MTF Price/Volume % [Anan]Hello friends,
This is a multi-timeframe table with these features:
Display price change percentage compared with the last timeframe candle close.
Display price change percentage compared with the last timeframe candle close MA.
Displays change percentage compared with the last timeframe candle volume.
Displays change percentage compared with the last timeframe candle volume MA.
Change type/length of MA for Price/Volume.
Full control of Panel position and size.
Full control of displaying any row or column.
ค้นหาในสคริปต์สำหรับ " TABLE "
Average Daily Range TableThis is the last script to complete Vladimir Poltoratskiy's setup found in his books.
Poltoratskiy argues that you should not take any fractal corridors higher than 50% of the Average Daily Range. To be honest, even 40% is a lot, because then, your target will be 160% ADR away from your entry and one "fracture" just can't be enough to predict moves this big.
I chose a table to visually represent the indicator because it doesn't change its value during the day. It takes far less room on the chart.
There are also two simple moving averages. You may use the as an indicator if the relative volatility as of late is extremely low and in that case, perhaps, expect an increase in the coming days. They are applied to the Average Daily Range, not one day range!
PAC newThis indicator will alert you when a candle goes above or below the price action channel (PAC) but only on the first or second candle after a colour change in candle.
When price is above the price action channel that is a bullish sign, when price is below the PAC that is a bearish sign.
The idea is that a sudden change in price is a cause to investigate further price action moving in that direction so the indicator aims to identify reversal
Scalping strategy that works on 5 min chart and aims to gain 10 pips. Do not act on every signal. Further investigation is required, for example by looking at RSI oversolf and overbought levels. For example, at an oversold area, a buy signal is more valid
Table: Forex Central Bank Interest RatesThis tool shows CB Interest Rates for USD, JPY, CAD, CHF, EUR, GBP, NZD, AUD - basically all the majors.
Use override and input your own value if it is changed and I haven't updated the script yet.
Month/Month Percentage % Change, Historical; Seasonal TendencyTable of monthly % changes in Average Price over the last 10 years (or the 10 yrs prior to input year).
Useful for gauging seasonal tendencies of an asset; backtesting monthly volatility and bullish/bearish tendency.
~~User Inputs~~
Choose measure of average: sma(close), sma(ohlc4), vwap(close), vwma(close).
Show last 10yrs, with 10yr average % change, or to just show single year.
Chose input year; with the indicator auto calculating the prior 10 years.
Choose color for labels and size for labels; choose +Ve value color and -Ve value color.
Set 'Daily bars in month': 21 for Forex/Commodities/Indices; 30 for Crypto.
Set precision: decimal places
~~notes~~
-designed for use on Daily timeframe (tradingview is buggy on monthly timeframe calculations, and less precise on weekly timeframe calculations).
-where Current month of year has not occurred yet, will print 9yr average.
-calculates the average change of displayed month compared to the previous month: i.e. Jan22 value represents whole of Jan22 compared to whole of Dec21.
-table displays on the chart over the input year; so for ES, with 2010 selected; shows values from 2001-2010, displaying across 2010-2011 on the chart.
-plots on seperate right hand side scale, so can be shrunk and dragged vertically.
-thanks to @gabx11 for the suggestion which inspired me to write this
Koalafied Risk ManagementTables and labels/lines showing trade levels and risk/reward. Use to manage trade risk compared to portfolio size.
Initial design optimised for tickers denominated against USD.
Multi-Session High/Low Trackertable that shows rth eth and full weekly range high and low with range difference from high and low
Table ATH and DayQuotes in the middle of a chartJust important things at a glance ..
AlltimeHigh and Daily High/Low
LapseBacktestingTableLibrary "LapseBacktestingMetrics"
This library provides a robust set of quantitative backtesting and performance evaluation functions for Pine Script strategies. It’s designed to help traders, quants, and developers assess risk, return, and robustness through detailed statistical metrics — including Sharpe, Sortino, Omega, drawdowns, and trade efficiency.
Built to enhance any trading strategy’s evaluation framework, this library allows you to visualize performance with the quantlapseTable() function, producing an interactive on-chart performance table.
Credit to EliCobra and BikeLife76 for original concept inspiration.
curve(disp_ind)
Retrieves a selected performance curve of your strategy.
Parameters:
disp_ind (simple string): Type of curve to plot. Options include "Equity", "Open Profit", "Net Profit", "Gross Profit".
Returns: (float) Corresponding performance curve value.
cleaner(disp_ind, plot)
Filters and displays selected strategy plots for clean visualization.
Parameters:
disp_ind (simple string): Type of display.
plot (simple float): Strategy plot variable.
Returns: (float) Filtered plot value.
maxEquityDrawDown()
Calculates the maximum equity drawdown during the strategy’s lifecycle.
Returns: (float) Maximum equity drawdown percentage.
maxTradeDrawDown()
Computes the worst intra-trade drawdown among all closed trades.
Returns: (float) Maximum intra-trade drawdown percentage.
consecutive_wins()
Finds the highest number of consecutive winning trades.
Returns: (int) Maximum consecutive wins.
consecutive_losses()
Finds the highest number of consecutive losing trades.
Returns: (int) Maximum consecutive losses.
no_position()
Counts the maximum consecutive bars where no position was held.
Returns: (int) Maximum flat days count.
long_profit()
Calculates total profit generated by long positions as a percentage of initial capital.
Returns: (float) Total long profit %.
short_profit()
Calculates total profit generated by short positions as a percentage of initial capital.
Returns: (float) Total short profit %.
prev_month()
Measures the previous month’s profit or loss based on equity change.
Returns: (float) Monthly equity delta.
w_months()
Counts the number of profitable months in the backtest.
Returns: (int) Total winning months.
l_months()
Counts the number of losing months in the backtest.
Returns: (int) Total losing months.
checktf()
Returns the time-adjusted scaling factor used in Sharpe and Sortino ratio calculations based on chart timeframe.
Returns: (float) Annualization multiplier.
stat_calc()
Performs complete statistical computation including drawdowns, Sharpe, Sortino, Omega, trade stats, and profit ratios.
Returns: (array)
.
f_colors(x, nv)
Generates a color gradient for performance values, supporting dynamic table visualization.
Parameters:
x (simple string): Metric label name.
nv (simple float): Metric numerical value.
Returns: (color) Gradient color value for table background.
quantlapseTable(option, position)
Displays an interactive Performance Table summarizing all major backtesting metrics.
Includes Sharpe, Sortino, Omega, Profit Factor, drawdowns, profitability %, and trade statistics.
Parameters:
option (simple string): Table type — "Full", "Simple", or "None".
position (simple string): Table position — "Top Left", "Middle Right", "Bottom Left", etc.
Returns: (table) On-chart performance visualization table.
This library empowers advanced quantitative evaluation directly within Pine Script®, ideal for strategy developers seeking deeper performance diagnostics and intuitive on-chart metrics.
Multi-Symbol EMA Crossover Scanner with Multi-Timeframe AnalysisDescription
What This Indicator Does:
This indicator is a comprehensive market scanner that monitors up to 10 symbols simultaneously across 4 different timeframes (15-minute, 1-hour, 4-hour, and daily) to detect exponential moving average (EMA) crossovers in real-time. Instead of manually checking multiple charts and timeframes for EMA crossover signals, this scanner automatically does the work for you and presents all detected signals in a clean, organized table that updates continuously throughout the trading session.
Key Features:
Multi-Symbol Monitoring: Scan up to 10 different symbols at once (stocks, forex, crypto, or any TradingView symbol)
Multi-Timeframe Analysis: Simultaneously tracks 4 timeframes (15m, 1H, 4H, 1D) with toggle options to enable/disable each
Comprehensive EMA Pairs: Detects crossovers between all major EMA combinations: 20×50, 20×100, 20×200, 50×100, 50×200, and 100×200
Real-Time Signal Feed: Displays the most recent signals in a sorted table (newest first) with timestamp, direction, price, and EMA pair information
Session Filter: Built-in time filter (default 10:00-18:00) to focus on specific trading hours and avoid pre-market/after-hours noise
Pagination System: Navigate through signals using a page selector when you have more signals than fit in one view
Signal Statistics: Footer displays total signals, bullish/bearish breakdown, and page navigation hints
Customizable Display: Choose table position (4 corners), signals per page (5-20), and maximum signal history (10-100)
How It Works:
The scanner uses the request.security() function to fetch EMA data from multiple symbols and timeframes simultaneously. For each symbol-timeframe combination, it calculates four exponential moving averages (20, 50, 100, and 200 periods) and monitors for crossovers:
Bullish Crossovers (▲ Green):
Faster EMA crosses above slower EMA
Indicates potential upward momentum
Common entry signals for long positions
Bearish Crossovers (▼ Red):
Faster EMA crosses below slower EMA
Indicates potential downward momentum
Common entry signals for short positions or exits
The scanner prioritizes crossovers involving faster EMAs (20×50) over slower ones (100×200), as faster crossovers typically generate more frequent signals. Each detected crossover is stored with its timestamp, allowing the scanner to sort signals chronologically and remove duplicates within the same timeframe.
Signal Table Columns:
Sym: Symbol name (abbreviated, e.g., "ASELS" instead of "BIST:ASELS")
TF: Timeframe where the crossover occurred (15m, 1h, 4h, 1D)
⏰: Exact time of the crossover (HH:MM format in Istanbul timezone)
↕: Direction indicator (▲ bullish green / ▼ bearish red)
₺: Price level where the crossover occurred (average of the two EMAs)
MA: Which EMA pair crossed (e.g., "20×50", "50×200")
How to Use:
For Day Traders:
Enable 15m and 1h timeframes
Monitor symbols from your watchlist
Use crossovers as entry timing signals in the direction of the larger trend
Adjust the time filter to match your trading session (e.g., market open to 2 hours before close)
For Swing Traders:
Enable 4h and 1D timeframes
Focus on 50×200 and 100×200 crossovers (golden/death crosses)
Look for multiple timeframe confluence (same symbol showing bullish crossovers on both 4h and 1D)
Use as a pre-market scanner to identify potential setups for the day
For Multi-Market Traders:
Mix symbols from different markets (stocks, forex, crypto)
Use the scanner to identify which markets are showing the most momentum
Track relative strength by comparing crossover frequency across symbols
Identify rotation opportunities when one asset shows bullish signals while another shows bearish
Setup Recommendations:
Default BIST (Turkish Stock Market) Setup:
The code comes pre-configured with 10 popular BIST stocks:
ASELS, EKGYO, THYAO, AKBNK, PGSUS, ASTOR, OTKAR, ALARK, ISCTR, BIMAS
For US Stocks:
Replace with symbols like: NASDAQ:AAPL, NASDAQ:TSLA, NASDAQ:NVDA, NYSE:JPM, etc.
For Forex:
Use pairs like: FX:EURUSD, FX:GBPUSD, FX:USDJPY, OANDA:XAUUSD, etc.
For Crypto:
Use exchanges like: BINANCE:BTCUSDT, COINBASE:ETHUSD, BINANCE:SOLUSDT, etc.
Settings Guide:
Symbol List (10 inputs):
Enter any valid TradingView symbol in "EXCHANGE:TICKER" format
Use symbols you actively trade or monitor
Mix different asset classes if desired
Timeframe Toggles:
15 Minutes: High-frequency signals, best for day trading
1 Hour: Balanced frequency, good for intraday swing trades
4 Hours: Lower frequency, quality swing trade signals
1 Day: Low frequency, major trend changes only
Time Filter:
Start Hour (10): Beginning of your trading session
End Hour (18): End of your trading session
Prevents signals during low-liquidity periods
Adjust to match your market's active hours
Display Settings:
Table Position: Choose corner placement (doesn't interfere with other indicators)
Max Signals (40): Total historical signals to keep in memory
Signals Per Page (10): How many rows to show at once
Page Number: Navigate through signal history (auto-adjusts to available pages)
What Makes This Original:
Multi-symbol scanners exist on TradingView, but this indicator's originality comes from:
Comprehensive EMA Pair Coverage: Most scanners focus on 1-2 EMA pairs, this monitors 6 different combinations simultaneously
Unified Multi-Timeframe View: Presents signals from 4 timeframes in a single, chronologically sorted feed rather than separate panels
Session-Aware Filtering: Built-in time filter prevents signal overload from 24-hour markets
Smart Pagination: Handles large signal volumes gracefully with page navigation instead of scrolling
Signal Deduplication: Prevents the same crossover from appearing multiple times if it persists across several bars
Price-at-Cross Recording: Captures the exact price where the crossover occurred, not just that it happened
Real-Time Statistics: Live tracking of bullish vs bearish signal distribution
Trading Strategy Examples:
Trend Confirmation Strategy:
Find a symbol showing bullish crossover on 1D (major trend change)
Wait for pullback
Enter when 1h shows bullish crossover (confirmation)
Exit when 1h shows bearish crossover
Multi-Timeframe Confluence:
Look for symbols appearing multiple times with same direction
Example: ASELS shows ▲ on both 4h and 1D = strong bullish signal
Avoid symbols showing conflicting signals (▲ on 1h but ▼ on 4h)
Rotation Scanner:
Monitor 10+ symbols from the same sector
Identify which are turning bullish (▲) first
Enter leaders, avoid laggards
Rotate out when crossovers turn bearish (▼)
Important Considerations:
Not a Complete System: EMA crossovers should be confirmed with price action, volume, and support/resistance analysis
Whipsaw Risk: During consolidation, EMAs can cross back and forth frequently (especially on 15m timeframe)
Lag: EMAs are lagging indicators; crossovers occur after the move has already begun
False Signals: More common during sideways markets; work best in trending environments
Symbol Limits: TradingView has limits on request.security() calls; this scanner uses 40 calls (10 symbols × 4 timeframes)
Performance: On lower-end devices, scanning 10 symbols across 4 timeframes may cause slight delays in chart updates
Best Practices:
Start with 5 symbols and 2 timeframes, then expand as you get comfortable
Use in conjunction with a main chart for price context
Don't trade every signal—filter for high-quality setups
Backtest your favorite EMA pairs on your symbols to understand their reliability
Adjust the time filter to exclude lunch hours if your market has low midday volume
Check the footer statistics—if you're getting 50+ signals per day, tighten your time filter or reduce symbols
Technical Notes:
Uses lookahead=barmerge.lookahead_off to prevent future data leakage
Signals are stored in arrays and sorted by timestamp (newest first)
Automatic daily reset clears old signals to prevent memory buildup
Table dynamically resizes based on signal count
All times displayed in Europe/Istanbul timezone (configurable in code)
TraderDemircan Trend Based Fibonacci + XABCD FormationDescription
TraderDemircan Trend-Based Fibonacci + XABCD Formation is an original open-source indicator that combines trend-based Fibonacci projections with an automated XABC structure detection engine.
The script focuses on identifying swing high → swing low transitions in a downtrend, generating Fibonacci levels and projecting a potential C-target extension based on harmonic geometry.
This indicator is designed for traders who want a clearer visual structure of how retracement, continuation, and harmonic projections interact inside trending markets.
🧩 What the Script Does
1. Detects the Most Recent X–A Swing (Trend High → Trend Low)
The indicator automatically scans a user-defined lookback range to identify:
X: Most recent significant swing high
A: The lowest low after X within the lookback window
This creates the foundational XA leg used for both Fibonacci levels and harmonic projections.
2. Determines a Dynamic B-Point Retracement
The script measures the market’s current retracement relative to the XA leg:
If price retraces below 0.50, B becomes the 0.50 level
If price retraces above 0.50, B becomes the 0.382 level
The algorithm ensures proper harmonic logic by validating that price stays below the B-level, preventing invalid structures.
3. Projects a Harmonic C-Target
Using harmonic extension logic, the script calculates:
C = B − (X − A)
This projects a symmetrical continuation leg relative to XA, giving traders an estimated “C-completion zone."
The C-target is displayed visually and numerically in an on-chart info table.
4. Plots Full Trend-Based Fibonacci Levels
The indicator draws Fibonacci levels from 0.0 to 2.618
Users can independently enable/disable each level, adjust line styling, choose color themes, add price labels, and display retracement percentages.
🎨 Visualization & Usability
The script includes:
Clean, customizable Fibonacci layout
Optional price labels & percentage labels
Extendable line options
Highlighted XABC structure
Dashed projection lines for the C-target
A compact info table showing X, A, B, C prices
This helps traders visually track structural market progression with clarity.
📘 Conceptual Foundation
This indicator is based on:
Trend-retracement logic using classic Fibonacci ratios
Structural swing identification
Basic harmonic symmetry (XA → BC projection)
Downtrend-based continuation expectations
It does not attempt to identify full harmonic patterns (like Gartley, Bat, or Crab), but instead focuses on the trend-based XABC segment and projected continuation targets.
🔧 Inputs Overview
Key user controls include:
Lookback window for pivot detection
Individual Fibonacci level visibility toggles
Color controls & line styling
Label display options
XABC formation display toggle
C-target on/off
All parameter names in the script are English; if translations appear in inputs, their English equivalents are included here to comply with TradingView publication rules.
⚠️ Limitations & Notes
The indicator does not predict future price direction.
It does not repaint, but pivot detection naturally depends on completed bars.
The C-projection is a geometric estimate, not a trading signal.
No forward-looking or non-causal data is used.
This tool is intended for structural analysis, not automated strategy execution.
📎 How to Use It
Add the indicator to a clean chart.
Observe the most recent X → A swing.
Watch how price interacts with 0.382 / 0.5 retracement to form the B-point.
Use the projected C-target as a reference zone for potential continuation completions.
Combine with your own trend, momentum, or volume methods for confirmation.
✔ Originality
This script is fully original and not derived from any pre-existing public script.
It combines:
Automated dynamic trend-based Fibonacci framework
Custom XABC structure detection
Harmonic-style C projection logic
Fully customizable visualization system
The indicator is intended to add meaningful analytical value to the community beyond standard Fibonacci tools.
YCGH Drawdown PercentilesWhat This Indicator Does?
The Drawdown Percentiles indicator tracks how far below the all-time high (ATH) a stock or asset is currently trading, then displays statistical percentiles of historical drawdowns in a customizable table.
Percentile Analysis: It collects up to 5,000 historical bars of drawdown data, sorts them, and calculates user-selected percentiles (default: 10th, 30th, 50th) to show where current drawdowns rank historically.
Visual Display: A table shows each percentile threshold with color-coded drawdown values, helping you quickly assess whether the current drawdown is typical or extreme compared to historical patterns.
Practical Use Cases
Risk Assessment: Identify if current drawdowns fall within normal ranges or represent extreme conditions requiring position adjustments.
Entry/Exit Timing: Use percentile rankings to time entries during historically shallow drawdowns (better conditions) and reduce exposure during deep drawdowns.
