200/100 vs 190/80 EMA [jarederaj]Track the 200/100 EMA cross Vs the 180/90 EMA cross. Also, see the dates when these periods start on the chart.
Cerca negli script per "track"
Consecutive Highs/LowsTrack consecutive new highs/lows outside the Donchian range. Fans of the oldschool Turtle Strategy should enjoy the visualization.
Same logic as my "Walking the Bands" script, just with Donchian breaks instead of Bollinger tags.
Altcoin PortfolioTrack your altcoin portfolio balance in Fiat currency.
Make sure to open the data window to the right of your charts, it makes everything alot easier to read at a glance.
To learn more about customizing this script to fit your portfolio, watch the video here: youtu.be
To get more cool scripts and up-to-date information about Autoview, join us in slack slack.with.pink
As per the usual, we hope this script helps with your trading venture.
Good luck, and happy trading.
Pivot Points. High & Lows By Reversal Percentage# Pivot Points. High & Lows By Reversal Percentage by Jal9000
📚 Library: pivots
This Pine Script library provides a robust function for identifying and tracking pivot points (reversal points) in price data, suitable for integration into custom trading indicators and strategies.
🛠️ Main Features:
- ✅ Identifies pivot highs and lows based on configurable price movement thresholds.
- ✅ Supports multiple configurations (timeframes and range percentages) within a single script.
- ✅ Compatible with request.security for multi-timeframe analysis.
- ✅ Configurable to use candle body (open/close) or full candle range (high/low) with ignore_wick .
- ✅ Returns both confirmed and temporary pivots for flexible integration.
🎯 Purpose:
The pivots library enables Pine Script developers to easily add pivot point detection to their scripts. It identifies significant price reversals by evaluating price movements against a minimum range threshold ( min_range_pct ) and confirming reversals based on a percentage ( reversal_pct ) of the prior trend’s magnitude. The library supports multiple calls with different settings, making it ideal for multi-timeframe strategies.
How It Works:
The library’s f_calculatePivot function tracks price movements to detect pivot points:
- Minimum Range Threshold : A potential pivot is considered if the price moves beyond the min_range_pct percentage of the current high (for a high pivot) or low (for a low pivot), ensuring sufficient movement.
- Reversal Confirmation : A pivot is confirmed if the price reverses from the potential pivot by at least the reversal_pct percentage of the distance between the last confirmed pivot and the current potential pivot, measuring the retracement relative to the prior trend’s magnitude.
- The function alternates between tracking highs (in an uptrend) and lows (in a downtrend), updating the trend when a pivot is confirmed.
- State management uses an array of pivot_state objects, allowing independent calculations for different timeframes and min_range_pct values.
Typical Use Cases:
- Detecting swing highs and lows for trend-following or reversal strategies.
- Visualizing pivot points across multiple timeframes using request.security .
- Enhancing custom indicators with reliable pivot detection.
- Building strategies that use confirmed pivots as entry/exit signals.
Who Should Use It:
This library is designed for Pine Script developers who want to incorporate robust pivot point detection into their indicators or strategies without managing complex state logic.
Important Note:
This library does not generate buy/sell signals on its own. It provides pivot detection tools to be used alongside your existing trading logic.
⚠️ Disclaimer:
This library is for educational and informational purposes only. It does not constitute financial advice.
## Technical Reference
Functions:
f_calculatePivot(series float _high, series float _low, float _min_range_pct, float _reversal_pct) →
- Parameters:
- _high : The high price series (e.g., high or math.max(open, close) ).
- _low : The low price series (e.g., low or math.min(open, close) ).
- _min_range_pct : The minimum percentage price movement to consider a potential pivot.
- _reversal_pct : The percentage of the prior trend’s distance required to confirm a pivot.
- Returns:
- A tuple containing:
- isNewPivot : Boolean indicating if a new pivot was confirmed.
- last_confirmed_pivot : The most recent confirmed pivot (type pivot ).
- temp_pivot : The current temporary pivot (type pivot ).
Types:
- pivot
- Fields:
- idx (series int) : Bar index of the pivot.
- typ (series int) : Type of pivot ( PIVOT_HIGH or PIVOT_LOW ).
- prc (series float) : Price of the pivot.
- tme (series int) : Timestamp of the pivot.
✨ Example of use:
//@version=5
indicator("Pivot Example", overlay=true)
import your_username/pivots/1
min_range_pct = input.float(20.0, "Min Range %")
reversal_pct = input.float(30.0, "Reversal %")
ignore_wick = input.bool(true, "Ignore wick")
h = ignore_wick ? math.max(open, close) : high
l = ignore_wick ? math.min(open, close) : low
= pivots.f_calculatePivot(h, l, min_range_pct, reversal_pct)
var pivot prev_pivot = na
if is_new_pivot
if not na(prev_pivot) and not na(new_pivot)
line.new(prev_pivot.idx, prev_pivot.prc, new_pivot.idx, new_pivot.prc, color=color.blue)
prev_pivot := new_pivot
## Release Notes
v1
- Initial release of the pivots library with f_calculatePivot function for detecting pivot points and supporting multiple configurations and timeframes.
Enigma UnlockedENIGMA Indicator: A Comprehensive Market Bias & Success Tracker
The ENIGMA Indicator is a powerful tool designed for traders who aim to identify market bias, track price movements, and evaluate trade performance using multiple timeframes. It combines multiple indicators and advanced logic to provide real-time insights into market trends, helping traders make more informed decisions.
Key Features
1. Multi-Timeframe Bias Calculation:
The ENIGMA Indicator tracks the market bias across multiple timeframes—Daily (D), 4-Hour (H4), 1-Hour (H1), 30-Minute (30M), 15-Minute (15M), 5-Minute (5M), and 1-Minute (1M).
How the Bias is Created:
The Bias is a key feature of the ENIGMA Indicator and is determined by comparing the current price with previous price levels for each timeframe.
- Bullish Bias (1): The market is considered **bullish** if the **current closing price** is higher than the **previous timeframe’s high**. This suggests that the market is trending upwards, and buyers are in control.
- Bearish Bias (-1): The market is considered **bearish** if the **current closing price** is lower than the **previous timeframe’s low**. This suggests that the market is trending downwards, and sellers are in control.
- Neutral Bias (0): The market is considered **neutral** if the price is between the **previous high** and **previous low**, indicating indecision or a range-bound market.
This bias calculation is performed independently for each timeframe. The **Bias** for each timeframe is then displayed in the **Bias Table** on your chart, providing a clear view of market direction across multiple timeframes.
2. **Customizable Table Display:**
- The indicator provides a table that displays the bias for each selected timeframe, clearly marking whether the market is **Bullish**, **Bearish**, or **Neutral**.
- Users can choose where to place the table on the chart: top-left, top-right, bottom-left, bottom-right, or center positions, allowing for easy and personalized chart management.
3. **Win/Loss Tracker:**
- The table also tracks the **success rate** of **buy** and **sell** trades based on price retests of key bias levels.
- For each period (Day, Week, Month), it tracks how often the price has moved in the direction of the initial bias, counting **Buy Wins**, **Sell Wins**, **Buy Losses**, and **Sell Losses**.
- This helps traders assess the effectiveness of the market bias over time and adjust their strategies accordingly.
#### **How the Success Calculation Determines the Success Rate:**
The **Success Calculation** is designed to track how often the price follows the direction of the market bias. It does this by evaluating how the price retests key levels associated with the identified market bias:
1. **Buy Success Calculation**:
- The success of a **Buy Trade** is determined when the price breaks above the **previous high** after a **bullish bias** has been identified.
- If the price continues to move higher (i.e., makes a new high) after breaking the previous high, the **buy trade is considered successful**.
- The indicator tracks how many times this condition is met and counts it as a **Buy Win**.
2. **Sell Success Calculation**:
- The success of a **Sell Trade** is determined when the price breaks below the **previous low** after a **bearish bias** has been identified.
- If the price continues to move lower (i.e., makes a new low) after breaking the previous low, the **sell trade is considered successful**.
- The indicator tracks how many times this condition is met and counts it as a **Sell Win**.
3. **Failure Calculations**:
- If the price does not move as expected (i.e., it does not continue in the direction of the identified bias), the trade is considered a **loss** and is tracked as **Buy Loss** or **Sell Loss**, depending on whether it was a bullish or bearish trade.
The ENIGMA Indicator keeps a running tally of **Buy Wins**, **Sell Wins**, **Buy Losses**, and **Sell Losses** over a set period (which can be customized to Days, Weeks, or Months). These statistics are updated dynamically in the **Bias Table**, allowing you to track your success rate in real-time and gain insights into the effectiveness of the market bias.
#### **Customizable Period Tracking:**
- The ENIGMA Indicator allows you to set custom tracking periods (e.g., 30 days, 2 weeks, etc.). The performance metrics reset after each tracking period, helping you monitor your success in different market conditions.
5. **Interactive Settings:**
- **Lookback Period**: Define how many bars the indicator should consider for bias calculations.
- **Success Tracking**: Set the number of candles to track for calculating the win/loss performance.
- **Time Threshold**: Set a time threshold to help define the period during which price retests are considered valid.
- **Info Tooltip**: You can enable the information tool in the settings to view detailed explanations of how wins and losses are calculated, ensuring you understand how the indicator works and how the results are derived.
#### **How to Use the ENIGMA Indicator:**
1. **Install the Indicator**:
- Add the ENIGMA Indicator to your chart. It will automatically calculate and display the bias for multiple timeframes.
2. **Interpret the Bias Table**:
- The bias table will show whether the market is **Bullish**, **Bearish**, or **Neutral** across different timeframes.
- Look for alignment between the timeframes—when multiple timeframes show the same bias, it may indicate a stronger trend.
3. **Use the Win/Loss Tracker**:
- Track how well your trades align with the bias using the **Win/Loss Tracker**. This helps you refine your strategy by understanding which timeframes and biases lead to higher success rates.
- For example, if you see a high number of **Buy Wins** and a low number of **Sell Wins**, you may decide to focus more on buying during bullish trends and avoid selling during bearish retracements.
4. **Track Your Period Performance**:
- The indicator will automatically track your performance over the set period (Days, Weeks, Months). Use this data to adjust your approach and evaluate the effectiveness of your trading strategy.
5. **Position the Table**:
- Customize the placement of the table on your chart based on your preferences. You can choose from options like **Top Left**, **Top Right**, **Bottom Left**, **Bottom Right**, or **Center** to keep the chart uncluttered.
6. **Adjust Settings**:
- Modify the indicator settings according to your trading style. You can adjust the **Lookback Period**, **Number of Candles to Track**, and **Time Threshold** to match the pace of your trading.
7. **Use the Info Tooltip**:
- Enable the **Info Tool** in the settings to understand how the Buy/Sell Wins and Losses are calculated. The tooltip provides a breakdown of how the indicator tracks price movements and calculates the success rate.
**Conclusion:**
The **ENIGMA Indicator** is designed to help traders make informed decisions by providing a clear view of the market bias and performance data. With the ability to track bias across multiple timeframes and evaluate your trading success, it can be a powerful tool for refining your trading strategies.
Whether you're looking to focus on a single timeframe or analyze multiple timeframes for a stronger bias, the ENIGMA Indicator adapts to your needs, providing both real-time market insights and performance feedback.
taLibrary "ta"
█ OVERVIEW
This library holds technical analysis functions calculating values for which no Pine built-in exists.
Look first. Then leap.
█ FUNCTIONS
cagr(entryTime, entryPrice, exitTime, exitPrice)
It calculates the "Compound Annual Growth Rate" between two points in time. The CAGR is a notional, annualized growth rate that assumes all profits are reinvested. It only takes into account the prices of the two end points — not drawdowns, so it does not calculate risk. It can be used as a yardstick to compare the performance of two instruments. Because it annualizes values, the function requires a minimum of one day between the two end points (annualizing returns over smaller periods of times doesn't produce very meaningful figures).
Parameters:
entryTime : The starting timestamp.
entryPrice : The starting point's price.
exitTime : The ending timestamp.
exitPrice : The ending point's price.
Returns: CAGR in % (50 is 50%). Returns `na` if there is not >=1D between `entryTime` and `exitTime`, or until the two time points have not been reached by the script.
█ v2, Mar. 8, 2022
Added functions `allTimeHigh()` and `allTimeLow()` to find the highest or lowest value of a source from the first historical bar to the current bar. These functions will not look ahead; they will only return new highs/lows on the bar where they occur.
allTimeHigh(src)
Tracks the highest value of `src` from the first historical bar to the current bar.
Parameters:
src : (series int/float) Series to track. Optional. The default is `high`.
Returns: (float) The highest value tracked.
allTimeLow(src)
Tracks the lowest value of `src` from the first historical bar to the current bar.
Parameters:
src : (series int/float) Series to track. Optional. The default is `low`.
Returns: (float) The lowest value tracked.
█ v3, Sept. 27, 2022
This version includes the following new functions:
aroon(length)
Calculates the values of the Aroon indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: ( [float, float ]) A tuple of the Aroon-Up and Aroon-Down values.
coppock(source, longLength, shortLength, smoothLength)
Calculates the value of the Coppock Curve indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
longLength (simple int) : (simple int) Number of bars for the fast ROC value (length).
shortLength (simple int) : (simple int) Number of bars for the slow ROC value (length).
smoothLength (simple int) : (simple int) Number of bars for the weigted moving average value (length).
Returns: (float) The oscillator value.
dema(source, length)
Calculates the value of the Double Exponential Moving Average (DEMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The double exponentially weighted moving average of the `source`.
dema2(src, length)
An alternate Double Exponential Moving Average (Dema) function to `dema()`, which allows a "series float" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The double exponentially weighted moving average of the `src`.
dm(length)
Calculates the value of the "Demarker" indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
donchian(length)
Calculates the values of a Donchian Channel using `high` and `low` over a given `length`.
Parameters:
length (int) : (series int) Number of bars (length).
Returns: ( [float, float, float ]) A tuple containing the channel high, low, and median, respectively.
ema2(src, length)
An alternate ema function to the `ta.ema()` built-in, which allows a "series float" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int/float) Number of bars (length).
Returns: (float) The exponentially weighted moving average of the `src`.
eom(length, div)
Calculates the value of the Ease of Movement indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
div (simple int) : (simple int) Divisor used for normalzing values. Optional. The default is 10000.
Returns: (float) The oscillator value.
frama(source, length)
The Fractal Adaptive Moving Average (FRAMA), developed by John Ehlers, is an adaptive moving average that dynamically adjusts its lookback period based on fractal geometry.
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The fractal adaptive moving average of the `source`.
ft(source, length)
Calculates the value of the Fisher Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
ht(source)
Calculates the value of the Hilbert Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
Returns: (float) The oscillator value.
ichimoku(conLength, baseLength, senkouLength)
Calculates values of the Ichimoku Cloud indicator, including tenkan, kijun, senkouSpan1, senkouSpan2, and chikou. NOTE: offsets forward or backward can be done using the `offset` argument in `plot()`.
Parameters:
conLength (int) : (series int) Length for the Conversion Line (Tenkan). The default is 9 periods, which returns the mid-point of the 9 period Donchian Channel.
baseLength (int) : (series int) Length for the Base Line (Kijun-sen). The default is 26 periods, which returns the mid-point of the 26 period Donchian Channel.
senkouLength (int) : (series int) Length for the Senkou Span 2 (Leading Span B). The default is 52 periods, which returns the mid-point of the 52 period Donchian Channel.
Returns: ( [float, float, float, float, float ]) A tuple of the Tenkan, Kijun, Senkou Span 1, Senkou Span 2, and Chikou Span values. NOTE: by default, the senkouSpan1 and senkouSpan2 should be plotted 26 periods in the future, and the Chikou Span plotted 26 days in the past.
ift(source)
Calculates the value of the Inverse Fisher Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
Returns: (float) The oscillator value.
kvo(fastLen, slowLen, trigLen)
Calculates the values of the Klinger Volume Oscillator.
Parameters:
fastLen (simple int) : (simple int) Length for the fast moving average smoothing parameter calculation.
slowLen (simple int) : (simple int) Length for the slow moving average smoothing parameter calculation.
trigLen (simple int) : (simple int) Length for the trigger moving average smoothing parameter calculation.
Returns: ( [float, float ]) A tuple of the KVO value, and the trigger value.
pzo(length)
Calculates the value of the Price Zone Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
rms(source, length)
Calculates the Root Mean Square of the `source` over the `length`.
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The RMS value.
rwi(length)
Calculates the values of the Random Walk Index.
Parameters:
length (simple int) : (simple int) Lookback and ATR smoothing parameter length.
Returns: ( [float, float ]) A tuple of the `rwiHigh` and `rwiLow` values.
stc(source, fast, slow, cycle, d1, d2)
Calculates the value of the Schaff Trend Cycle indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
fast (simple int) : (simple int) Length for the MACD fast smoothing parameter calculation.
slow (simple int) : (simple int) Length for the MACD slow smoothing parameter calculation.
cycle (simple int) : (simple int) Number of bars for the Stochastic values (length).
d1 (simple int) : (simple int) Length for the initial %D smoothing parameter calculation.
d2 (simple int) : (simple int) Length for the final %D smoothing parameter calculation.
