RSI OTT).
Details: RISOTTO comprised of two lines: the "Support Line," which is the VIDYA of the given source's RSI value, calculated based on the length parameter, and the "RISOTTO Line," a trailing stop of the Support Line. The market is considered in an uptrend when the Support Line is above the RISOTTO Line, and in a downtrend if it is below.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `50`.
rsiLength (simple int) : (simple int) Number of bars used for RSI calculation. Default value is `100`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.2`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `risottoLine`.
Example:
= ottlib.risotto(source = close, length = 50, rsiLength = 100, multiplier = 0.2)
longCondition = ta.crossover(supportLine, risottoLine)
shortCondition = ta.crossunder(supportLine, risottoLine)
• sott(source, kLength, dLength, multiplier)
Short Definition: Calculates SOTT (Stochastic OTT).
Details: SOTT is comprised of two lines: the "Support Line," which is the VIDYA of the given source's Stochastic value, based on the %K and %D lengths, and the "SOTT Line," serving as the trailing stop of the Support Line. The market is considered in an uptrend when the Support Line is above the SOTT Line, and in a downtrend when it is below.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
kLength (simple int) : (simple int) Stochastic %K length. Default value is `500`.
dLength (simple int) : (simple int) Stochastic %D length. Default value is `200`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.5`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `sottLine`.
Example:
= ottlib.sott(source = close, kLength = 500, dLength = 200, multiplier = 0.5)
longCondition = ta.crossover(supportLine, sottLine)
shortCondition = ta.crossunder(supportLine, sottLine)
• hottlott(length, multiplier)
Short Definition: Calculates HOTT & LOTT (Highest & Lowest OTT).
Details: HOTT & LOTT are composed of two lines: the "HOTT Line", which is the OTT of the highest price's VIDYA, and the "LOTT Line", the OTT of the lowest price's VIDYA. A high price surpassing the HOTT Line can be considered a long signal, while a low price dropping below the LOTT Line may indicate a short signal.
Parameters:
length (simple int) : (simple int) Number of bars to lookback. Default value is `20`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.6`.
Returns: ( [ float, float ]) Tuple of `hottLine` and `lottLine`.
Example:
= ottlib.hottlott(length = 20, multiplier = 0.6)
longCondition = ta.crossover(high, hottLine)
shortCondition = ta.crossunder(low, lottLine)
• rott(source, length, multiplier)
Short Definition: Calculates ROTT (Relative OTT).
Details: ROTT comprises two lines: the "Support Line", which is the VIDYA of the given source, and the "ROTT Line", the OTT of the Support Line's VIDYA. The market is considered in an uptrend if the Support Line is above the ROTT Line, and in a downtrend if it is below. ROTT is similar to OTT, but the key difference is that the ROTT Line is derived from the VIDYA of two bars of Support Line, not directly from it.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `200`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.1`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `rottLine`.
Example:
= ottlib.rott(source = close, length = 200, multiplier = 0.1)
isUpTrend = supportLine > rottLine
isDownTrend = supportLine < rottLine
• ft(source, length, majorMultiplier, minorMultiplier)
Short Definition: Calculates Fırsatçı Trend (Opportunist Trend).
Details: FT is comprised of two lines: the "Support Line", which is the VIDYA of the given source, and the "FT Line", a trailing stop of the Support Line calculated using both minor and major trend values. The market is considered in an uptrend when the Support Line is above the FT Line, and in a downtrend when it is below.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `30`.
majorMultiplier (simple float) : (simple float) Percent of major trend. Default value is `3.6`.
minorMultiplier (simple float) : (simple float) Percent of minor trend. Default value is `1.8`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `ftLine`.
Example:
= ottlib.ft(source = close, length = 30, majorMultiplier = 3.6, minorMultiplier = 1.8)
longCondition = ta.crossover(supportLine, ftLine)
shortCondition = ta.crossunder(supportLine, ftLine)
█ CUSTOM OTT CREATION
Users can create custom OTT implementations using f_ottTrail function in this library. The example code which uses EMA of 7 period as moving average and calculates OTT based of it is below.
Source Code:
//@version=5
indicator("Custom OTT", shorttitle = "COTT", overlay = true)
import ismailcarlik/ottlib/1 as ottlib
src = input.source(close, title = "Source")
length = input.int(7, title = "Length", minval = 1)
multiplier = input.float(2.0, title = "Multiplier", minval = 0.1)
support = ta.ema(source = src, length = length)
ott = ottlib.f_ottTrail(source = support, multiplier = multiplier)
pSupport = plot(support, title = "Moving Average Line (Support)", color = color.blue)
pOtt = plot(ott, title = "Custom OTT Line", color = color.orange)
fillColor = support >= ott ? color.new(color.green, 60) : color.new(color.red, 60)
fill(pSupport, pOtt, color = fillColor, title = "Direction")
Result:
█ DISCLAIMER
Trading is risky and most of the day traders lose money eventually. This library and its functions are only for educational purposes and should not be construed as financial advice. Past performances does not guarantee future results.
A_Traders_Edge__LibraryLibrary "A_Traders_Edge__Library"
- A Trader's Edge (ATE)_Library was created to assist in constructing Market Overview Scanners (MOS)
LabelLocation(_firstLocation)
This function is used when there's a desire to print an assets ALERT LABELS at a set location on the scale that will
NOT change throughout the progression of the script. This is created so that if a lot of alerts are triggered, they
will stay relatively visible and not overlap each other. Ex. If you set your '_firstLocation' parameter as 1, since
there are a max of 40 assets that can be scanned, the 1st asset's location is assigned the value in the '_firstLocation' parameter,
the 2nd asset's location is the (1st asset's location+1)...and so on. If your first location is set to 81 then
the 1st asset is 81 and 2nd is 82 and so on until the 40th location = 120(in this particular example).
Parameters:
_firstLocation (simple int) : (simple int)
Optional(starts at 1 if no parameter added).
Location that you want the first asset to print its label if is triggered to do so.
ie. loc2=loc1+1, loc3=loc2+1, etc.