Strategy Comparison: Compare drawdown patterns across different assets or trading strategies to evaluate risk-adjusted performance.
dhruv private 91400//@version=5
//
VERSION = '7.9-X'// 2024.3.20
strategy(
'LE VAN DO® - Swing Signals & Overlays Private™ 7.9-X',
shorttitle = 'LE VAN DO® - Swing Signals & Overlays Private™ 7.9-X' + VERSION,
overlay = true,
explicit_plot_zorder = true,
pyramiding = 0,
default_qty_type = strategy.percent_of_equity,
default_qty_value = 50,
calc_on_every_tick = false,
process_orders_on_close = true,
max_bars_back = 500,
initial_capital = 5000,
commission_type = strategy.commission.percent,
commission_value = 0.02,
max_lines_count = 500
)
//Truncate Function
truncate(number, decimals) =>
factor = math.pow(10, decimals)
int(number * factor) / factor
//
// === INPUTS ===
TPSType = input.string('Trailing', 'What TPS should be taken : ', options = )
setupType = input.string('Open/Close', title='What Trading Setup should be taken : ', options= )
scolor = input(true, title='Show coloured Bars to indicate Trend?')
almaRibbon = input(false, title='Enable Ribbon?')
//tradeType = input.string('BOTH', title='What trades should be taken : ', options= )
// === /INPUTS ===
// Display the probabilities in a table
//text01_ = str.tostring(timeframe.multiplier * intRes, '####')
//t = timenow + math.round(ta.change(time) * 25)
//var label lab01 = na
//label.delete(lab01)
//lab01 := label.new(t, close, text=text01_, style=label.style_label_left, yloc=yloc.price, xloc=xloc.bar_time, textalign=text.align_left, textcolor=color.white)
// Constants colours that include fully non-transparent option.
green100 = #008000FF
lime100 = #66bb6a
red100 = #f7525f
blue100 = #0000FFFF
aqua100 = #00FFFFFF
darkred100 = #8B0000FF
gray100 = #808080FF
/////////////////////////////////////////////
// Create non-repainting security function
rp_security(_symbol, _res, _src) =>
request.security(_symbol, _res, _src )
//
f_tfInMinutes() =>
_tfInMinutes = timeframe.period == '1' ? '3' : timeframe.period == '3' ? '5' : timeframe.period == '5' ? '15' : timeframe.period == '15' ? '30' : timeframe.period == '30' ? '60' : timeframe.period == '60' ? '240' : 'D'
_tfInMinutes
my_time1 = f_tfInMinutes()
tfmult = 18 //input.int(18, "Input Timeframe Multiplier")
f_resInMinutes() =>
_resInMinutes = timeframe.multiplier * (
timeframe.isseconds ? 1. / 60. :
timeframe.isminutes ? 1. :
timeframe.isdaily ? 1440. :
timeframe.isweekly ? 10080. :
timeframe.ismonthly ? 43800. : na)
my_time = str.tostring(f_resInMinutes()*tfmult)
useSource = close //input.string('Close', 'What Source to be used?', options = )
enableFilter = input(true, "Enable Backtesting Range Filtering")
fromDate = input.time(timestamp("01 Jan 2023 00:00 +0300"), "Start Date")
toDate = input.time(timestamp("31 Dec 2099 00:00 +0300"), "End Date")
tradeDateIsAllowed = not enableFilter or (time >= fromDate and time <= toDate)
filter1 = 'Filter with Atr'
filter2 = 'Filter with RSI'
filter3 = 'Atr or RSI'
filter4 = 'Atr and RSI'
filter5 = 'No Filtering'
filter6 = 'Entry Only in sideways market(By ATR or RSI)'
filter7 = 'Entry Only in sideways market(By ATR and RSI)'
typefilter = input.string(filter5, title='Sideways Filtering Input', options= , group='Strategy Options')
RSI = truncate(ta.rsi(close, input.int(7, group='RSI Filterring')), 2)
toplimitrsi = input.int(45, title='TOP Limit', group='RSI Filterring')
botlimitrsi = input.int(10, title='BOT Limit', group='RSI Filterring')
//ST = input.bool(true, title='Show Supertrend?', group='Supertrend Indicator')
//period = input.int(1440, group='Supertrend Indicator')
//mult = input.float(2.612, group='Supertrend Indicator')
atrfiltLen = 5 //input.int(5, minval=1, title='atr Length', group='Sideways Filtering Input')
atrMaType = 'EMA' //input.string('EMA', options= , group='Sideways Filtering Input', title='atr Moving Average Type')
atrMaLen = 5 //input.int(5, minval=1, title='atr MA Length', group='Sideways Filtering Input')
//filtering
atra = request.security(syminfo.tickerid, '', ta.atr(atrfiltLen))
atrMa = atrMaType == 'EM' ? ta.ema(atra, atrMaLen) : ta.sma(atra, atrMaLen)
updm = ta.change(high)
downdm = -ta.change(low)
plusdm = na(updm) ? na : updm > downdm and updm > 0 ? updm : 0
minusdm = na(downdm) ? na : downdm > updm and downdm > 0 ? downdm : 0
cndSidwayss1 = atra >= atrMa
cndSidwayss2 = RSI > toplimitrsi or RSI < botlimitrsi
cndSidways = cndSidwayss1 or cndSidwayss2
cndSidways1 = cndSidwayss1 and cndSidwayss2
Sidwayss1 = atra <= atrMa
Sidwayss2 = RSI < toplimitrsi and RSI > botlimitrsi
Sidways = Sidwayss1 or Sidwayss2
Sidways1 = Sidwayss1 and Sidwayss2
trendType = typefilter == filter1 ? cndSidwayss1 : typefilter == filter2 ? cndSidwayss2 : typefilter == filter3 ? cndSidways : typefilter == filter4 ? cndSidways1 : typefilter == filter5 ? RSI > 0 : typefilter == filter6 ? Sidways : typefilter == filter7 ? Sidways1 : na
// === /INPUTS ===
tf = my_time //input('15')
r = ticker.heikinashi(syminfo.tickerid)
openSeriesAlt = request.security(r, tf, open, lookahead=barmerge.lookahead_on)
closeSeriesAlt = request.security(r, tf, close, lookahead=barmerge.lookahead_on)
//openP = plot(almaRibbon ? openSeriesAlt : na, color=color.new(color.lime, 0), linewidth=3)
//closeP = plot(almaRibbon ? closeSeriesAlt : na, color=color.new(color.red, 0), linewidth=3)
BUYOC = ta.crossover(closeSeriesAlt, openSeriesAlt) and setupType == "Open/Close" and trendType
SELLOC = ta.crossunder(closeSeriesAlt, openSeriesAlt) and setupType == "Open/Close" and trendType
//strategy.entry('sell', direction=strategy.short, qty=trade_size, comment='sell', when=sel_entry)
//strategy.entry('buy', direction=strategy.long, qty=trade_size, comment='buy', when=buy_entry)
//trendColour = closeSeriesAlt > openSeriesAlt ? color.green : color.red
//bcolour = closeSeriesAlt > openSeriesAlt ? lime100 : red100
//barcolor(scolor ? bcolour : na, title='Bar Colours')
//closeP = plot(almaRibbon ? closeSeriesAlt : na, title='Close Series', color=color.new(trendColour, 20), linewidth=2, style=plot.style_line)
//openP = plot(almaRibbon ? openSeriesAlt : na, title='Open Series', color=color.new(trendColour, 20), linewidth=2, style=plot.style_line)
//fill(closeP, openP, color=color.new(trendColour, 80))
//
//rt = input(true, title="ATR Based REnko is the Default, UnCheck to use Traditional ATR?")
atrLen = 3 //input.int(3, title="RENKO_ATR", group = "Renko Settings")
isATR = true //input.bool(true, title="RENKO_USE_RENKO_ATR", group = "Renko Settings")
tradLen1 = 1000 //input.int(1000, title="RENKO_TRADITIONAL", group = "Renko Settings")
//Code to be implemented in V2
//mul = input(1, "Number Of minticks")
//value = mul * syminfo.mintick
tradLen = tradLen1 * 1
param = isATR ? ticker.renko(syminfo.tickerid, "ATR", atrLen) : ticker.renko(syminfo.tickerid, "Traditional", tradLen)
renko_close = request.security(param, my_time, close, lookahead=barmerge.lookahead_on)
renko_open = request.security(param, my_time, open, lookahead=barmerge.lookahead_on)
//============================================
//Sniper------------------------------------------------------------------------------------------------------------------------------------- // Signal 2
//============================================
//============================================
//EMA_CROSS-------------------------------------------------------------------------------------------------------------------------------- // Signal 4
//============================================
EMA1_length=input.int(2, "EMA1_length", group = "Renko Settings")
EMA2_length=input.int(10, "EMA2_length", group = "Renko Settings")
a = ta.ema(renko_close, EMA1_length)
b = ta.ema(renko_close, EMA2_length)
//BUY = ta.cross(a, b) and a > b and renko_open < renko_close
//SELL = ta.cross(a, b) and a < b and renko_close < renko_open
///////////////////////////////
// Determine long and short conditions
BUYR = ta.crossover(a, b) and setupType == "Renko" and trendType
SELLR = ta.crossunder(a, b) and setupType == "Renko" and trendType
sel_color = setupType == "Open/Close" ? closeSeriesAlt < openSeriesAlt : setupType == "Renko" ? renko_close < renko_open : na
buy_color = setupType == "Open/Close" ? closeSeriesAlt > openSeriesAlt : setupType == "Renko" ? renko_close > renko_open : na
sel_entry = setupType == "Open/Close" ? SELLOC : setupType == "Renko" ? SELLR : na
buy_entry = setupType == "Open/Close" ? BUYOC : setupType == "Renko" ? BUYR : na
trendColour = buy_color ? color.green : color.red
bcolour = buy_color ? lime100 : red100
barcolor(scolor ? bcolour : na, title='Bar Colours')
p11=plot(almaRibbon and setupType == "Open/Close" ? closeSeriesAlt : almaRibbon and setupType == "Renko" ? renko_close : na, style=plot.style_circles, linewidth=1, color=color.new(trendColour, 80), title="RENKO_1")
p22=plot(almaRibbon and setupType == "Open/Close" ? openSeriesAlt : almaRibbon and setupType == "Renko" ? renko_open : na, style=plot.style_circles, linewidth=1, color=color.new(trendColour, 80), title="RENKO_2")
fill(p11, p22, color=color.new(trendColour, 50), title="RENKO_fill")
//
lxTrigger = false
sxTrigger = false
leTrigger = buy_entry
seTrigger = sel_entry
// === /ALERT conditions.
buy = leTrigger //ta.crossover(closeSeriesAlt, openSeriesAlt)
sell = seTrigger //ta.crossunder(closeSeriesAlt, openSeriesAlt)
varip wasLong = false
varip wasShort = false
if barstate.isconfirmed
wasLong := false
else
if buy
wasLong := true
if barstate.isconfirmed
wasShort := false
else
if sell
wasShort := true
plotshape(wasLong, color = color.yellow)
plotshape(wasShort, color = color.yellow)
//plotshape(almaRibbon ? buy : na, title = "Buy", text = 'Buy', style = shape.labelup, location = location.belowbar, color= #39ff14, textcolor = #FFFFFF, size = size.tiny)
//plotshape(almaRibbon ? sell : na, title = "Exit", text = 'Exit', style = shape.labeldown, location = location.abovebar, color= #ff1100, textcolor = #FFFFFF, size = size.tiny)
// === STRATEGY ===
i_alert_txt_entry_long = "Short Exit" //input.text_area(defval = "Short Exit", title = "Long Entry Message", group = "Alerts")
i_alert_txt_exit_long = "Long Exit" //input.text_area(defval = "Long Exit", title = "Long Exit Message", group = "Alerts")
i_alert_txt_entry_short = "Go Short" //input.text_area(defval = "Go Short", title = "Short Entry Message", group = "Alerts")
i_alert_txt_exit_short = "Go Long" //input.text_area(defval = "Go Long", title = "Short Exit Message", group = "Alerts")
// Entries and Exits with TP/SL
//tradeType
if buy and TPSType == "Trailing" and tradeDateIsAllowed
strategy.close("Short" , alert_message = i_alert_txt_exit_short)
strategy.entry("Long" , strategy.long , alert_message = i_alert_txt_entry_long)
if sell and TPSType == "Trailing" and tradeDateIsAllowed
strategy.close("Long" , alert_message = i_alert_txt_exit_long)
strategy.entry("Short" , strategy.short, alert_message = i_alert_txt_entry_short)
//tradeType
if buy and TPSType == "Options" and tradeDateIsAllowed
// strategy.close("Short" , alert_message = i_alert_txt_exit_short)
strategy.entry("Long" , strategy.long , alert_message = i_alert_txt_entry_long)
if sell and TPSType == "Options" and tradeDateIsAllowed
strategy.close("Long" , alert_message = i_alert_txt_exit_long)
// strategy.entry("Short" , strategy.short, alert_message = i_alert_txt_entry_short)
G_RISK = '■ ' + 'Risk Management'
//#region ———— <↓↓↓ G_RISK ↓↓↓> {
//ATR SL Settings
atrLength = 20 //input.int(20, minval=1, title='ATR Length')
profitFactor = 2.5 //input(2.5, title='Take Profit Factor')
stopFactor = 1 //input(1.0, title='Stop Loss Factor')
// Calculate ATR
tpatrValue = ta.atr(atrLength)
// Calculate take profit and stop loss levels for buy signals
takeProfit1_buy = 1 * profitFactor * tpatrValue //close + profitFactor * atrValue
takeProfit2_buy = 2 * profitFactor * tpatrValue //close + 2 * profitFactor * atrValue
takeProfit3_buy = 3 * profitFactor * tpatrValue //close + 3 * profitFactor * atrValue
stopLoss_buy = close - takeProfit1_buy //stopFactor * tpatrValue
// Calculate take profit and stop loss levels for sell signals
takeProfit1_sell = 1 * profitFactor * tpatrValue //close - profitFactor * atrValue
takeProfit2_sell = 2 * profitFactor * tpatrValue //close - 2 * profitFactor * atrValue
takeProfit3_sell = 3 * profitFactor * tpatrValue //close - 3 * profitFactor * atrValue
stopLoss_sell = close + takeProfit1_sell //stopFactor * tpatrValue
// ———————————
//Tooltip
T_LVL = '(%) Exit Level'
T_QTY = '(%) Adjust trade exit volume'
T_MSG = 'Paste JSON message for your bot'
//Webhook Message
O_LEMSG = 'Long Entry'
O_LXMSGSL = 'Long SL'
O_LXMSGTP1 = 'Long TP1'
O_LXMSGTP2 = 'Long TP2'
O_LXMSGTP3 = 'Long TP3'
O_LXMSG = 'Long Exit'
O_SEMSG = 'Short Entry'
O_SXMSGSL = 'Short SL'
O_SXMSGA = 'Short TP1'
O_SXMSGB = 'Short TP2'
O_SXMSGC = 'Short TP3'
O_SXMSGX = 'Short Exit'
// on whole pips) for forex currency pairs.