Returns: (float) The oscillator value.
stochFull(periodK, smoothK, periodD)
Calculates the %K and %D values of the Full Stochastic indicator.
Parameters:
periodK (simple int) : (simple int) Number of bars for Stochastic calculation. (length).
smoothK (simple int) : (simple int) Number of bars for smoothing of the %K value (length).
periodD (simple int) : (simple int) Number of bars for smoothing of the %D value (length).
Returns: ( [float, float ]) A tuple of the slow %K and the %D moving average values.
stochRsi(lengthRsi, periodK, smoothK, periodD, source)
Calculates the %K and %D values of the Stochastic RSI indicator.
Parameters:
lengthRsi (simple int) : (simple int) Length for the RSI smoothing parameter calculation.
periodK (simple int) : (simple int) Number of bars for Stochastic calculation. (length).
smoothK (simple int) : (simple int) Number of bars for smoothing of the %K value (length).
periodD (simple int) : (simple int) Number of bars for smoothing of the %D value (length).
source (float) : (series int/float) Series of values to process. Optional. The default is `close`.
Returns: ( [float, float ]) A tuple of the slow %K and the %D moving average values.
supertrend(factor, atrLength, wicks)
Calculates the values of the SuperTrend indicator with the ability to take candle wicks into account, rather than only the closing price.
Parameters:
factor (float) : (series int/float) Multiplier for the ATR value.
atrLength (simple int) : (simple int) Length for the ATR smoothing parameter calculation.
wicks (simple bool) : (simple bool) Condition to determine whether to take candle wicks into account when reversing trend, or to use the close price. Optional. Default is false.
Returns: ( [float, int ]) A tuple of the superTrend value and trend direction.
szo(source, length)
Calculates the value of the Sentiment Zone Oscillator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
t3(source, length, vf)
Calculates the value of the Tilson Moving Average (T3).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
vf (simple float) : (simple float) Volume factor. Affects the responsiveness.
Returns: (float) The Tilson moving average of the `source`.
t3Alt(source, length, vf)
An alternate Tilson Moving Average (T3) function to `t3()`, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
vf (simple float) : (simple float) Volume factor. Affects the responsiveness.
Returns: (float) The Tilson moving average of the `source`.
tema(source, length)
Calculates the value of the Triple Exponential Moving Average (TEMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The triple exponentially weighted moving average of the `source`.
tema2(source, length)
An alternate Triple Exponential Moving Average (TEMA) function to `tema()`, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The triple exponentially weighted moving average of the `source`.
trima(source, length)
Calculates the value of the Triangular Moving Average (TRIMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The triangular moving average of the `source`.
trima2(src, length)
An alternate Triangular Moving Average (TRIMA) function to `trima()`, which allows a "series int" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int) Number of bars (length).
Returns: (float) The triangular moving average of the `src`.
trix(source, length, signalLength, exponential)
Calculates the values of the TRIX indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
signalLength (simple int) : (simple int) Length for smoothing the signal line.
exponential (simple bool) : (simple bool) Condition to determine whether exponential or simple smoothing is used. Optional. The default is `true` (exponential smoothing).
Returns: ( [float, float, float ]) A tuple of the TRIX value, the signal value, and the histogram.
uo(fastLen, midLen, slowLen)
Calculates the value of the Ultimate Oscillator.
Parameters:
fastLen (simple int) : (series int) Number of bars for the fast smoothing average (length).
midLen (simple int) : (series int) Number of bars for the middle smoothing average (length).
slowLen (simple int) : (series int) Number of bars for the slow smoothing average (length).
Returns: (float) The oscillator value.
vhf(source, length)
Calculates the value of the Vertical Horizontal Filter.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
vi(length)
Calculates the values of the Vortex Indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: ( [float, float ]) A tuple of the viPlus and viMinus values.
vzo(length)
Calculates the value of the Volume Zone Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
williamsFractal(period)
Detects Williams Fractals.
Parameters:
period (int) : (series int) Number of bars (length).
Returns: ( [bool, bool ]) A tuple of an up fractal and down fractal. Variables are true when detected.
wpo(length)
Calculates the value of the Wave Period Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
█ v7, Nov. 2, 2023
This version includes the following new and updated functions:
atr2(length)
An alternate ATR function to the `ta.atr()` built-in, which allows a "series float" `length` argument.
Parameters:
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The ATR value.
changePercent(newValue, oldValue)
Calculates the percentage difference between two distinct values.
Parameters:
newValue (float) : (series int/float) The current value.
oldValue (float) : (series int/float) The previous value.
Returns: (float) The percentage change from the `oldValue` to the `newValue`.
donchian(length)
Calculates the values of a Donchian Channel using `high` and `low` over a given `length`.
Parameters:
length (int) : (series int) Number of bars (length).
Returns: ( [float, float, float ]) A tuple containing the channel high, low, and median, respectively.
highestSince(cond, source)
Tracks the highest value of a series since the last occurrence of a condition.
Parameters:
cond (bool) : (series bool) A condition which, when `true`, resets the tracking of the highest `source`.
source (float) : (series int/float) Series of values to process. Optional. The default is `high`.
Returns: (float) The highest `source` value since the last time the `cond` was `true`.
lowestSince(cond, source)
Tracks the lowest value of a series since the last occurrence of a condition.
Parameters:
cond (bool) : (series bool) A condition which, when `true`, resets the tracking of the lowest `source`.
source (float) : (series int/float) Series of values to process. Optional. The default is `low`.
Returns: (float) The lowest `source` value since the last time the `cond` was `true`.
relativeVolume(length, anchorTimeframe, isCumulative, adjustRealtime)
Calculates the volume since the last change in the time value from the `anchorTimeframe`, the historical average volume using bars from past periods that have the same relative time offset as the current bar from the start of its period, and the ratio of these volumes. The volume values are cumulative by default, but can be adjusted to non-accumulated with the `isCumulative` parameter.
Parameters:
length (simple int) : (simple int) The number of periods to use for the historical average calculation.
anchorTimeframe (simple string) : (simple string) The anchor timeframe used in the calculation. Optional. Default is "D".
isCumulative (simple bool) : (simple bool) If `true`, the volume values will be accumulated since the start of the last `anchorTimeframe`. If `false`, values will be used without accumulation. Optional. The default is `true`.
adjustRealtime (simple bool) : (simple bool) If `true`, estimates the cumulative value on unclosed bars based on the data since the last `anchor` condition. Optional. The default is `false`.
Returns: ( [float, float, float ]) A tuple of three float values. The first element is the current volume. The second is the average of volumes at equivalent time offsets from past anchors over the specified number of periods. The third is the ratio of the current volume to the historical average volume.
rma2(source, length)
An alternate RMA function to the `ta.rma()` built-in, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The rolling moving average of the `source`.
supertrend2(factor, atrLength, wicks)
An alternate SuperTrend function to `supertrend()`, which allows a "series float" `atrLength` argument.
Parameters:
factor (float) : (series int/float) Multiplier for the ATR value.
atrLength (float) : (series int/float) Length for the ATR smoothing parameter calculation.
wicks (simple bool) : (simple bool) Condition to determine whether to take candle wicks into account when reversing trend, or to use the close price. Optional. Default is `false`.
Returns: ( [float, int ]) A tuple of the superTrend value and trend direction.
vStop(source, atrLength, atrFactor)
Calculates an ATR-based stop value that trails behind the `source`. Can serve as a possible stop-loss guide and trend identifier.
Parameters:
source (float) : (series int/float) Series of values that the stop trails behind.
atrLength (simple int) : (simple int) Length for the ATR smoothing parameter calculation.
atrFactor (float) : (series int/float) The multiplier of the ATR value. Affects the maximum distance between the stop and the `source` value. A value of 1 means the maximum distance is 100% of the ATR value. Optional. The default is 1.
Returns: ( [float, bool ]) A tuple of the volatility stop value and the trend direction as a "bool".
vStop2(source, atrLength, atrFactor)
An alternate Volatility Stop function to `vStop()`, which allows a "series float" `atrLength` argument.
Parameters:
source (float) : (series int/float) Series of values that the stop trails behind.
atrLength (float) : (series int/float) Length for the ATR smoothing parameter calculation.
atrFactor (float) : (series int/float) The multiplier of the ATR value. Affects the maximum distance between the stop and the `source` value. A value of 1 means the maximum distance is 100% of the ATR value. Optional. The default is 1.
Returns: ( [float, bool ]) A tuple of the volatility stop value and the trend direction as a "bool".
Removed Functions:
allTimeHigh(src)
Tracks the highest value of `src` from the first historical bar to the current bar.
allTimeLow(src)
Tracks the lowest value of `src` from the first historical bar to the current bar.
trima2(src, length)
An alternate Triangular Moving Average (TRIMA) function to `trima()`, which allows a
"series int" length argument.
light_logLight Log - A Defensive Programming Library for Pine Script
Overview
The Light Log library transforms Pine Script development by introducing structured logging and defensive programming patterns typically found in enterprise languages like C#. This library addresses a fundamental challenge in Pine Script: the lack of sophisticated error handling and debugging tools that developers expect when building complex trading systems.
At its core, Light Log provides three transformative capabilities that work together to create more reliable and maintainable code. First, it wraps all native Pine Script types in error-aware containers, allowing values to carry validation state alongside their data. Second, it offers a comprehensive logging system with severity levels and conditional rendering. Third, it includes defensive programming utilities that catch errors early and make code self-documenting.
The Philosophy of Errors as Values
Traditional Pine Script error handling relies on runtime errors that halt execution, making it difficult to build resilient systems that can gracefully handle edge cases. Light Log introduces a paradigm shift by treating errors as first-class values that flow through your program alongside regular data.
When you wrap a value using Light Log's type system, you're not just storing data – you're creating a container that can carry both the value and its validation state. For example, when you call myNumber.INT() , you receive an INT object that contains both the integer value and a Log object that can describe any issues with that value. This approach, inspired by functional programming languages, allows errors to propagate through calculations without causing immediate failures.
Consider how this changes error handling in practice. Instead of a calculation failing catastrophically when it encounters invalid input, it can produce a result object that contains both the computed value (which might be na) and a detailed log explaining what went wrong. Subsequent operations can check has_error() to decide whether to proceed or handle the error condition gracefully.
The Typed Wrapper System
Light Log provides typed wrappers for every native Pine Script type: INT, FLOAT, BOOL, STRING, COLOR, LINE, LABEL, BOX, TABLE, CHART_POINT, POLYLINE, and LINEFILL. These wrappers serve multiple purposes beyond simple value storage.
Each wrapper type contains two fields: the value field v holds the actual data, while the error field e contains a Log object that tracks the value's validation state. This dual nature enables powerful programming patterns. You can perform operations on wrapped values and accumulate error information along the way, creating an audit trail of how values were processed.
The wrapper system includes convenient methods for converting between wrapped and unwrapped values. The extension methods like INT() , FLOAT() , etc., make it easy to wrap existing values, while the from_INT() , from_FLOAT() methods extract the underlying values when needed. The has_error() method provides a consistent interface for checking whether any wrapped value has encountered issues during processing.
The Log Object: Your Debugging Companion
The Log object represents the heart of Light Log's debugging capabilities. Unlike simple string concatenation for error messages, the Log object provides a structured approach to building, modifying, and rendering diagnostic information.
Each Log object carries three essential pieces of information: an error type (info, warning, error, or runtime_error), a message string that can be built incrementally, and an active flag that controls conditional rendering. This structure enables sophisticated logging patterns where you can build up detailed diagnostic information throughout your script's execution and decide later whether and how to display it.
The Log object's methods support fluent chaining, allowing you to build complex messages in a readable way. The write() and write_line() methods append text to the log, while new_line() adds formatting. The clear() method resets the log for reuse, and the rendering methods ( render_now() , render_condition() , and the general render() ) control when and how messages appear.
Defensive Programming Made Easy
Light Log's argument validation functions transform how you write defensive code. Instead of cluttering your functions with verbose validation logic, you can use concise, self-documenting calls that make your intentions clear.
The argument_error() function provides strict validation that halts execution when conditions aren't met – perfect for catching programming errors early. For less critical issues, argument_log_warning() and argument_log_error() record problems without stopping execution, while argument_log_info() provides debug visibility into your function's behavior.
These functions follow a consistent pattern: they take a condition to check, the function name, the argument name, and a descriptive message. This consistency makes error messages predictable and helpful, automatically formatting them to show exactly where problems occurred.
Building Modular, Reusable Code
Light Log encourages a modular approach to Pine Script development by providing tools that make functions more self-contained and reliable. When functions validate their inputs and return wrapped values with error information, they become true black boxes that can be safely composed into larger systems.
The void_return() function addresses Pine Script's requirement that all code paths return a value, even in error handling branches. This utility function provides a clean way to satisfy the compiler while making it clear that a particular code path should never execute.
The static log pattern, initialized with init_static_log() , enables module-wide error tracking. You can create a persistent Log object that accumulates information across multiple function calls, building a comprehensive diagnostic report that helps you understand complex behaviors in your indicators and strategies.
Real-World Applications
In practice, Light Log shines when building sophisticated trading systems. Imagine developing a complex indicator that processes multiple data streams, performs statistical calculations, and generates trading signals. With Light Log, each processing stage can validate its inputs, perform calculations, and pass along both results and diagnostic information.
For example, a moving average calculation might check that the period is positive, that sufficient data exists, and that the input series contains valid values. Instead of failing silently or throwing runtime errors, it can return a FLOAT object that contains either the calculated average or a detailed explanation of why the calculation couldn't be performed.
Strategy developers benefit even more from Light Log's capabilities. Complex entry and exit logic often involves multiple conditions that must all be satisfied. With Light Log, each condition check can contribute to a comprehensive log that explains exactly why a trade was or wasn't taken, making strategy debugging and optimization much more straightforward.
Performance Considerations
While Light Log adds a layer of abstraction over raw Pine Script values, its design minimizes performance impact. The wrapper objects are lightweight, containing only two fields. The logging operations only consume resources when actually rendered, and the conditional rendering system ensures that production code can run with logging disabled for maximum performance.
The library follows Pine Script best practices for performance, using appropriate data structures and avoiding unnecessary operations. The var keyword in init_static_log() ensures that persistent logs don't create new objects on every bar, maintaining efficiency even in real-time calculations.
Getting Started
Adopting Light Log in your Pine Script projects is straightforward. Import the library, wrap your critical values, add validation to your functions, and use Log objects to track important events. Start small by adding logging to a single function, then expand as you see the benefits of better error visibility and code organization.
Remember that Light Log is designed to grow with your needs. You can use as much or as little of its functionality as makes sense for your project. Even simple uses, like adding argument validation to key functions, can significantly improve code reliability and debugging ease.
Transform your Pine Script development experience with Light Log – because professional trading systems deserve professional development tools.
Light Log Technical Deep Dive: Advanced Patterns and Architecture
Understanding Errors as Values
The concept of "errors as values" represents a fundamental shift in how we think about error handling in Pine Script. In traditional Pine Script development, errors are events – they happen at a specific moment in time and immediately interrupt program flow. Light Log transforms errors into data – they become information that flows through your program just like any other value.
This transformation has profound implications. When errors are values, they can be stored, passed between functions, accumulated, transformed, and inspected. They become part of your program's data flow rather than exceptions to it. This approach, popularized by languages like Rust with its Result type and Haskell with its Either monad, brings functional programming's elegance to Pine Script.
Consider a practical example. Traditional Pine Script might calculate a momentum indicator like this:
momentum = close - close
If period is invalid or if there isn't enough historical data, this calculation might produce na or cause subtle bugs. With Light Log's approach:
calculate_momentum(src, period)=>
result = src.FLOAT()
if period <= 0
result.e.write("Invalid period: must be positive", true, ErrorType.error)
result.v := na
else if bar_index < period
result.e.write("Insufficient data: need " + str.tostring(period) + " bars", true, ErrorType.warning)
result.v := na
else
result.v := src - src
result.e.write("Momentum calculated successfully", false, ErrorType.info)
result
Now the function returns not just a value but a complete computational result that includes diagnostic information. Calling code can make intelligent decisions based on both the value and its associated metadata.
The Monad Pattern in Pine Script
While Pine Script lacks the type system features to implement true monads, Light Log brings monadic thinking to Pine Script development. The wrapped types (INT, FLOAT, etc.) act as computational contexts that carry both values and metadata through a series of transformations.
The key insight of monadic programming is that you can chain operations while automatically propagating context. In Light Log, this context is the error state. When you have a FLOAT that contains an error, operations on that FLOAT can check the error state and decide whether to proceed or propagate the error.
This pattern enables what functional programmers call "railway-oriented programming" – your code follows a success track when all is well but can switch to an error track when problems occur. Both tracks lead to the same destination (a result with error information), but they take different paths based on the validity of intermediate values.