Returns: Returns 40 output variables each being a different location to print the labels so that an asset is asssigned to
a particular location on the scale. Regardless of if you have the maximum amount of assets being screened (40 max), this
function will output 40 locations… So there needs to be 40 variables assigned in the tuple in this function. What I
mean by that is you need to have 40 output location variables within your tuple (ie. between the ' ') regarless of
if your scanning 40 assets or not. If you only have 20 assets in your scripts input settings, then only the first 20
variables within the ' ' Will be assigned to a value location and the other 20 will be assigned 'NA', but their
variables still need to be present in the tuple.
SeparateTickerids(_string)
You must form this single tickerID input string exactly as described in the scripts info panel (little gray 'i' that
is circled at the end of the settings in the settings/input panel that you can hover your cursor over this 'i' to read the
details of that particular input). IF the string is formed correctly then it will break up this single string parameter into
a total of 40 separate strings which will be all of the tickerIDs that the script is using in your MO scanner.
Parameters:
_string (simple string) : (string)
A maximum of 40 Tickers (ALL joined as 1 string for the input parameter) that is formulated EXACTLY as described
within the tooltips of the TickerID inputs in my MOS Scanner scripts:
assets = input.text_area(tIDset1, title="TickerID (MUST READ TOOLTIP)", tooltip="Accepts 40 TICKERID's for each
copy of the script on the chart. TEXT FORMATTING RULES FOR TICKERID'S:
(1) To exclude the EXCHANGE NAME in the Labels, de-select the next input option.
(2) MUST have a space (' ') AFTER each TickerID.
(3) Capitalization in the Labels will match cap of these TickerID's.
(4) If your asset has a BaseCurrency & QuoteCurrency (ie. ADAUSDT ) BUT you ONLY want Labels
to show BaseCurrency(ie.'ADA'), include a FORWARD SLASH ('/') between the Base & Quote (ie.'ADA/USDT')", display=display.none)
Returns: Returns 40 output variables of the different strings of TickerID's (ie. you need to output 40 variables within the
tuple ' ' regardless of if you were scanning using all possible (40) assets or not.
If your scanning for less than 40 assets, then once the variables are assigned to all of the tickerIDs, the rest
of the 40 variables in the tuple will be assigned "NA".
TickeridForLabelsAndSecurity(_includeExchange, _ticker)
This function accepts the TickerID Name as its parameter and produces a single string that will be used in all of your labels.
Parameters:
_includeExchange (simple bool) : (bool)
Optional(if parameter not included in function it defaults to false ).
Used to determine if the Exchange name will be included in all labels/triggers/alerts.
_ticker (simple string) : (string)
For this parameter, input the varible named '_coin' from your 'f_main()' function for this parameter. It is the raw
Ticker ID name that will be processed.
Returns: ( )
Returns 2 output variables:
1st ('_securityTickerid') is to be used in the 'request.security()' function as this string will contain everything
TV needs to pull the correct assets data.
2nd ('lblTicker') is to be used in all of the labels in your MOS as it will only contain what you want your labels
to show as determined by how the tickerID is formulated in the MOS's input.
InvalidTID(_tablePosition, _stackVertical, _close, _securityTickerid, _invalidArray)
This is to add a table in the middle right of your chart that prints all the TickerID's that were either not formulated
correctly in the '_source' input or that is not a valid symbol and should be changed.
Parameters:
_tablePosition (simple string) : (string)
Optional(if parameter not included, it defaults to position.middle_right). Location on the chart you want the table printed.
Possible strings include: position.top_center, position.top_left, position.top_right, position.middle_center,
position.middle_left, position.middle_right, position.bottom_center, position.bottom_left, position.bottom_right.
_stackVertical (simple bool) : (bool)
Optional(if parameter not included, it defaults to true). All of the assets that are counted as INVALID will be
created in a list. If you want this list to be prited as a column then input 'true' here.
_close (float) : (float)
If you want them printed as a single row then input 'false' here.
This should be the closing value of each of the assets being tested to determine in the TickerID is valid or not.
_securityTickerid (string) : (string)
Throughout the entire charts updates, if a '_close' value is never regestered then the logic counts the asset as INVALID.
This will be the 1st TickerID varible (named _securityTickerid) outputted from the tuple of the TickeridForLabels()
function above this one.
_invalidArray (string ) : (array string)
Input the array from the original script that houses all of the invalidArray strings.
Returns: (na)
Returns a table with the screened assets Invalid TickerID's. Table draws automatically if any are Invalid, thus,
no output variable to deal with.
LabelSizes(_barCnt, _lblSzRfrnce)
This function sizes your Alert Trigger Labels according to the amount of Printed Bars the chart has printed within
a set time period, while also keeping in mind the smallest relative reference size you input in the 'lblSzRfrnceInput'
parameter of this function. A HIGHER % of Printed Bars(aka...more trades occurring for that asset on the exchange),
the LARGER the Name Label will print, potentially showing you the better opportunities on the exchange to avoid
exchange manipulation liquidations.
*** SHOULD NOT be used as size of labels that are your asset Name Labels next to each asset's Line Plot...
if your MOS includes these as you want these to be the same size for every asset so the larger ones dont cover the
smaller ones if the plots are all close to each other ***
Parameters:
_barCnt (float) : (float)
Get the 1st variable('barCnt') from the 'PrintedBarCount' function's tuple and input it as this functions 1st input
parameter which will directly affect the size of the 2nd output variable ('alertTrigLabel') outputted by this function.
_lblSzRfrnce (string) : (string)
Optional(if parameter not included, it defaults to size.small). This will be the size of the 1st variable outputted
by this function ('assetNameLabel') BUT also affects the 2nd variable outputted by this function.
Returns: ( )
Returns 2 variables:
1st output variable ('AssetNameLabel') is assigned to the size of the 'lblSzRfrnceInput' parameter.
2nd output variable('alertTrigLabel') can be of variying sizes depending on the 'barCnt' parameter...BUT the smallest
size possible for the 2nd output variable ('alertTrigLabel') will be the size set in the 'lblSzRfrnceInput' parameter.
AssetColor()
This function is used to assign 40 different colors to 40 variables to be used for the different labels/plots.
Returns: Returns 40 output variables each with a different color assigned to them to be used in your plots & labels.
Regardless of if you have the maximum amount of assets your scanning(40 max) or less,
this function will assign 40 colors to 40 variables that you have between the ' '.