pip_size = syminfo.mintick * (syminfo.type == "forex" ? 10 : 1)
// On the last historical bar, show the instrument's pip size
//if barstate.islastconfirmedhistory
// label.new(x=bar_index + 2, y=close, style=label.style_label_left,
// color=color.navy, textcolor=color.white, size=size.large,
// text=syminfo.ticker + "'s pip size is:\n" +
// str.tostring(pip_size))
// ——————————— | | | Line length guide |
i_lxLvlTP1 = leTrigger ? takeProfit1_buy : seTrigger ? takeProfit1_sell : na //input.float (1, 'Level TP1' , group = G_RISK, tooltip = T_LVL)
i_lxQtyTP1 = input.float (50, 'Qty TP1' , group = G_RISK, tooltip = T_QTY)
i_lxLvlTP2 = leTrigger ? takeProfit2_buy : seTrigger ? takeProfit2_sell : na //input.float (1.5, 'Level TP2' , group = G_RISK, tooltip = T_LVL)
i_lxQtyTP2 = input.float (30, 'Qty TP2' , group = G_RISK, tooltip = T_QTY)
i_lxLvlTP3 = leTrigger ? takeProfit3_buy : seTrigger ? takeProfit3_sell : na //input.float (2, 'Level TP3' , group = G_RISK, tooltip = T_LVL)
i_lxQtyTP3 = input.float (20, 'Qty TP3' , group = G_RISK, tooltip = T_QTY)
i_lxLvlSL = leTrigger ? takeProfit1_buy : seTrigger ? takeProfit1_sell : na //input.float (0.5, 'Stop Loss' , group = G_RISK, tooltip = T_LVL)
i_sxLvlTP1 = i_lxLvlTP1
i_sxQtyTP1 = i_lxQtyTP1
i_sxLvlTP2 = i_lxLvlTP2
i_sxQtyTP2 = i_lxQtyTP2
i_sxLvlTP3 = i_lxLvlTP3
i_sxQtyTP3 = i_lxQtyTP3
i_sxLvlSL = i_lxLvlSL
G_MSG = '■ ' + 'Webhook Message'
i_leMsg = O_LEMSG //input.string (O_LEMSG ,'Long Entry' , group = G_MSG, tooltip = T_MSG)
i_lxMsgSL = O_LXMSGSL //input.string (O_LXMSGSL ,'Long SL' , group = G_MSG, tooltip = T_MSG)
i_lxMsgTP1 = O_LXMSGTP1 //input.string (O_LXMSGTP1,'Long TP1' , group = G_MSG, tooltip = T_MSG)
i_lxMsgTP2 = O_LXMSGTP2 //input.string (O_LXMSGTP2,'Long TP2' , group = G_MSG, tooltip = T_MSG)
i_lxMsgTP3 = O_LXMSGTP3 //input.string (O_LXMSGTP3,'Long TP3' , group = G_MSG, tooltip = T_MSG)
i_lxMsg = O_LXMSG //input.string (O_LXMSG ,'Long Exit' , group = G_MSG, tooltip = T_MSG)
i_seMsg = O_SEMSG //input.string (O_SEMSG ,'Short Entry' , group = G_MSG, tooltip = T_MSG)
i_sxMsgSL = O_SXMSGSL //input.string (O_SXMSGSL ,'Short SL' , group = G_MSG, tooltip = T_MSG)
i_sxMsgTP1 = O_SXMSGA //input.string (O_SXMSGA ,'Short TP1' , group = G_MSG, tooltip = T_MSG)
i_sxMsgTP2 = O_SXMSGB //input.string (O_SXMSGB ,'Short TP2' , group = G_MSG, tooltip = T_MSG)
i_sxMsgTP3 = O_SXMSGC //input.string (O_SXMSGC ,'Short TP3' , group = G_MSG, tooltip = T_MSG)
i_sxMsg = O_SXMSGX //input.string (O_SXMSGX ,'Short Exit' , group = G_MSG, tooltip = T_MSG)
i_src = close
G_DISPLAY = 'Display'
//
i_alertOn = true //input.bool (true, 'Alert Labels On/Off' , group = G_DISPLAY)
i_barColOn = true //input.bool (true, 'Bar Color On/Off' , group = G_DISPLAY)
// ———————————
// @function Calculate the Take Profit line, and the crossover or crossunder
f_tp(_condition, _conditionValue, _leTrigger, _seTrigger, _src, _lxLvlTP, _sxLvlTP)=>
var float _tpLine = 0.0
_topLvl = _src + _lxLvlTP //TPSType == "Fixed %" ? _src + (_src * (_lxLvlTP / 100)) : _src + _lxLvlTP
_botLvl = _src - _lxLvlTP //TPSType == "Fixed %" ? _src - (_src * (_sxLvlTP / 100)) : _src - _sxLvlTP
_tpLine := _condition != _conditionValue and _leTrigger ? _topLvl :
_condition != -_conditionValue and _seTrigger ? _botLvl :
nz(_tpLine )
// @function Similar to "ta.crossover" or "ta.crossunder"
f_cross(_scr1, _scr2, _over)=>
_cross = _over ? _scr1 > _scr2 and _scr1 < _scr2 :
_scr1 < _scr2 and _scr1 > _scr2
// ———————————
//
var float condition = 0.0
var float slLine = 0.0
var float entryLine = 0.0
//
entryLine := leTrigger and condition <= 0.0 ? close :
seTrigger and condition >= 0.0 ? close : nz(entryLine )
//
slTopLvl = TPSType == "Fixed %" ? i_src + (i_src * (i_lxLvlSL / 100)) : i_src + i_lxLvlSL
slBotLvl = TPSType == "Fixed %" ? i_src - (i_src * (i_sxLvlSL / 100)) : i_src - i_lxLvlSL
slLine := condition <= 0.0 and leTrigger ? slBotLvl :
condition >= 0.0 and seTrigger ? slTopLvl : nz(slLine )
slLong = f_cross(low, slLine, false)
slShort = f_cross(high, slLine, true )
//
= f_tp(condition, 1.2,leTrigger, seTrigger, i_src, i_lxLvlTP3, i_sxLvlTP3)
= f_tp(condition, 1.1,leTrigger, seTrigger, i_src, i_lxLvlTP2, i_sxLvlTP2)
= f_tp(condition, 1.0,leTrigger, seTrigger, i_src, i_lxLvlTP1, i_sxLvlTP1)
tp3Long = f_cross(high, tp3Line, true )
tp3Short = f_cross(low, tp3Line, false)
tp2Long = f_cross(high, tp2Line, true )
tp2Short = f_cross(low, tp2Line, false)
tp1Long = f_cross(high, tp1Line, true )
tp1Short = f_cross(low, tp1Line, false)
switch
leTrigger and condition <= 0.0 => condition := 1.0
seTrigger and condition >= 0.0 => condition := -1.0
tp3Long and condition == 1.2 => condition := 1.3
tp3Short and condition == -1.2 => condition := -1.3
tp2Long and condition == 1.1 => condition := 1.2
tp2Short and condition == -1.1 => condition := -1.2
tp1Long and condition == 1.0 => condition := 1.1
tp1Short and condition == -1.0 => condition := -1.1
slLong and condition >= 1.0 => condition := 0.0
slShort and condition <= -1.0 => condition := 0.0
lxTrigger and condition >= 1.0 => condition := 0.0
sxTrigger and condition <= -1.0 => condition := 0.0
longE = leTrigger and condition <= 0.0 and condition == 1.0
shortE = seTrigger and condition >= 0.0 and condition == -1.0
longX = lxTrigger and condition >= 1.0 and condition == 0.0
shortX = sxTrigger and condition <= -1.0 and condition == 0.0
longSL = slLong and condition >= 1.0 and condition == 0.0
shortSL = slShort and condition <= -1.0 and condition == 0.0
longTP3 = tp3Long and condition == 1.2 and condition == 1.3
shortTP3 = tp3Short and condition == -1.2 and condition == -1.3
longTP2 = tp2Long and condition == 1.1 and condition == 1.2
shortTP2 = tp2Short and condition == -1.1 and condition == -1.2
longTP1 = tp1Long and condition == 1.0 and condition == 1.1
shortTP1 = tp1Short and condition == -1.0 and condition == -1.1
// ——————————— {
//
if strategy.position_size <= 0 and longE and TPSType == "ATR" and tradeDateIsAllowed
strategy.entry( 'Long', strategy.long, alert_message = i_leMsg, comment = 'LE')
if strategy.position_size > 0 and condition == 1.0 and TPSType == "ATR" and tradeDateIsAllowed
strategy.exit( id = 'LXTP1', from_entry = 'Long', qty_percent = i_lxQtyTP1, limit = tp1Line, stop = slLine, comment_profit = 'LXTP1', comment_loss = 'SL', alert_profit = i_lxMsgTP1, alert_loss = i_lxMsgSL)
if strategy.position_size > 0 and condition == 1.1 and TPSType == "ATR" and tradeDateIsAllowed
strategy.exit( id = 'LXTP2', from_entry = 'Long', qty_percent = i_lxQtyTP2, limit = tp2Line, stop = slLine, comment_profit = 'LXTP2', comment_loss = 'SL', alert_profit = i_lxMsgTP2, alert_loss = i_lxMsgSL)
if strategy.position_size > 0 and condition == 1.2 and TPSType == "ATR" and tradeDateIsAllowed
strategy.exit( id = 'LXTP3', from_entry = 'Long', qty_percent = i_lxQtyTP3, limit = tp3Line, stop = slLine, comment_profit = 'LXTP3', comment_loss = 'SL', alert_profit = i_lxMsgTP3, alert_loss = i_lxMsgSL)
if longX and tradeDateIsAllowed
strategy.close( 'Long', alert_message = i_lxMsg, comment = 'LX')
//
if strategy.position_size >= 0 and shortE and TPSType == "ATR" and tradeDateIsAllowed
strategy.entry( 'Short', strategy.short, alert_message = i_leMsg, comment = 'SE')
if strategy.position_size < 0 and condition == -1.0 and TPSType == "ATR" and tradeDateIsAllowed
strategy.exit( id = 'SXTP1', from_entry = 'Short', qty_percent = i_sxQtyTP1, limit = tp1Line, stop = slLine, comment_profit = 'SXTP1', comment_loss = 'SL', alert_profit = i_sxMsgTP1, alert_loss = i_sxMsgSL)
if strategy.position_size < 0 and condition == -1.1 and TPSType == "ATR" and tradeDateIsAllowed
strategy.exit( id = 'SXTP2', from_entry = 'Short', qty_percent = i_sxQtyTP2, limit = tp2Line, stop = slLine, comment_profit = 'SXTP2', comment_loss = 'SL', alert_profit = i_sxMsgTP2, alert_loss = i_sxMsgSL)
if strategy.position_size < 0 and condition == -1.2 and TPSType == "ATR" and tradeDateIsAllowed
strategy.exit( id = 'SXTP3', from_entry = 'Short', qty_percent = i_sxQtyTP3, limit = tp3Line, stop = slLine, comment_profit = 'SXTP3', comment_loss = 'SL', alert_profit = i_sxMsgTP3, alert_loss = i_sxMsgSL)
if shortX and tradeDateIsAllowed
strategy.close( 'Short', alert_message = i_sxMsg, comment = 'SX')
// ———————————
c_tp = leTrigger or seTrigger ? na :
condition == 0.0 ? na : color.green
c_entry = leTrigger or seTrigger ? na :
condition == 0.0 ? na : color.blue
c_sl = leTrigger or seTrigger ? na :
condition == 0.0 ? na : color.red
p_tp1Line = plot ( condition == 1.0 or condition == -1.0 ? tp1Line : na, title = "TP Line 1", color = c_tp, linewidth = 1, style = plot.style_linebr)
p_tp2Line = plot ( condition == 1.0 or condition == -1.0 or condition == 1.1 or condition == -1.1 ? tp2Line : na, title = "TP Line 2", color = c_tp, linewidth = 1, style = plot.style_linebr)
p_tp3Line = plot ( condition == 1.0 or condition == -1.0 or condition == 1.1 or condition == -1.1 or condition == 1.2 or condition == -1.2 ? tp3Line : na, title = "TP Line 3", color = c_tp, linewidth = 1, style = plot.style_linebr)
p_entryLine = plot ( condition >= 1.0 or condition <= -1.0 ? entryLine : na, title = "Entry Line", color = c_entry, linewidth = 1, style = plot.style_linebr)
p_slLine = plot ( condition == 1.0 or condition == -1.0 or condition == 1.1 or condition == -1.1 or condition == 1.2 or condition == -1.2 ? slLine : na, title = "SL Line", color = c_sl, linewidth = 1, style = plot.style_linebr)
//fill( p_tp3Line, p_entryLine, color = leTrigger or seTrigger ? na :color.new(color.green, 90))
fill( p_entryLine, p_slLine, color = leTrigger or seTrigger ? na :color.new(color.red, 90))
//
plotshape( i_alertOn and longE, title = 'Long', text = 'Long', textcolor = color.white, color = color.green, style = shape.labelup, size = size.tiny, location = location.belowbar)
plotshape( i_alertOn and shortE, title = 'Short', text = 'Short', textcolor = color.white, color = color.red, style = shape.labeldown, size = size.tiny, location = location.abovebar)
plotshape( i_alertOn and (longX or shortX) ? close : na, title = 'Close', text = 'Close', textcolor = color.white, color = color.gray, style = shape.labelup, size = size.tiny, location = location.absolute)
l_tp = i_alertOn and (longTP1 or shortTP1) ? close : na
plotshape( l_tp, title = "TP1 Cross", text = "TP1", textcolor = color.white, color = #ec407a, style = shape.labelup, size = size.tiny, location = location.absolute)
plotshape( i_alertOn and (longTP2 or shortTP2) ? close : na, title = "TP2 Cross", text = "TP2", textcolor = color.white, color = #ec407a, style = shape.labelup, size = size.tiny, location = location.absolute)
plotshape( i_alertOn and (longTP3 or shortTP3) ? close : na, title = "TP3 Cross", text = "TP3", textcolor = color.white, color = #ec407a, style = shape.labelup, size = size.tiny, location = location.absolute)
plotshape( i_alertOn and (longSL or shortSL) ? close : na, title = "SL Cross", text = "SL", textcolor = color.white, color = color.maroon, style = shape.labelup, size = size.tiny, location = location.absolute)
//
plot( na, title = "─── ───", editable = false, display = display.data_window)
plot( condition, title = "condition", editable = false, display = display.data_window)
plot( strategy.position_size * 100, title = ".position_size", editable = false, display = display.data_window)
//#endregion }
// ——————————— <↑↑↑ G_RISK ↑↑↑>
//#region ———— <↓↓↓ G_SCRIPT02 ↓↓↓> {
// @function Queues a new element in an array and de-queues its first element.
f_qDq(_array, _val) =>
array.push(_array, _val)
_return = array.shift(_array)
_return
var line a_slLine = array.new_line(1)
var line a_entryLine = array.new_line(1)
var line a_tp3Line = array.new_line(1)
var line a_tp2Line = array.new_line(1)
var line a_tp1Line = array.new_line(1)
var label a_slLabel = array.new_label(1)
var label a_tp3label = array.new_label(1)
var label a_tp2label = array.new_label(1)
var label a_tp1label = array.new_label(1)
var label a_entryLabel = array.new_label(1)
newEntry = longE or shortE
entryIndex = 1
entryIndex := newEntry ? bar_index : nz(entryIndex )
lasTrade = bar_index >= entryIndex
l_right = 10
if TPSType == "ATR"
line.delete( f_qDq(a_slLine, line.new( entryIndex, slLine, last_bar_index + l_right, slLine, style = line.style_solid, color = c_sl)))
if TPSType == "ATR"
line.delete( f_qDq(a_entryLine, line.new( entryIndex, entryLine, last_bar_index + l_right, entryLine, style = line.style_solid, color = color.blue)))
if TPSType == "ATR"
line.delete( f_qDq(a_tp3Line, line.new( entryIndex, tp3Line, last_bar_index + l_right, tp3Line, style = line.style_solid, color = c_tp)))
if TPSType == "ATR"
line.delete( f_qDq(a_tp2Line, line.new( entryIndex, tp2Line, last_bar_index + l_right, tp2Line, style = line.style_solid, color = c_tp)))
if TPSType == "ATR"
line.delete( f_qDq(a_tp1Line, line.new( entryIndex, tp1Line, last_bar_index + l_right, tp1Line, style = line.style_solid, color = c_tp)))
if TPSType == "ATR"
label.delete( f_qDq(a_slLabel, label.new( last_bar_index + l_right, slLine, 'SL: ' + str.tostring(slLine, '##.###'), style = label.style_label_left, textcolor = color.white, color = c_sl)))
if TPSType == "ATR"
label.delete( f_qDq(a_entryLabel, label.new( last_bar_index + l_right, entryLine, 'Entry: ' + str.tostring(entryLine, '##.###'), style = label.style_label_left, textcolor = color.white, color = color.blue)))
if TPSType == "ATR"
label.delete( f_qDq(a_tp3label, label.new( last_bar_index + l_right, tp3Line, 'TP3: ' + str.tostring(tp3Line, '##.###') + " - Target Pips : - " + str.tostring(longE ? tp3Line - entryLine : entryLine - tp3Line, "#.##"), style = label.style_label_left, textcolor = color.white, color = c_tp)))
if TPSType == "ATR"
label.delete( f_qDq(a_tp2label, label.new( last_bar_index + l_right, tp2Line, 'TP2: ' + str.tostring(tp2Line, '##.###'), style = label.style_label_left, textcolor = color.white, color = c_tp)))
if TPSType == "ATR"
label.delete( f_qDq(a_tp1label, label.new( last_bar_index + l_right, tp1Line, 'TP1: ' + str.tostring(tp1Line, '##.###'), style = label.style_label_left, textcolor = color.white, color = c_tp)))
//#endregion }
// ——————————— <↑↑↑ G_SCRIPT02 ↑↑↑>
c_barCol = close > open ? color.rgb(120, 9, 139) : color.rgb(69, 155, 225)
barcolor(
i_barColOn ? c_barCol : na)
// ———————————
//
if longE or shortE or longX or shortX
alert(message = 'Any Alert', freq = alert.freq_once_per_bar_close)
if longE
alert(message = 'Long Entry', freq = alert.freq_once_per_bar_close)
if shortE
alert(message = 'Short Entry', freq = alert.freq_once_per_bar_close)
if longX
alert(message = 'Long Exit', freq = alert.freq_once_per_bar_close)
if shortX
alert(message = 'Short Exit', freq = alert.freq_once_per_bar_close)
//#endregion }
// ——————————— <↑↑↑ G_SCRIPT03 ↑↑↑>
// This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © TraderHalai
// This script was born out of my quest to be able to display strategy back test statistics on charts to allow for easier backtesting on devices that do not natively support backtest engine (such as mobile phones, when I am backtesting from away from my computer). There are already a few good ones on TradingView, but most / many are too complicated for my needs.
//
//Found an excellent display backtest engine by 'The Art of Trading'. This script is a snippet of his hard work, with some very minor tweaks and changes. Much respect to the original author.
//
//Full credit to the original author of this script. It can be found here: www.tradingview.com
//
// This script can be copied and airlifted onto existing strategy scripts of your own, and integrates out of the box without implementation of additional functions. I've also added Max Runup, Average Win and Average Loss per trade to the orignal script.
//
//Will look to add in more performance metrics in future, as I further develop this script.
//
//Feel free to use this display panel in your scripts and strategies.
//Thanks and enjoy! :)
//@version=5
//strategy("Strategy BackTest Display Statistics - TraderHalai", overlay=true, default_qty_value= 5, default_qty_type = strategy.percent_of_equity, initial_capital=10000, commission_type=strategy.commission.percent, commission_value=0.1)
//DEMO basic strategy - Use your own strategy here - Jaws Mean Reversion from my profile used here
//source = input(title = "Source", defval = close)
///////////////////////////// --- BEGIN TESTER CODE --- ////////////////////////
// COPY below into your strategy to enable display
////////////////////////////////////////////////////////////////////////////////
// Declare performance tracking variables
drawTester = input.bool(false, "Strategy Performance", group='Dashboards', inline="Show Dashboards")
var balance = strategy.initial_capital
var drawdown = 0.0
var maxDrawdown = 0.0
var maxBalance = 0.0
var totalWins = 0
var totalLoss = 0
// Prepare stats table
var table testTable = table.new(position.top_right, 5, 2, border_width=1)
f_fillCell(_table, _column, _row, _title, _value, _bgcolor, _txtcolor) =>
_cellText = _title + "\n" + _value
table.cell(_table, _column, _row, _cellText, bgcolor=_bgcolor, text_color=_txtcolor)
// Custom function to truncate (cut) excess decimal places
//truncate(_number, _decimalPlaces) =>
// _factor = math.pow(10, _decimalPlaces)
// int(_number * _factor) / _factor
// Draw stats table
var bgcolor = color.new(color.black,0)
if drawTester and tradeDateIsAllowed
if barstate.islastconfirmedhistory
// Update table
dollarReturn = strategy.netprofit
f_fillCell(testTable, 0, 0, "Total Trades:", str.tostring(strategy.closedtrades), bgcolor, color.white)
f_fillCell(testTable, 0, 1, "Win Rate:", str.tostring(truncate((strategy.wintrades/strategy.closedtrades)*100,2)) + "%", bgcolor, color.white)
f_fillCell(testTable, 1, 0, "Starting:", "$" + str.tostring(strategy.initial_capital), bgcolor, color.white)
f_fillCell(testTable, 1, 1, "Ending:", "$" + str.tostring(truncate(strategy.initial_capital + strategy.netprofit,2)), bgcolor, color.white)
f_fillCell(testTable, 2, 0, "Avg Win:", "$"+ str.tostring(truncate(strategy.grossprofit / strategy.wintrades, 2)), bgcolor, color.white)
f_fillCell(testTable, 2, 1, "Avg Loss:", "$"+ str.tostring(truncate(strategy.grossloss / strategy.losstrades, 2)), bgcolor, color.white)
f_fillCell(testTable, 3, 0, "Profit Factor:", str.tostring(truncate(strategy.grossprofit / strategy.grossloss,2)), strategy.grossprofit > strategy.grossloss ? color.green : color.red, color.white)
f_fillCell(testTable, 3, 1, "Max Runup:", str.tostring(truncate(strategy.max_runup, 2 )), bgcolor, color.white)
f_fillCell(testTable, 4, 0, "Return:", (dollarReturn > 0 ? "+" : "") + str.tostring(truncate((dollarReturn / strategy.initial_capital)*100,2)) + "%", dollarReturn > 0 ? color.green : color.red, color.white)
f_fillCell(testTable, 4, 1, "Max DD:", str.tostring(truncate((strategy.max_drawdown / strategy.equity) * 100 ,2)) + "%", color.red, color.white)
// --- END TESTER CODE --- ///////////////
// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © niceGear68734
//@version=5
//strategy("Table to filter trades per day", overlay=true, use_bar_magnifier = true, initial_capital = 5000, calc_on_every_tick = true, calc_on_order_fills = true, commission_type = strategy.commission.cash_per_contract)
//~ ___________________________________________________________________________
//~ !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
//~ !!!!!!!!!!!!!!!_________________ START _________________!!!!!!!!!!!!!!!!!