Composable Error Handling
Light Log's design encourages composition – building complex functionality from simpler, well-tested components. Each component can validate its inputs, perform its calculation, and return a result with appropriate error information. Higher-level functions can then combine these results intelligently.
Consider building a complex trading signal from multiple indicators:
generate_signal(src, fast_period, slow_period, signal_period) =>
log = init_static_log(ErrorType.info)
// Calculate components with error tracking
fast_ma = calculate_ma(src, fast_period)
slow_ma = calculate_ma(src, slow_period)
// Check for errors in components
if fast_ma.has_error()
log.write_line("Fast MA error: " + fast_ma.e.message, true)
if slow_ma.has_error()
log.write_line("Slow MA error: " + slow_ma.e.message, true)
// Proceed with calculation if no errors
signal = 0.0.FLOAT()
if not (fast_ma.has_error() or slow_ma.has_error())
macd_line = fast_ma.v - slow_ma.v
signal_line = calculate_ma(macd_line, signal_period)
if signal_line.has_error()
log.write_line("Signal line error: " + signal_line.e.message, true)
signal.e := log
else
signal.v := macd_line - signal_line.v
log.write("Signal generated successfully")
else
signal.e := log
signal.v := na
signal
This composable approach makes complex calculations more reliable and easier to debug. Each component is responsible for its own validation and error reporting, and the composite function orchestrates these components while maintaining comprehensive error tracking.
The Static Log Pattern
The init_static_log() function introduces a powerful pattern for maintaining state across function calls. In Pine Script, the var keyword creates variables that persist across bars but are initialized only once. Light Log leverages this to create logging objects that can accumulate information throughout a script's execution.
This pattern is particularly valuable for debugging complex strategies where you need to understand behavior across multiple bars. You can create module-level logs that track important events:
// Module-level diagnostic log
diagnostics = init_static_log(ErrorType.info)
// Track strategy decisions across bars
check_entry_conditions() =>
diagnostics.clear() // Start fresh each bar
diagnostics.write_line("Bar " + str.tostring(bar_index) + " analysis:")
if close > sma(close, 20)
diagnostics.write_line("Price above SMA20", false)
else
diagnostics.write_line("Price below SMA20 - no entry", true, ErrorType.warning)
if volume > sma(volume, 20) * 1.5
diagnostics.write_line("Volume surge detected", false)
else
diagnostics.write_line("Normal volume", false)
// Render diagnostics based on verbosity setting
if debug_mode
diagnostics.render_now()
Advanced Validation Patterns
Light Log's argument validation functions enable sophisticated precondition checking that goes beyond simple null checks. You can implement complex validation logic while keeping your code readable:
validate_price_data(open_val, high_val, low_val, close_val) =>
argument_error(na(open_val) or na(high_val) or na(low_val) or na(close_val),
"validate_price_data", "OHLC values", "contain na values")
argument_error(high_val < low_val,
"validate_price_data", "high/low", "high is less than low")
argument_error(close_val > high_val or close_val < low_val,
"validate_price_data", "close", "is outside high/low range")
argument_log_warning(high_val == low_val,
"validate_price_data", "high/low", "are equal (no range)")
This validation function documents its requirements clearly and fails fast with helpful error messages when assumptions are violated. The mix of errors (which halt execution) and warnings (which allow continuation) provides fine-grained control over how strict your validation should be.
Performance Optimization Strategies
While Light Log adds abstraction, careful design minimizes overhead. Understanding Pine Script's execution model helps you use Light Log efficiently.
Pine Script executes once per bar, so operations that seem expensive in traditional programming might have negligible impact. However, when building real-time systems, every optimization matters. Light Log provides several patterns for efficient use:
Lazy Evaluation: Log messages are only built when they'll be rendered. Use conditional logging to avoid string concatenation in production:
if debug_mode
log.write_line("Calculated value: " + str.tostring(complex_calculation))
Selective Wrapping: Not every value needs error tracking. Wrap values at API boundaries and critical calculation points, but use raw values for simple operations:
// Wrap at boundaries
input_price = close.FLOAT()
validated_period = validate_period(input_period).INT()
// Use raw values internally
sum = 0.0
for i = 0 to validated_period.v - 1
sum += close
Error Propagation: When errors occur early, avoid expensive calculations:
process_data(input) =>
validated = validate_input(input)
if validated.has_error()
validated // Return early with error
else
// Expensive processing only if valid
perform_complex_calculation(validated)
Integration Patterns
Light Log integrates smoothly with existing Pine Script code. You can adopt it incrementally, starting with critical functions and expanding coverage as needed.
Boundary Validation: Add Light Log at the boundaries of your system – where user input enters and where final outputs are produced. This catches most errors while minimizing changes to existing code.
Progressive Enhancement: Start by adding argument validation to existing functions. Then wrap return values. Finally, add comprehensive logging. Each step improves reliability without requiring a complete rewrite.
Testing and Debugging: Use Light Log's conditional rendering to create debug modes for your scripts. Production users see clean output while developers get detailed diagnostics:
// User input for debug mode
debug = input.bool(false, "Enable debug logging")
// Conditional diagnostic output
if debug
diagnostics.render_now()
else
diagnostics.render_condition() // Only shows errors/warnings
Future-Proofing Your Code
Light Log's patterns prepare your code for Pine Script's evolution. As Pine Script adds more sophisticated features, code that uses structured error handling and defensive programming will adapt more easily than code that relies on implicit assumptions.
The type wrapper system, in particular, positions your code to take advantage of potential future features or more sophisticated type inference. By thinking in terms of wrapped values and error propagation today, you're building code that will remain maintainable and extensible tomorrow.
Light Log doesn't just make your Pine Script better today – it prepares it for the trading systems you'll need to build tomorrow.
Library "light_log"
A lightweight logging and defensive programming library for Pine Script.
Designed for modular and extensible scripts, this utility provides structured runtime validation,
conditional logging, and reusable `Log` objects for centralized error propagation.
It also introduces a typed wrapping system for all native Pine values (e.g., `INT`, `FLOAT`, `LABEL`),
allowing values to carry errors alongside data. This enables functional-style flows with built-in
validation tracking, error detection (`has_error()`), and fluent chaining.
Inspired by structured logging patterns found in systems like C#, it reduces boilerplate,
enforces argument safety, and encourages clean, maintainable code architecture.
method INT(self, error_type)
Wraps an `int` value into an `INT` struct with an optional log severity.
Namespace types: series int, simple int, input int, const int
Parameters:
self (int) : The raw `int` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: An `INT` object containing the value and a default Log instance.
method FLOAT(self, error_type)
Wraps a `float` value into a `FLOAT` struct with an optional log severity.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : The raw `float` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `FLOAT` object containing the value and a default Log instance.
method BOOL(self, error_type)
Wraps a `bool` value into a `BOOL` struct with an optional log severity.
Namespace types: series bool, simple bool, input bool, const bool
Parameters:
self (bool) : The raw `bool` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `BOOL` object containing the value and a default Log instance.
method STRING(self, error_type)
Wraps a `string` value into a `STRING` struct with an optional log severity.
Namespace types: series string, simple string, input string, const string
Parameters:
self (string) : The raw `string` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `STRING` object containing the value and a default Log instance.
method COLOR(self, error_type)
Wraps a `color` value into a `COLOR` struct with an optional log severity.
Namespace types: series color, simple color, input color, const color
Parameters:
self (color) : The raw `color` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `COLOR` object containing the value and a default Log instance.
method LINE(self, error_type)
Wraps a `line` object into a `LINE` struct with an optional log severity.
Namespace types: series line
Parameters:
self (line) : The raw `line` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `LINE` object containing the value and a default Log instance.
method LABEL(self, error_type)
Wraps a `label` object into a `LABEL` struct with an optional log severity.
Namespace types: series label
Parameters:
self (label) : The raw `label` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `LABEL` object containing the value and a default Log instance.
method BOX(self, error_type)
Wraps a `box` object into a `BOX` struct with an optional log severity.
Namespace types: series box
Parameters:
self (box) : The raw `box` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `BOX` object containing the value and a default Log instance.
method TABLE(self, error_type)
Wraps a `table` object into a `TABLE` struct with an optional log severity.
Namespace types: series table
Parameters:
self (table) : The raw `table` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `TABLE` object containing the value and a default Log instance.
method CHART_POINT(self, error_type)
Wraps a `chart.point` value into a `CHART_POINT` struct with an optional log severity.
Namespace types: chart.point
Parameters:
self (chart.point) : The raw `chart.point` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `CHART_POINT` object containing the value and a default Log instance.
method POLYLINE(self, error_type)
Wraps a `polyline` object into a `POLYLINE` struct with an optional log severity.
Namespace types: series polyline, series polyline, series polyline, series polyline
Parameters:
self (polyline) : The raw `polyline` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `POLYLINE` object containing the value and a default Log instance.
method LINEFILL(self, error_type)
Wraps a `linefill` object into a `LINEFILL` struct with an optional log severity.
Namespace types: series linefill
Parameters:
self (linefill) : The raw `linefill` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `LINEFILL` object containing the value and a default Log instance.
method from_INT(self)
Extracts the integer value from an INT wrapper.
Namespace types: INT
Parameters:
self (INT) : The wrapped INT instance.
Returns: The underlying `int` value.
method from_FLOAT(self)
Extracts the float value from a FLOAT wrapper.
Namespace types: FLOAT
Parameters:
self (FLOAT) : The wrapped FLOAT instance.
Returns: The underlying `float` value.
method from_BOOL(self)
Extracts the boolean value from a BOOL wrapper.
Namespace types: BOOL
Parameters:
self (BOOL) : The wrapped BOOL instance.
Returns: The underlying `bool` value.
method from_STRING(self)
Extracts the string value from a STRING wrapper.
Namespace types: STRING
Parameters:
self (STRING) : The wrapped STRING instance.
Returns: The underlying `string` value.
method from_COLOR(self)
Extracts the color value from a COLOR wrapper.
Namespace types: COLOR
Parameters:
self (COLOR) : The wrapped COLOR instance.
Returns: The underlying `color` value.
method from_LINE(self)
Extracts the line object from a LINE wrapper.
Namespace types: LINE
Parameters:
self (LINE) : The wrapped LINE instance.
Returns: The underlying `line` object.
method from_LABEL(self)
Extracts the label object from a LABEL wrapper.
Namespace types: LABEL
Parameters:
self (LABEL) : The wrapped LABEL instance.
Returns: The underlying `label` object.
method from_BOX(self)
Extracts the box object from a BOX wrapper.
Namespace types: BOX
Parameters:
self (BOX) : The wrapped BOX instance.
Returns: The underlying `box` object.
method from_TABLE(self)
Extracts the table object from a TABLE wrapper.
Namespace types: TABLE
Parameters:
self (TABLE) : The wrapped TABLE instance.
Returns: The underlying `table` object.
method from_CHART_POINT(self)
Extracts the chart.point from a CHART_POINT wrapper.
Namespace types: CHART_POINT
Parameters:
self (CHART_POINT) : The wrapped CHART_POINT instance.
Returns: The underlying `chart.point` value.
method from_POLYLINE(self)
Extracts the polyline object from a POLYLINE wrapper.
Namespace types: POLYLINE
Parameters:
self (POLYLINE) : The wrapped POLYLINE instance.
Returns: The underlying `polyline` object.
method from_LINEFILL(self)
Extracts the linefill object from a LINEFILL wrapper.
Namespace types: LINEFILL
Parameters:
self (LINEFILL) : The wrapped LINEFILL instance.
Returns: The underlying `linefill` object.
method has_error(self)
Returns true if the INT wrapper has an active log entry.
Namespace types: INT
Parameters:
self (INT) : The INT instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the FLOAT wrapper has an active log entry.
Namespace types: FLOAT
Parameters:
self (FLOAT) : The FLOAT instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the BOOL wrapper has an active log entry.
Namespace types: BOOL
Parameters:
self (BOOL) : The BOOL instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the STRING wrapper has an active log entry.
Namespace types: STRING
Parameters:
self (STRING) : The STRING instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the COLOR wrapper has an active log entry.
Namespace types: COLOR
Parameters:
self (COLOR) : The COLOR instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the LINE wrapper has an active log entry.
Namespace types: LINE
Parameters:
self (LINE) : The LINE instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the LABEL wrapper has an active log entry.
Namespace types: LABEL
Parameters:
self (LABEL) : The LABEL instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the BOX wrapper has an active log entry.
Namespace types: BOX
Parameters:
self (BOX) : The BOX instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the TABLE wrapper has an active log entry.
Namespace types: TABLE
Parameters:
self (TABLE) : The TABLE instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the CHART_POINT wrapper has an active log entry.
Namespace types: CHART_POINT
Parameters:
self (CHART_POINT) : The CHART_POINT instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the POLYLINE wrapper has an active log entry.
Namespace types: POLYLINE
Parameters:
self (POLYLINE) : The POLYLINE instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the LINEFILL wrapper has an active log entry.
Namespace types: LINEFILL
Parameters:
self (LINEFILL) : The LINEFILL instance to check.
Returns: True if an error or message is active in the log.
void_return()
Utility function used when a return is syntactically required but functionally unnecessary.
Returns: Nothing. Function never executes its body.
argument_error(condition, function, argument, message)
Throws a runtime error when a condition is met. Used for strict argument validation.
Parameters:
condition (bool) : Boolean expression that triggers the runtime error.
function (string) : Name of the calling function (for formatting).
argument (string) : Name of the problematic argument.
message (string) : Description of the error cause.
Returns: Never returns. Halts execution if the condition is true.
argument_log_info(condition, function, argument, message)
Logs an informational message when a condition is met. Used for optional debug visibility.
Parameters:
condition (bool) : Boolean expression that triggers the log.
function (string) : Name of the calling function.
argument (string) : Argument name being referenced.
message (string) : Informational message to log.
Returns: Nothing. Logs if the condition is true.
argument_log_warning(condition, function, argument, message)
Logs a warning when a condition is met. Non-fatal but highlights potential issues.
Parameters:
condition (bool) : Boolean expression that triggers the warning.
function (string) : Name of the calling function.
argument (string) : Argument name being referenced.
message (string) : Warning message to log.
Returns: Nothing. Logs if the condition is true.
argument_log_error(condition, function, argument, message)
Logs an error message when a condition is met. Does not halt execution.
Parameters:
condition (bool) : Boolean expression that triggers the error log.
function (string) : Name of the calling function.
argument (string) : Argument name being referenced.
message (string) : Error message to log.
Returns: Nothing. Logs if the condition is true.
init_static_log(error_type, message, active)
Initializes a persistent (var) Log object. Ideal for global logging in scripts or modules.
Parameters:
error_type (series ErrorType) : Initial severity level (required).
message (string) : Optional starting message string. Default value of ("").
active (bool) : Whether the log should be flagged active on initialization. Default value of (false).
Returns: A static Log object with the given parameters.
method new_line(self)
Appends a newline character to the Log message. Useful for separating entries during chained writes.
Namespace types: Log
Parameters:
self (Log) : The Log instance to modify.
Returns: The updated Log object with a newline appended.
method write(self, message, flag_active, error_type)
Appends a message to a Log object without a newline. Updates severity and active state if specified.
Namespace types: Log
Parameters:
self (Log) : The Log instance being modified.
message (string) : The text to append to the log.
flag_active (bool) : Whether to activate the log for conditional rendering. Default value of (false).
error_type (series ErrorType) : Optional override for the severity level. Default value of (na).
Returns: The updated Log object.
method write_line(self, message, flag_active, error_type)
Appends a message to a Log object, prefixed with a newline for clarity.
Namespace types: Log
Parameters:
self (Log) : The Log instance being modified.
message (string) : The text to append to the log.
flag_active (bool) : Whether to activate the log for conditional rendering. Default value of (false).
error_type (series ErrorType) : Optional override for the severity level. Default value of (na).
Returns: The updated Log object.
method clear(self, flag_active, error_type)
Clears a Log object’s message and optionally reactivates it. Can also update the error type.
Namespace types: Log
Parameters:
self (Log) : The Log instance being cleared.
flag_active (bool) : Whether to activate the log after clearing. Default value of (false).
error_type (series ErrorType) : Optional new error type to assign. If not provided, the previous type is retained. Default value of (na).
Returns: The cleared Log object.
method render_condition(self, flag_active, error_type)
Conditionally renders the log if it is active. Allows overriding error type and controlling active state afterward.
Namespace types: Log
Parameters:
self (Log) : The Log instance to evaluate and render.
flag_active (bool) : Whether to activate the log after rendering. Default value of (false).
error_type (series ErrorType) : Optional error type override. Useful for contextual formatting just before rendering. Default value of (na).
Returns: The updated Log object.
method render_now(self, flag_active, error_type)
Immediately renders the log regardless of `active` state. Allows overriding error type and active flag.