PrintedBarCount(_time, _barCntLength, _barCntPercentMin)
The Printed BarCount Filter looks back a User Defined amount of minutes and calculates the % of bars that have printed
out of the TOTAL amount of bars that COULD HAVE been printed within the same amount of time.
Parameters:
_time (int) : (int)
The time associated with the chart of the particular asset that is being screened at that point.
_barCntLength (int) : (int)
The amount of time (IN MINUTES) that you want the logic to look back at to calculate the % of bars that have actually
printed in the span of time you input into this parameter.
_barCntPercentMin (int) : (int)
The minimum % of Printed Bars of the asset being screened has to be GREATER than the value set in this parameter
for the output variable 'bc_gtg' to be true.
Returns: ( )
Returns 2 outputs:
1st is the % of Printed Bars that have printed within the within the span of time you input in the '_barCntLength' parameter.
2nd is true/false according to if the Printed BarCount % is above the threshold that you input into the '_barCntPercentMin' parameter.
RCI(_rciLength, _source, _interval)
You will see me using this a lot. DEFINITELY my favorite oscillator to utilize for SO many different things from
timing entries/exits to determining trends.Calculation of this indicator based on Spearmans Correlation.
Parameters:
_rciLength (int) : (int)
Amount of bars back to use in RCI calculations.
_source (float) : (float)
Source to use in RCI calculations (can use ANY source series. Ie, open,close,high,low,etc).
_interval (int) : (int)
Optional(if parameter not included, it defaults to 3). RCI calculation groups bars by this amount and then will.
rank these groups of bars.
Returns: (float)
Returns a single RCI value that will oscillates between -100 and +100.
RCIAVG(firstLength, _amtBtLengths, _rciSMAlen, _source, _interval)
20 RCI's are averaged together to get this RCI Avg (Rank Correlation Index Average). Each RCI (of the 20 total RCI)
has a progressively LARGER Lookback Length. Though the RCI Lengths are not individually adjustable,
there are 2 factors that ARE:
(1) the Lookback Length of the 1st RCI and
(2) the amount of values between one RCI's Lookback Length and the next.
*** If you set 'firstLength' to it's default of 200 and '_amtBtLengths' to it's default of 120 (aka AMOUNT BETWEEN LENGTHS=120)...
then RCI_2 Length=320, RCI_3 Length=440, RCI_4 Length=560, and so on.
Parameters:
firstLength (int) : (int)
Optional(if parameter is not included when the function is called, then it defaults to 200).
This parameter is the Lookback Length for the 1st RCI used in the RCI Avg.
_amtBtLengths (int) : (int)
Optional(if parameter not included when the function is called, then it defaults to 120).
This parameter is the value amount between each of the progressively larger lengths used for the 20 RCI's that
are averaged in the RCI Avg.
***** BEWARE ***** Too large of a value here will cause the calc to look back too far, causing an error(thus the value must be lowered)
_rciSMAlen (int) : (int)
Unlike the Single RCI Function, this function smooths out the end result using an SMA with a length value that is this parameter.
_source (float) : (float)
Source to use in RCI calculations (can use ANY source series. Ie, open,close,high,low,etc).
_interval (int) : (int)
Optional(if parameter not included, it defaults to 3). Within the RCI calculation, bars next to each other are grouped together
and then these groups are Ranked against each other. This parameter is the number of adjacent bars that are grouped together.
Returns: (float)
Returns a single RCI value that is the Avg of many RCI values that will oscillate between -100 and +100.
PercentChange(_startingValue, _endingValue)
This is a quick function to calculate how much % change has occurred between the '_startingValue' and the '_endingValue'
that you input into the function.
Parameters:
_startingValue (float) : (float)
The source value to START the % change calculation from.
_endingValue (float) : (float)
The source value to END the % change caluclation from.
Returns: Returns a single output being the % value between 0-100 (with trailing numbers behind a decimal). If you want only
a certain amount of numbers behind the decimal, this function needs to be put within a formatting function to do so.
Rescale(_source, _oldMin, _oldMax, _newMin, _newMax)
Rescales series with a known '_oldMin' & '_oldMax'. Use this when the scale of the '_source' to
rescale is known (bounded).
Parameters:
_source (float) : (float)
Source to be normalized.
_oldMin (int) : (float)
The known minimum of the '_source'.
_oldMax (int) : (float)
The known maximum of the '_source'.
_newMin (int) : (float)
What you want the NEW minimum of the '_source' to be.
_newMax (int) : (float)
What you want the NEW maximum of the '_source' to be.
Returns: Outputs your previously bounded '_source', but now the value will only move between the '_newMin' and '_newMax'
values you set in the variables.
Normalize_Historical(_source, _minimumLvl, _maximumLvl)
Normalizes '_source' that has a previously unknown min/max(unbounded) determining the max & min of the '_source'
FROM THE ENTIRE CHARTS HISTORY. ]
Parameters:
_source (float) : (float)
Source to be normalized.
_minimumLvl (int) : (float)
The Lower Boundary Level.
_maximumLvl (int) : (float)
The Upper Boundary Level.
Returns: Returns your same '_source', but now the value will MOSTLY stay between the minimum and maximum values you set in the
'_minimumLvl' and '_maximumLvl' variables (ie. if the source you input is an RSI...the output is the same RSI value but
instead of moving between 0-100 it will move between the maxand min you set).
Normailize_Local(_source, _length, _minimumLvl, _maximumLvl)
Normalizes series with previously unknown min/max(unbounded). Much like the Normalize_Historical function above this one,
but rather than using the Highest/Lowest Values within the ENTIRE charts history, this on looks for the Highest/Lowest
values of '_source' within the last ___ bars (set by user as/in the '_length' parameter. ]
Parameters:
_source (float) : (float)
Source to be normalized.
_length (int) : (float)
The amount of bars to look back to determine the highest/lowest '_source' value.
_minimumLvl (int) : (float)
The Lower Boundary Level.
_maximumLvl (int) : (float)
The Upper Boundary Level.
Returns: Returns a single output variable being the previously unbounded '_source' that is now normalized and bound between
the values used for '_minimumLvl'/'_maximumLvl' of the '_source' within the user defined lookback period.