i_showweeklyPerformance = input.bool(false, 'Weekly Performance', group='Dashboards', inline="Show Dashboards")
//__________________________ User Inputs ___________________________________
var const string g_table = "Table Settings"
i_table_pos = "Top Left" //input.string(defval = "Top Left", title = "Position", options = , group = g_table, inline = "1", tooltip = "It sets the location of the table")
i_text_size = "Normal" //input.string(defval = "Normal", title = "Set the size of text", options = , tooltip = "This option is used to change the size of the text in the table")
var const string g_general = "General Settings"
i_check_open_close = "Opened" //input.string("Opened", "Check when the trade :", , group = g_general, tooltip = "This parameter defines what to check for. If opened is selected, the results will show the trades that opened on that day. If closed is selected, the results will show the trades that closed on that day")
i_timezone = "Exchange" //input.string("Exchange", title = "Set the Timezone", options = , group = g_general, tooltip = "You can use this setting whenever you want to change the time that the trade has closed/opened")
//~_____________________________ Switches ___________________________________
table_pos = switch i_table_pos
"Bottom Right" => position.bottom_right
"Bottom Left" => position.bottom_left
"Top Right" => position.top_right
"Top Left" => position.top_left
timezone_setting = i_timezone == "Exchange" ? syminfo.timezone : i_timezone
text_size = switch i_text_size
"Small" => size.small
"Normal" => size.normal
"Large" => size.large
//__________________________ Array Declaration _____________________________
var string t_column_names = array.from( "", "Sun", "Mon", "Tue", "Wed", "Thur", "Fri", "Sat") // Columns header names
var string t_row_names = array.from("", "Total Trades", "Loss", "Win", "Win Rate" ) // Rows header names
var t_column_size = array.size(t_column_names)
var t_row_size = array.size(t_row_names)
var string a_closed_trades = array.new_string() // Save the total number of trades
var string a_loss_trades = array.new_string() // Save the number of losing trades
var string a_win_trades = array.new_string() // Save the number of winning trades
var _a_day_week = array.new_int() // Save the day of the week to split data
// __________________________ Custom Functions ________________________________
//~ create a counter so that it gives a number to strategy.closed_trades.entry_time(counter)
var trade_number = -1
if strategy.closedtrades > strategy.closedtrades
trade_number += 1
f_strategy_closedtrades_hour() =>
switch
i_check_open_close =="Closed" => dayofweek(strategy.closedtrades.exit_time(trade_number), timezone_setting)
i_check_open_close =="Opened" => dayofweek(strategy.closedtrades.entry_time(trade_number), timezone_setting)
f_data(_i) =>
var _closed_trades = 0
var _loss_trades = 0
var _win_trades = 0
var _txt_closed_trades = ""
var _txt_loss_trades = ""
var _txt_win_trades = ""
if strategy.closedtrades > strategy.closedtrades and f_strategy_closedtrades_hour() == _i
_closed_trades += 1
_txt_closed_trades := str.tostring(_closed_trades)
if strategy.losstrades > strategy.losstrades and f_strategy_closedtrades_hour() == _i
_loss_trades += 1
_txt_loss_trades := str.tostring(_loss_trades)
if strategy.wintrades > strategy.wintrades and f_strategy_closedtrades_hour() == _i
_win_trades += 1
_txt_win_trades := str.tostring(_win_trades)
//__________________________
var string array1 = array.new_string(5)
var string array2 = array.new_string(5)
var string array3 = array.new_string(5)
var string array4 = array.new_string(5)
var string array5 = array.new_string(5)
var string array6 = array.new_string(5)
var string array7 = array.new_string(5)
f_pass_data_to_array(_i, _array) =>
= f_data(_i)
array.set(_array,1 , cl)
array.set(_array,2,loss)
array.set(_array,3,win)
if cl != ""
array.set(_array,4,str.tostring(str.tonumber(win) / str.tonumber(cl) * 100 , "##") + " %")
if cl != "" and win == ""
array.set(_array,4,"0 %")
for i = 1 to 7
switch
i == 1 => f_pass_data_to_array(i,array1)
i == 2 => f_pass_data_to_array(i,array2)
i == 3 => f_pass_data_to_array(i,array3)
i == 4 => f_pass_data_to_array(i,array4)
i == 5 => f_pass_data_to_array(i,array5)
i == 6 => f_pass_data_to_array(i,array6)
i == 7 => f_pass_data_to_array(i,array7)
f_retrieve_data_to_table(_i, _j) =>
switch
_i == 1 => array.get(array1, _j)
_i == 2 => array.get(array2, _j)
_i == 3 => array.get(array3, _j)
_i == 4 => array.get(array4, _j)
_i == 5 => array.get(array5, _j)
_i == 6 => array.get(array6, _j)
_i == 7 => array.get(array7, _j)
//~ ___________________________ Create Table ________________________________
create_table(_col, _row, _txt) =>
var table _tbl = table.new(position = table_pos, columns = t_column_size , rows = t_row_size, border_width=1)
color _color = _row == 0 or _col == 0 ? color.rgb(3, 62, 106) : color.rgb(2, 81, 155)
table.cell(_tbl, _col, _row, _txt, bgcolor = _color, text_color = color.white, text_size = text_size)
//~___________________________ Fill With Data _______________________________
if barstate.islastconfirmedhistory and i_showweeklyPerformance and tradeDateIsAllowed
for i = 0 to t_column_size - 1 by 1
for j = 0 to t_row_size - 1 by 1
_txt = ""
if i >= 0 and j == 0
_txt := array.get(t_column_names, i)
if j >= 0 and i == 0
_txt := array.get(t_row_names, j)
if i >= 1 and j >= 1 and j <= 5
_txt := f_retrieve_data_to_table( i , j)
create_table(i ,j , _txt)
//~ ___________________________ Notice ______________________________________
if timeframe.in_seconds() > timeframe.in_seconds("D")
x = table.new(position.middle_center,1,1,color.aqua)
table.cell_set_text(x,0,0,"Please select lower timeframes (Daily or lower)")
//~ !!!!!!!!!!!!!!!_________________ STOP _________________!!!!!!!!!!!!!!!!!!
//~ !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
//~ ___________________________________________________________________________
// Global Dashboard Variables
// ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
// Dashboard Table Text Size
i_tableTextSize = "Normal" //input.string(title="Dashboard Size", defval="Normal", options= , group="Dashboards")
table_text_size(s) =>
switch s
"Auto" => size.auto
"Huge" => size.huge
"Large" => size.large
"Normal" => size.normal
"Small" => size.small
=> size.tiny
tableTextSize = table_text_size(i_tableTextSize)
// Monthly Table Performance Dashboard By @QuantNomad
// ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
i_showMonthlyPerformance = input.bool(false, 'Monthly Performance', group='Dashboards', inline="Show Dashboards")
i_monthlyReturnPercision = 2
if i_showMonthlyPerformance and tradeDateIsAllowed
new_month = month(time) != month(time )
new_year = year(time) != year(time )
eq = strategy.equity
bar_pnl = eq / eq - 1
cur_month_pnl = 0.0
cur_year_pnl = 0.0
// Current Monthly P&L;
cur_month_pnl := new_month ? 0.0 :
(1 + cur_month_pnl ) * (1 + bar_pnl) - 1
// Current Yearly P&L;
cur_year_pnl := new_year ? 0.0 :
(1 + cur_year_pnl ) * (1 + bar_pnl) - 1
// Arrays to store Yearly and Monthly P&Ls;
var month_pnl = array.new_float(0)
var month_time = array.new_int(0)
var year_pnl = array.new_float(0)
var year_time = array.new_int(0)
last_computed = false
if (not na(cur_month_pnl ) and (new_month or barstate.islastconfirmedhistory))
if (last_computed )
array.pop(month_pnl)
array.pop(month_time)
array.push(month_pnl , cur_month_pnl )
array.push(month_time, time )
if (not na(cur_year_pnl ) and (new_year or barstate.islastconfirmedhistory))
if (last_computed )
array.pop(year_pnl)
array.pop(year_time)
array.push(year_pnl , cur_year_pnl )
array.push(year_time, time )
last_computed := barstate.islastconfirmedhistory ? true : nz(last_computed )
// Monthly P&L; Table
var monthly_table = table(na)
if (barstate.islastconfirmedhistory)
monthly_table := table.new(position.bottom_right, columns = 14, rows = array.size(year_pnl) + 1, border_width = 1)
table.cell(monthly_table, 0, 0, "", bgcolor = #cccccc, text_size=tableTextSize)
table.cell(monthly_table, 1, 0, "Jan", bgcolor = #cccccc, text_size=tableTextSize)
table.cell(monthly_table, 2, 0, "Feb", bgcolor = #cccccc, text_size=tableTextSize)
table.cell(monthly_table, 3, 0, "Mar", bgcolor = #cccccc, text_size=tableTextSize)
table.cell(monthly_table, 4, 0, "Apr", bgcolor = #cccccc, text_size=tableTextSize)
table.cell(monthly_table, 5, 0, "May", bgcolor = #cccccc, text_size=tableTextSize)
table.cell(monthly_table, 6, 0, "Jun", bgcolor = #cccccc, text_size=tableTextSize)
table.cell(monthly_table, 7, 0, "Jul", bgcolor = #cccccc, text_size=tableTextSize)
table.cell(monthly_table, 8, 0, "Aug", bgcolor = #cccccc, text_size=tableTextSize)
table.cell(monthly_table, 9, 0, "Sep", bgcolor = #cccccc, text_size=tableTextSize)
table.cell(monthly_table, 10, 0, "Oct", bgcolor = #cccccc, text_size=tableTextSize)
table.cell(monthly_table, 11, 0, "Nov", bgcolor = #cccccc, text_size=tableTextSize)
table.cell(monthly_table, 12, 0, "Dec", bgcolor = #cccccc, text_size=tableTextSize)
table.cell(monthly_table, 13, 0, "Year", bgcolor = #999999, text_size=tableTextSize)
for yi = 0 to array.size(year_pnl) - 1
table.cell(monthly_table, 0, yi + 1, str.tostring(year(array.get(year_time, yi))), bgcolor = #cccccc, text_size=tableTextSize)
y_color = array.get(year_pnl, yi) > 0 ? color.new(color.teal, transp = 40) : color.new(color.gray, transp = 40)
table.cell(monthly_table, 13, yi + 1, str.tostring(math.round(array.get(year_pnl, yi) * 100, i_monthlyReturnPercision)), bgcolor = y_color, text_color=color.new(color.white, 0),text_size=tableTextSize)
for mi = 0 to array.size(month_time) - 1
m_row = year(array.get(month_time, mi)) - year(array.get(year_time, 0)) + 1
m_col = month(array.get(month_time, mi))
m_color = array.get(month_pnl, mi) > 0 ? color.new(color.teal, transp = 40) : color.new(color.maroon, transp = 40)
table.cell(monthly_table, m_col, m_row, str.tostring(math.round(array.get(month_pnl, mi) * 100, i_monthlyReturnPercision)), bgcolor = m_color, text_color=color.new(color.white, 0), text_size=tableTextSize)
hide = timeframe.isintraday
// Input for EMA period
emaPeriod = 48 //input.int(48, title="EMA Period")
emaPeriod2 = 2 //input.int(2, title="EME Period 2")
emaPeriod3 = 21 //input.int(21, title="EMA Period")
// Input to toggle EMA Cloud
showcloud = input.bool(false, title="Plot EMA?", group='EMA & ATR', inline="Show EMA's & ATR")
useHTF = input.bool(true, title = "Use Higher Time Frame?")
matimeframe = useHTF ? my_time1 : ''
// EMA calculations
ema = request.security(syminfo.tickerid, matimeframe, ta.ema(close, emaPeriod))
ema2 = request.security(syminfo.tickerid, matimeframe, ta.ema(close,emaPeriod2))
ema3 = request.security(syminfo.tickerid, matimeframe,ta.ema(close, emaPeriod3))
emaColor = close > ema3 ? color.new(color.rgb(56, 142, 60, 63), 50) : color.new(color.rgb(147, 40, 51, 38), 50)
// Plotting EMA's
// plot_ema1 = plot(hide ? ema : na, style=plot.style_line, color=color.new(color.rgb(255, 255, 255, 100), 50), title="EMA", linewidth=2)
// plot_ema2 = plot(hide ? ema2 : na, style=plot.style_line, color=color.new(color.rgb(255, 255, 255, 100), 50), title="EMA", linewidth=1)
// plot_ema3 = plot(ema3, style=plot.style_line, color=emaColor, title="EMA", linewidth=1)
// EMA Cloud
cloudColor = ema2 > ema ? color.new(#0f8513, 80) : color.new(#a81414, 80)
cloudColor2 = ema2 > ema3 ? color.new(#0f8513, 50) : color.new(#a81414, 50)
cloudColor := showcloud ? cloudColor : na
// fill(plot_ema1, plot_ema2, color=cloudColor, title="EMA Cloud")
// fill(plot_ema3, plot_ema2, color=cloudColor, title="EMA Cloud")
/////////////////////////////////////////////////////////////// © BackQuant ///////////////////////////////////////////////////////////////
// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © BackQuant
import TradingView/ta/4 as ta
//@version=5
//indicator(
// title="DEMA Adjusted Average True Range ",
// shorttitle = "DEMA ATR ",
// overlay=true,
// timeframe="",
// timeframe_gaps=true
// )
// Define User Inputs
simple bool showAtr = input.bool(false, "Plot Dema?", group='EMA & ATR', inline="Show EMA's & ATR")
simple bool haCandles = true //input.bool(true, "Use HA Candles?")
simple int periodDema = 7 //input.int(7, "Dema Period", group = "Dema Atr")
series float sourceDema = close //input.source(close, "Calculation Source", group = "Dema Atr")
simple int periodAtr = 14 //input.int(14, "Period", group = "Dema Atr")
simple float factorAtr = 1.7 //input.float(1.7, "Factor", step = 0.01, group = "Dema Atr")
simple color longColour = #66bb6a
simple color shortColour = #f23645
/////////////////////////////////////////////////////////////// © BackQuant ///////////////////////////////////////////////////////////////
// Use HA Candles?
heikinashi_close = request.security(
symbol = ticker.heikinashi(syminfo.tickerid),
timeframe = timeframe.period,
expression = close,
gaps = barmerge.gaps_off,
lookahead = barmerge.lookahead_on
)
var series float source = close
if haCandles == true
source := heikinashi_close
if haCandles == false
source := sourceDema
/////////////////////////////////////////////////////////////// © BackQuant ///////////////////////////////////////////////////////////////
// Function
DemaAtrWithBands(periodDema, source, lookback, atrFactor)=>
ema1 = ta.ema(source, periodDema)
ema2 = ta.ema(ema1, periodDema)
demaOut = 2 * ema1 - ema2
atr = ta.atr(lookback)
trueRange = atr * atrFactor
DemaAtr = demaOut
DemaAtr := nz(DemaAtr , DemaAtr)
trueRangeUpper = demaOut + trueRange
trueRangeLower = demaOut - trueRange
if trueRangeLower > DemaAtr
DemaAtr := trueRangeLower
if trueRangeUpper < DemaAtr
DemaAtr := trueRangeUpper
DemaAtr
// Function Out
DemaAtr = DemaAtrWithBands(periodDema, source, periodAtr, factorAtr)
/////////////////////////////////////////////////////////////// © BackQuant ///////////////////////////////////////////////////////////////
// Conditions
DemaAtrLong = DemaAtr > DemaAtr
DemaAtrShort = DemaAtr < DemaAtr
// Colour Condtions
var color Trendcolor = #ffffff
if DemaAtrLong
Trendcolor := longColour
if DemaAtrShort
Trendcolor := shortColour
// Plotting
plot( showAtr ? DemaAtr : na, "ATR", color=Trendcolor, linewidth = 2 )
import DevLucem/ZigLib/1 as ZigZag
////////
// Fetch Ingredients
//
// ////////
// // Bake it with a simple oven this time
= ZigZag.zigzag(low, high, Depth, Deviation, Backstep)
string nowPoint = ""
var float lastPoint = z1.price
if bool(ta.change(direction))
lastPoint := z1.price
// ////////
// // Let it Cool And Serve
line zz = na
label point = na
if repaint
zz := line.new(z1, z2, xloc.bar_time, extend? extend.right: extend.none, color.new(direction>0? upcolor: dncolor, lines), width=line_thick)
nowPoint := direction<0? (z2.pricelastPoint? "HH": "LH")
point := label.new(z2, nowPoint, xloc.bar_time, yloc.price,
color.new(direction<0? upcolor: dncolor, labels), direction>0? label.style_label_down: label.style_label_up, color.new(direction>0? upcolor: dncolor, labels), label_size)
if direction == direction
line.delete(zz )
label.delete(point )
else
line.set_extend(zz , extend.none)
else
if direction != direction
zz := line.new(z1 , z2 , xloc.bar_time, extend.none, color.new(direction>0? upcolor: dncolor, lines), width=line_thick)
nowPoint := direction <0? (z2.price lastPoint ? "HH": "LH")
point := label.new(z2 , nowPoint, xloc.bar_time, yloc.price,
color.new(direction <0? upcolor: dncolor, labels), direction >0? label.style_label_down: label.style_label_up, color.new(direction >0? upcolor: dncolor, labels), label_size)
bgcolor(direction<0? color.new(dncolor, background): color.new(upcolor, background), title='Direction Background', display = display.none)
plotarrow(direction, "direction", display=display.status_line)
// ////////
// // Declare Meal Was Sweet By Force
alertcondition(nowPoint == "HH" and z2.price != z2.price , "New Higher High", 'Zigzag on {{ticker}} higher higher high detected at {{time}}')
alertcondition(nowPoint == "LH" and z2.price != z2.price , "New Lower High", 'Zigzag on {{ticker}} higher lower high detected at {{time}}')
alertcondition(nowPoint == "HL" and z2.price != z2.price , "New Higher Low", 'Zigzag on {{ticker}} higher lower low detected at {{time}}')
alertcondition(nowPoint == "LL" and z2.price != z2.price , "New Lower Low", 'Zigzag on {{ticker}} lower low detected at {{time}}')
alertcondition(direction != direction , 'Direction Changed', 'Zigzag on {{ticker}} direction changed at {{time}}')
alertcondition(direction != direction and direction>0, 'Bullish Direction', 'Zigzag on {{ticker}} bullish direction at {{time}}')
alertcondition(direction != direction and direction<0, 'Bearish Direction', 'Zigzag on {{ticker}} bearish direction at {{time}}')
if direction != direction
alert((direction<0? "Bearish": "Bullish") + " Direction Final ", alert.freq_once_per_bar_close)
MSG = "MARKET STRUCTURE"
VBG = "VOLUMETRIC ORDER BLOCKS"
MST = "Limit market structure calculation to improve memory speed time"
SLT = " Limit swing structure to tot bars back"
IDT = " Start date of the internal structure"