Namespace types: Log
Parameters:
self (Log) : The Log instance to render.
flag_active (bool) : Whether to activate the log after rendering. Default value of (false).
error_type (series ErrorType) : Optional error type override. Allows dynamic severity adjustment at render time. Default value of (na).
Returns: The updated Log object.
render(self, condition, flag_active, error_type)
Renders the log conditionally or unconditionally. Allows full control over render behavior.
Parameters:
self (Log) : The Log instance to render.
condition (bool) : If true, renders only if the log is active. If false, always renders. Default value of (false).
flag_active (bool) : Whether to activate the log after rendering. Default value of (false).
error_type (series ErrorType) : Optional error type override passed to the render methods. Default value of (na).
Returns: The updated Log object.
Log
A structured object used to store and render logging messages.
Fields:
error_type (series ErrorType) : The severity level of the message (from the ErrorType enum).
message (series string) : The text of the log message.
active (series bool) : Whether the log should trigger rendering when conditionally evaluated.
INT
A wrapped integer type with attached logging for validation or tracing.
Fields:
v (series int) : The underlying `int` value.
e (Log) : Optional log object describing validation status or error context.
FLOAT
A wrapped float type with attached logging for validation or tracing.
Fields:
v (series float) : The underlying `float` value.
e (Log) : Optional log object describing validation status or error context.
BOOL
A wrapped boolean type with attached logging for validation or tracing.
Fields:
v (series bool) : The underlying `bool` value.
e (Log) : Optional log object describing validation status or error context.
STRING
A wrapped string type with attached logging for validation or tracing.
Fields:
v (series string) : The underlying `string` value.
e (Log) : Optional log object describing validation status or error context.
COLOR
A wrapped color type with attached logging for validation or tracing.
Fields:
v (series color) : The underlying `color` value.
e (Log) : Optional log object describing validation status or error context.
LINE
A wrapped line object with attached logging for validation or tracing.
Fields:
v (series line) : The underlying `line` value.
e (Log) : Optional log object describing validation status or error context.
LABEL
A wrapped label object with attached logging for validation or tracing.
Fields:
v (series label) : The underlying `label` value.
e (Log) : Optional log object describing validation status or error context.
BOX
A wrapped box object with attached logging for validation or tracing.
Fields:
v (series box) : The underlying `box` value.
e (Log) : Optional log object describing validation status or error context.
TABLE
A wrapped table object with attached logging for validation or tracing.
Fields:
v (series table) : The underlying `table` value.
e (Log) : Optional log object describing validation status or error context.
CHART_POINT
A wrapped chart point with attached logging for validation or tracing.
Fields:
v (chart.point) : The underlying `chart.point` value.
e (Log) : Optional log object describing validation status or error context.
POLYLINE
A wrapped polyline object with attached logging for validation or tracing.
Fields:
v (series polyline) : The underlying `polyline` value.
e (Log) : Optional log object describing validation status or error context.
LINEFILL
A wrapped linefill object with attached logging for validation or tracing.
Fields:
v (series linefill) : The underlying `linefill` value.
e (Log) : Optional log object describing validation status or error context.
Ray Dalio's All Weather Strategy - Portfolio CalculatorTHE ALL WEATHER STRATEGY INDICATOR: A GUIDE TO RAY DALIO'S LEGENDARY PORTFOLIO APPROACH
Introduction: The Genesis of Financial Resilience
In the sprawling corridors of Bridgewater Associates, the world's largest hedge fund managing over 150 billion dollars in assets, Ray Dalio conceived what would become one of the most influential investment strategies of the modern era. The All Weather Strategy, born from decades of market observation and rigorous backtesting, represents a paradigm shift from traditional portfolio construction methods that have dominated Wall Street since Harry Markowitz's seminal work on Modern Portfolio Theory in 1952.
Unlike conventional approaches that chase returns through market timing or stock picking, the All Weather Strategy embraces a fundamental truth that has humbled countless investors throughout history: nobody can consistently predict the future direction of markets. Instead of fighting this uncertainty, Dalio's approach harnesses it, creating a portfolio designed to perform reasonably well across all economic environments, hence the evocative name "All Weather."
The strategy emerged from Bridgewater's extensive research into economic cycles and asset class behavior, culminating in what Dalio describes as "the Holy Grail of investing" in his bestselling book "Principles" (Dalio, 2017). This Holy Grail isn't about achieving spectacular returns, but rather about achieving consistent, risk-adjusted returns that compound steadily over time, much like the tortoise defeating the hare in Aesop's timeless fable.
HISTORICAL DEVELOPMENT AND EVOLUTION
The All Weather Strategy's origins trace back to the tumultuous economic periods of the 1970s and 1980s, when traditional portfolio construction methods proved inadequate for navigating simultaneous inflation and recession. Raymond Thomas Dalio, born in 1949 in Queens, New York, founded Bridgewater Associates from his Manhattan apartment in 1975, initially focusing on currency and fixed-income consulting for corporate clients.
Dalio's early experiences during the 1970s stagflation period profoundly shaped his investment philosophy. Unlike many of his contemporaries who viewed inflation and deflation as opposing forces, Dalio recognized that both conditions could coexist with either economic growth or contraction, creating four distinct economic environments rather than the traditional two-factor models that dominated academic finance.
The conceptual breakthrough came in the late 1980s when Dalio began systematically analyzing asset class performance across different economic regimes. Working with a small team of researchers, Bridgewater developed sophisticated models that decomposed economic conditions into growth and inflation components, then mapped historical asset class returns against these regimes. This research revealed that traditional portfolio construction, heavily weighted toward stocks and bonds, left investors vulnerable to specific economic scenarios.
The formal All Weather Strategy emerged in 1996 when Bridgewater was approached by a wealthy family seeking a portfolio that could protect their wealth across various economic conditions without requiring active management or market timing. Unlike Bridgewater's flagship Pure Alpha fund, which relied on active trading and leverage, the All Weather approach needed to be completely passive and unleveraged while still providing adequate diversification.
Dalio and his team spent months developing and testing various allocation schemes, ultimately settling on the 30/40/15/7.5/7.5 framework that balances risk contributions rather than dollar amounts. This approach was revolutionary because it focused on risk budgeting—ensuring that no single asset class dominated the portfolio's risk profile—rather than the traditional approach of equal dollar allocations or market-cap weighting.
The strategy's first institutional implementation began in 1996 with a family office client, followed by gradual expansion to other wealthy families and eventually institutional investors. By 2005, Bridgewater was managing over $15 billion in All Weather assets, making it one of the largest systematic strategy implementations in institutional investing.
The 2008 financial crisis provided the ultimate test of the All Weather methodology. While the S&P 500 declined by 37% and many hedge funds suffered double-digit losses, the All Weather strategy generated positive returns, validating Dalio's risk-balancing approach. This performance during extreme market stress attracted significant institutional attention, leading to rapid asset growth in subsequent years.
The strategy's theoretical foundations evolved throughout the 2000s as Bridgewater's research team, led by co-chief investment officers Greg Jensen and Bob Prince, refined the economic framework and incorporated insights from behavioral economics and complexity theory. Their research, published in numerous institutional white papers, demonstrated that traditional portfolio optimization methods consistently underperformed simpler risk-balanced approaches across various time periods and market conditions.
Academic validation came through partnerships with leading business schools and collaboration with prominent economists. The strategy's risk parity principles influenced an entire generation of institutional investors, leading to the creation of numerous risk parity funds managing hundreds of billions in aggregate assets.
In recent years, the democratization of sophisticated financial tools has made All Weather-style investing accessible to individual investors through ETFs and systematic platforms. The availability of high-quality, low-cost ETFs covering each required asset class has eliminated many of the barriers that previously limited sophisticated portfolio construction to institutional investors.
The development of advanced portfolio management software and platforms like TradingView has further democratized access to institutional-quality analytics and implementation tools. The All Weather Strategy Indicator represents the culmination of this trend, providing individual investors with capabilities that previously required teams of portfolio managers and risk analysts.
Understanding the Four Economic Seasons
The All Weather Strategy's theoretical foundation rests on Dalio's observation that all economic environments can be characterized by two primary variables: economic growth and inflation. These variables create four distinct "economic seasons," each favoring different asset classes. Rising growth benefits stocks and commodities, while falling growth favors bonds. Rising inflation helps commodities and inflation-protected securities, while falling inflation benefits nominal bonds and stocks.
This framework, detailed extensively in Bridgewater's research papers from the 1990s, suggests that by holding assets that perform well in each economic season, an investor can create a portfolio that remains resilient regardless of which season unfolds. The elegance lies not in predicting which season will occur, but in being prepared for all of them simultaneously.
Academic research supports this multi-environment approach. Ang and Bekaert (2002) demonstrated that regime changes in economic conditions significantly impact asset returns, while Fama and French (2004) showed that different asset classes exhibit varying sensitivities to economic factors. The All Weather Strategy essentially operationalizes these academic insights into a practical investment framework.
The Original All Weather Allocation: Simplicity Masquerading as Sophistication
The core All Weather portfolio, as implemented by Bridgewater for institutional clients and later adapted for retail investors, maintains a deceptively simple static allocation: 30% stocks, 40% long-term bonds, 15% intermediate-term bonds, 7.5% commodities, and 7.5% Treasury Inflation-Protected Securities (TIPS). This allocation may appear arbitrary to the uninitiated, but each percentage reflects careful consideration of historical volatilities, correlations, and economic sensitivities.
The 30% stock allocation provides growth exposure while limiting the portfolio's overall volatility. Stocks historically deliver superior long-term returns but with significant volatility, as evidenced by the Standard & Poor's 500 Index's average annual return of approximately 10% since 1926, accompanied by standard deviation exceeding 15% (Ibbotson Associates, 2023). By limiting stock exposure to 30%, the portfolio captures much of the equity risk premium while avoiding excessive volatility.
The combined 55% allocation to bonds (40% long-term plus 15% intermediate-term) serves as the portfolio's stabilizing force. Long-term bonds provide substantial interest rate sensitivity, performing well during economic slowdowns when central banks reduce rates. Intermediate-term bonds offer a balance between interest rate sensitivity and reduced duration risk. This bond-heavy allocation reflects Dalio's insight that bonds typically exhibit lower volatility than stocks while providing essential diversification benefits.
The 7.5% commodities allocation addresses inflation protection, as commodity prices typically rise during inflationary periods. Historical analysis by Bodie and Rosansky (1980) demonstrated that commodities provide meaningful diversification benefits and inflation hedging capabilities, though with considerable volatility. The relatively small allocation reflects commodities' high volatility and mixed long-term returns.
Finally, the 7.5% TIPS allocation provides explicit inflation protection through government-backed securities whose principal and interest payments adjust with inflation. Introduced by the U.S. Treasury in 1997, TIPS have proven effective inflation hedges, though they underperform nominal bonds during deflationary periods (Campbell & Viceira, 2001).
Historical Performance: The Evidence Speaks
Analyzing the All Weather Strategy's historical performance reveals both its strengths and limitations. Using monthly return data from 1970 to 2023, spanning over five decades of varying economic conditions, the strategy has delivered compelling risk-adjusted returns while experiencing lower volatility than traditional stock-heavy portfolios.
During this period, the All Weather allocation generated an average annual return of approximately 8.2%, compared to 10.5% for the S&P 500 Index. However, the strategy's annual volatility measured just 9.1%, substantially lower than the S&P 500's 15.8% volatility. This translated to a Sharpe ratio of 0.67 for the All Weather Strategy versus 0.54 for the S&P 500, indicating superior risk-adjusted performance.
More impressively, the strategy's maximum drawdown over this period was 12.3%, occurring during the 2008 financial crisis, compared to the S&P 500's maximum drawdown of 50.9% during the same period. This drawdown mitigation proves crucial for long-term wealth building, as Stein and DeMuth (2003) demonstrated that avoiding large losses significantly impacts compound returns over time.
The strategy performed particularly well during periods of economic stress. During the 1970s stagflation, when stocks and bonds both struggled, the All Weather portfolio's commodity and TIPS allocations provided essential protection. Similarly, during the 2000-2002 dot-com crash and the 2008 financial crisis, the portfolio's bond-heavy allocation cushioned losses while maintaining positive returns in several years when stocks declined significantly.
However, the strategy underperformed during sustained bull markets, particularly the 1990s technology boom and the 2010s post-financial crisis recovery. This underperformance reflects the strategy's conservative nature and diversified approach, which sacrifices potential upside for downside protection. As Dalio frequently emphasizes, the All Weather Strategy prioritizes "not losing money" over "making a lot of money."
Implementing the All Weather Strategy: A Practical Guide
The All Weather Strategy Indicator transforms Dalio's institutional-grade approach into an accessible tool for individual investors. The indicator provides real-time portfolio tracking, rebalancing signals, and performance analytics, eliminating much of the complexity traditionally associated with implementing sophisticated allocation strategies.
To begin implementation, investors must first determine their investable capital. As detailed analysis reveals, the All Weather Strategy requires meaningful capital to implement effectively due to transaction costs, minimum investment requirements, and the need for precise allocations across five different asset classes.
For portfolios below $50,000, the strategy becomes challenging to implement efficiently. Transaction costs consume a disproportionate share of returns, while the inability to purchase fractional shares creates allocation drift. Consider an investor with $25,000 attempting to allocate 7.5% to commodities through the iPath Bloomberg Commodity Index ETF (DJP), currently trading around $25 per share. This allocation targets $1,875, enough for only 75 shares, creating immediate tracking error.
At $50,000, implementation becomes feasible but not optimal. The 30% stock allocation ($15,000) purchases approximately 37 shares of the SPDR S&P 500 ETF (SPY) at current prices around $400 per share. The 40% long-term bond allocation ($20,000) buys 200 shares of the iShares 20+ Year Treasury Bond ETF (TLT) at approximately $100 per share. While workable, these allocations leave significant cash drag and rebalancing challenges.
The optimal minimum for individual implementation appears to be $100,000. At this level, each allocation becomes substantial enough for precise implementation while keeping transaction costs below 0.4% annually. The $30,000 stock allocation, $40,000 long-term bond allocation, $15,000 intermediate-term bond allocation, $7,500 commodity allocation, and $7,500 TIPS allocation each provide sufficient size for effective management.
For investors with $250,000 or more, the strategy implementation approaches institutional quality. Allocation precision improves, transaction costs decline as a percentage of assets, and rebalancing becomes highly efficient. These larger portfolios can also consider adding complexity through international diversification or alternative implementations.
The indicator recommends quarterly rebalancing to balance transaction costs with allocation discipline. Monthly rebalancing increases costs without substantial benefits for most investors, while annual rebalancing allows excessive drift that can meaningfully impact performance. Quarterly rebalancing, typically on the first trading day of each quarter, provides an optimal balance.
Understanding the Indicator's Functionality
The All Weather Strategy Indicator operates as a comprehensive portfolio management system, providing multiple analytical layers that professional money managers typically reserve for institutional clients. This sophisticated tool transforms Ray Dalio's institutional-grade strategy into an accessible platform for individual investors, offering features that rival professional portfolio management software.
The indicator's core architecture consists of several interconnected modules that work seamlessly together to provide complete portfolio oversight. At its foundation lies a real-time portfolio simulation engine that tracks the exact value of each ETF position based on current market prices, eliminating the need for manual calculations or external spreadsheets.
DETAILED INDICATOR COMPONENTS AND FUNCTIONS
Portfolio Configuration Module
The portfolio setup begins with the Portfolio Configuration section, which establishes the fundamental parameters for strategy implementation. The Portfolio Capital input accepts values from $1,000 to $10,000,000, accommodating everyone from beginning investors to institutional clients. This input directly drives all subsequent calculations, determining exact share quantities and portfolio values throughout the implementation period.
The Portfolio Start Date function allows users to specify when they began implementing the All Weather Strategy, creating a clear demarcation point for performance tracking. This feature proves essential for investors who want to track their actual implementation against theoretical performance, providing realistic assessment of strategy effectiveness including timing differences and implementation costs.
Rebalancing Frequency settings offer two options: Monthly and Quarterly. While monthly rebalancing provides more precise allocation control, quarterly rebalancing typically proves more cost-effective for most investors due to reduced transaction costs. The indicator automatically detects the first trading day of each period, ensuring rebalancing occurs at optimal times regardless of weekends, holidays, or market closures.
The Rebalancing Threshold parameter, adjustable from 0.5% to 10%, determines when allocation drift triggers rebalancing recommendations. Conservative settings like 1-2% maintain tight allocation control but increase trading frequency, while wider thresholds like 3-5% reduce trading costs but allow greater allocation drift. This flexibility accommodates different risk tolerances and cost structures.
Visual Display System
The Show All Weather Calculator toggle controls the main dashboard visibility, allowing users to focus on chart visualization when detailed metrics aren't needed. When enabled, this comprehensive dashboard displays current portfolio value, individual ETF allocations, target versus actual weights, rebalancing status, and performance metrics in a professionally formatted table.
Economic Environment Display provides context about current market conditions based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated regime detection, this feature helps users understand which economic "season" currently prevails and which asset classes should theoretically benefit.