ATR GOD Strategy by TradeSmart (PineConnector-compatible)This is a highly-customizable trading strategy made by TradeSmart, focusing mainly on ATR-based indicators and filters. The strategy is mainly intended for trading forex , and has been optimized using the Deep Backtest feature on the 2018.01.01 - 2023.06.01 interval on the EUR/USD (FXCM) 15M chart, with a Slippage value of 3, and a Commission set to 0.00004 USD per contract. The strategy is also made compatible with PineConnector , to provide an easy option to automate the strategy using a connection to MetaTrader. See tooltips for details on how to set up the bot, and check out our website for a detailed guide with images on how to automate the strategy.
The strategy was implemented using the following logic:
Entry strategy:
A total of 4 Supertrend values can be used to determine the entry logic. There is option to set up all 4 Supertrend parameters individually, as well as their potential to be used as an entry signal/or a trend filter. Long/Short entry signals will be determined based on the selected potential Supertrend entry signals, and filtered based on them being in an uptrend/downtrend (also available for setup). Please use the provided tooltips for each setup to see every detail.
Exit strategy:
4 different types of Stop Losses are available: ATR-based/Candle Low/High Based/Percentage Based/Pip Based. Additionally, Force exiting can also be applied, where there is option to set up 4 custom sessions, and exits will happen after the session has closed.
Parameters of every indicator used in the strategy can be tuned in the strategy settings as follows:
Plot settings:
Plot Signals: true by default, Show all Long and Short signals on the signal candle
Plot SL/TP lines: false by default, Checking this option will result in the TP and SL lines to be plotted on the chart.
Supertrend 1-4:
All the parameters of the Supertrends can be set up here, as well as their individual role in the entry logic.
Exit Strategy:
ATR Based Stop Loss: true by default
ATR Length (of the SL): 100 by default
ATR Smoothing (of the SL): RMA/SMMA by default
Candle Low/High Based Stop Loss: false by default, recent lowest or highest point (depending on long/short position) will be used to calculate stop loss value. Set 'Base Risk Multiplier' to 1 if you would like to use the calculated value as is. Setting it to a different value will count as an additional multiplier.
Candle Lookback (of the SL): 50 by default
Percentage Based Stop Loss: false by default, Set the stop loss to current price - % of current price (long) or price + % of current price (short).
Percentage (of the SL): 0.3 by default
Pip Based Stop Loss: Set the stop loss to current price - x pips (long) or price + x pips (short). Set 'Base Risk Multiplier' to 1 if you would like to use the calculated value as is. Setting it to a different value will count as an additional multiplier.
Pip (of the SL): 10 by default
Base Risk Multiplier: 4.5 by default, the stop loss will be placed at this risk level (meaning in case of ATR SL that the ATR value will be multiplied by this factor and the SL will be placed that value away from the entry level)
Risk to Reward Ratio: 1.5 by default, the take profit level will be placed such as this Risk/Reward ratio is met
Force Exiting:
4 total Force exit on custom session close options: none applied by default. If enabled, trades will close automatically after the set session is closed (on next candle's open).
Base Setups:
Allow Long Entries: true by default
Allow Short Entries: true by default
Order Size: 10 by default
Order Type: Capital Percentage by default, allows adjustment on how the position size is calculated: Cash: only the set cash amount will be used for each trade Contract(s): the adjusted number of contracts will be used for each trade Capital Percentage: a % of the current available capital will be used for each trade
ATR Limiter:
Use ATR Limiter: true by default, Only enter into any position (long/short) if ATR value is higher than the Low Boundary and lower than the High Boundary.
ATR Limiter Length: 50 by default
ATR Limiter Smoothing: RMA/SMMA by default
High Boundary: 1000 by default
Low Boundary: 0.0003 by default
MA based calculation: ATR value under MA by default, If not Unspecified, an MA is calculated with the ATR value as source. Only enter into position (long/short) if ATR value is higher/lower than the MA.
MA Type: RMA/SMMA by default
MA Length: 400 by default
Waddah Attar Filter:
Explosion/Deadzone relation: Not specified by default, Explosion over Deadzone: trades will only happen if the explosion line is over the deadzone line; Explosion under Deadzone: trades will only happen if the explosion line is under the deadzone line; Not specified: the opening of trades will not be based on the relation between the explosion and deadzone lines.
Limit trades based on trends: Not specified by default, Strong Trends: only enter long if the WA bar is colored green (there is an uptrend and the current bar is higher then the previous); only enter short if the WA bar is colored red (there is a downtrend and the current bar is higher then the previous); Soft Trends: only enter long if the WA bar is colored lime (there is an uptrend and the current bar is lower then the previous); only enter short if the WA bar is colored orange (there is a downtrend and the current bar is lower then the previous); All Trends: only enter long if the WA bar is colored green or lime (there is an uptrend); only enter short if the WA bar is colored red or orange (there is a downtrend); Not specified: the color of the WA bar (trend) is not relevant when considering entries.
WA bar value: Not specified by default, Over Explosion and Deadzone: only enter trades when the WA bar value is over the Explosion and Deadzone lines; Not specified: the relation between the explosion/deadzone lines to the value of the WA bar will not be used to filter opening trades.
Sensitivity: 150 by default
Fast MA Type: SMA by default
Fast MA Length: 10 by default
Slow MA Type: SMA
Slow MA Length: 20 by default
Channel MA Type: EMA by default
BB Channel Length: 20 by default
BB Stdev Multiplier: 2 by default
Trend Filter:
Use long trend filter 1: false by default, Only enter long if price is above Long MA.
Show long trend filter 1: false by default, Plot the selected MA on the chart.
TF1 - MA Type: EMA by default
TF1 - MA Length: 120 by default
TF1 - MA Source: close by default
Use short trend filter 1: false by default, Only enter long if price is above Long MA.
Show short trend filter 1: false by default, Plot the selected MA on the chart.