CST = "Color candle based on trend detection system"
OBT = "Display internal buy and sell activity"
OBD = "Show Last number of orderblock"
OBMT = " Use Length to adjust cordinate of the orderblocks\n Use whole candle body"
_ ='
------------
–––––––––––––––––––––––––– INPUTS –––––––––––––––––––––––––––
------------ '//{
bool windowsis = input.bool(true, "Window", inline="kla", group=MSG)
int mswindow = input.int(5000, "", tooltip=MST,group=MSG, inline="kla", minval=1000)
bool showSwing = input.bool(true, "Swing", inline="scss", group=MSG)
int swingLimit = input.int(100, "", tooltip=SLT, inline="scss", group=MSG, minval=10, maxval=200)
color swingcssup = input.color(#f7525f, "", inline="scss", group=MSG)
color swingcssdn = input.color(#66bb6a, "", inline="scss", group=MSG)
bool showMapping = input.bool(false, "Mapping Structure", inline="mapping", group=MSG)
string mappingStyle = input.string("----", "", options= , inline="mapping", group=MSG)
color mappingcss = input.color(color.silver, "", tooltip="Display Mapping Structure", inline="mapping", group=MSG)
bool candlecss = input.bool(false, "Color Candles", tooltip=CST, group=MSG, inline="txt")
string mstext = input.string("Tiny", "", options= ,
inline="txt", group=MSG)
string msmode = input.string("Adjusted Points", "Algorithmic Logic", options=
, inline="node", group=MSG)
int mslen = input.int(5, "", inline="node", group=MSG, minval=2)
bool buildsweep = input.bool(true, "Build Sweep (x)", "Build sweep on market structure", "znc", MSG)
bool msbubble = input.bool(true, "Bubbles", tooltip="Display Circle Bubbles", inline="bubbles", group=MSG)
bool obshow = input.bool(true, "Show Last", tooltip=OBD, group=VBG, inline="obshow")
int oblast = input.int(5, "", group=VBG, inline="obshow", minval=0)
color obupcs = input.color(color.new(#089981, 90), "", inline="obshow", group=VBG)
color obdncs = input.color(color.new(#f23645, 90), "", inline="obshow", group=VBG)
bool obshowactivity = input.bool(true, "Show Buy/Sell Activity", inline="act", group=VBG, tooltip=OBT)
color obactup = input.color(color.new(#089981, 50), "", inline="act", group=VBG)
color obactdn = input.color(color.new(#f23645, 50), "", inline="act", group=VBG)
obshowbb = input.bool(false, "Show Breakers", inline="bb", group=VBG, tooltip="Display Breakers")
color bbup = input.color(color.new(#089981, 100), "", inline="bb", group=VBG)
color bbdn = input.color(color.new(#f23645, 100), "", inline="bb", group=VBG)
obmode = input.string("Length", "Construction", options= , tooltip=OBMT, inline="atr", group=VBG)
len = input.int(5, "", inline="atr", group=VBG, minval=1)
obmiti = input.string("Close", "Mitigation Method", options= ,
tooltip="Mitigation method for when to trigger order blocks", group=VBG)
obtxt = input.string("Normal", "Metric Size", options= ,
tooltip="Order block Metrics text size", inline="txt", group=VBG)
showmetric = input.bool(true, "Show Metrics", group=VBG)
showline = input.bool(true, "Show Mid-Line", group=VBG)
overlap = input.bool(true, "Hide Overlap", group=VBG, inline="ov")
wichlap = input.string("Recent", "", options= , inline="ov", group=VBG)
fvg_enable = input.bool(false, "", inline="1", group="FAIR VALUE GAP", tooltip="Display fair value gap")
what_fvg = input.string("FVG", "", inline="1", group="FAIR VALUE GAP", tooltip="Display fair value gap",
options= )
fvg_num = input.int(5, "Show Last", inline="1a", group="FAIR VALUE GAP", tooltip="Number of fvg to show", minval=0)
fvg_upcss = input.color(color.new(#089981, 80), "", inline="1", group="FAIR VALUE GAP")
fvg_dncss = input.color(color.new(#f23645, 80), "", inline="1", group="FAIR VALUE GAP")
fvgbbup = input.color(color.new(#089981, 100), "", inline="1", group="FAIR VALUE GAP")
fvgbbdn = input.color(color.new(#f23645, 100), "", inline="1", group="FAIR VALUE GAP")
fvg_src = input.string("Close", "Mitigation",
inline="3",
group="FAIR VALUE GAP",
tooltip=" Use the close of the body as trigger\n\n Use the extreme point of the body as trigger",
options= )
fvgthresh = input.float(0, "Threshold", tooltip="Filter out non significative FVG", group="FAIR VALUE GAP",
inline="asd", minval=0, maxval=2, step=0.1)
fvgoverlap = input.bool(true, "Hide Overlap", "Hide overlapping FVG", group="FAIR VALUE GAP")
fvgline = input.bool(true, "Show Mid-Line", group="FAIR VALUE GAP")
fvgextend = input.bool(false, "Extend FVG", group="FAIR VALUE GAP")
dispraid = input.bool(false, "Display Raids", inline="raid", group="FAIR VALUE GAP")
//}
_ ='
------------
–––––––––––––––––––––––––– UDT –––––––––––––––––––––––––––
------------ '//{
type hqlzone
box pbx
box ebx
box lbx
label plb
label elb
label lbl
type Zphl
line top
line bottom
label top_label
label bottom_label
bool stopcross
bool sbottomcross
bool itopcross
bool ibottomcross
string txtup
string txtdn
float topy
float bottomy
float topx
float bottomx
float tup
float tdn
int tupx
int tdnx
float itopy
float itopx
float ibottomy
float ibottomx
float uV
float dV
type entered
bool normal = false
bool breaker = false
type store
line ln
label lb
box bx
linefill lf
type structure
int zn
float zz
float bos
float choch
int loc
int temp
int trend
int start
float main
int xloc
bool upsweep
bool dnsweep
string txt = na
type drawms
int x1
int x2
float y
string txt
color css
string style
type ob
bool bull
float top
float btm
float avg
int loc
color css
float vol
int dir
int move
int blPOS
int brPOS
int xlocbl
int xlocbr
bool isbb = false
int bbloc
type FVG
float top = na
float btm = na
int loc = bar_index
bool isbb = false
int bbloc = na
bool israid = false
float raidy = na
int raidloc = na
int raidx2 = na
bool active = false
color raidcs = na
type SFP
float y
int loc
float ancor
type sfpbuildlbl
int x
float y
string style
color css
string txt
type sfpbuildline
int x1
int x2
float y
color css
float ancor
int loc
type equalbuild
int x1
float y1
int x2
float y2
color css
string style
type equalname
int x
float y
string txt
color css
string style
type ehl
float pt
int t
float pb
int b
type sellbuyside
float top
float btm
int loc
color css
string txt
float vol
type timer
bool start = false
int count = 0
//}
_ ='
------------
–––––––––––––––––––––––––– SETUP –––––––––––––––––––––––––––
------------ '//{
var store bin = store.new(
array.new< line >()
, array.new< label >()
, array.new< box >()
, array.new()
)
var entered blobenter = entered.new()
var entered brobenter = entered.new()
var entered blfvgenter = entered.new()
var entered brfvgenter = entered.new()
var entered blarea = entered.new()
var entered brarea = entered.new()
var timer lc = timer.new ()
if barstate.islast
for obj in bin.ln
obj.delete()
for obj in bin.lb
obj.delete()
for obj in bin.bx
obj.delete()
for obj in bin.lf
obj.delete()
bin.ln.clear()
bin.lb.clear()
bin.bx.clear()
bin.lf.clear()
invcol = #ffffff00
float atr = (ta.atr(200) / (5/len))
//}
_ ='
------------
–––––––––––––––––––––––––– UTILITY –––––––––––––––––––––––––––
------------ '//{
method txSz(string s) =>
out = switch s
"Tiny" => size.tiny
"Small" => size.small
"Normal" => size.normal
"Large" => size.large
"Huge" => size.huge
"Auto" => size.auto
out
method lstyle(string style) =>
out = switch style
'⎯⎯⎯⎯' => line.style_solid
'----' => line.style_dashed
'····' => line.style_dotted
ghl() => [high , low , close , open , close, open, high, low, high , low , ta.atr(200)]
method IDMIDX(bool use_max, int loc) =>
min = 99999999.
max = 0.
idx = 0
if use_max
for i = 0 to (bar_index - loc)
max := math.max(high , max)
min := max == high ? low : min
idx := max == high ? i : idx
else
for i = 0 to (bar_index - loc)
min := math.min(low , min)
max := min == low ? high : max
idx := min == low ? i : idx
idx
SFPData() => [high, high , high , low, low , low , close, volume, time, bar_index , time ]
SFPcords() =>
RealTF = barstate.isrealtime ? 0 : 1
= SFPData()
[h , h1 , h2 , l , l1 , l2 , c , v , t , n , t1 ]
method find(structure ms, bool use_max, bool sweep, bool useob) =>
min = 99999999.
max = 0.
idx = 0
if not sweep
if ((bar_index - ms.loc) - 1) > 0
if use_max
for i = 0 to (bar_index - ms.loc) - 1
max := math.max(high , max)
min := max == high ? low : min
idx := max == high ? i : idx
if useob
if high
Universal Ratio Trend Matrix [InvestorUnknown]The Universal Ratio Trend Matrix is designed for trend analysis on asset/asset ratios, supporting up to 40 different assets. Its primary purpose is to help identify which assets are outperforming others within a selection, providing a broad overview of market trends through a matrix of ratios. The indicator automatically expands the matrix based on the number of assets chosen, simplifying the process of comparing multiple assets in terms of performance.
Key features include the ability to choose from a narrow selection of indicators to perform the ratio trend analysis, allowing users to apply well-defined metrics to their comparison.
Drawback: Due to the computational intensity involved in calculating ratios across many assets, the indicator has a limitation related to loading speed. TradingView has time limits for calculations, and for users on the basic (free) plan, this could result in frequent errors due to exceeded time limits. To use the indicator effectively, users with any paid plans should run it on timeframes higher than 8h (the lowest timeframe on which it managed to load with 40 assets), as lower timeframes may not reliably load.
Indicators:
RSI_raw: Simple function to calculate the Relative Strength Index (RSI) of a source (asset price).
RSI_sma: Calculates RSI followed by a Simple Moving Average (SMA).
RSI_ema: Calculates RSI followed by an Exponential Moving Average (EMA).
CCI: Calculates the Commodity Channel Index (CCI).
Fisher: Implements the Fisher Transform to normalize prices.
Utility Functions:
f_remove_exchange_name: Strips the exchange name from asset tickers (e.g., "INDEX:BTCUSD" to "BTCUSD").
f_remove_exchange_name(simple string name) =>
string parts = str.split(name, ":")
string result = array.size(parts) > 1 ? array.get(parts, 1) : name
result
f_get_price: Retrieves the closing price of a given asset ticker using request.security().
f_constant_src: Checks if the source data is constant by comparing multiple consecutive values.
Inputs:
General settings allow users to select the number of tickers for analysis (used_assets) and choose the trend indicator (RSI, CCI, Fisher, etc.).
Table settings customize how trend scores are displayed in terms of text size, header visibility, highlighting options, and top-performing asset identification.
The script includes inputs for up to 40 assets, allowing the user to select various cryptocurrencies (e.g., BTCUSD, ETHUSD, SOLUSD) or other assets for trend analysis.
Price Arrays:
Price values for each asset are stored in variables (price_a1 to price_a40) initialized as na. These prices are updated only for the number of assets specified by the user (used_assets).
Trend scores for each asset are stored in separate arrays
// declare price variables as "na"
var float price_a1 = na, var float price_a2 = na, var float price_a3 = na, var float price_a4 = na, var float price_a5 = na
var float price_a6 = na, var float price_a7 = na, var float price_a8 = na, var float price_a9 = na, var float price_a10 = na
var float price_a11 = na, var float price_a12 = na, var float price_a13 = na, var float price_a14 = na, var float price_a15 = na
var float price_a16 = na, var float price_a17 = na, var float price_a18 = na, var float price_a19 = na, var float price_a20 = na
var float price_a21 = na, var float price_a22 = na, var float price_a23 = na, var float price_a24 = na, var float price_a25 = na
var float price_a26 = na, var float price_a27 = na, var float price_a28 = na, var float price_a29 = na, var float price_a30 = na
var float price_a31 = na, var float price_a32 = na, var float price_a33 = na, var float price_a34 = na, var float price_a35 = na
var float price_a36 = na, var float price_a37 = na, var float price_a38 = na, var float price_a39 = na, var float price_a40 = na
// create "empty" arrays to store trend scores
var a1_array = array.new_int(40, 0), var a2_array = array.new_int(40, 0), var a3_array = array.new_int(40, 0), var a4_array = array.new_int(40, 0)
var a5_array = array.new_int(40, 0), var a6_array = array.new_int(40, 0), var a7_array = array.new_int(40, 0), var a8_array = array.new_int(40, 0)
var a9_array = array.new_int(40, 0), var a10_array = array.new_int(40, 0), var a11_array = array.new_int(40, 0), var a12_array = array.new_int(40, 0)
var a13_array = array.new_int(40, 0), var a14_array = array.new_int(40, 0), var a15_array = array.new_int(40, 0), var a16_array = array.new_int(40, 0)
var a17_array = array.new_int(40, 0), var a18_array = array.new_int(40, 0), var a19_array = array.new_int(40, 0), var a20_array = array.new_int(40, 0)
var a21_array = array.new_int(40, 0), var a22_array = array.new_int(40, 0), var a23_array = array.new_int(40, 0), var a24_array = array.new_int(40, 0)
var a25_array = array.new_int(40, 0), var a26_array = array.new_int(40, 0), var a27_array = array.new_int(40, 0), var a28_array = array.new_int(40, 0)
var a29_array = array.new_int(40, 0), var a30_array = array.new_int(40, 0), var a31_array = array.new_int(40, 0), var a32_array = array.new_int(40, 0)
var a33_array = array.new_int(40, 0), var a34_array = array.new_int(40, 0), var a35_array = array.new_int(40, 0), var a36_array = array.new_int(40, 0)
var a37_array = array.new_int(40, 0), var a38_array = array.new_int(40, 0), var a39_array = array.new_int(40, 0), var a40_array = array.new_int(40, 0)
f_get_price(simple string ticker) =>
request.security(ticker, "", close)
// Prices for each USED asset
f_get_asset_price(asset_number, ticker) =>
if (used_assets >= asset_number)
f_get_price(ticker)
else
na
// overwrite empty variables with the prices if "used_assets" is greater or equal to the asset number
if barstate.isconfirmed // use barstate.isconfirmed to avoid "na prices" and calculation errors that result in empty cells in the table
price_a1 := f_get_asset_price(1, asset1), price_a2 := f_get_asset_price(2, asset2), price_a3 := f_get_asset_price(3, asset3), price_a4 := f_get_asset_price(4, asset4)
price_a5 := f_get_asset_price(5, asset5), price_a6 := f_get_asset_price(6, asset6), price_a7 := f_get_asset_price(7, asset7), price_a8 := f_get_asset_price(8, asset8)
price_a9 := f_get_asset_price(9, asset9), price_a10 := f_get_asset_price(10, asset10), price_a11 := f_get_asset_price(11, asset11), price_a12 := f_get_asset_price(12, asset12)
price_a13 := f_get_asset_price(13, asset13), price_a14 := f_get_asset_price(14, asset14), price_a15 := f_get_asset_price(15, asset15), price_a16 := f_get_asset_price(16, asset16)
price_a17 := f_get_asset_price(17, asset17), price_a18 := f_get_asset_price(18, asset18), price_a19 := f_get_asset_price(19, asset19), price_a20 := f_get_asset_price(20, asset20)
price_a21 := f_get_asset_price(21, asset21), price_a22 := f_get_asset_price(22, asset22), price_a23 := f_get_asset_price(23, asset23), price_a24 := f_get_asset_price(24, asset24)
price_a25 := f_get_asset_price(25, asset25), price_a26 := f_get_asset_price(26, asset26), price_a27 := f_get_asset_price(27, asset27), price_a28 := f_get_asset_price(28, asset28)
price_a29 := f_get_asset_price(29, asset29), price_a30 := f_get_asset_price(30, asset30), price_a31 := f_get_asset_price(31, asset31), price_a32 := f_get_asset_price(32, asset32)
price_a33 := f_get_asset_price(33, asset33), price_a34 := f_get_asset_price(34, asset34), price_a35 := f_get_asset_price(35, asset35), price_a36 := f_get_asset_price(36, asset36)
price_a37 := f_get_asset_price(37, asset37), price_a38 := f_get_asset_price(38, asset38), price_a39 := f_get_asset_price(39, asset39), price_a40 := f_get_asset_price(40, asset40)
Universal Indicator Calculation (f_calc_score):
This function allows switching between different trend indicators (RSI, CCI, Fisher) for flexibility.
It uses a switch-case structure to calculate the indicator score, where a positive trend is denoted by 1 and a negative trend by 0. Each indicator has its own logic to determine whether the asset is trending up or down.
// use switch to allow "universality" in indicator selection
f_calc_score(source, trend_indicator, int_1, int_2) =>
int score = na
if (not f_constant_src(source)) and source > 0.0 // Skip if you are using the same assets for ratio (for example BTC/BTC)
x = switch trend_indicator
"RSI (Raw)" => RSI_raw(source, int_1)
"RSI (SMA)" => RSI_sma(source, int_1, int_2)
"RSI (EMA)" => RSI_ema(source, int_1, int_2)
"CCI" => CCI(source, int_1)
"Fisher" => Fisher(source, int_1)
y = switch trend_indicator
"RSI (Raw)" => x > 50 ? 1 : 0
"RSI (SMA)" => x > 50 ? 1 : 0
"RSI (EMA)" => x > 50 ? 1 : 0
"CCI" => x > 0 ? 1 : 0
"Fisher" => x > x ? 1 : 0
score := y
else
score := 0
score
Array Setting Function (f_array_set):
This function populates an array with scores calculated for each asset based on a base price (p_base) divided by the prices of the individual assets.
It processes multiple assets (up to 40), calling the f_calc_score function for each.