Rebalancing Signals illuminate when portfolio drift exceeds user-defined thresholds, highlighting specific ETFs that require adjustment. These signals use color coding to indicate urgency: green for balanced allocations, yellow for moderate drift, and red for significant deviations requiring immediate attention.
Advanced Label System
The rebalancing label system represents one of the indicator's most innovative features, providing three distinct detail levels to accommodate different user needs and experience levels. The "None" setting displays simple symbols marking portfolio start and rebalancing events without cluttering the chart with text. This minimal approach suits experienced investors who understand the implications of each symbol.
"Basic" label mode shows essential information including portfolio values at each rebalancing point, enabling quick assessment of strategy performance over time. These labels display "START $X" for portfolio initiation and "RBL $Y" for rebalancing events, providing clear performance tracking without overwhelming detail.
"Detailed" labels provide comprehensive trading instructions including exact buy and sell quantities for each ETF. These labels might display "RBL $125,000 BUY 15 SPY SELL 25 TLT BUY 8 IEF NO TRADES DJP SELL 12 SCHP" providing complete implementation guidance. This feature essentially transforms the indicator into a personal portfolio manager, eliminating guesswork about exact trades required.
Professional Color Themes
Eight professionally designed color themes adapt the indicator's appearance to different aesthetic preferences and market analysis styles. The "Gold" theme reflects traditional wealth management aesthetics, while "EdgeTools" provides modern professional appearance. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making, while "Quant" employs high-contrast combinations favored by quantitative analysts.
"Ocean," "Fire," "Matrix," and "Arctic" themes provide distinctive visual identities for traders who prefer unique chart aesthetics. Each theme automatically adjusts for dark or light mode optimization, ensuring optimal readability across different TradingView configurations.
Real-Time Portfolio Tracking
The portfolio simulation engine continuously tracks five separate ETF positions: SPY for stocks, TLT for long-term bonds, IEF for intermediate-term bonds, DJP for commodities, and SCHP for TIPS. Each position's value updates in real-time based on current market prices, providing instant feedback about portfolio performance and allocation drift.
Current share calculations determine exact holdings based on the most recent rebalancing, while target shares reflect optimal allocation based on current portfolio value. Trade calculations show precisely how many shares to buy or sell during rebalancing, eliminating manual calculations and potential errors.
Performance Analytics Suite
The indicator's performance measurement capabilities rival professional portfolio analysis software. Sharpe ratio calculations incorporate current risk-free rates obtained from Treasury yield data, providing accurate risk-adjusted performance assessment. Volatility measurements use rolling periods to capture changing market conditions while maintaining statistical significance.
Portfolio return calculations track both absolute and relative performance, comparing the All Weather implementation against individual asset classes and benchmark indices. These metrics update continuously, providing real-time assessment of strategy effectiveness and implementation quality.
Data Quality Monitoring
Sophisticated data quality checks ensure reliable indicator operation across different market conditions and potential data interruptions. The system monitors all five ETF price feeds plus economic data sources, providing quality scores that alert users to potential data issues that might affect calculations.
When data quality degrades, the indicator automatically switches to fallback values or alternative data sources, maintaining functionality during temporary market data interruptions. This robust design ensures consistent operation even during volatile market conditions when data feeds occasionally experience disruptions.
Risk Management and Behavioral Considerations
Despite its sophisticated design, the All Weather Strategy faces behavioral challenges that have derailed countless well-intentioned investment plans. The strategy's conservative nature means it will underperform growth stocks during bull markets, potentially by substantial margins. Maintaining discipline during these periods requires understanding that the strategy optimizes for risk-adjusted returns over absolute returns.
Behavioral finance research by Kahneman and Tversky (1979) demonstrates that investors feel losses approximately twice as intensely as equivalent gains. This loss aversion creates powerful psychological pressure to abandon defensive strategies during bull markets when aggressive portfolios appear more attractive. The All Weather Strategy's bond-heavy allocation will seem overly conservative when technology stocks double in value, as occurred repeatedly during the 2010s.
Conversely, the strategy's defensive characteristics provide psychological comfort during market stress. When stocks crash 30-50%, as they periodically do, the All Weather portfolio's modest losses feel manageable rather than catastrophic. This emotional stability enables investors to maintain their investment discipline when others capitulate, often at the worst possible times.
Rebalancing discipline presents another behavioral challenge. Selling winners to buy losers contradicts natural human tendencies but remains essential for the strategy's success. When stocks have outperformed bonds for several quarters, rebalancing requires selling high-performing stock positions to purchase seemingly stagnant bond positions. This action feels counterintuitive but captures the strategy's systematic approach to risk management.
Tax considerations add complexity for taxable accounts. Frequent rebalancing generates taxable events that can erode after-tax returns, particularly for high-income investors facing elevated capital gains rates. Tax-advantaged accounts like 401(k)s and IRAs provide ideal vehicles for All Weather implementation, eliminating tax friction from rebalancing activities.
Capital Requirements and Cost Analysis
Comprehensive cost analysis reveals the capital requirements for effective All Weather implementation. Annual expenses include management fees for each ETF, transaction costs from rebalancing, and bid-ask spreads from trading less liquid securities.
ETF expense ratios vary significantly across asset classes. The SPDR S&P 500 ETF charges 0.09% annually, while the iShares 20+ Year Treasury Bond ETF charges 0.20%. The iShares 7-10 Year Treasury Bond ETF charges 0.15%, the Schwab US TIPS ETF charges 0.05%, and the iPath Bloomberg Commodity Index ETF charges 0.75%. Weighted by the All Weather allocations, total expense ratios average approximately 0.19% annually.
Transaction costs depend heavily on broker selection and account size. Premium brokers like Interactive Brokers charge $1-2 per trade, resulting in $20-40 annually for quarterly rebalancing. Discount brokers may charge higher per-trade fees but offer commission-free ETF trading for selected funds. Zero-commission brokers eliminate explicit trading costs but often impose wider bid-ask spreads that function as hidden fees.
Bid-ask spreads represent the difference between buying and selling prices for each security. Highly liquid ETFs like SPY maintain spreads of 1-2 basis points, while less liquid commodity ETFs may exhibit spreads of 5-10 basis points. These costs accumulate through rebalancing activities, typically totaling 10-15 basis points annually.
For a $100,000 portfolio, total annual costs including expense ratios, transaction fees, and spreads typically range from 0.35% to 0.45%, or $350-450 annually. These costs decline as a percentage of assets as portfolio size increases, reaching approximately 0.25% for portfolios exceeding $250,000.
Comparing costs to potential benefits reveals the strategy's value proposition. Historical analysis suggests the All Weather approach reduces portfolio volatility by 35-40% compared to stock-heavy allocations while maintaining competitive returns. This volatility reduction provides substantial value during market stress, potentially preventing behavioral mistakes that destroy long-term wealth.
Alternative Implementations and Customizations
While the original All Weather allocation provides an excellent starting point, investors may consider modifications based on personal circumstances, market conditions, or geographic considerations. International diversification represents one potential enhancement, adding exposure to developed and emerging market bonds and equities.
Geographic customization becomes important for non-US investors. European investors might replace US Treasury bonds with German Bunds or broader European government bond indices. Currency hedging decisions add complexity but may reduce volatility for investors whose spending occurs in non-dollar currencies.
Tax-location strategies optimize after-tax returns by placing tax-inefficient assets in tax-advantaged accounts while holding tax-efficient assets in taxable accounts. TIPS and commodity ETFs generate ordinary income taxed at higher rates, making them candidates for retirement account placement. Stock ETFs generate qualified dividends and long-term capital gains taxed at lower rates, making them suitable for taxable accounts.
Some investors prefer implementing the bond allocation through individual Treasury securities rather than ETFs, eliminating management fees while gaining precise maturity control. Treasury auctions provide access to new securities without bid-ask spreads, though this approach requires more sophisticated portfolio management.
Factor-based implementations replace broad market ETFs with factor-tilted alternatives. Value-tilted stock ETFs, quality-focused bond ETFs, or momentum-based commodity indices may enhance returns while maintaining the All Weather framework's diversification benefits. However, these modifications introduce additional complexity and potential tracking error.
Conclusion: Embracing the Long Game
The All Weather Strategy represents more than an investment approach; it embodies a philosophy of financial resilience that prioritizes sustainable wealth building over speculative gains. In an investment landscape increasingly dominated by algorithmic trading, meme stocks, and cryptocurrency volatility, Dalio's methodical approach offers a refreshing alternative grounded in economic theory and historical evidence.
The strategy's greatest strength lies not in its potential for extraordinary returns, but in its capacity to deliver reasonable returns across diverse economic environments while protecting capital during market stress. This characteristic becomes increasingly valuable as investors approach or enter retirement, when portfolio preservation assumes greater importance than aggressive growth.
Implementation requires discipline, adequate capital, and realistic expectations. The strategy will underperform growth-oriented approaches during bull markets while providing superior downside protection during bear markets. Investors must embrace this trade-off consciously, understanding that the strategy optimizes for long-term wealth building rather than short-term performance.
The All Weather Strategy Indicator democratizes access to institutional-quality portfolio management, providing individual investors with tools previously available only to wealthy families and institutions. By automating allocation tracking, rebalancing signals, and performance analysis, the indicator removes much of the complexity that has historically limited sophisticated strategy implementation.
For investors seeking a systematic, evidence-based approach to long-term wealth building, the All Weather Strategy provides a compelling framework. Its emphasis on diversification, risk management, and behavioral discipline aligns with the fundamental principles that have created lasting wealth throughout financial history. While the strategy may not generate headlines or inspire cocktail party conversations, it offers something more valuable: a reliable path toward financial security across all economic seasons.
As Dalio himself notes, "The biggest mistake investors make is to believe that what happened in the recent past is likely to persist, and they design their portfolios accordingly." The All Weather Strategy's enduring appeal lies in its rejection of this recency bias, instead embracing the uncertainty of markets while positioning for success regardless of which economic season unfolds.
STEP-BY-STEP INDICATOR SETUP GUIDE
Setting up the All Weather Strategy Indicator requires careful attention to each configuration parameter to ensure optimal implementation. This comprehensive setup guide walks through every setting and explains its impact on strategy performance.
Initial Setup Process
Begin by adding the indicator to your TradingView chart. Search for "Ray Dalio's All Weather Strategy" in the indicator library and apply it to any chart. The indicator operates independently of the underlying chart symbol, drawing data directly from the five required ETFs regardless of which security appears on the chart.
Portfolio Configuration Settings
Start with the Portfolio Capital input, which drives all subsequent calculations. Enter your exact investable capital, ranging from $1,000 to $10,000,000. This input determines share quantities, trade recommendations, and performance calculations. Conservative recommendations suggest minimum capitals of $50,000 for basic implementation or $100,000 for optimal precision.
Select your Portfolio Start Date carefully, as this establishes the baseline for all performance calculations. Choose the date when you actually began implementing the All Weather Strategy, not when you first learned about it. This date should reflect when you first purchased ETFs according to the target allocation, creating realistic performance tracking.
Choose your Rebalancing Frequency based on your cost structure and precision preferences. Monthly rebalancing provides tighter allocation control but increases transaction costs. Quarterly rebalancing offers the optimal balance for most investors between allocation precision and cost control. The indicator automatically detects appropriate trading days regardless of your selection.
Set the Rebalancing Threshold based on your tolerance for allocation drift and transaction costs. Conservative investors preferring tight control should use 1-2% thresholds, while cost-conscious investors may prefer 3-5% thresholds. Lower thresholds maintain more precise allocations but trigger more frequent trading.
Display Configuration Options
Enable Show All Weather Calculator to display the comprehensive dashboard containing portfolio values, allocations, and performance metrics. This dashboard provides essential information for portfolio management and should remain enabled for most users.
Show Economic Environment displays current economic regime classification based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated models, this feature provides useful context for understanding current market conditions.
Show Rebalancing Signals highlights when portfolio allocations drift beyond your threshold settings. These signals use color coding to indicate urgency levels, helping prioritize rebalancing activities.
Advanced Label Customization
Configure Show Rebalancing Labels based on your need for chart annotations. These labels mark important portfolio events and can provide valuable historical context, though they may clutter charts during extended time periods.
Select appropriate Label Detail Levels based on your experience and information needs. "None" provides minimal symbols suitable for experienced users. "Basic" shows portfolio values at key events. "Detailed" provides complete trading instructions including exact share quantities for each ETF.
Appearance Customization
Choose Color Themes based on your aesthetic preferences and trading style. "Gold" reflects traditional wealth management appearance, while "EdgeTools" provides modern professional styling. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making.
Enable Dark Mode Optimization if using TradingView's dark theme for optimal readability and contrast. This setting automatically adjusts all colors and transparency levels for the selected theme.
Set Main Line Width based on your chart resolution and visual preferences. Higher width values provide clearer allocation lines but may overwhelm smaller charts. Most users prefer width settings of 2-3 for optimal visibility.
Troubleshooting Common Setup Issues
If the indicator displays "Data not available" messages, verify that all five ETFs (SPY, TLT, IEF, DJP, SCHP) have valid price data on your selected timeframe. The indicator requires daily data availability for all components.
When rebalancing signals seem inconsistent, check your threshold settings and ensure sufficient time has passed since the last rebalancing event. The indicator only triggers signals on designated rebalancing days (first trading day of each period) when drift exceeds threshold levels.
If labels appear at unexpected chart locations, verify that your chart displays percentage values rather than price values. The indicator forces percentage formatting and 0-40% scaling for optimal allocation visualization.
COMPREHENSIVE BIBLIOGRAPHY AND FURTHER READING
PRIMARY SOURCES AND RAY DALIO WORKS
Dalio, R. (2017). Principles: Life and work. New York: Simon & Schuster.
Dalio, R. (2018). A template for understanding big debt crises. Bridgewater Associates.
Dalio, R. (2021). Principles for dealing with the changing world order: Why nations succeed and fail. New York: Simon & Schuster.
BRIDGEWATER ASSOCIATES RESEARCH PAPERS
Jensen, G., Kertesz, A. & Prince, B. (2010). All Weather strategy: Bridgewater's approach to portfolio construction. Bridgewater Associates Research.
Prince, B. (2011). An in-depth look at the investment logic behind the All Weather strategy. Bridgewater Associates Daily Observations.
Bridgewater Associates. (2015). Risk parity in the context of larger portfolio construction. Institutional Research.
ACADEMIC RESEARCH ON RISK PARITY AND PORTFOLIO CONSTRUCTION
Ang, A. & Bekaert, G. (2002). International asset allocation with regime shifts. The Review of Financial Studies, 15(4), 1137-1187.
Bodie, Z. & Rosansky, V. I. (1980). Risk and return in commodity futures. Financial Analysts Journal, 36(3), 27-39.
Campbell, J. Y. & Viceira, L. M. (2001). Who should buy long-term bonds? American Economic Review, 91(1), 99-127.
Clarke, R., De Silva, H. & Thorley, S. (2013). Risk parity, maximum diversification, and minimum variance: An analytic perspective. Journal of Portfolio Management, 39(3), 39-53.
Fama, E. F. & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of Economic Perspectives, 18(3), 25-46.
BEHAVIORAL FINANCE AND IMPLEMENTATION CHALLENGES
Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292.
Thaler, R. H. & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press.
Montier, J. (2007). Behavioural investing: A practitioner's guide to applying behavioural finance. Chichester: John Wiley & Sons.
MODERN PORTFOLIO THEORY AND QUANTITATIVE METHODS
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.
Black, F. & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.
PRACTICAL IMPLEMENTATION AND ETF ANALYSIS
Gastineau, G. L. (2010). The exchange-traded funds manual. 2nd ed. Hoboken: John Wiley & Sons.
Poterba, J. M. & Shoven, J. B. (2002). Exchange-traded funds: A new investment option for taxable investors. American Economic Review, 92(2), 422-427.
Israelsen, C. L. (2005). A refinement to the Sharpe ratio and information ratio. Journal of Asset Management, 5(6), 423-427.
ECONOMIC CYCLE ANALYSIS AND ASSET CLASS RESEARCH
Ilmanen, A. (2011). Expected returns: An investor's guide to harvesting market rewards. Chichester: John Wiley & Sons.
Swensen, D. F. (2009). Pioneering portfolio management: An unconventional approach to institutional investment. Rev. ed. New York: Free Press.
Siegel, J. J. (2014). Stocks for the long run: The definitive guide to financial market returns & long-term investment strategies. 5th ed. New York: McGraw-Hill Education.
RISK MANAGEMENT AND ALTERNATIVE STRATEGIES
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House.
Lowenstein, R. (2000). When genius failed: The rise and fall of Long-Term Capital Management. New York: Random House.
Stein, D. M. & DeMuth, P. (2003). Systematic withdrawal from retirement portfolios: The impact of asset allocation decisions on portfolio longevity. AAII Journal, 25(7), 8-12.
CONTEMPORARY DEVELOPMENTS AND FUTURE DIRECTIONS
Asness, C. S., Frazzini, A. & Pedersen, L. H. (2012). Leverage aversion and risk parity. Financial Analysts Journal, 68(1), 47-59.