TF2 - MA Type: EMA by default
TF2 - MA Length: 120 by default
TF2 - MA Source: close by default
Volume Filter:
Only enter trades where volume is higher then the volume-based MA: true by default, a set type of MA will be calculated with the volume as source, and set length
MA Type: RMA/SMMA by default
MA Length: 200 by default
Date Range Limiter:
Limit Between Dates: false by default
Start Date: Jan 01 2023 00:00:00 by default
End Date: Jun 24 2023 00:00:00 by default
Session Limiter:
Show session plots: false by default, show market sessions on chart: Sidney (red), Tokyo (orange), London (yellow), New York (green)
Use session limiter: false by default, if enabled, trades will only happen in the ticked sessions below.
Sidney session: false by default, session between: 15:00 - 00:00 (EST)
Tokyo session: false by default, session between: 19:00 - 04:00 (EST)
London session: false by default, session between: 03:00 - 11:00 (EST)
New York session: false by default, session between: 08:00 - 17:00 (EST)
Trading Time:
Limit Trading Time: true by default, tick this together with the options below to enable limiting based on day and time
Valid Trading Days Global: 123567 by default, if the Limit Trading Time is on, trades will only happen on days that are present in this field. If any of the not global Valid Trading Days is used, this field will be neglected. Values represent days: Sunday (1), Monday (2), ..., Friday (6), Saturday(7) To trade on all days use: 123457
(1) Valid Trading Days: false, 123456 by default, values represent days: Sunday (1), Monday (2), ..., Friday (6), Saturday(7) The script will trade on days that are present in this field. Please make sure that this field and also (1) Valid Trading Hours Between is checked
(1) Valid Trading Hours Between: false, 1800-2000 by default, hours between which the trades can happen. The time is always in the exchange's timezone
All other options are also disabled by default
PineConnector Automation:
Use PineConnector Automation: false by default, In order for the connection to MetaTrader to work, you will need do perform prerequisite steps, you can follow our full guide at our website, or refer to the official PineConnector Documentation. To set up PineConnector Automation on the TradingView side, you will need to do the following:
1. Fill out the License ID field with your PineConnector License ID;
2. Fill out the Risk (trading volume) with the desired volume to be traded in each trade (the meaning of this value depends on the EA settings in Metatrader. Follow the detailed guide for additional information);
3. After filling out the fields, you need to enable the 'Use PineConnector Automation' option (check the box in the strategy settings);
4. Check if the chart has updated and you can see the appropriate order comments on your chart;
5. Create an alert with the strategy selected as Condition, and the Message as {{strategy.order.comment}} (should be there by default);
6. Enable the Webhook URL in the Notifications section, set it as the official PineConnector webhook address and enjoy your connection with MetaTrader.
License ID: 60123456789 by default
Risk (trading volume): 1 by default
NOTE! Fine-tuning/re-optimization is highly recommended when using other asset/timeframe combinations.
VolatilityIndicatorsLibrary "VolatilityIndicators"
This is a library of Volatility Indicators .
It aims to facilitate the grouping of this category of indicators, and also offer the customized supply of
the parameters and sources, not being restricted to just the closing price.
@Thanks and credits:
1. Dynamic Zones: Leo Zamansky, Ph.D., and David Stendahl
2. Deviation: Karl Pearson (code by TradingView)
3. Variance: Ronald Fisher (code by TradingView)
4. Z-score: Veronique Valcu (code by HPotter)
5. Standard deviation: Ronald Fisher (code by TradingView)
6. ATR (Average True Range): J. Welles Wilder (code by TradingView)
7. ATRP (Average True Range Percent): millerrh
8. Historical Volatility: HPotter
9. Min-Max Scale Normalization: gorx1
10. Mean Normalization: gorx1
11. Standardization: gorx1
12. Scaling to unit length: gorx1
13. LS Volatility Index: Alexandre Wolwacz (Stormer), Fabrício Lorenz, Fábio Figueiredo (Vlad) (code by me)
14. Bollinger Bands: John Bollinger (code by TradingView)
15. Bollinger Bands %: John Bollinger (code by TradingView)
16. Bollinger Bands Width: John Bollinger (code by TradingView)
dev(source, length, anotherSource)
Deviation. Measure the difference between a source in relation to another source
Parameters:
source (float)
length (simple int) : (int) Sequential period to calculate the deviation
anotherSource (float) : (float) Source to compare
Returns: (float) Bollinger Bands Width
variance(src, mean, length, biased, degreesOfFreedom)
Variance. A statistical measurement of the spread between numbers in a data set. More specifically,
variance measures how far each number in the set is from the mean (average), and thus from every other number in the set.
Variance is often depicted by this symbol: σ2. It is used by both analysts and traders to determine volatility and market security.
Parameters:
src (float) : (float) Source to calculate variance
mean (float) : (float) Mean (Moving average)
length (simple int) : (int) The sequential period to calcule the variance (number of values in data set)
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length. Only applies when biased parameter is defined as true.
Returns: (float) Standard deviation
stDev(src, length, mean, biased, degreesOfFreedom)
Measure the Standard deviation from a source in relation to it's moving average.
In this implementation, you pass the average as a parameter, allowing a more personalized calculation.
Parameters:
src (float) : (float) Source to calculate standard deviation
length (simple int) : (int) The sequential period to calcule the standard deviation
mean (float) : (float) Moving average.
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
else uses unbiased sample variance (n-1 or another value, as long as it is in the range between 1 and n-1), where n=length.
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length.
Returns: (float) Standard deviation
zscore(src, mean, length, biased, degreesOfFreedom)
Z-Score. A z-score is a statistical measurement that indicates how many standard deviations a data point is from
the mean of a data set. It is also known as a standard score. The formula for calculating a z-score is (x - μ) / σ,
where x is the individual data point, μ is the mean of the data set, and σ is the standard deviation of the data set.
Z-scores are useful in identifying outliers or extreme values in a data set. A positive z-score indicates that the
data point is above the mean, while a negative z-score indicates that the data point is below the mean. A z-score of
0 indicates that the data point is equal to the mean.
Z-scores are often used in hypothesis testing and determining confidence intervals. They can also be used to compare
data sets with different units or scales, as the z-score standardizes the data. Overall, z-scores provide a way to
measure the relative position of a data point in a data
Parameters:
src (float) : (float) Source to calculate z-score
mean (float) : (float) Moving average.
length (simple int) : (int) The sequential period to calcule the standard deviation
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
else uses unbiased sample variance (n-1 or another value, as long as it is in the range between 1 and n-1), where n=length.