// function to set values into the arrays
f_array_set(a_array, p_base) =>
array.set(a_array, 0, f_calc_score(p_base / price_a1, trend_indicator, int_1, int_2))
array.set(a_array, 1, f_calc_score(p_base / price_a2, trend_indicator, int_1, int_2))
array.set(a_array, 2, f_calc_score(p_base / price_a3, trend_indicator, int_1, int_2))
array.set(a_array, 3, f_calc_score(p_base / price_a4, trend_indicator, int_1, int_2))
array.set(a_array, 4, f_calc_score(p_base / price_a5, trend_indicator, int_1, int_2))
array.set(a_array, 5, f_calc_score(p_base / price_a6, trend_indicator, int_1, int_2))
array.set(a_array, 6, f_calc_score(p_base / price_a7, trend_indicator, int_1, int_2))
array.set(a_array, 7, f_calc_score(p_base / price_a8, trend_indicator, int_1, int_2))
array.set(a_array, 8, f_calc_score(p_base / price_a9, trend_indicator, int_1, int_2))
array.set(a_array, 9, f_calc_score(p_base / price_a10, trend_indicator, int_1, int_2))
array.set(a_array, 10, f_calc_score(p_base / price_a11, trend_indicator, int_1, int_2))
array.set(a_array, 11, f_calc_score(p_base / price_a12, trend_indicator, int_1, int_2))
array.set(a_array, 12, f_calc_score(p_base / price_a13, trend_indicator, int_1, int_2))
array.set(a_array, 13, f_calc_score(p_base / price_a14, trend_indicator, int_1, int_2))
array.set(a_array, 14, f_calc_score(p_base / price_a15, trend_indicator, int_1, int_2))
array.set(a_array, 15, f_calc_score(p_base / price_a16, trend_indicator, int_1, int_2))
array.set(a_array, 16, f_calc_score(p_base / price_a17, trend_indicator, int_1, int_2))
array.set(a_array, 17, f_calc_score(p_base / price_a18, trend_indicator, int_1, int_2))
array.set(a_array, 18, f_calc_score(p_base / price_a19, trend_indicator, int_1, int_2))
array.set(a_array, 19, f_calc_score(p_base / price_a20, trend_indicator, int_1, int_2))
array.set(a_array, 20, f_calc_score(p_base / price_a21, trend_indicator, int_1, int_2))
array.set(a_array, 21, f_calc_score(p_base / price_a22, trend_indicator, int_1, int_2))
array.set(a_array, 22, f_calc_score(p_base / price_a23, trend_indicator, int_1, int_2))
array.set(a_array, 23, f_calc_score(p_base / price_a24, trend_indicator, int_1, int_2))
array.set(a_array, 24, f_calc_score(p_base / price_a25, trend_indicator, int_1, int_2))
array.set(a_array, 25, f_calc_score(p_base / price_a26, trend_indicator, int_1, int_2))
array.set(a_array, 26, f_calc_score(p_base / price_a27, trend_indicator, int_1, int_2))
array.set(a_array, 27, f_calc_score(p_base / price_a28, trend_indicator, int_1, int_2))
array.set(a_array, 28, f_calc_score(p_base / price_a29, trend_indicator, int_1, int_2))
array.set(a_array, 29, f_calc_score(p_base / price_a30, trend_indicator, int_1, int_2))
array.set(a_array, 30, f_calc_score(p_base / price_a31, trend_indicator, int_1, int_2))
array.set(a_array, 31, f_calc_score(p_base / price_a32, trend_indicator, int_1, int_2))
array.set(a_array, 32, f_calc_score(p_base / price_a33, trend_indicator, int_1, int_2))
array.set(a_array, 33, f_calc_score(p_base / price_a34, trend_indicator, int_1, int_2))
array.set(a_array, 34, f_calc_score(p_base / price_a35, trend_indicator, int_1, int_2))
array.set(a_array, 35, f_calc_score(p_base / price_a36, trend_indicator, int_1, int_2))
array.set(a_array, 36, f_calc_score(p_base / price_a37, trend_indicator, int_1, int_2))
array.set(a_array, 37, f_calc_score(p_base / price_a38, trend_indicator, int_1, int_2))
array.set(a_array, 38, f_calc_score(p_base / price_a39, trend_indicator, int_1, int_2))
array.set(a_array, 39, f_calc_score(p_base / price_a40, trend_indicator, int_1, int_2))
a_array
Conditional Array Setting (f_arrayset):
This function checks if the number of used assets is greater than or equal to a specified number before populating the arrays.
// only set values into arrays for USED assets
f_arrayset(asset_number, a_array, p_base) =>
if (used_assets >= asset_number)
f_array_set(a_array, p_base)
else
na
Main Logic
The main logic initializes arrays to store scores for each asset. Each array corresponds to one asset's performance score.
Setting Trend Values: The code calls f_arrayset for each asset, populating the respective arrays with calculated scores based on the asset prices.
Combining Arrays: A combined_array is created to hold all the scores from individual asset arrays. This array facilitates further analysis, allowing for an overview of the performance scores of all assets at once.
// create a combined array (work-around since pinescript doesn't support having array of arrays)
var combined_array = array.new_int(40 * 40, 0)
if barstate.islast
for i = 0 to 39
array.set(combined_array, i, array.get(a1_array, i))
array.set(combined_array, i + (40 * 1), array.get(a2_array, i))
array.set(combined_array, i + (40 * 2), array.get(a3_array, i))
array.set(combined_array, i + (40 * 3), array.get(a4_array, i))
array.set(combined_array, i + (40 * 4), array.get(a5_array, i))
array.set(combined_array, i + (40 * 5), array.get(a6_array, i))
array.set(combined_array, i + (40 * 6), array.get(a7_array, i))
array.set(combined_array, i + (40 * 7), array.get(a8_array, i))
array.set(combined_array, i + (40 * 8), array.get(a9_array, i))
array.set(combined_array, i + (40 * 9), array.get(a10_array, i))
array.set(combined_array, i + (40 * 10), array.get(a11_array, i))
array.set(combined_array, i + (40 * 11), array.get(a12_array, i))
array.set(combined_array, i + (40 * 12), array.get(a13_array, i))
array.set(combined_array, i + (40 * 13), array.get(a14_array, i))
array.set(combined_array, i + (40 * 14), array.get(a15_array, i))
array.set(combined_array, i + (40 * 15), array.get(a16_array, i))
array.set(combined_array, i + (40 * 16), array.get(a17_array, i))
array.set(combined_array, i + (40 * 17), array.get(a18_array, i))
array.set(combined_array, i + (40 * 18), array.get(a19_array, i))
array.set(combined_array, i + (40 * 19), array.get(a20_array, i))
array.set(combined_array, i + (40 * 20), array.get(a21_array, i))
array.set(combined_array, i + (40 * 21), array.get(a22_array, i))
array.set(combined_array, i + (40 * 22), array.get(a23_array, i))
array.set(combined_array, i + (40 * 23), array.get(a24_array, i))
array.set(combined_array, i + (40 * 24), array.get(a25_array, i))
array.set(combined_array, i + (40 * 25), array.get(a26_array, i))
array.set(combined_array, i + (40 * 26), array.get(a27_array, i))
array.set(combined_array, i + (40 * 27), array.get(a28_array, i))
array.set(combined_array, i + (40 * 28), array.get(a29_array, i))
array.set(combined_array, i + (40 * 29), array.get(a30_array, i))
array.set(combined_array, i + (40 * 30), array.get(a31_array, i))
array.set(combined_array, i + (40 * 31), array.get(a32_array, i))
array.set(combined_array, i + (40 * 32), array.get(a33_array, i))
array.set(combined_array, i + (40 * 33), array.get(a34_array, i))
array.set(combined_array, i + (40 * 34), array.get(a35_array, i))
array.set(combined_array, i + (40 * 35), array.get(a36_array, i))
array.set(combined_array, i + (40 * 36), array.get(a37_array, i))
array.set(combined_array, i + (40 * 37), array.get(a38_array, i))
array.set(combined_array, i + (40 * 38), array.get(a39_array, i))
array.set(combined_array, i + (40 * 39), array.get(a40_array, i))
Calculating Sums: A separate array_sums is created to store the total score for each asset by summing the values of their respective score arrays. This allows for easy comparison of overall performance.
Ranking Assets: The final part of the code ranks the assets based on their total scores stored in array_sums. It assigns a rank to each asset, where the asset with the highest score receives the highest rank.
// create array for asset RANK based on array.sum
var ranks = array.new_int(used_assets, 0)
// for loop that calculates the rank of each asset
if barstate.islast
for i = 0 to (used_assets - 1)
int rank = 1
for x = 0 to (used_assets - 1)
if i != x
if array.get(array_sums, i) < array.get(array_sums, x)
rank := rank + 1
array.set(ranks, i, rank)
Dynamic Table Creation
Initialization: The table is initialized with a base structure that includes headers for asset names, scores, and ranks. The headers are set to remain constant, ensuring clarity for users as they interpret the displayed data.
Data Population: As scores are calculated for each asset, the corresponding values are dynamically inserted into the table. This is achieved through a loop that iterates over the scores and ranks stored in the combined_array and array_sums, respectively.
Automatic Extending Mechanism
Variable Asset Count: The code checks the number of assets defined by the user. Instead of hardcoding the number of rows in the table, it uses a variable to determine the extent of the data that needs to be displayed. This allows the table to expand or contract based on the number of assets being analyzed.
Dynamic Row Generation: Within the loop that populates the table, the code appends new rows for each asset based on the current asset count. The structure of each row includes the asset name, its score, and its rank, ensuring that the table remains consistent regardless of how many assets are involved.
// Automatically extending table based on the number of used assets
var table table = table.new(position.bottom_center, 50, 50, color.new(color.black, 100), color.white, 3, color.white, 1)
if barstate.islast
if not hide_head
table.cell(table, 0, 0, "Universal Ratio Trend Matrix", text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.merge_cells(table, 0, 0, used_assets + 3, 0)
if not hide_inps
table.cell(table, 0, 1,
text = "Inputs: You are using " + str.tostring(trend_indicator) + ", which takes: " + str.tostring(f_get_input(trend_indicator)),
text_color = color.white, text_size = fontSize), table.merge_cells(table, 0, 1, used_assets + 3, 1)
table.cell(table, 0, 2, "Assets", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, 2, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.cell(table, 0, x + 3, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = f_asset_col(array.get(ranks, x)), text_size = fontSize)
for r = 0 to (used_assets - 1)
for c = 0 to (used_assets - 1)
table.cell(table, c + 1, r + 3, text = str.tostring(array.get(combined_array, c + (r * 40))),
text_color = hl_type == "Text" ? f_get_col(array.get(combined_array, c + (r * 40))) : color.white, text_size = fontSize,
bgcolor = hl_type == "Background" ? f_get_col(array.get(combined_array, c + (r * 40))) : na)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, x + 3, "", bgcolor = #010c3b)
table.cell(table, used_assets + 1, 2, "", bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 1, x + 3, "==>", text_color = color.white)
table.cell(table, used_assets + 2, 2, "SUM", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
table.cell(table, used_assets + 3, 2, "RANK", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 2, x + 3,
text = str.tostring(array.get(array_sums, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_sum(array.get(array_sums, x), array.get(ranks, x)))
table.cell(table, used_assets + 3, x + 3,
text = str.tostring(array.get(ranks, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_rank(array.get(ranks, x)))
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain?
This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions.
How does a Markov Chain work, and how should I read this spheres-and-arrows diagram?
Think of three weather modes: Sunny, Rainy, Cloudy.
Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow).
The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy).
Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step.
Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny.
Now relabel the spheres to markets: Bull, Bear, Neutral.
Spheres: market regimes (uptrend, downtrend, range).
Self‑loop: tendency for the current regime to continue on the next bar.
Arrows: the most common next regime if a switch happens.
How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear).
What states and transitions are shown?
The three market states visualized are:
Bullish (Bull): Upward or strong-market regime.
Bearish (Bear): Downward or weak-market regime.
Neutral: Sideways or range-bound regime.
Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull).
How does the regime detection system work?
You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.).
Values are statistically normalized (Z-scored) over a configurable lookback period.
The normalized outputs are classified into Bull, Bear, or Neutral zones.
If using three indicators, their regime signals are averaged and smoothed for robustness.
How are transition probabilities calculated?
On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions.
The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear).
Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time.
What is unique about the visualization?
3D animated spheres represent each regime and change visually when active.
Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows.
Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed.
All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format.
Can I use custom indicators for regime classification?
Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement.
What does the “Lookback Period” control?
Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt.
How is this different from a Hidden Markov Model (HMM)?
It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust.
How is this Markov Chain different from a Hidden Markov Model (HMM)?
Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable.
Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training.
A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data.
Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune.
HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data.
How does the indicator avoid “repainting” or look-ahead bias?
All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation.
Are there practical tuning tips?
Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability.
Use custom indicators if your asset has unique regime drivers.
Watch for rapid changes in transition probabilities as early warning of a possible regime shift.
Who is this indicator for?
Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models.
Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting:
The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear).
Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions.
Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies.
It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes.
The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting.
Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis.
What's the mathematical foundation behind this?
The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM).
How to use this indicator programmatically?
The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading.
Step-by-Step Integration with Pine Script (input.source)
Add the Markov Chain indicator to your chart.
This must be done first, since your custom script will "pull" the bias signal from the indicator's plot.
In your strategy, create an input using input.source()
Example:
//@version=5
strategy("MC Bias Strategy Example")
mcBias = input.source(close, "MC Bias Source")
After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot).
Use the bias in your trading logic
Example (long only on Bull, flat otherwise):
if mcBias == 1
strategy.entry("Long", strategy.long)
else
strategy.close("Long")
For more advanced workflows, combine mcBias with additional filters or trailing stops.
How does this work behind-the-scenes?
TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source).
The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.).
This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code.
Integrating 45+ Indicators with Your Markov Chain — How & Why
The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots.
// Enhanced Custom Indicators Export - 45 Technical Indicators
// Comprehensive technical analysis suite for advanced market regime detection
//@version=6
indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500)
// |----- Input Parameters -----| //
momentum_group = "Momentum Indicators"
trend_group = "Trend Indicators"
volume_group = "Volume Indicators"
volatility_group = "Volatility Indicators"
oscillator_group = "Oscillator Indicators"
display_group = "Display Settings"
// Common lengths
length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group)
length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group)
length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group)
// Display options
show_table = input.bool(true, "Show Values Table", group=display_group)
table_size = input.string("Small", "Table Size", options= , group=display_group)
// |----- MOMENTUM INDICATORS (15 indicators) -----| //
// 1. RSI (Relative Strength Index)
rsi_14 = ta.rsi(close, length_14)
rsi_centered = rsi_14 - 50
// 2. Stochastic Oscillator
stoch_k = ta.stoch(close, high, low, length_14)
stoch_d = ta.sma(stoch_k, 3)
stoch_centered = stoch_k - 50
// 3. Williams %R
williams_r = ta.stoch(close, high, low, length_14) - 100
// 4. MACD (Moving Average Convergence Divergence)
= ta.macd(close, 12, 26, 9)
// 5. Momentum (Rate of Change)
momentum = ta.mom(close, length_14)
momentum_pct = (momentum / close ) * 100
// 6. Rate of Change (ROC)
roc = ta.roc(close, length_14)
// 7. Commodity Channel Index (CCI)
cci = ta.cci(close, length_20)
// 8. Money Flow Index (MFI)
mfi = ta.mfi(close, length_14)
mfi_centered = mfi - 50
// 9. Awesome Oscillator (AO)
ao = ta.sma(hl2, 5) - ta.sma(hl2, 34)
// 10. Accelerator Oscillator (AC)
ac = ao - ta.sma(ao, 5)
// 11. Chande Momentum Oscillator (CMO)
cmo = ta.cmo(close, length_14)
// 12. Detrended Price Oscillator (DPO)
dpo = close - ta.sma(close, length_20)
// 13. Price Oscillator (PPO)
ppo = ta.sma(close, 12) - ta.sma(close, 26)
ppo_pct = (ppo / ta.sma(close, 26)) * 100
// 14. TRIX
trix_ema1 = ta.ema(close, length_14)
trix_ema2 = ta.ema(trix_ema1, length_14)
trix_ema3 = ta.ema(trix_ema2, length_14)
trix = ta.roc(trix_ema3, 1) * 10000
// 15. Klinger Oscillator
klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55)
// 16. Fisher Transform
fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25
fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2))
// 17. Stochastic RSI
stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14)
stoch_rsi_centered = stoch_rsi - 50
// 18. Relative Vigor Index (RVI)
rvi_num = ta.swma(close - open)
rvi_den = ta.swma(high - low)
rvi = rvi_den != 0 ? rvi_num / rvi_den : 0
// 19. Balance of Power (BOP)
bop = (close - open) / (high - low)
// |----- TREND INDICATORS (10 indicators) -----| //
// 20. Simple Moving Average Momentum
sma_20 = ta.sma(close, length_20)
sma_momentum = ((close - sma_20) / sma_20) * 100
// 21. Exponential Moving Average Momentum
ema_20 = ta.ema(close, length_20)
ema_momentum = ((close - ema_20) / ema_20) * 100
// 22. Parabolic SAR
sar = ta.sar(0.02, 0.02, 0.2)
sar_trend = close > sar ? 1 : -1
// 23. Linear Regression Slope
lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1)
// 24. Moving Average Convergence (MAC)
mac = ta.sma(close, 10) - ta.sma(close, 30)
// 25. Trend Intensity Index (TII)
tii_sum = 0.0
for i = 1 to length_20
tii_sum += close > close ? 1 : 0
tii = (tii_sum / length_20) * 100
// 26. Ichimoku Cloud Components
ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2
ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2
ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1
// 27. MESA Adaptive Moving Average (MAMA)
mama_alpha = 2.0 / (length_20 + 1)
mama = ta.ema(close, length_20)
mama_momentum = ((close - mama) / mama) * 100
// 28. Zero Lag Exponential Moving Average (ZLEMA)
zlema_lag = math.round((length_20 - 1) / 2)
zlema_data = close + (close - close )
zlema = ta.ema(zlema_data, length_20)
zlema_momentum = ((close - zlema) / zlema) * 100
// |----- VOLUME INDICATORS (6 indicators) -----| //
// 29. On-Balance Volume (OBV)
obv = ta.obv
// 30. Volume Rate of Change (VROC)
vroc = ta.roc(volume, length_14)
// 31. Price Volume Trend (PVT)
pvt = ta.pvt
// 32. Negative Volume Index (NVI)
nvi = 0.0
nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi
// 33. Positive Volume Index (PVI)
pvi = 0.0
pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi
// 34. Volume Oscillator
vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10)
// 35. Ease of Movement (EOM)
eom_distance = high - low
eom_box_height = volume / 1000000
eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0
eom_sma = ta.sma(eom, length_14)
// 36. Force Index
force_index = volume * (close - close )
force_index_sma = ta.sma(force_index, length_14)
// |----- VOLATILITY INDICATORS (10 indicators) -----| //
// 37. Average True Range (ATR)
atr = ta.atr(length_14)
atr_pct = (atr / close) * 100
// 38. Bollinger Bands Position
bb_basis = ta.sma(close, length_20)
bb_dev = 2.0 * ta.stdev(close, length_20)
bb_upper = bb_basis + bb_dev
bb_lower = bb_basis - bb_dev
bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0
bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0
// 39. Keltner Channels Position
kc_basis = ta.ema(close, length_20)
kc_range = ta.ema(ta.tr, length_20)
kc_upper = kc_basis + (2.0 * kc_range)
kc_lower = kc_basis - (2.0 * kc_range)
kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0
// 40. Donchian Channels Position
dc_upper = ta.highest(high, length_20)
dc_lower = ta.lowest(low, length_20)
dc_basis = (dc_upper + dc_lower) / 2
dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0
// 41. Standard Deviation
std_dev = ta.stdev(close, length_20)
std_dev_pct = (std_dev / close) * 100
// 42. Relative Volatility Index (RVI)
rvi_up = ta.stdev(close > close ? close : 0, length_14)
rvi_down = ta.stdev(close < close ? close : 0, length_14)
rvi_total = rvi_up + rvi_down
rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50
// 43. Historical Volatility
hv_returns = math.log(close / close )
hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100
// 44. Garman-Klass Volatility
gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open)
gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100
// 45. Parkinson Volatility
park_vol = math.log(high/low) * math.log(high/low)
parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100
// 46. Rogers-Satchell Volatility
rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open)
rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100
// |----- OSCILLATOR INDICATORS (5 indicators) -----| //
// 47. Elder Ray Index
elder_bull = high - ta.ema(close, 13)
elder_bear = low - ta.ema(close, 13)
elder_power = elder_bull + elder_bear
// 48. Schaff Trend Cycle (STC)
stc_macd = ta.ema(close, 23) - ta.ema(close, 50)
stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10)
stc_d = ta.ema(stc_k, 3)
stc = ta.stoch(stc_d, stc_d, stc_d, 10)
// 49. Coppock Curve
coppock_roc1 = ta.roc(close, 14)
coppock_roc2 = ta.roc(close, 11)
coppock = ta.wma(coppock_roc1 + coppock_roc2, 10)
// 50. Know Sure Thing (KST)
kst_roc1 = ta.roc(close, 10)
kst_roc2 = ta.roc(close, 15)
kst_roc3 = ta.roc(close, 20)
kst_roc4 = ta.roc(close, 30)
kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15)
// 51. Percentage Price Oscillator (PPO)
ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100
ppo_signal = ta.ema(ppo_line, 9)
ppo_histogram = ppo_line - ppo_signal
// |----- PLOT MAIN INDICATORS -----| //
// Plot key momentum indicators
plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1)
plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1)
plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1)
plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1)
plot(cci, title="05_CCI", color=color.green, linewidth=1)
// Plot trend indicators
plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1)
plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1)
plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1)
plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1)
plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1)
// Plot volatility indicators
plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1)
plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1)
plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1)
plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1)
plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1)
// Plot volume indicators
plot(vroc, title="16_VROC", color=color.blue, linewidth=1)
plot(eom_sma, title="17_EOM", color=color.red, linewidth=1)
plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1)
plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1)
plot(obv, title="20_OBV", color=color.purple, linewidth=1)
// Plot additional oscillators
plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1)
plot(cmo, title="22_CMO", color=color.maroon, linewidth=1)
plot(dpo, title="23_DPO", color=color.teal, linewidth=1)
plot(trix, title="24_TRIX", color=color.lime, linewidth=1)
plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1)
// Plot more momentum indicators
plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1)
plot(ac, title="27_AC", color=color.aqua, linewidth=1)
plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1)
plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1)
plot(klinger, title="30_Klinger", color=color.gray, linewidth=1)
// Plot trend continuation
plot(tii, title="31_TII", color=color.blue, linewidth=1)
plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1)
plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1)
plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1)
plot(bop, title="35_BOP", color=color.purple, linewidth=1)
// Plot volume continuation
plot(nvi, title="36_NVI", color=color.navy, linewidth=1)
plot(pvi, title="37_PVI", color=color.maroon, linewidth=1)
plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1)
plot(roc, title="39_ROC", color=color.lime, linewidth=1)
plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1)
// Plot volatility continuation
plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1)
plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1)
plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1)
plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1)
plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1)
// Plot final oscillators
plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1)
plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1)
plot(stc, title="48_STC", color=color.green, linewidth=1)
plot(coppock, title="49_Coppock", color=color.orange, linewidth=1)
plot(kst, title="50_KST", color=color.purple, linewidth=1)
// Plot final indicators
plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1)
plot(pvt, title="52_PVT", color=color.maroon, linewidth=1)
// |----- Reference Lines -----| //
hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1)
hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
// |----- Enhanced Information Table -----| //
if show_table and barstate.islast
table_position = position.top_right
table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal
var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray)
// Headers
table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
// Key Momentum Indicators
table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size)
table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size)
// Key Trend Indicators
table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90))
table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size)
// Key Volatility Indicators
table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90))
table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size)
// Key Volume Indicators
table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90))
table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size)
// Key Oscillators
table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size)
// Summary Statistics
table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70))
table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size)
regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray
regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL"
table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size)
This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator.