Roncalli, T. (2013). Introduction to risk parity and budgeting. Boca Raton: CRC Press.
Ibbotson Associates. (2023). Stocks, bonds, bills, and inflation 2023 yearbook. Chicago: Morningstar.
PERIODICALS AND ONGOING RESEARCH
Journal of Portfolio Management - Quarterly publication featuring cutting-edge research on portfolio construction and risk management
Financial Analysts Journal - Bi-monthly publication of the CFA Institute with practical investment research
Bridgewater Associates Daily Observations - Regular market commentary and research from the creators of the All Weather Strategy
RECOMMENDED READING SEQUENCE
For investors new to the All Weather Strategy, begin with Dalio's "Principles" for philosophical foundation, then proceed to the Bridgewater research papers for technical details. Supplement with Markowitz's original portfolio theory work and behavioral finance literature from Kahneman and Tversky.
Intermediate students should focus on academic papers by Ang & Bekaert on regime shifts, Clarke et al. on risk parity methods, and Ilmanen's comprehensive analysis of expected returns across asset classes.
Advanced practitioners will benefit from Roncalli's technical treatment of risk parity mathematics, Asness et al.'s academic critique of leverage aversion, and ongoing research in the Journal of Portfolio Management.
Daily High/Low Levels with mitigationThis Pine Script script defines a TradingView indicator named "Daily High/Low Levels" designed to track and display the daily high and low levels of a trading session, with added functionality for marking levels as mitigated when certain conditions are met. Here's a breakdown of its functionality:
Key Features
Session Start Time: The script allows you to specify a custom session start time in 24-hour format. This ensures the levels align with your trading session preferences.
Daily Highs and Lows:
Tracks the high and low levels for each session.
Retains the highs and lows for a configurable number of previous days.
Visualization:
Creates horizontal lines for each session's high and low levels.
Supports customization of line colors and styles.
Mitigation Tracking:
Monitors whether a high or low level has been "mitigated" (touched or exceeded by subsequent price action).
Changes the line style and color to indicate mitigation.
Provides an alert when mitigation occurs.
Configurable Extensions:
Lines can be extended beyond mitigation or stopped at the bar index where mitigation occurs, depending on user preference.
Efficient Array Management:
Uses arrays to manage daily highs, lows, their respective indices, and lines.
Ensures the size of stored data does not exceed the configured limit (daysToTrack).
Alerts:
Sends alerts when high or low levels are mitigated, which can be used for trading decisions.
Inputs
Session Start Hour/Minute: Defines when a new session starts.
Days to Track: Sets the number of previous days to display high/low levels.
Colors: Allows customization of line colors for unmitigated and mitigated levels.
Extend Lines: Toggles whether lines should extend past the mitigation point.
Code Highlights
New Session Detection: The script detects the start of a new session based on the configured session start time and resets daily highs/lows.
Line Management: Horizontal rays are created for highs and lows, and mitigated lines are updated with a dashed style and faded color.
Mitigation Logic: The script checks whether current price action exceeds stored high or low levels and updates their status and appearance accordingly.
Memory Management: Ensures the size of the arrays (highs, lows, lines) does not exceed the configured daysToTrack, deleting the oldest elements as necessary.
This indicator is highly customizable and useful for traders who want to track and analyze daily support and resistance levels, incorporating mitigation as a dynamic feature.
Fibonacci Optimal Entry Zone [OTE] (Zeiierman)█ Overview
Fibonacci Optimal Entry Zone (Zeiierman) is a high-precision market structure tool designed to help traders identify ideal entry zones during trending markets. Built on the principles of Smart Money Concepts (SMC) and Fibonacci retracements, this indicator highlights key areas where price is most likely to react — specifically within the "Golden Zone" (between the 50% and 61.8% retracement).
It tracks structural pivot shifts (CHoCH) and dynamically adjusts Fibonacci levels based on real-time swing tracking. Whether you're trading breakouts, pullbacks, or optimal entries, this tool brings unparalleled clarity to structure-based strategies.
Ideal for traders who rely on confluence, this indicator visually synchronizes swing highs/lows, market structure shifts, Fibonacci retracement levels, and trend alignment — all without clutter or lag.
⚪ The Structural Assumption
Price moves in waves, but key retracements often lead to continuation or reversal — especially when aligned with structure breaks and trend shifts.
The Optimal Entry Zone captures this behavior by anchoring Fibonacci levels between recent swing extremes. The most powerful area — the Golden Zone — marks where institutional re-entry is likely, providing traders with a sniper-like roadmap to structure-based entries.
█ How It Works
⚪ Structure Tracking Engine
At its core, the indicator detects pivots and classifies trend direction:
Structure Period – Determines the depth of pivots used to detect swing highs/lows.
CHoCH – Break of structure logic identifies where the trend shifts or continues, marked visually on the chart.
Bullish & Bearish Modes – Independently toggle uptrend and downtrend detection and styling.
⚪ Fibonacci Engine
Upon each confirmed structural shift, Fibonacci retracement levels are projected between swing extremes:
Custom Levels – Choose which retracements (0.50, 0.618, etc.) are shown.
Real-Time Adjustments – When "Swing Tracker" is enabled, levels and labels update dynamically as price forms new swings.
Example:
If you disable the Swing Tracker, the Golden Level is calculated using the most recent confirmed swing high and low.
If you enable the Swing Tracker, the Golden Level is calculated from the latest swing high or low, making it more adaptive as the trend evolves in real time.
█ How to Use
⚪ Structure-Based Entry
Wait for CHoCH events and use the resulting Fibonacci projection to identify entry points. Enter trades as price taps into the Golden Zone, especially when confluence forms with swing structure or order blocks.
⚪ Real-Time Reaction Tracking
Enable Swing Tracker to keep the tool live — constantly updating zones as price shifts. This is especially useful for scalpers or intraday traders who rely on fresh swing zones.
█ Settings
Structure Period – Number of bars used to define swing pivots. Larger values = stronger structure.
Swing Tracker – Auto-updates fib levels as new highs/lows form.
Show Previous Levels – Keep older fib zones on chart or reset with each structure shift.
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Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Leverage Aware Trade OptimizerWelcome to the Leverage-Aware Trade Optimizer (LATO)! I’m thrilled to have you exploring this dynamic algorithm! LATO combines advanced market oscillation tracking, leverage-aware trade optimization, and real-time market analysis to help you make smarter, more informed trading decisions. Whether you're just starting or you’re an experienced trader, LATO provides powerful tools and insights to enhance your strategies. LATO is here to support you in optimizing your trades with precision, so feel free to dive in and explore all the features. Let’s make your trading experience as effective and rewarding as possible. Safe trading!
Leverage-Aware Trade Optimizer (LATO)
Short Title: LATO
Category: Trading Tools / Technical Analysis
Overview
The Leverage-Aware Trade Optimizer (LATO) is a powerful algorithm designed to track and analyze market oscillations, identify reversal zones, and provide dynamic trading levels for optimal decision-making. With built-in risk management features, LATO enhances traders’ ability to make well-informed decisions based on a comprehensive range of market indicators, including price oscillations, probabilities, and leverage-related risks.
Key Features
Comprehensive Market Oscillation Tracking: LATO utilizes advanced indicators such as the Indexed Position Oscillator (IPO), Candle Relative Percentage (CRP), and Oscillating Range Indicator (ORI) to track price fluctuations and detect key market oscillations, providing a detailed view of price movements.
Dynamic Price Levels for Trading Decisions: The script calculates critical price levels such as WAP, WBP, XAP, and XBP. These weighted and expanded prices help identify potential support and resistance zones for accurate trade entries and exits.
Reversal Detection and Trend Identification: LATO is designed to recognize top and bottom reversal zones using user-defined thresholds (e.g., upper_reversal, lower_reversal). The algorithm signals potential trend changes with event markers such as UP, DOWN, UIP, and DIP, enabling traders to anticipate market reversals.
Risk and Leverage Mapping: By estimating liquidation levels for various leverage values (5x, 10x, 20x, etc.), LATO assists in risk management, helping traders visualize leverage exposure and optimize their trades according to risk tolerance.
Integrated Visualization and Event Labels: LATO enhances visual analysis by plotting key levels, trend lines, and event markers on the chart. Custom labels summarize critical values, including SOD (Sell Odds), BOD (Buy Odds), ORI (Oscillating Range Indicator), and PVI (Price Volatility Index), offering a quick, actionable summary for traders.
User Inputs
Orders Deviation (order_deviation): Controls the deviation for calculating trade levels.
Top Reversal (upper_reversal): Sets the threshold for the upper reversal zone.
Bottom Reversal (lower_reversal): Sets the threshold for the lower reversal zone.
How It Works
LATO tracks market oscillations through the Indexed Position Oscillator (IPO) and Candle Relative Percentage (CRP), dynamically adjusting as the market fluctuates. The algorithm then identifies key levels using weighted prices (e.g., WAP, WBP) and generates reversal signals based on defined thresholds.
Once the Leverage-Aware Trade Optimizer (LATO) is applied to a chart, it automatically calculates dynamic support and resistance levels and identifies potential buying or selling opportunities. The script also plots liquidation zones based on different leverage levels and visualizes these areas through color-coded lines.
Use Case Scenarios
Trend Reversal Detection: Identify when the market is likely to reverse based on the ORI and price action.
Dynamic Price Levels: Use the weighted price levels and trend lines to pinpoint entry/exit points.
Leverage Risk Management: Monitor liquidation levels and use them for managing risk while trading with leverage.
Oscillation Tracking: Track key oscillations for detecting overbought or oversold conditions.
Alert Setup for LATO
You can set up alerts based on the key conditions like UP, DOWN, UIP, and DIP, as well as specific market movements.
Down Trend Alert (DOWN): Alerts when there’s a downtrend, triggered by a crossover of WBP and BL5, with specific conditions for ORI and SOD.
Up Trend Alert (UP): Alerts when there’s an uptrend, triggered by a crossunder of WAP and SL5, with ORI below -0.5.
Upper Reversal Alert (UIP): Alerts when ORI crosses below the lower_reversal threshold.
Downward Reversal Alert (DIP): Alerts when ORI crosses above the upper_reversal threshold.
Conclusion
The Leverage-Aware Trade Optimizer (LATO) is a comprehensive trading tool designed for traders seeking to optimize their trade entries and exits. By combining multiple indicators, dynamic price levels, and reversal zone detection, LATO offers an advanced approach to market analysis and decision-making. Whether you’re trading with leverage or simply looking for trend confirmation, LATO provides the insights you need to maximize your trading potential.
Notes
This script is designed to be used on any time frame.
Adjust the order_deviation parameter based on the asset volatility you are trading.
The reversal thresholds (upper and lower) should be fine-tuned depending on market conditions.
Globex Trap w/ percentage [SLICKRICK]Globex Trap w/ Percentage
Overview
The Globex Trap w/ Percentage indicator is a powerful tool designed to help traders identify high-probability trading opportunities by analyzing price action during the Globex (overnight) session and regular trading hours. By combining Globex session ranges with Supply & Demand zones, this indicator highlights potential "trap" areas where significant price reactions may occur. Additionally, it calculates the Globex session range as a percentage of the daily Average True Range (ATR), providing valuable context for assessing market volatility.
This indicator is ideal for traders in futures markets or other instruments traded during Globex sessions, offering a visual and analytical edge for spotting key price levels and potential reversals or breakouts.
Key Features
Globex Session Tracking:
Visualizes the high and low of the Globex session (default: 3:00 PM to 6:30 AM PST) with customizable time settings.
Displays a semi-transparent box to mark the Globex range, with labels for "Globex High" and "Globex Low."
Calculates the Globex range as a percentage of the daily ATR, displayed as a label for quick reference.
Supply & Demand Zones:
Identifies Supply & Demand zones during regular trading hours (default: 6:00 AM to 8:00 AM PST) with customizable time settings.
Draws semi-transparent boxes to highlight these zones, aiding in the identification of key support and resistance areas.
Trap Area Identification:
Highlights potential trap zones where Globex ranges and Supply & Demand zones overlap, indicating areas where price may reverse or consolidate due to trapped traders.
Customizable Settings:
Adjust Globex and Supply & Demand session times to suit your trading preferences.
Toggle visibility of Globex and Supply & Demand zones independently.
Customize box colors for better chart readability.
Set the lookback period (default: 10 days) to control how many historical zones are displayed.
Configure the ATR length (default: 14) for the percentage calculation.
PST Timezone Default:
All times are based on Pacific Standard Time (PST) by default, ensuring accurate session tracking for users in this timezone or those aligning with U.S. West Coast market hours.
Recommended Usage
Timeframes: Best used on 1-hour charts or lower (e.g., 15-minute, 5-minute) for precise entry and exit points.
Markets: Optimized for futures (e.g., ES, NQ, CL) and other instruments traded during Globex sessions.
Historical Data: Ensure at least 10 days of historical data for optimal visualization of zones.
Strategy Integration: Use the indicator to identify potential reversals or breakouts at Globex highs/lows or Supply & Demand zones. The ATR percentage provides context for whether the Globex range is significant relative to typical daily volatility.
How It Works
Globex Session:
Tracks the high and low prices during the user-defined Globex session (default: 3:00 PM to 6:30 AM PST).
When the session ends, a box is drawn from the start to the end of the session, capturing the high and low prices.
Labels are placed at the midpoint of the session, showing "Globex High," "Globex Low," and the range as a percentage of the daily ATR (e.g., "75.23% of Daily ATR").
Supply & Demand Zones:
Tracks the high and low prices during the user-defined regular trading hours (default: 6:00 AM to 8:00 AM PST).
Draws a box to mark these zones, which often act as key support or resistance levels.
ATR Percentage:
Calculates the Globex range (high minus low) and divides it by the daily ATR to express it as a percentage.
This metric helps traders gauge whether the overnight price movement is significant compared to the instrument’s typical volatility.
Time Handling:
Uses PST (UTC-8) for all time calculations, ensuring accurate session timing for users aligning with this timezone.
Properly handles overnight sessions that cross midnight, ensuring seamless tracking.
Input Settings
Globex Session Settings:
Show Globex Session: Enable/disable Globex session visualization (default: true).
Globex Start/End Time: Set the start and end times for the Globex session (default: 3:00 PM to 6:30 AM PST).
Globex Box Color: Customize the color of the Globex session box (default: semi-transparent gray).
Supply & Demand Zone Settings:
Show Supply & Demand Zone: Enable/disable zone visualization (default: true).
Zone Start/End Time: Set the start and end times for Supply & Demand zones (default: 6:00 AM to 8:00 AM PST).
Zone Box Color: Customize the color of the zone box (default: semi-transparent aqua).
General Settings:
Days to Look Back: Number of historical days to display zones (default: 10).
ATR Length: Period for calculating the daily ATR (default: 14).
Notes
All times are in Pacific Standard Time (PST). Adjust the start and end times if your market operates in a different timezone or if you prefer different session windows.
The indicator is optimized for instruments with active Globex sessions, such as futures. Results may vary for non-24/5 markets.
A typo in the label "Globe Low" (should be "Globex Low") will be corrected in future updates.
Ensure your TradingView chart is set to display sufficient historical data to view the full lookback period.
Why Use This Indicator?
The Globex Trap w/ Percentage indicator provides a unique combination of session-based range analysis, Supply & Demand zone identification, and volatility context via the ATR percentage. Whether you’re a day trader, swing trader, or scalper, this tool helps you:
Pinpoint key price levels where institutional traders may act.
Assess the significance of overnight price movements relative to daily volatility.
Identify potential trap zones for high-probability setups.
Customize the indicator to fit your trading style and market preferences.
0830-0845 High/Low Marker (Accurate Start + History)This indicator marks the high and low of the 15-minute candle between 08:30 and 08:45 (local time) of the trading session. The high and low are tracked dynamically, with the lines drawn once the 08:45 candle closes.
Key Features:
Session-based Tracking: Automatically tracks and records the high and low of the 15-minute period starting at 08:30 and ending at 08:45.
Excludes 08:45 High : If a high is created exactly at 08:45, the indicator will ignore it and use the highest value before 08:45, ensuring it only references the price action during the specified window.
Line Extension : The high and low lines are drawn and extended to the right for a user-defined number of bars, making them visible beyond the session's close.
Customizable Parameters : Adjust the start and end times of the session, line colors, and line width to fit your preferences.
Use Case :
Ideal for traders who focus on the price action during the early part of the trading session (08:30 to 08:45) and want to track significant levels of support and resistance from that period.
The extended lines help identify potential price zones for the rest of the session or the trading day.
PnL MonitorThe PnL Monitor is a customizable tool designed to help traders track the Profit and Loss (PnL) of up to 20 currency pairs or assets in real-time. This script provides a clear and organized table that displays the entry price, and PnL percentage for each pair, making it an essential tool for monitoring open positions or tracking potential trades.