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length.
Returns: (float) Z-score
atr(source, length)
ATR: Average True Range. Customized version with source parameter.
Parameters:
source (float) : (float) Source
length (simple int) : (int) Length (number of bars back)
Returns: (float) ATR
atrp(length, sourceP)
ATRP (Average True Range Percent)
Parameters:
length (simple int) : (int) Length (number of bars back) for ATR
sourceP (float) : (float) Source for calculating percentage relativity
Returns: (float) ATRP
atrp(source, length, sourceP)
ATRP (Average True Range Percent). Customized version with source parameter.
Parameters:
source (float) : (float) Source for ATR
length (simple int) : (int) Length (number of bars back) for ATR
sourceP (float) : (float) Source for calculating percentage relativity
Returns: (float) ATRP
historicalVolatility(lengthATR, lengthHist)
Historical Volatility
Parameters:
lengthATR (simple int) : (int) Length (number of bars back) for ATR
lengthHist (simple int) : (int) Length (number of bars back) for Historical Volatility
Returns: (float) Historical Volatility
historicalVolatility(source, lengthATR, lengthHist)
Historical Volatility
Parameters:
source (float) : (float) Source for ATR
lengthATR (simple int) : (int) Length (number of bars back) for ATR
lengthHist (simple int) : (int) Length (number of bars back) for Historical Volatility
Returns: (float) Historical Volatility
minMaxNormalization(src, numbars)
Min-Max Scale Normalization. Maximum and minimum values are taken from the sequential range of
numbars bars back, where numbars is a number defined by the user.
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
Returns: (float) Normalized value
minMaxNormalization(src, numbars, minimumLimit, maximumLimit)
Min-Max Scale Normalization. Maximum and minimum values are taken from the sequential range of
numbars bars back, where numbars is a number defined by the user.
In this implementation, the user explicitly provides the desired minimum (min) and maximum (max) values for the scale,
rather than using the minimum and maximum values from the data.
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
minimumLimit (simple float) : (float) Minimum value to scale
maximumLimit (simple float) : (float) Maximum value to scale
Returns: (float) Normalized value
meanNormalization(src, numbars, mean)
Mean Normalization
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
mean (float) : (float) Mean of source
Returns: (float) Normalized value
standardization(src, mean, stDev)
Standardization (Z-score Normalization). How "outside the mean" values relate to the standard deviation (ratio between first and second)
Parameters:
src (float) : (float) Source to normalize
mean (float) : (float) Mean of source
stDev (float) : (float) Standard Deviation
Returns: (float) Normalized value
scalingToUnitLength(src, numbars)
Scaling to unit length
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
Returns: (float) Normalized value
lsVolatilityIndex(movingAverage, sourceHvol, lengthATR, lengthHist, lenNormal, lowerLimit, upperLimit)
LS Volatility Index. Measures the volatility of price in relation to an average.
Parameters:
movingAverage (float) : (float) A moving average
sourceHvol (float) : (float) Source for calculating the historical volatility
lengthATR (simple int) : (float) Length for calculating the ATR (Average True Range)
lengthHist (simple int) : (float) Length for calculating the historical volatility
lenNormal (simple int) : (float) Length for normalization
lowerLimit (simple int)
upperLimit (simple int)
Returns: (float) LS Volatility Index
lsVolatilityIndex(sourcePrice, movingAverage, sourceHvol, lengthATR, lengthHist, lenNormal, lowerLimit, upperLimit)
LS Volatility Index. Measures the volatility of price in relation to an average.
Parameters:
sourcePrice (float) : (float) Source for measure the distance
movingAverage (float) : (float) A moving average
sourceHvol (float) : (float) Source for calculating the historical volatility
lengthATR (simple int) : (float) Length for calculating the ATR (Average True Range)
lengthHist (simple int) : (float) Length for calculating the historical volatility
lenNormal (simple int)
lowerLimit (simple int)
upperLimit (simple int)
Returns: (float) LS Volatility Index
bollingerBands(src, length, mult, basis)
Bollinger Bands. A Bollinger Band is a technical analysis tool defined by a set of lines plotted
two standard deviations (positively and negatively) away from a simple moving average (SMA) of the security's price,
but can be adjusted to user preferences. In this version you can pass a customized basis (moving average), not only SMA.
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) A tuple of Bollinger Bands, where index 1=basis; 2=basis+dev; 3=basis-dev; and dev=multiplier*stdev
bollingerBands(src, length, aMult, basis)
Bollinger Bands. A Bollinger Band is a technical analysis tool defined by a set of lines plotted
two standard deviations (positively and negatively) away from a simple moving average (SMA) of the security's price,
but can be adjusted to user preferences. In this version you can pass a customized basis (moving average), not only SMA.
Also, various multipliers can be passed, thus getting more bands (instead of just 2).
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
aMult (float ) : (float ) An array of multiplies used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands, where:
index 1=basis; 2=basis+dev1; 3=basis-dev1; 4=basis+dev2, 5=basis-dev2, 6=basis+dev2, 7=basis-dev2, Nup=basis+devN, Nlow=basis-devN
and dev1, dev2, devN are ```multiplier N * stdev```
bollingerBandsB(src, length, mult, basis)
Bollinger Bands %B - or Percent Bandwidth (%B).
Quantify or display where price (or another source) is in relation to the bands.
%B can be useful in identifying trends and trading signals.
Calculation:
%B = (Current Price - Lower Band) / (Upper Band - Lower Band)
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) Bollinger Bands %B
bollingerBandsB(src, length, aMult, basis)
Bollinger Bands %B - or Percent Bandwidth (%B).
Quantify or display where price (or another source) is in relation to the bands.
%B can be useful in identifying trends and trading signals.