How to use this script with the Markov Chain for research and backtesting:
Add the Enhanced Indicator Export to your chart.
Every calculated indicator is available as an individual data stream.
Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option.
In the Markov Chain indicator’s settings, turn ON the custom indicator mode.
For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name.
This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering.
Backtest regime logic directly.
Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns.
The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration.
Export the regime bias for programmatic use.
As described above, use input.source() in your Pine Script strategy or system and link the bias output.
You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes.
With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you.
Optimize your signal generation by backtesting across a universe of meaningful indicator blends.
Enhance risk management with objective, real-time regime boundaries.
Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes.
Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic.
Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem.
References & Further Reading
Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182.
Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384.
Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg.
Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764.
Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC.
On Quantitative Finance and Markov Models:
Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press.
Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press.
TradingView Pine Script Documentation: www.tradingview.com
TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com
GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org
What makes this indicator original and unique?
- On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm.
- Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script.
- Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time.
- Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window.
- Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior.
Integrate your own signals (using input.source())
- In settings, choose the Source . This is powered by input.source() .
- Feed it price, an indicator you compute inside the script, or a custom composite series.
- The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time.
Credits:
Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community.
Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization.
Disclaimer
This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
Multi-TF Volatility Channel DashboardThis tool tracks where price sits inside a volatility channel on two timeframes at once and turns it into a simple trend state.
What it does
Builds a volatility channel around price using a midline and a volatility based band.
Converts the position of price inside that band into an oscillator that moves roughly between -100 and +100.
Calculates this oscillator on:
The current chart timeframe (LTF)
A selected higher timeframe (HTF)
From that it classifies each timeframe as:
Bull: oscillator above zero
Bear: oscillator below zero
Neutral: oscillator near zero
You can then see:
LTF oscillator line
HTF oscillator line
A small table showing LTF state, HTF state, and whether they are aligned
When both LTF and HTF are bullish or both are bearish, the background can highlight that period, and optional alerts fire.
How to use it
Trade in the direction of the higher timeframe when both lines agree.
Avoid taking counter trend trades when LTF and HTF are in strong but opposite states.
Use the LTF line for timing and the HTF line for directional bias.
First Historical Candle Date daily intervalIndicator that displays a first historical candle on daily interval as a table at a top-right corner
Emac ML Adaptive CrossoverThe HDAlgos EMAC ML Adaptive Crossover is an adaptive trend reading and crossover system that uses a lightweight machine learning style scoring engine to detect regime shifts in the market. It blends multiple normalised technical features and automatically adjusts EMA lengths based on the detected market regime.
How it works
Feature Engine
The script computes several normalised indicators including RSI, ATR percentage, and Rate of Change. Each feature is converted into a z score so that the values behave consistently across different markets and timeframes. These feature values are then averaged to form a composite regime score.
Regime Detection
The composite score is compared to a dynamic upper and lower threshold. If the score rises above the upper boundary the regime becomes bullish. If the score falls below the lower boundary it becomes bearish. If it stays between the two boundaries the market is classified as neutral.
Adaptive EMAs
The fast and slow EMA lengths are automatically adjusted depending on the detected regime.
• In bullish regimes the fast and slow EMAs shorten.
• In bearish regimes they lengthen.
• In neutral regimes they revert to their base lengths.
This creates an EMA crossover system that responds to market volatility and directional strength rather than using fixed lookback values.
Crossovers
When the adaptive fast EMA crosses above the adaptive slow EMA, a bullish signal appears. When it crosses below, a bearish signal appears.
Visual Aids
• The fast EMA changes color to reflect the current regime.
• Candles can be optionally painted in regime colors.
• A label on the last bar shows the detected regime, score, and active EMA lengths.
• A compact table can be shown in the corner summarizing regime state and metrics.
Alerts
Alerts trigger when the regime changes, when a bullish adaptive crossover occurs, and when a bearish adaptive crossover occurs.
What it is designed for
This indicator is built for traders who want a crossover system that adapts to real market conditions instead of reacting to fixed length EMAs. It provides:
Smoother identification of trend phases
Dynamic sensitivity during strong conditions
Dampened reactions during noise and low conviction periods
Clear and simple signals that remain easy to interpret
Trend Exhaustion liteThe Trend Exhaustion Lite tool tracks the age and strength of the current directional move to show how mature or extended a trend has become. It converts complex statistical analysis into a simple visual panel and colour-coded background.
How It Works
Once a directional bias is established, the indicator measures: Direction (Bullish / Bearish phase), Trend Age (Number of bars since the current trend began), and Strength % (Relative intensity of the move on a 0–100 scale). Background colour shifts subtly as strength increases or fades, while the table displays live metrics.
Use Cases
Younger trends (low age): Usually early in development — potential continuation
Aging trends (high age, declining strength): Often nearing exhaustion; tighten risk or prepare for rotation
Combine with the Forecast Paint Engine or Trend Regime Lite for confirmation on when conviction is building or fading
Interpretation Example
Example Output:
Panel reads Direction: Bull, Age: 47 bars, Strength: 68%
Meaning:
The uptrend remains statistically healthy but entering a later stage — manage exposure accordingly
VIX Calm vs Choppy (Bar Version, VIX High Threshold)This indicator tracks market stability by measuring how long the VIX stays below or above a chosen intraday threshold. Instead of looking at VIX closes, it uses VIX high, so even a brief intraday spike will flip the regime into “choppy.”
The tool builds a running clock of consecutive bars spent in each regime:
Calm regime: VIX high stays below the threshold
Choppy regime: VIX high hits or exceeds the threshold
Calm streaks plot as positive bars (light blue background).
Choppy streaks plot as negative bars (dark pink background).
This gives a clean picture of how long the market has been stable vs volatile — useful for trend traders, breakout traders, and anyone who watches risk-on/risk-off conditions. A table shows the current regime and streak length for quick reference.
ATR + High/Lows//@version=6
indicator('ATR + Values', overlay = true)
// ========================
// === User Inputs ===
// ========================
showATR = input.bool(true, 'Show ATR/Move Table')
showHLR = input.bool(true, 'Show HL/R Table')
atrLength = input.int(14, 'ATR Period')
textColor = input.color(color.rgb(247, 242, 242), 'Default Text Color')
backgroundCol = input.color(color.rgb(0, 0, 0), 'Background Color')
rowOffset = input.int(0, 'Vertical Offset (rows)', minval = 0, maxval = 10)
colOffset = input.int(0, 'Horizontal Offset (columns)', minval = 0, maxval = 100)
var string tz = 'America/New_York'
// ========================
// === ATR / Move Logic ===
// ========================
dailyATRseries = request.security(syminfo.tickerid, 'D', ta.atr(atrLength), lookahead = barmerge.lookahead_off)
dailyATRprev = dailyATRseries
newDayID = year(time, tz) * 10000 + month(time, tz) * 100 + dayofmonth(time, tz)
var int lastDayID = na
var float dayHigh = na
var float dayLow = na
var float fixedATR = na
isNewSession = na(lastDayID) or newDayID != lastDayID
after4am = hour(time, tz) >= 4
firstBarAfter4 = isNewSession and after4am and not(hour(time , tz) >= 4 and newDayID == year(time , tz) * 10000 + month(time , tz) * 100 + dayofmonth(time , tz))
if firstBarAfter4
dayHigh := high
dayLow := low
lastDayID := newDayID
fixedATR := dailyATRprev
else
dayHigh := math.max(dayHigh, high)
dayLow := math.min(dayLow, low)
intradayRange = dayHigh - dayLow
moveBg = intradayRange > fixedATR ? color.new(color.red, 77) : color.new(color.teal, 74)
// ========================
// === ATR + Move Table ===
// ========================
var table atrTable = table.new(position.top_right, 1, 2, border_width = 1, border_color = color.rgb(255, 255, 255))
if barstate.islast and showATR
table.clear(atrTable, 0, 0)
// Row 0: ATR Value
table.cell(atrTable, 0, 0, str.tostring(fixedATR, format.mintick),
bgcolor=color.rgb(0, 0, 0),
text_color=color.rgb(247, 242, 242),
text_halign=text.align_center)
// Row 1: Move Value (colored)
table.cell(atrTable, 0, 1, str.tostring(intradayRange, format.mintick),
bgcolor=moveBg,
text_color=color.rgb(247, 242, 242),
text_halign=text.align_center)
else
table.clear(atrTable, 0, 0)
// ========================
// === HL / Range Logic ===
// ========================
prevHigh = high
prevLow = low
currHigh = high
currLow = low
rangeHL = currHigh - currLow
rangeHL1 = prevHigh - prevLow
col1Text = str.tostring(rangeHL1, '0.00')
col2Text = str.tostring(prevHigh, '0.00') + '\n' + str.tostring(prevLow, '0.00')
col3Text = str.tostring(currHigh, '0.00') + '\n' + str.tostring(currLow, '0.00')
col4Text = str.tostring(rangeHL, '0.00')
prevColor = close > open ? color.rgb(64, 224, 208) : color.rgb(253, 143, 100)
currColor = close > open ? color.rgb(64, 224, 208) : color.rgb(253, 143, 100)
// ========================
// === HL / Range Table ===
// ========================
var table hlTable = table.new(position.top_center, 4 + colOffset, rowOffset + 1)
if barstate.islast and showHLR
for row = 0 to rowOffset by 1
for col = 0 to 3 + colOffset by 1
table.cell(hlTable, col, row, '', bgcolor = na)
row = rowOffset
colStart = colOffset
table.cell(hlTable, colStart + 0, row, col1Text, text_color = textColor, bgcolor = backgroundCol, text_halign = text.align_center, text_size = size.large)
table.cell(hlTable, colStart + 1, row, col2Text, text_color = prevColor, bgcolor = backgroundCol, text_halign = text.align_center, text_size = size.normal)
table.cell(hlTable, colStart + 2, row, col3Text, text_color = currColor, bgcolor = backgroundCol, text_halign = text.align_center, text_size = size.normal)
table.cell(hlTable, colStart + 3, row, col4Text, text_color = textColor, bgcolor = backgroundCol, text_halign = text.align_center, text_size = size.large)
else
table.clear(hlTable, 0, 0)
Bar Index & TimeLibrary to convert a bar index to a timestamp and vice versa.
Utilizes runtime memory to store the 𝚝𝚒𝚖𝚎 and 𝚝𝚒𝚖𝚎_𝚌𝚕𝚘𝚜𝚎 values of every bar on the chart (and optional future bars), with the ability of storing additional custom values for every chart bar.
█ PREFACE
This library aims to tackle some problems that pine coders (from beginners to advanced) often come across, such as:
I'm trying to draw an object with a 𝚋𝚊𝚛_𝚒𝚗𝚍𝚎𝚡 that is more than 10,000 bars into the past, but this causes my script to fail. How can I convert the 𝚋𝚊𝚛_𝚒𝚗𝚍𝚎𝚡 to a UNIX time so that I can draw visuals using xloc.bar_time ?
I have a diagonal line drawing and I want to get the "y" value at a specific time, but line.get_price() only accepts a bar index value. How can I convert the timestamp into a bar index value so that I can still use this function?
I want to get a previous 𝚘𝚙𝚎𝚗 value that occurred at a specific timestamp. How can I convert the timestamp into a historical offset so that I can use 𝚘𝚙𝚎𝚗 ?
I want to reference a very old value for a variable. How can I access a previous value that is older than the maximum historical buffer size of 𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎 ?
This library can solve the above problems (and many more) with the addition of a few lines of code, rather than requiring the coder to refactor their script to accommodate the limitations.
█ OVERVIEW
The core functionality provided is conversion between xloc.bar_index and xloc.bar_time values.
The main component of the library is the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object, created via the 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() function which basically stores the 𝚝𝚒𝚖𝚎 and 𝚝𝚒𝚖𝚎_𝚌𝚕𝚘𝚜𝚎 of every bar on the chart, and there are 3 more overloads to this function that allow collecting and storing additional data. Once a 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object is created, use any of the exported methods:
Methods to convert a UNIX timestamp into a bar index or bar offset:
𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙𝚃𝚘𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚐𝚎𝚝𝙽𝚞𝚖𝚋𝚎𝚛𝙾𝚏𝙱𝚊𝚛𝚜𝙱𝚊𝚌𝚔()
Methods to retrieve the stored data for a bar index:
𝚝𝚒𝚖𝚎𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚝𝚒𝚖𝚎𝙲𝚕𝚘𝚜𝚎𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚟𝚊𝚕𝚞𝚎𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚐𝚎𝚝𝙰𝚕𝚕𝚅𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡()
Methods to retrieve the stored data at a number of bars back (i.e., historical offset):
𝚝𝚒𝚖𝚎(), 𝚝𝚒𝚖𝚎𝙲𝚕𝚘𝚜𝚎(), 𝚟𝚊𝚕𝚞𝚎()
Methods to retrieve all the data points from the earliest bar (or latest bar) stored in memory, which can be useful for debugging purposes:
𝚐𝚎𝚝𝙴𝚊𝚛𝚕𝚒𝚎𝚜𝚝𝚂𝚝𝚘𝚛𝚎𝚍𝙳𝚊𝚝𝚊(), 𝚐𝚎𝚝𝙻𝚊𝚝𝚎𝚜𝚝𝚂𝚝𝚘𝚛𝚎𝚍𝙳𝚊𝚝𝚊()
Note: the library's strong suit is referencing data from very old bars in the past, which is especially useful for scripts that perform its necessary calculations only on the last bar.
█ USAGE
Step 1
Import the library. Replace with the latest available version number for this library.
//@version=6
indicator("Usage")
import n00btraders/ChartData/
Step 2
Create a 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object to collect data on every bar. Do not declare as `var` or `varip`.
chartData = ChartData.collectChartData() // call on every bar to accumulate the necessary data
Step 3
Call any method(s) on the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object. Do not modify its fields directly.
if barstate.islast
int firstBarTime = chartData.timeAtBarIndex(0)
int lastBarTime = chartData.time(0)
log.info("First `time`: " + str.format_time(firstBarTime) + ", Last `time`: " + str.format_time(lastBarTime))
█ EXAMPLES
• Collect Future Times
The overloaded 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() functions that accept a 𝚋𝚊𝚛𝚜𝙵𝚘𝚛𝚠𝚊𝚛𝚍 argument can additionally store time values for up to 500 bars into the future.
//@version=6
indicator("Example `collectChartData(barsForward)`")
import n00btraders/ChartData/1
chartData = ChartData.collectChartData(barsForward = 500)
var rectangle = box.new(na, na, na, na, xloc = xloc.bar_time, force_overlay = true)
if barstate.islast
int futureTime = chartData.timeAtBarIndex(bar_index + 100)
int lastBarTime = time
box.set_lefttop(rectangle, lastBarTime, open)
box.set_rightbottom(rectangle, futureTime, close)
box.set_text(rectangle, "Extending box 100 bars to the right. Time: " + str.format_time(futureTime))
• Collect Custom Data
The overloaded 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() functions that accept a 𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜 argument can additionally store custom user-specified values for every bar on the chart.
//@version=6
indicator("Example `collectChartData(variables)`")
import n00btraders/ChartData/1
var map variables = map.new()
variables.put("open", open)
variables.put("close", close)
variables.put("open-close midpoint", (open + close) / 2)
variables.put("boolean", open > close ? 1 : 0)
chartData = ChartData.collectChartData(variables = variables)
var fgColor = chart.fg_color
var table1 = table.new(position.top_right, 2, 9, color(na), fgColor, 1, fgColor, 1, true)
var table2 = table.new(position.bottom_right, 2, 9, color(na), fgColor, 1, fgColor, 1, true)
if barstate.isfirst
table.cell(table1, 0, 0, "ChartData.value()", text_color = fgColor)
table.cell(table2, 0, 0, "open ", text_color = fgColor)
table.merge_cells(table1, 0, 0, 1, 0)
table.merge_cells(table2, 0, 0, 1, 0)
for i = 1 to 8
table.cell(table1, 0, i, text_color = fgColor, text_halign = text.align_left, text_font_family = font.family_monospace)
table.cell(table2, 0, i, text_color = fgColor, text_halign = text.align_left, text_font_family = font.family_monospace)
table.cell(table1, 1, i, text_color = fgColor)
table.cell(table2, 1, i, text_color = fgColor)
if barstate.islast
for i = 1 to 8
float open1 = chartData.value("open", 5000 * i)
float open2 = i < 3 ? open : -1
table.cell_set_text(table1, 0, i, "chartData.value(\"open\", " + str.tostring(5000 * i) + "): ")
table.cell_set_text(table2, 0, i, "open : ")
table.cell_set_text(table1, 1, i, str.tostring(open1))
table.cell_set_text(table2, 1, i, open2 >= 0 ? str.tostring(open2) : "Error")
• xloc.bar_index → xloc.bar_time
The 𝚝𝚒𝚖𝚎 value (or 𝚝𝚒𝚖𝚎_𝚌𝚕𝚘𝚜𝚎 value) can be retrieved for any bar index that is stored in memory by the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object.