Key Features:
Multi-Asset Tracking:
Monitor up to 20 currency pairs or assets simultaneously. Simply input the pair symbol and your entry price, and the script will calculate the PnL in real-time.
Dynamic Table Positioning:
Choose where the table appears on your chart with the Table Position input. Options include:
Top Left
Top Right
Bottom Left
Bottom Right
Real-Time PnL Calculation:
The script fetches the current price of each pair and calculates the PnL percentage based on your entry price. Positive PnL is highlighted in green, while negative PnL is highlighted in red.
Exchange and Pair Separation:
The script automatically separates the exchange name (if provided) from the pair symbol, making it easier to identify the source of the data.
Customizable Inputs:
Add or remove pairs as needed.
Leave the price field blank for pairs you don’t want to track.
How to Use:
Input Your Pairs:
In the script settings, input the symbol of the pair (e.g., NASDAQ:AAPL or BTCUSD) and your entry price. Leave the price field blank for pairs you don’t want to track.
Choose Table Position:
Select where you want the table to appear on your chart.
Monitor PnL:
The table will automatically update with the current price and PnL percentage for each pair.
Why Use This Script?
Efficiency: Track multiple pairs in one place without switching charts.
Clarity: Easily identify profitable and losing positions at a glance.
Flexibility: Customize the table to fit your trading style and preferences.
Ideal For:
Forex, crypto, and stock traders managing multiple positions.
Smarter Money Concepts - MTF IFVGs [PhenLabs]📊 Smarter Money Concepts - MTF IFVG
Version: PineScript™ v6
📌 Description
This multi-timeframe indicator identifies Inverse Fair Value Gaps (IFVGs) and their inversions across simultaneous chart intervals, helping traders spot liquidity voids and potential reversal zones. By analyzing price action through the lens of institutional order flow patterns, it solves the problem of manual gap tracking across timeframes while incorporating volatility-adjusted parameters and psychological level analysis for higher-probability setups.
🚀 Points of Innovation
• Multi-Timeframe Engine - Simultaneous analysis of 3 higher timeframes
• Adaptive Parameters - Auto-adjusts to market volatility conditions
• Quality Scoring System - Ranks gaps using RVI strength and size metrics
• Inversion Tracking - Monitors failed gaps for counter-trend signals
• Render Optimization - Prevents chart clutter with smart gap management
🔧 Core Components
FVG Detection Logic: Identifies gaps using customizable price source (Close/Wick)
Inversion Tracker: Manages failed gaps and generates counter signals
Multi-Timeframe Engine: Processes 3 independent higher timeframe analyses
Dashboard System: Real-time display of active gaps across all timeframes
🔥 Key Features
• Volatility-adjusted gap size filters (ATR-based)
• Customizable timeframe confluence analysis
• Color-coded quality scoring
• Non-repainting inversion signals
• Mobile-optimized visual rendering
🎨 Visualization
• Colored Boxes: Translucent zones show active gaps (green/bullish, red/bearish)
• Midline Plot: Dashed gray line marks gap midpoint for price targets
• Inversion Markers: Intense colors show failed gaps (dark red/bullish failure, bright green/bearish failure)
• HTF Differentiation: Higher timeframe gaps shown in blue/teal hues
📖 Usage Guidelines
Multi-Timeframe Settings
• Higher Timeframe 1
Default: 30 | Range: Any > Chart TF | Controls primary confluence timeframe
• Show All Timeframes
Default: True | Toggles multi-TF gap displays
Gap Settings
• Source
Default: Close | Options: | Determines gap measurement method
• RVI Period
Default: 14 | Range: 1-50 | Sets momentum confirmation sensitivity
• RVI Value
Default 0.1 | 0 to see all IFVGs | Increase min RVI to see the most powerful IFVGs
✅ Best Use Cases
• Identifying confluence across timeframes
• Spotting institutional order blocks
• High-probability reversal trading
• Trend continuation confirmation
• Volatility breakout setups
⚠️ Limitations
• Repaints historical gap zones
• Requires understanding of FVG concepts
• Higher timeframe data latency
• Quality scores rely on RVI/ATR settings
💡 What Makes This Unique
First FVG indicator with true multi-timeframe processing
Adaptive parameters that auto-adjust to volatility
Quantifiable quality scoring system
Professional-grade dashboard with HTF tracking
🔬 How It Works
Gap Detection: Identifies FVGs using price relationships and RVI confirmation
Inversion Tracking: Monitors price breaches to flag failed gaps
Quality Assessment: Scores gaps based on size, momentum, and location
Adaptive Filtering: Adjusts parameters using ATR-based volatility analysis
Multi-TF Synthesis: Correlates gaps across user-selected timeframes
Visual Rendering: Displays only relevant, active gaps to prevent clutter
💡 Note:
Start with default settings and gradually adjust parameters after observing market interactions. Focus on gaps with quality scores above 7 that align with higher timeframe trends. Combine with price action at psychological levels for highest-probability setups. Remember that higher timeframe gaps generally carry more significance than current chart gaps.
Multi-Sector Trend AnalysisThis script, titled "Multi-Sector Trend Analysis: Track Sector Momentum and Trends," is designed to assist traders and investors in monitoring multiple sectors of the stock market simultaneously. It leverages technical analysis by incorporating trend detection and momentum indicators like moving averages and the Relative Strength Index (RSI) to offer insights into the price action of various market sectors.
Core Features:
1. Sector-Based Analysis: The script covers 20 major sectors from the NSE (National Stock Exchange) such as Auto, Banking, Energy, FMCG, IT, Pharma, and others. Users can customize which sectors they wish to analyze using the available input fields.
Technical Indicators: The script uses two core technical indicators to detect trends and momentum:
2. Moving Averages: The script calculates both fast and slow exponential moving averages (EMAs). These are critical for identifying short- and long-term price trends and crossovers, helping detect shifts in momentum.
3. Relative Strength Index (RSI): A well-known momentum indicator that shows whether a stock is overbought or oversold. This script uses a 14-period RSI to gauge the strength of each sector.
4. Trend Detection: The script identifies whether the current market trend is "Up" or "Down" based on the relationship between the fast and slow EMAs (i.e., whether the fast EMA is above or below the slow EMA). It highlights this trend visually in a table format, allowing quick and easy trend recognition.
5. Gain/Loss Tracking: This feature calculates the percentage gain or loss since the last EMA crossover (a key point in trend change), giving users a sense of how much the price has moved since the trend shifted.
6. Customizable Table for Display: The script displays the analyzed data in a table format, where users can view each sector's:
Symbol
Trend (Up or Down)
RSI Value
Gain/Loss Since the Last EMA Crossover
This table is customizable in terms of size and color theme (dark or light), providing flexibility in presentation for different charting styles.
How It Works:
Sector Selection: Users can input up to 20 different sector symbols for analysis.
Moving Averages: Users can define the period lengths for both the fast and slow EMAs to suit their trading strategies.
Table Options: Choose between different table sizes and opt for a dark theme to enhance the visual appearance on charts.
How to Use:
Select the symbols (sectors) that you want to track. The script includes pre-configured symbols for major sectors on the NSE, but you can modify these to suit your needs.
Adjust the fast and slow EMA lengths to your preference. A common setting would be 3 for the fast EMA and 4 for the slow EMA, but more conservative traders might opt for higher values.
Customize the table size and theme based on your preference, whether you want a compact table or a larger one for easier readability.
Why Use This Script:
This script is ideal for traders looking to:
Monitor multiple market sectors simultaneously.
Identify key trends across sectors quickly.
Understand momentum and detect potential reversals through RSI and EMA crossovers.
Stay informed on sector performance using a clear visual table that tracks gains or losses.
By using this script, traders can gain better insights into sector-based trading strategies, improve their sector rotation tactics, and stay informed about the broader market environment. It provides a powerful yet easy-to-use tool for both beginner and advanced traders.
Contrarian Market Structure BreakMarket Structure Break application was inspired and adapted from Market Structure Oscillator indicator developed by Lux Algo. So much credit to their work.
This indicator pairs nicely with the Contrarian 100 MA and can be located here:
Indicator Description: Contrarian Market Structure BreakOverview
The "Contrarian Market Structure Break" indicator is a versatile tool tailored for traders seeking to identify potential reversal opportunities by analyzing market structure across multiple timeframes. Built on Institutional Concepts of Structure (ICT), this indicator detects Break of Structure (BOS) and Change of Character (CHoCH) patterns across short-term, intermediate-term, and long-term swings, plotting them with customizable lines and labels. It generates contrarian buy and sell signals when price breaks key swing levels, with a unique "Blue Dot Tracker" to monitor consecutive buy signals for trend confirmation. Optimized for the daily timeframe, this indicator is adaptable to other timeframes with proper testing, making it ideal for traders of forex, stocks, or cryptocurrencies.
How It Works
The indicator combines three key components to provide a comprehensive view of market dynamics: Multi-Timeframe Market Structure Analysis: It identifies swing highs and lows across short-term, intermediate-term, and long-term periods, plotting BOS (continuation) and CHoCH (reversal) events with customizable line styles and labels.
Contrarian Signal Generation: Buy and sell signals are triggered when the price crosses below swing lows (buy) or above swing highs (sell), indicating potential reversals in overextended markets.
Blue Dot Tracker: A unique feature that counts consecutive buy signals ("blue dots") and highlights a "Hold Investment" state with a yellow background when three or more buy signals occur, suggesting a potential trend continuation.
Signals are visualized as small circles below (buy) or above (sell) price bars, and a table in the bottom-right corner displays the blue dot count and recommended action (Hold or Flip Investment), enhancing decision-making clarity.
Mathematical Concepts Swing Detection: The indicator identifies swing highs and lows by comparing price patterns over three bars, ensuring robust detection of pivot points. A swing high occurs when the middle bar’s high is higher than the surrounding bars, and a swing low occurs when the middle bar’s low is lower.
Market Structure Logic: BOS is detected when the price breaks a prior swing high (bullish) or low (bearish) in the direction of the current trend, while CHoCH signals a potential reversal when the price breaks a swing level against the trend. These are calculated across three timeframes for a multi-dimensional perspective.
Blue Dot Tracker: This feature counts consecutive buy signals and tracks the entry price. If three or more buy signals occur without a sell signal, the indicator enters a "Hold Investment" state, marked by a yellow background, until the price exceeds the entry price or a sell signal occurs.
Entry and Exit Rules Buy Signal (Blue Dot Below Bar): Triggered when the closing price crosses below a swing low on either the intermediate-term or long-term timeframe, suggesting an oversold condition and potential reversal upward. Short-term signals can be enabled but are disabled by default to reduce noise.
Sell Signal (White Dot Above Bar): Triggered when the closing price crosses above a swing high on either the intermediate-term or long-term timeframe, indicating an overbought condition and potential reversal downward.
Blue Dot Tracker Logic: After a buy signal, the indicator increments a blue dot counter and records the entry price. If three or more consecutive buy signals occur (blueDotCount ≥ 3), the indicator enters a "Hold Investment" state, highlighted with a yellow background, suggesting a potential trend continuation. The "Hold Investment" state ends when the price exceeds the entry price or a sell signal occurs, resetting the counter.
Exit Rules: Traders can exit buy positions when a sell signal appears, the price exceeds the entry price during a "Hold Investment" state, or based on additional confirmation from BOS/CHoCH patterns or other technical analysis tools. Always use proper risk management.
Recommended Usage
The indicator is optimized for the daily timeframe, where it effectively captures significant reversal and continuation patterns in trending or ranging markets. It can be adapted to other timeframes (e.g., 1H, 4H, 15M) with careful testing of settings, particularly enabling/disabling short-term structure analysis to suit market conditions. Backtesting is recommended to optimize performance for your chosen asset and timeframe.
Customization Options Market Structure Display: Toggle short-term, intermediate-term, and long-term structures on or off, with customizable line styles (solid, dashed, dotted) and colors for bullish and bearish breaks.
Labels: Enable or disable BOS/CHoCH labels for each timeframe to reduce chart clutter.
Signal Visibility: Hide buy/sell signals if desired for a cleaner chart.
Blue Dot Tracker: Monitor the blue dot count and action (Hold or Flip Investment) via the table display, which is fully customizable in terms of position and appearance.
Why Use This Indicator?
The "Contrarian Market Structure Break" indicator offers a robust framework for identifying high-probability reversal and continuation setups using ICT principles. Its multi-timeframe analysis, clear signal visualization, and innovative Blue Dot Tracker provide traders with actionable insights into market dynamics. Whether you're a swing trader or a day trader, this indicator’s flexibility and intuitive design make it a valuable addition to your trading arsenal.
Note for TradingView Moderators
This script complies with TradingView's House Rules by providing an educational and transparent description without performance claims or guarantees. It is designed to assist traders in technical analysis and should be used alongside proper risk management and personal research. The code is original, well-documented, and includes customizable inputs and clear visual outputs to enhance the user experience.
Tips for Users:
Backtest thoroughly on your chosen asset and timeframe to validate signal reliability. Combine with other indicators or price action analysis for confirmation of entries and exits. Adjust timeframe settings and enable/disable short-term structures to match market volatility and your trading style.
Hope the "Contrarian Market Structure Break" indicator enhances your trading strategy and helps you navigate the markets with confidence! Happy trading!
HMA Buy Sell Signals - Profit ManagerNote : Settings should be adjusted according to the selected time frame. Try to find the best setting according to the profitability rate
Overall Functionality
This script combines several trading tools to create a comprehensive system for trend analysis, trade execution, and performance tracking. Users can identify market trends using specific moving averages and RSI indicators while managing profit and loss levels automatically.
Trend Detection and Trade Signals
Hull Moving Averages (HMA):
Two HMAs (a faster one and a slower one) are used to determine the market trend.
A buy signal is generated when the faster HMA crosses above the slower HMA.
Conversely, a sell signal is triggered when the faster HMA crosses below the slower one.
Visual Feedback:
Trend lines on the chart change color to reflect the trend direction (e.g., green for upward trends and red for downward trends).
Trade Levels and Management
Entry, Take-Profit, and Stop-Loss Levels:
When the trend shifts upwards, the script calculates entry, take-profit, and stop-loss levels based on the opening price.
Similarly, for downward trends, these levels are determined for short trades.
Commission Tracking:
Each trade includes a commission cost, which is factored into net profit and loss calculations.
Dynamic Labels:
Entry, take-profit, and stop-loss levels are visually marked on the chart for easier tracking.
Performance Tracking
Profit and Loss Tracking:
The script keeps a running total of profits, losses, and commissions for both long and short trades.
It also calculates the net profit after all costs are considered.
Performance Table:
A table is displayed on the chart summarizing:
The number of trades.
Total profit and loss for long and short positions.
Commission costs.
Net profit.
Fractal Support and Resistance
Dynamic Lines:
The script identifies the most recent significant highs and lows using fractals.
It draws support and resistance lines that automatically update as new fractals form.
Simplified Visuals:
The chart always shows the last two support and resistance lines, keeping the visualization clean and focused.
RSI-Based Signals
Overbought and Oversold Levels:
RSI is used to identify overbought (above 80) and oversold (below 20) conditions.
The script generates buy signals at oversold levels and sell signals at overbought levels.
Chart Indicators:
Arrows and labels appear on the chart to highlight these RSI-based opportunities.
Customization
The script allows users to customize key parameters such as:
Moving average lengths for trend detection.
Take-profit and stop-loss percentages.
Timeframes for backtesting.
Starting capital and commission rates.
Conclusion
This script is a versatile tool for traders, combining trend detection, automated trade management, and visual feedback. It simplifies decision-making by providing clear signals and tracking performance metrics, making it suitable for both beginners and experienced traders.
* The most recently drawn fractals represent potential support and resistance levels. If the price aligns with these levels at the time of entering a trade, it may indicate a likelihood of reversal. In such cases, it’s advisable to either avoid entering the trade altogether or proceed with increased caution.
RTH Levels: VWAP + PDH/PDL + ONH/ONL + IBAlgo Index — Levels Pro (ONH/ONL • PDH/PDL • VWAP±Bands • IB • Gaps)
Purpose. A session-aware, non-repainting levels tool for intraday decision-making. Designed for futures and indices, with clean visuals, alerts, and a one-click Minimal Mode for screenshot-ready charts.
What it plots
• PDH/PDL (RTH-only) – Prior Regular Trading Hours high/low, computed intraday and frozen at the RTH close (no 24h mix-ups, no repainting).
• ONH/ONL – Prior Overnight high/low, held throughout RTH.
• RTH VWAP with ±σ bands – Volume-weighted variance, reset each RTH.
• Initial Balance (IB) – First N minutes of RTH, plus 1.5× / 2.0× extensions after IB completes.
• Today’s RTH Open & Prior RTH Close – With gap detection and “gap filled” alert.
• Killzone shading – NY Open (09:30–10:30 ET) and Lunch (11:15–13:30 ET).
• Values panel (top-right) – Each level with live distance in points & ticks.
• Right-edge level tags – With anti-overlap (stagger + vertical jitter).