Calculation
%B = (Current Price - Lower Band) / (Upper Band - Lower Band)
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
aMult (float ) : (float ) Array of multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands %B. The number of results in this array is equal the numbers of multipliers passed via parameter.
bollingerBandsW(src, length, mult, basis)
Bollinger Bands Width. Serve as a way to quantitatively measure the width between the Upper and Lower Bands
Calculation:
Bollinger Bands Width = (Upper Band - Lower Band) / Middle Band
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) Sequential period to calculate the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) Bollinger Bands Width
bollingerBandsW(src, length, aMult, basis)
Bollinger Bands Width. Serve as a way to quantitatively measure the width between the Upper and Lower Bands
Calculation
Bollinger Bands Width = (Upper Band - Lower Band) / Middle Band
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) Sequential period to calculate the standard deviation
aMult (float ) : (float ) Array of multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands Width. The number of results in this array is equal the numbers of multipliers passed via parameter.
dinamicZone(source, sampleLength, pcntAbove, pcntBelow)
Get Dynamic Zones
Parameters:
source (float) : (float) Source
sampleLength (simple int) : (int) Sample Length
pcntAbove (simple float) : (float) Calculates the top of the dynamic zone, considering that the maximum values are above x% of the sample
pcntBelow (simple float) : (float) Calculates the bottom of the dynamic zone, considering that the minimum values are below x% of the sample
Returns: A tuple with 3 series of values: (1) Upper Line of Dynamic Zone;
(2) Lower Line of Dynamic Zone; (3) Center of Dynamic Zone (x = 50%)
Examples:
Sniffer
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Overview
A vast majority of modern data analysis & modelling techniques rely upon the idea of hidden patterns, wether it is some type of visualisation tool or some form of a complex machine learning algorithm, the one thing that they have in common is the belief, that patterns tell us what’s hidden behind plain numbers. The same philosophy has been adopted by many traders & investors worldwide, there’s an entire school of thought that operates purely based on chart patterns. This is where Sniffer comes in, it is a tool designed to simplify & quantify the job of pattern recognition on any given price chart, by combining various factors & techniques that generate high-quality results.
This tool analyses bars selected by the user, and highlights bar clusters on the chart that exhibit similar behaviour across multiple dimensions. It can detect a single candle pattern like hammers or dojis, or it can handle multiple candles like morning/evening stars or double tops/bottoms, and many more. In fact, the tool is completely independent of such specific candle formations, instead, it works on the idea of vector similarity and generates a degree of similarity for every single combination of candles. Only the top-n matches are highlighted, users get to choose which patterns they want to analyse and to what degree, by customising the feature-space.
Background
In the world of trading, a common use-case is to scan a price chart for some specific candlestick formations & price structures, and then the chart is further analysed in reference to these events. Traders are often trying to answer questions like, when was the last time price showed similar behaviour, what are the instances similar to what price is doing right now, what happens when price forms a pattern like this, what were some of other indicators doing when this happened last(RSI, CCI, ADX etc), and many other abstract ideas to have a stronger confluence or to confirm a bias.Having such a context can be vital in making better informed decisions, but doing this manually on a chart that has thousands of candles can have many disadvantages. It’s tedious, human errors are rather likely, and even if it’s done with pin-point accuracy, chances are that we’ll miss out on many pieces of information. This is the thought that gave birth to Sniffer .
Sniffer tries to provide a general solution for pattern-based analysis by deploying vector-similarity computation techniques, that cover the full-breadth of a price chart and generate a list of top-n matches based on the criteria selected by the user. Most of these techniques come from the data science space, where vector similarity is often implemented to solve classification & clustering problems. Sniffer uses same principles of vector comparison, and computes a degree of similarity for every single candle formation within the selected range, and as a result generates a similarity matrix that captures how similar or dissimilar a set of candles is to the input set selected by the user.
How It Works
A brief overview of how the tool is implemented:
- Every bar is processed, and a set of features are mapped to it.
- Bars selected by the user are captured, and saved for later use.
- Once the all the bars have been processed, candles are back-tracked and degree of similarity is computed for every single bar(max-limit is 5000 bars).
- Degree of similarity is computed by comparing attributes like price range, candle breadth & volume etc.
- Similarity matrix is sorted and top-n results are highlighted on the chart through boxes of different colors.
A brief overview of the features space for bars:
- Range: Difference between high & low
- Body: Difference between close & open
- Volume: Traded volume for that candle
- Head: Upper wick for green candles & lower wick for red candles
- Tail: Lower wick for green candles & upper wick for red candles
- BTR: Body to Range ratio
- HTR: Head to Range ratio
- TTR: Tail to Range ratio
- HTB: Head to Body ratio
- TTB: Tail to Body ratio
- ROC: Rate of change for HL2 for four different periods
- RSI: Relative Strength Index
- CCI: Commodity Channel Index
- Stochastic: Stochastic Index
- ADX: DMI+, DMI- & ADX
A brief overview of how degree of similarity is calculated:
- Each bar set is compared to the inout bar set within the selected feature space
- Features are represented as vectors, and distance between the vectors is calculated
- Shorter the distance, greater the similarity
- Different distance calculation methods are available to choose from, such as Cosine, Euclidean, Lorentzian, Manhattan, & Pearson
- Each method is likely to generate slightly different results, users are expected to select the method & the feature space that best fits their use-case
How To Use It
- Usage of this tool is relatively straightforward, users can add this indicator to their chart and similar clusters will be highlighted automatically
- Users need to select a time range that will be treated as input, and bars within that range become the input formation for similarity calculations
- Boxes will be draw around the clusters that fit the matching criteria
- Boxes are color-coded, green color boxes represent the top one-third of the top-n matches, yellow boxes represent the middle third, red boxes are for bottom third, and white box represents user-input
- Boxes colors will be adjusted as you adjust input parameters, such as number of matches or look-back period
User Settings
Users can configure the following options:
- Select the time-range to set input bars
- Select the look-back period, number of candles to backtrack for similarity search
- Select the number of top-n matches to show on the chart
- Select the method for similarity calculation
- Adjust the feature space, this enables addition of custom features, such as pattern recognition, technical indicators, rate of change etc
- Toggle verbosity, shows degree of similarity as a percentage value inside the box
Top Features
- Pattern Agnostic: Designed to work with variable number of candles & complex patterns
- Customisable Feature Space: Users get to add custom features to each bar
- Comprehensive Comparison: Generates a degree of similarity for all possible combinations
Final Note
- Similarity matches will be shown only within last 4500 bars.
- In theory, it is possible to compute similarity for any size candle formations, indicator has been tested with formations of 50+ candles, but it is recommended to select smaller range for faster & cleaner results.