• Price-scale tags – Native trackprice markers that always “stick” to the axis.
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New in v6.4
• Minimal Mode: one click for a clean look (thinner lines, VWAP bands/IB extensions hidden, on-chart right-edge labels off; price-scale tags remain).
• Theme presets: Dark Hi-Contrast / Light Minimal / Futures Classic / Muted Dark.
• Anti-overlap controls: horizontal staggering, vertical jitter, and baseline offset to keep tags readable even when levels cluster.
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Quick start (2 minutes)
1. Add to chart → keep defaults.
2. Sessions (ET):
• RTH Session default: 09:30–16:00 (US equities cash hours).
• Overnight Session default: 18:00–09:29.
Adjust for your market if you use different “day” hours (e.g., many use 08:20–13:30 ET for COMEX Gold).
3. Theme & Minimal Mode: pick a Theme Preset; enable Minimal Mode for screenshots.
4. Visibility: toggle PD/ON/VWAP/IB/References/Panel to taste.
5. Right-edge labels: turn Show Right-Edge Labels on. If they crowd, tune:
• Anti-overlap: min separation (ticks)
• Horizontal offset per tag (bars)
• Vertical jitter per step (ticks)
• Right-edge baseline offset (bars)
6. Alerts: open Add alert → Condition: and pick the events you want.
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How levels are computed (no repainting)
• PDH/PDL: Intraday H/L are accumulated only while in RTH and saved at RTH close for “yesterday’s” values.
• ONH/ONL: Accumulated across the defined Overnight window and then held during RTH.
• RTH VWAP & ±σ: Volume-weighted mean and standard deviation, reset at the RTH open.
• IB: First N minutes of RTH (default 60). Extensions (1.5×/2.0×) appear after IB completes.
• Gaps: Today’s RTH open vs prior RTH close; “Gap Filled” triggers when price trades back to prior close.
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Practical playbooks (how to trade around the levels)
1) PDH/PDL interactions
• Rejection: Price taps PDH/PDL then closes back inside → mean-reversion toward VWAP/IB.
• Acceptance: Close/hold beyond PDH/PDL with momentum → continuation to next HTF/IB target.
• Alert: PD Touch/Break.
2) ONH/ONL “taken”
• Often one ON extreme is taken during RTH. ONH Taken / ONL Taken → check if it’s a clean break or sweep & reclaim.
• Sweep + reclaim near VWAP can fuel rotations through the ON range.
3) VWAP ±σ framework
• Balanced: First tag of ±1σ often reverts toward VWAP.
• Trend: Persistent trade beyond ±1σ + IB break → target ±2σ/±3σ.
• Alerts: VWAP Cross and VWAP Reject (cross then immediate fail back).
4) IB breaks
• After IB completes, a clean IB break commonly targets 1.5× and sometimes 2.0×.
• Quick return inside IB = possible fade back to the opposite IB edge/VWAP.
• Alerts: IB Break Up / Down.
5) Gaps
• Gap-and-go: Opening drive away from prior close + VWAP support → trend until IB completion.
• Gap-fill: Weak open and VWAP overhead/underfoot → trade toward prior close; manage on Gap Filled alert.
Pro tip: Stack confluences (e.g., ONL sweep + VWAP reclaim + IB hold) and respect your execution rules (e.g., require a 5-minute close in direction, or your order-flow confirmation).
⸻
Inputs you’ll actually touch
• Sessions (ET): Session Timezone, RTH Session, Overnight Session.
• Visibility: toggles for PD/ON/VWAP/IB/Ref/Panel.
• VWAP bands: set σ multipliers (±1/±2/±3).
• IB: duration (minutes) and extension multipliers (1.5× / 2.0×).
• Style & Theme: Theme Preset, Main Line Width, Trackprice, Minimal Mode, and anti-overlap controls.
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Alerts included
• PD Touch/Break — High ≥ PDH or Low ≤ PDL
• ONH Taken / ONL Taken — First in-RTH take of ONH/ONL
• VWAP Cross — Close crosses VWAP
• VWAP Reject — Cross then immediate fail back
• IB Break Up / Down — Break of IB High/Low after IB completes
• Gap Filled — Price trades back to prior RTH close
Setup: Add alert → Condition: Algo Index — Levels Pro → choose event → message → Notify on app/email.
⸻
Panel guide
The top-right panel shows each level plus live distance from last price:
LevelValue (Δpoints | Δticks)
Coloring: green if level is below current price, red if above.
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Styling & screenshot tips
• Use Theme Preset that matches your chart.
• For dark charts, “Dark Hi-Contrast” with Main Line Width = 3 works well.
• Enable Trackprice for crisp axis tags that always stick to the right edge.
• Turn on Minimal Mode for cleaner screenshots (no VWAP bands or IB extensions, on-chart tags off; price-scale tags remain).
• If tags crowd, increase min separation (ticks) to 30–60 and horizontal offset to 3–5; add vertical jitter (4–12 ticks) and/or push tags farther right with baseline offset (bars).
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Behavior & limitations
• Levels are computed incrementally; tables refresh on the last bar for efficiency.
• Right-edge labels are placed at bar_index + offset and do not track extra right-margin scrolling (TradingView limitation). The price-scale tags (from trackprice) do track the axis.
• “RTH” is what you define in inputs. If your market uses different day hours, change the session strings so PDH/PDL reflect your definition of “yesterday’s session.”
⸻
FAQ
Q: My PDH/PDL don’t match the daily chart.
A: By design this uses RTH-only highs/lows, not 24h daily bars. Adjust sessions if you want a different definition.
Q: Right-edge tags overlap or don’t sit at the far right.
A: Increase min separation / horizontal offset / vertical jitter and/or push tags farther with baseline offset. If you want markers that always hug the axis, rely on Trackprice.
Q: Can I change killzones?
A: Yes—edit the session strings in settings or request a version with user inputs for custom windows.
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Disclaimer
Educational use only. This is not financial advice. Always apply your own risk management and confirmation rules.
⸻
Enjoy it? Please ⭐ the script and share screenshots using Minimal Mode + a Theme Preset that fits your style.
ICT Panther (By Obicrypto) V1 ICT Panther Indicator: Full and Detailed Description
The ICT Panther Indicator, created by Obicrypto, is an advanced technical analysis tool designed specifically for traders looking to identify key price action events based on institutional trading techniques, particularly in the context of the Inner Circle Trader (ICT) methodology. This indicator helps traders spot market structure breaks, order blocks, and potential trade opportunities driven by institutional behaviors in the market. Here's a detailed breakdown of its features and how it works:
What Does the ICT Panther Indicator Do?
1. Market Structure Breaks (MSB) Identification:
The ICT Panther identifies critical points where the market changes direction, commonly referred to as a break of structure (BoS). When the price breaks above or below certain key levels (based on highs and lows or opens and closes), it signals a potential shift in market sentiment. These break-of-structure points are essential for traders to determine whether the market is likely to continue its trend or reverse.
2. Order Blocks Visualization:
The indicator plots demand (bullish) and supply (bearish) boxes, which represent areas where institutional traders might place significant buy or sell orders. These zones, known as order blocks, are areas where the price tends to pause or reverse, giving traders key insights into potential entry and exit points. The indicator shows these areas graphically as colored boxes on the chart, which can be used to plan trades based on market structure and price action.
3. Pivot Point Detection:
The ICT Panther identifies important pivot points by tracking higher highs and lower lows. These pivot points are critical in determining the strength of a trend and can help traders confirm the direction of the market. The indicator uses a unique algorithm to detect two levels of pivot points:
- First-Order Pivots: Major pivot points where the price makes notable highs and lows.
- Second-Order Pivots: Smaller pivot points, useful for detecting microtrends within the larger market structure.
4. Bullish and Bearish Break of Structure Lines:
When a significant market structure break (BoS) occurs, the indicator will automatically draw red lines (for bearish break of structure) and green lines (for bullish break of structure) at key price levels. These lines help traders quickly see where institutional moves have occurred in the past and where potential future price moves could originate from.
5. Tested and Filled Boxes:
The ICT Panther also has a built-in mechanism to dim previously tested order blocks. When the price tests an order block (returns to a previous demand or supply zone), the box's color dims to indicate that the area has already been tested, reducing its significance. If the price fully fills an order block, the box stops plotting, providing a clear and clutter-free chart.
Key Features
1. Market Structure Break (MSB) Trigger:
- The indicator allows users to select between highs/lows or opens/closes as the trigger for market structure breaks. This flexibility lets traders adjust the indicator to suit their personal trading style or the behavior of specific assets.
2. Order Block Detection and Visualization:
- The tool automatically plots bullish and bearish demand and supply boxes, representing institutional order blocks on the chart. These boxes provide visual cues for areas of potential price action, where institutional traders might be active.
3. Second-Order Pivot Highlighting:
- The ICT Panther offers an option to plot second-order pivots, highlighting smaller pivot points within the larger market structure. These pivots can be helpful for short-term traders who need to react to smaller price movements while still keeping the larger trend in mind.
4. Box Test and Fill Delays:
- Users can configure delays for box tests and box fills, meaning the indicator will only mark a box as tested or filled after a certain number of bars. This prevents false signals and helps confirm that a zone is truly significant in the market.
5. Customization and Visual Clarity:
- The indicator is highly customizable, allowing users to turn on or off various features like:
- Displaying second-order pivots.
- Highlighting candles that broke structure.
- Plotting market structure broke lines.
- Showing or hiding tested and filled demand boxes.
- Setting custom delays for box testing and filling to suit different market conditions.
6. Tested and Filled Order Block Visualization:
- The indicator visually adjusts the tested and filled order blocks, dimming tested zones and removing filled zones to avoid clutter on the chart. This ensures that traders can focus on active trading opportunities without distractions from historical data.
How Does It Work?
1. Detecting Market Structure Breaks (BoS):
- The indicator continuously tracks the market for key price action signals. When the price breaks through previous highs or lows (or opens and closes, depending on your selection), the indicator marks this as a break of structure. This is a critical signal used by institutional traders and retail traders alike to determine potential future price movements.
2. Order Block Identification:
- Whenever a bullish break of structure occurs, the indicator plots a green demand box to show the area where institutional buyers might have placed significant orders. Similarly, for a bearish break of structure, it plots a red supply box representing areas where institutional sellers are active.
3. Pivot Analysis and Tracking:
- As the market moves, the indicator continuously updates first-order and second-order pivot points based on highs and lows. These points help traders identify whether the market is trending or consolidating. Traders can use these pivot points in combination with the order blocks to make informed trading decisions.
4. Box Testing and Filling:
- When the price retests an existing order block, the box dims to show it has been tested. If the price fully fills the box, it is no longer shown, which helps traders focus on the most relevant, untested order blocks.
Benefits for Traders
- Improved Decision-Making: With clear visuals and advanced logic based on institutional trading strategies, this indicator provides a deeper understanding of market structure and price action.
- Reduced Clutter: The indicator intelligently manages the display of order blocks and pivot points, ensuring that traders focus only on the most relevant information.
- Adaptability: Whether you are a swing trader or a day trader, the ICT Panther can be adjusted to fit your trading style, offering robust and flexible tools for tracking market structure and order blocks.
- Institutional Edge: By identifying institutional-level order blocks and market structure breaks, traders using this indicator can trade in line with the strategies of large market participants.
Who Should Use the ICT Panther Indicator?
This indicator is ideal for:
- Crypto, Forex, and Stock Traders who want to incorporate institutional trading concepts into their strategies.
- Technical Analysts looking for precise tools to measure the market structure and price action.
- ICT Traders who follow the Inner Circle Trader methodology and want an advanced tool to automate and enhance their analysis.
- Price Action Traders seeking a reliable indicator to track pivot points, order blocks, and market structure breaks.
The ICT Panther Indicator is a powerful, versatile tool that brings institutional trading techniques to the fingertips of retail traders. Whether you are looking to identify key market structure breaks, order blocks, or crucial pivot points, this indicator offers detailed visualizations and customizable options to help you make more informed trading decisions. With its ability to track the activities of institutional traders, the ICT Panther Indicator equips traders with the insights needed to stay ahead of the market and trade with confidence.
With the ICT Panther Indicator, traders can follow the movements of institutional money, making it easier to predict market direction and capitalize on high-probability trading opportunities.
Enjoy it and share it with your friends!
Enhanced Candle Sticks [AlgoAlpha]🚀🌟 Introducing the Enhanced Candle Sticks by AlgoAlpha, a Pine Script tool designed to provide traders with an enhanced view of market dynamics through candlestick analysis. This script aims to visualise if price has hit the high or low of the candle first, aiding in back-testing, and to identify smaller trends using market structure.📊🔍
Key Features:
Timeframe Flexibility: Users can select their desired timeframe for analysis, offering a range of options from M15 to H12. This flexibility allows for detailed and specific timeframe analysis.
Micro Trend Identification: The script includes an option to enable 'MicroTrends', giving traders insights into smaller movements and trends within the larger market context.
Customizable Visuals: Traders can customize the colors of bullish and bearish candlesticks, enhancing visual clarity and personalizing the chart to their preferences.
State Tracking: The script tracks the 'state' of the market on lower timeframes to detect if the high or the low was formed first.
Warning System: When the selected timeframe does not match the chart timeframe, the script generates a warning, ensuring accurate analysis and preventing potential misinterpretations.
Usages:
Enhanced Back-testing: Users can now get a more accurate interpretation of the candlesticks by know if the high or the low came first (denoted with ⩚ or ⩛), especially in scenarios where the high and the low of the larger timeframe candle is touching both the take-profit and stop-loss levels.
Squeeze Analysis: Users can identify squeezes in price when the microtrend shows both an uptrend and a downtrend, possibly giving more insight into the market.
Lower Timeframe Market Structure Analysis: Microtrends form when the low of the candle is consecutively increasing and the high is consecutively falling, which means on a lower timeframe, price is forming higher lows or lower highs.
Basic Logic Explanation:
- The script starts by setting up the necessary parameters and importing the required library. Users can customize the timeframe, colors, and whether to enable micro trends and candlestick plotting.
- It then calculates the lower timeframe (1/12th of the current timeframe) for more detailed analysis. The `minutes` function helps in converting the selected timeframe into minutes.
- The script tracks new bars and calculates the highest and lowest values within an hour, using `ta.highestSince` and `ta.lowestSince`.
- It determines the market 'state' by checking if the current high is breaking the previous high and if the current low is breaking the previous low on lower timeframes to determine if the high or the low was formed first.
- The script uses the `plotchar` and `plotcandle` functions to visually represent these trends and states on the chart. This visual representation is key for quick and effective analysis.
Alerts:
Alerts can be set for microtrend formations:
This script is a valuable tool for traders looking to deepen their market analysis with enhanced candlestick visualization and micro trend tracking. 📈🔶💡
Gap Statistics (Zeiierman)█ Overview
The Gap Statistics (Zeiierman) indicator is crafted to monitor, analyze, and visually present price gaps on a trading chart. Price gaps are areas on a chart where the price jumps up or down from the previous close to the next open, creating a "gap" in the normal price pattern. This script delivers an extensive range of statistics related to these gaps, encompassing their size, direction (whether bullish or bearish), frequency of getting filled, as well as the average number of bars it takes for a gap to be filled. The indicator also visually represents the gaps, making it easier for traders to spot and analyze them.
█ How It Works
Gap Identification: The script identifies gaps by comparing the open price of a bar to the close price of the previous bar. If there is a discrepancy between the two, it is recognized as a gap.
Gap Classification: Once a gap is identified, it is classified based on its size (as a percentage of the previous close price) and direction (bullish or bearish). The gap is then added to a specific category based on its size.
Gap Tracking: The script keeps track of all identified gaps using arrays and user-defined types, storing details like their size, direction, and whether they have been filled.
Gap Filling: The script continuously monitors the price to check if any previously identified gaps get filled. A gap is considered filled if the price moves back into the gap area.
Statistics and Alerts: The script calculates various statistics like the total number of gaps, the number of filled gaps, the average number of bars it takes for a gap to fill, and the percentage of gaps that get filled. It also generates alerts when a new gap is identified or an existing gap gets filled.
█ How to Use
Gaps are often classified into four main types:
Common Gaps: These are not associated with any major news and are likely to get filled quickly.
Breakaway Gaps: These occur at the end of a price pattern and signal the beginning of a new trend.
Runaway Gaps: Also known as continuation gaps, these occur in the middle of a trend and signal a surge in interest in the stock.
Exhaustion Gaps: These occur near the end of a price pattern and signal a final attempt to hit new highs or lows.
The Gap Statistics (Zeiierman) indicator enhances a trader's ability to use gaps in their trading strategy in several ways:
Statistical Analysis: Traders get comprehensive statistics on gaps, such as their size, direction, and how often they get filled.
Performance Tracking: The indicator tracks how many bars it typically takes for a gap to fill, providing traders with an average timeframe for gap closure.
█ Settings
Display Gaps: Choose to display "All Gaps," "Active Gaps," or "None."
Show Gap Size: Toggle on/off the display of the gap size.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