- As you move to smaller time frames, selected time range will provide a larger number of candles as input, which can produce undesired results, it is advised to adjust your selection when you change time frames. Seeking suggestions on how to directly receive bars as user input, instead of time range.
- At times, users may see array index out of bound error when setting up this indicator, this generally happens when the input range is not properly configured. So, it should disappear after you select the input range, still trying to figure out where it is coming from, suggestions are welcome.
Credits
- @HeWhoMustNotBeNamed for publishing such a handy PineScript Logger, it certainly made the job a lot easier.
Oliver Velez IndicatorOliver Velez is a well-known trader and educator who has developed multiple trading strategies. One of them is the 20-200sma strategy, which is a basic moving average crossover strategy. The strategy involves using two simple moving averages (SMAs) - a short-term SMA with a period of 20 and a long-term SMA with a period of 200 - on a 2-minute timeframe chart.
When the short-term SMA crosses above the long-term SMA, it signals a potential bullish trend and traders may look for opportunities to enter a long position. Conversely, when the short-term SMA crosses below the long-term SMA, it signals a potential bearish trend and traders may look for opportunities to enter a short position.
Traders using this strategy may also look for additional confirmations, such as price action signals or other technical indicators, before entering or exiting a trade. It is important to note that no trading strategy can guarantee profits, and traders should always use risk management techniques to limit potential losses.
This script is an implementation of the 2 SMA's (can also choose other types of MA's), with Elephant Bar Indicator (EBI) and the Tail Bars Indicator in TradingView.
The Elephant Bar Indicator is a technical indicator used in trading to identify potential trend reversals in the market. It is named after the large size of the bullish or bearish candlestick that it represents. The Tail Bars Indicator is a pattern recognition technique that identifies candlestick patterns with long tails or wicks.
The script starts by defining the input parameters for both indicators. For the Elephant Bar Indicator, the user inputs the lookback period and the size multiplier. For the Tail Bars Indicator, the user inputs the tail ratio and opposite wick ratio.
Next, the script calculates the moving averages of the closing price over the defined short and long periods using the Moving Average function. The script then calculates the average candle size and volume over the lookback period.
The script then identifies the Elephant Bars and Tail Bars using the input parameters and additional conditions. For Elephant Bars, the script identifies bullish and bearish bars that meet certain criteria, such as a size greater than the average candle size and volume greater than the average volume.
For Tail Bars, the script identifies bullish and bearish bars that have long tails or wicks and meet certain criteria such as opposite wick size less than or equal to the tail size multiplied by the input opposite wick ratio.
Finally, the script plots the Elephant Bar and Tail Bar signals on the chart using different colors and shapes. The script also plots the moving averages and Keltner Channels to help traders identify potential trend reversals.
It is still under development, so please, if someone has ideas to add, more than welcome
True Trend Average BandsThis is the indicator I am most proud of. After reading Glenn Neely's book "Mastering Eliott Waves" / "Neowave" and chatting with @timwest who got acknowledged by Neely, we came up with the idea of an moving average which does calculate the real average price since a trend started. Addionally I adapted a method from Neely Neowave and Tim Wests TimeAtMode to not force a timeframe on a chart but instead let the charts data decide which timeframe to use, to then calculate the real average price since the trend started.
It took me a while to get this right and coded, so take a moment and dive deeper and you might learn something new.
We assume that the price is in multiple trends on multiple timeframes, this is caused by short term traders, long term traders and investors who trade on different timeframes. To find out in which timeframe the important trends are, we have to look out for significant lows and highs. Then we change the timeframe in the chart to a value so that we have 10 to 20 bars since the significant low/high. While new bars are printed, and we reach more than 20 bars, we have to switch to a higher timeframe so we have 10 to 20 bars again. In the chart you see two significant trends: a downtrend on the 3 week timeframe and an uptrend from the 2 month timeframe. Based on the logic I have described, these are the two important timeframes to watch right now for the spx (there is another uptrend in the yearly chart, which is not shown here).
Now that we understand how to find the important timeframes, let's look what the magic in this script is that tells us the real average price since a trend started.
I developed a new type of moving average, which includes only the prices since a trend started. The difference to the regular sma is that it will not include prices which happened before the significant low or high happened. For example, if a top happened in a market 10 days ago, the regular sma20 would be calculated by 10 bars which happened before the top and 10 bars which happened after the top. If we want to know the average price of the last 10 bars we manually have to change the ma20 to the ma10 which is annoying manual work, additionally even if we use the ma10 in this case, and we look at yesterday's bar the ma10 will include 9 bars from after the top and one bar before the top, so the ma10 would only show the real average price for the current bar which is not what we want.
To come up with a solution to this problem, the True Trend Average searches for the lowest/highest bar in a given period (20 bars). Then starts to calculate the average value since the low/high. For example: if the price reaches a new 20 day high and then trades below it, the day of the high will be the sma1, the day after it's the sma2, ... up to the maximum look back length.
This way, we always know what the average price would have been if someone sold/bought a little bit every bar of his investment since the high/low.
Why is this even important? Let's assume we missed selling the top or buying the low, and think it would have been at least better to buy/sell a little bit since the new trend started. Once the price reaches the true trend average again, we can buy/sell, and it would be as good as selling/buying a little bit every day. We find prices to buy the dip and sell the bounce, which are as good as scaling in/out.
There is a lot more we can learn from these price levels but I think it is better to let you figure out yourself what you can learn from the information given by this indicator. Think about how market participants who accumulate or distribute feel when prices are above or below certain levels.
Now that we understand this new type of moving average, let's look into the lines we see in the chart:
The upper red band line shows the true trend average high price since the last significant top within 20 bars.
The lower red band line shows the true trend average hl2 price since the last significant top within 20 bars.
The lower green band line shows the true trend average low price since the last significant low within 20 bars.
The upper green band line shows the true trend average hl2 price since the last significant low within 20 bars.
The centerline is the average between the upper red band and the lower green band.
The teal lines show 1 standard deviation from the outer bands.
Before today only a few people had access to this indicator, now that it is public and open source, I am curious if you will find it useful and what you will do with it. Please share your findings.
/edit: The chart only shows the 3week timeframe so here are the other two trends from the 2month and 1year timeframe