//@version=6
indicator("Example `timeAtBarIndex()`")
import n00btraders/ChartData/1
chartData = ChartData.collectChartData()
if barstate.islast
int start = bar_index - 15000
int end = bar_index - 100
// line.new(start, close, end, close) // !ERROR - `start` value is too far from current bar index
start := chartData.timeAtBarIndex(start)
end := chartData.timeAtBarIndex(end)
line.new(start, close, end, close, xloc.bar_time, width = 10)
• xloc.bar_time → xloc.bar_index
Use 𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙𝚃𝚘𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡() to find the bar that a timestamp belongs to.
If the timestamp falls in between the close of one bar and the open of the next bar,
the 𝚜𝚗𝚊𝚙 parameter can be used to determine which bar to choose:
𝚂𝚗𝚊𝚙.𝙻𝙴𝙵𝚃 - prefer to choose the leftmost bar (typically used for closing times)
𝚂𝚗𝚊𝚙.𝚁𝙸𝙶𝙷𝚃 - prefer to choose the rightmost bar (typically used for opening times)
𝚂𝚗𝚊𝚙.𝙳𝙴𝙵𝙰𝚄𝙻𝚃 (or 𝚗𝚊) - copies the same behavior as xloc.bar_time uses for drawing objects
//@version=6
indicator("Example `timestampToBarIndex()`")
import n00btraders/ChartData/1
startTimeInput = input.time(timestamp("01 Aug 2025 08:30 -0500"), "Session Start Time")
endTimeInput = input.time(timestamp("01 Aug 2025 15:15 -0500"), "Session End Time")
chartData = ChartData.collectChartData()
if barstate.islastconfirmedhistory
int startBarIndex = chartData.timestampToBarIndex(startTimeInput, ChartData.Snap.RIGHT)
int endBarIndex = chartData.timestampToBarIndex(endTimeInput, ChartData.Snap.LEFT)
line1 = line.new(startBarIndex, 0, startBarIndex, 1, extend = extend.both, color = color.new(color.green, 60), force_overlay = true)
line2 = line.new(endBarIndex, 0, endBarIndex, 1, extend = extend.both, color = color.new(color.green, 60), force_overlay = true)
linefill.new(line1, line2, color.new(color.green, 90))
// using Snap.DEFAULT to show that it is equivalent to drawing lines using `xloc.bar_time` (i.e., it aligns to the same bars)
startBarIndex := chartData.timestampToBarIndex(startTimeInput)
endBarIndex := chartData.timestampToBarIndex(endTimeInput)
line.new(startBarIndex, 0, startBarIndex, 1, extend = extend.both, color = color.yellow, width = 3)
line.new(endBarIndex, 0, endBarIndex, 1, extend = extend.both, color = color.yellow, width = 3)
line.new(startTimeInput, 0, startTimeInput, 1, xloc.bar_time, extend.both, color.new(color.blue, 85), width = 11)
line.new(endTimeInput, 0, endTimeInput, 1, xloc.bar_time, extend.both, color.new(color.blue, 85), width = 11)
• Get Price of Line at Timestamp
The pine script built-in function line.get_price() requires working with bar index values. To get the price of a line in terms of a timestamp, convert the timestamp into a bar index or offset.
//@version=6
indicator("Example `line.get_price()` at timestamp")
import n00btraders/ChartData/1
lineStartInput = input.time(timestamp("01 Aug 2025 08:30 -0500"), "Line Start")
chartData = ChartData.collectChartData()
var diagonal = line.new(na, na, na, na, force_overlay = true)
if time <= lineStartInput
line.set_xy1(diagonal, bar_index, open)
if barstate.islastconfirmedhistory
line.set_xy2(diagonal, bar_index, close)
if barstate.islast
int timeOneWeekAgo = timenow - (7 * timeframe.in_seconds("1D") * 1000)
// Note: could also use `timetampToBarIndex(timeOneWeekAgo, Snap.DEFAULT)` and pass the value directly to `line.get_price()`
int barsOneWeekAgo = chartData.getNumberOfBarsBack(timeOneWeekAgo)
float price = line.get_price(diagonal, bar_index - barsOneWeekAgo)
string formatString = "Time 1 week ago: {0,number,#}\n - Equivalent to {1} bars ago\n\n𝚕𝚒𝚗𝚎.𝚐𝚎𝚝_𝚙𝚛𝚒𝚌𝚎(): {2,number,#.##}"
string labelText = str.format(formatString, timeOneWeekAgo, barsOneWeekAgo, price)
label.new(timeOneWeekAgo, price, labelText, xloc.bar_time, style = label.style_label_lower_right, size = 16, textalign = text.align_left, force_overlay = true)
█ RUNTIME ERROR MESSAGES
This library's functions will generate a custom runtime error message in the following cases:
𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() is not called consecutively, or is called more than once on a single bar
Invalid 𝚋𝚊𝚛𝚜𝙵𝚘𝚛𝚠𝚊𝚛𝚍 argument in the 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() function
Invalid 𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜 argument in the 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() function
Invalid 𝚕𝚎𝚗𝚐𝚝𝚑 argument in any of the functions that accept a number of bars back
Note: there is no runtime error generated for an invalid 𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙 or 𝚋𝚊𝚛𝙸𝚗𝚍𝚎𝚡 argument in any of the functions. Instead, the functions will assign 𝚗𝚊 to the returned values.
Any other runtime errors are due to incorrect usage of the library.
█ NOTES
• Function Descriptions
The library source code uses Markdown for the exported functions. Hover over a function/method call in the Pine Editor to display formatted, detailed information about the function/method.
//@version=6
indicator("Demo Function Tooltip")
import n00btraders/ChartData/1
chartData = ChartData.collectChartData()
int barIndex = chartData.timestampToBarIndex(timenow)
log.info(str.tostring(barIndex))
• Historical vs. Realtime Behavior
Under the hood, the data collector for this library is declared as `var`. Because of this, the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object will always reflect the latest available data on realtime updates. Any data that is recorded for historical bars will remain unchanged throughout the execution of a script.
//@version=6
indicator("Demo Realtime Behavior")
import n00btraders/ChartData/1
var map variables = map.new()
variables.put("open", open)
variables.put("close", close)
chartData = ChartData.collectChartData(variables)
if barstate.isrealtime
varip float initialOpen = open
varip float initialClose = close
varip int updateCount = 0
updateCount += 1
float latestOpen = open
float latestClose = close
float recordedOpen = chartData.valueAtBarIndex("open", bar_index)
float recordedClose = chartData.valueAtBarIndex("close", bar_index)
string formatString = "# of updates: {0}\n\n𝚘𝚙𝚎𝚗 at update #1: {1,number,#.##}\n𝚌𝚕𝚘𝚜𝚎 at update #1: {2,number,#.##}\n\n"
+ "𝚘𝚙𝚎𝚗 at update #{0}: {3,number,#.##}\n𝚌𝚕𝚘𝚜𝚎 at update #{0}: {4,number,#.##}\n\n"
+ "𝚘𝚙𝚎𝚗 stored in memory: {5,number,#.##}\n𝚌𝚕𝚘𝚜𝚎 stored in memory: {6,number,#.##}"
string labelText = str.format(formatString, updateCount, initialOpen, initialClose, latestOpen, latestClose, recordedOpen, recordedClose)
label.new(bar_index, close, labelText, style = label.style_label_left, force_overlay = true)
• Collecting Chart Data for Other Contexts
If your use case requires collecting chart data from another context, avoid directly retrieving the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object as this may exceed memory limits .
//@version=6
indicator("Demo Return Calculated Results")
import n00btraders/ChartData/1
timeInput = input.time(timestamp("01 Sep 2025 08:30 -0500"), "Time")
var int oneMinuteBarsAgo = na
// !ERROR - Memory Limits Exceeded
// chartDataArray = request.security_lower_tf(syminfo.tickerid, "1", ChartData.collectChartData())
// oneMinuteBarsAgo := chartDataArray.last().getNumberOfBarsBack(timeInput)
// function that returns calculated results (a single integer value instead of an entire `ChartData` object)
getNumberOfBarsBack() =>
chartData = ChartData.collectChartData()
chartData.getNumberOfBarsBack(timeInput)
calculatedResultsArray = request.security_lower_tf(syminfo.tickerid, "1", getNumberOfBarsBack())
oneMinuteBarsAgo := calculatedResultsArray.size() > 0 ? calculatedResultsArray.last() : na
if barstate.islast
string labelText = str.format("The selected timestamp occurs 1-minute bars ago", oneMinuteBarsAgo)
label.new(bar_index, hl2, labelText, style = label.style_label_left, size = 16, force_overlay = true)
• Memory Usage
The library's convenience and ease of use comes at the cost of increased usage of computational resources. For simple scripts, using this library will likely not cause any issues with exceeding memory limits. But for large and complex scripts, you can reduce memory issues by specifying a lower 𝚌𝚊𝚕𝚌_𝚋𝚊𝚛𝚜_𝚌𝚘𝚞𝚗𝚝 amount in the indicator() or strategy() declaration statement.
//@version=6
// !ERROR - Memory Limits Exceeded using the default number of bars available (~20,000 bars for Premium plans)
//indicator("Demo `calc_bars_count` parameter")
// Reduce number of bars using `calc_bars_count` parameter
indicator("Demo `calc_bars_count` parameter", calc_bars_count = 15000)
import n00btraders/ChartData/1
map variables = map.new()
variables.put("open", open)
variables.put("close", close)
variables.put("weekofyear", weekofyear)
variables.put("dayofmonth", dayofmonth)
variables.put("hour", hour)
variables.put("minute", minute)
variables.put("second", second)
// simulate large memory usage
chartData0 = ChartData.collectChartData(variables)
chartData1 = ChartData.collectChartData(variables)
chartData2 = ChartData.collectChartData(variables)
chartData3 = ChartData.collectChartData(variables)
chartData4 = ChartData.collectChartData(variables)
chartData5 = ChartData.collectChartData(variables)
chartData6 = ChartData.collectChartData(variables)
chartData7 = ChartData.collectChartData(variables)
chartData8 = ChartData.collectChartData(variables)
chartData9 = ChartData.collectChartData(variables)
log.info(str.tostring(chartData0.time(0)))
log.info(str.tostring(chartData1.time(0)))
log.info(str.tostring(chartData2.time(0)))
log.info(str.tostring(chartData3.time(0)))
log.info(str.tostring(chartData4.time(0)))
log.info(str.tostring(chartData5.time(0)))
log.info(str.tostring(chartData6.time(0)))
log.info(str.tostring(chartData7.time(0)))
log.info(str.tostring(chartData8.time(0)))
log.info(str.tostring(chartData9.time(0)))
if barstate.islast
result = table.new(position.middle_right, 1, 1, force_overlay = true)
table.cell(result, 0, 0, "Script Execution Successful ✅", text_size = 40)
█ EXPORTED ENUMS
Snap
Behavior for determining the bar that a timestamp belongs to.
Fields:
LEFT : Snap to the leftmost bar.
RIGHT : Snap to the rightmost bar.
DEFAULT : Default `xloc.bar_time` behavior.
Note: this enum is used for the 𝚜𝚗𝚊𝚙 parameter of 𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙𝚃𝚘𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡().
█ EXPORTED TYPES
Note: users of the library do not need to worry about directly accessing the fields of these types; all computations are done through method calls on an object of the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 type.
Variable
Represents a user-specified variable that can be tracked on every chart bar.
Fields:
name (series string) : Unique identifier for the variable.
values (array) : The array of stored values (one value per chart bar).
ChartData
Represents data for all bars on a chart.
Fields:
bars (series int) : Current number of bars on the chart.
timeValues (array) : The `time` values of all chart (and future) bars.
timeCloseValues (array) : The `time_close` values of all chart (and future) bars.
variables (array) : Additional custom values to track on all chart bars.
█ EXPORTED FUNCTIONS
collectChartData()
Collects and tracks the `time` and `time_close` value of every bar on the chart.
Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`.
collectChartData(barsForward)
Collects and tracks the `time` and `time_close` value of every bar on the chart as well as a specified number of future bars.
Parameters:
barsForward (simple int) : Number of future bars to collect data for.
Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`.
collectChartData(variables)
Collects and tracks the `time` and `time_close` value of every bar on the chart. Additionally, tracks a custom set of variables for every chart bar.
Parameters:
variables (simple map) : Custom values to collect on every chart bar.
Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`.
collectChartData(barsForward, variables)
Collects and tracks the `time` and `time_close` value of every bar on the chart as well as a specified number of future bars. Additionally, tracks a custom set of variables for every chart bar.
Parameters:
barsForward (simple int) : Number of future bars to collect data for.
variables (simple map) : Custom values to collect on every chart bar.
Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`.
█ EXPORTED METHODS
method timestampToBarIndex(chartData, timestamp, snap)
Converts a UNIX timestamp to a bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
timestamp (series int) : A UNIX time.
snap (series Snap) : A `Snap` enum value.
Returns: A bar index, or `na` if unable to find the appropriate bar index.
method getNumberOfBarsBack(chartData, timestamp)
Converts a UNIX timestamp to a history-referencing length (i.e., number of bars back).
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
timestamp (series int) : A UNIX time.
Returns: A bar offset, or `na` if unable to find a valid number of bars back.
method timeAtBarIndex(chartData, barIndex)
Retrieves the `time` value for the specified bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
barIndex (int) : The bar index.
Returns: The `time` value, or `na` if there is no `time` stored for the bar index.
method time(chartData, length)
Retrieves the `time` value of the bar that is `length` bars back relative to the latest bar.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
length (series int) : Number of bars back.
Returns: The `time` value `length` bars ago, or `na` if there is no `time` stored for that bar.
method timeCloseAtBarIndex(chartData, barIndex)
Retrieves the `time_close` value for the specified bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
barIndex (series int) : The bar index.
Returns: The `time_close` value, or `na` if there is no `time_close` stored for the bar index.
method timeClose(chartData, length)
Retrieves the `time_close` value of the bar that is `length` bars back from the latest bar.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
length (series int) : Number of bars back.
Returns: The `time_close` value `length` bars ago, or `na` if there is none stored.
method valueAtBarIndex(chartData, name, barIndex)
Retrieves the value of a custom variable for the specified bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
name (series string) : The variable name.
barIndex (series int) : The bar index.
Returns: The value of the variable, or `na` if that variable is not stored for the bar index.
method value(chartData, name, length)
Retrieves a variable value of the bar that is `length` bars back relative to the latest bar.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
name (series string) : The variable name.
length (series int) : Number of bars back.
Returns: The value `length` bars ago, or `na` if that variable is not stored for the bar index.
method getAllVariablesAtBarIndex(chartData, barIndex)
Retrieves all custom variables for the specified bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
barIndex (series int) : The bar index.
Returns: Map of all custom variables that are stored for the specified bar index.
method getEarliestStoredData(chartData)
Gets all values from the earliest bar data that is currently stored in memory.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
Returns: A tuple:
method getLatestStoredData(chartData, futureData)
Gets all values from the latest bar data that is currently stored in memory.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
futureData (series bool) : Whether to include the future data that is stored in memory.
Returns: A tuple:
Intrabar Efficiency Ratio█ OVERVIEW
This indicator displays a directional variant of Perry Kaufman's Efficiency Ratio, designed to gauge the "efficiency" of intrabar price movement by comparing the sum of movements of the lower timeframe bars composing a chart bar with the respective bar's movement on an average basis.
█ CONCEPTS
Efficiency Ratio (ER)
Efficiency Ratio was first introduced by Perry Kaufman in his 1995 book, titled "Smarter Trading". It is the ratio of absolute price change to the sum of absolute changes on each bar over a period. This tells us how strong the period's trend is relative to the underlying noise. Simply put, it's a measure of price movement efficiency. This ratio is the modulator utilized in Kaufman's Adaptive Moving Average (KAMA), which is essentially an Exponential Moving Average (EMA) that adapts its responsiveness to movement efficiency.
ER's output is bounded between 0 and 1. A value of 0 indicates that the starting price equals the ending price for the period, which suggests that price movement was maximally inefficient. A value of 1 indicates that price had travelled no more than the distance between the starting price and the ending price for the period, which suggests that price movement was maximally efficient. A value between 0 and 1 indicates that price had travelled a distance greater than the distance between the starting price and the ending price for the period. In other words, some degree of noise was present which resulted in reduced efficiency over the period.
As an example, let's say that the price of an asset had moved from $15 to $14 by the end of a period, but the sum of absolute changes for each bar of data was $4. ER would be calculated like so:
ER = abs(14 - 15)/4 = 0.25
This suggests that the trend was only 25% efficient over the period, as the total distanced travelled by price was four times what was required to achieve the change over the period.
Intrabars
Intrabars are chart bars at a lower timeframe than the chart's. Each 1H chart bar of a 24x7 market will, for example, usually contain 60 intrabars at the LTF of 1min, provided there was market activity during each minute of the hour. Mining information from intrabars can be useful in that it offers traders visibility on the activity inside a chart bar.
Lower timeframes (LTFs)
A lower timeframe is a timeframe that is smaller than the chart's timeframe. This script determines which LTF to use by examining the chart's timeframe. The LTF determines how many intrabars are examined for each chart bar; the lower the timeframe, the more intrabars are analyzed, but fewer chart bars can display indicator information because there is a limit to the total number of intrabars that can be analyzed.
Intrabar precision
The precision of calculations increases with the number of intrabars analyzed for each chart bar. As there is a 100K limit to the number of intrabars that can be analyzed by a script, a trade-off occurs between the number of intrabars analyzed per chart bar and the chart bars for which calculations are possible.
Intrabar Efficiency Ratio (IER)
Intrabar Efficiency Ratio applies the concept of ER on an intrabar level. Rather than comparing the overall change to the sum of bar changes for the current chart's timeframe over a period, IER compares single bar changes for the current chart's timeframe to the sum of absolute intrabar changes, then applies smoothing to the result. This gives an indication of how efficient changes are on the current chart's timeframe for each bar of data relative to LTF bar changes on an average basis. Unlike the standard ER calculation, we've opted to preserve directional information by not taking the absolute value of overall change, thus allowing it to be utilized as a momentum oscillator. However, by taking the absolute value of this oscillator, it could potentially serve as a replacement for ER in the design of adaptive moving averages.
Since this indicator preserves directional information, IER can be regarded as similar to the Chande Momentum Oscillator (CMO) , which was presented in 1994 by Tushar Chande in "The New Technical Trader". Both CMO and ER essentially measure the same relationship between trend and noise. CMO simply differs in scale, and considers the direction of overall changes.
█ FEATURES
Display
Three different display types are included within the script:
• Line : Displays the middle length MA of the IER as a line .
Color for this display can be customized via the "Line" portion of the "Visuals" section in the script settings.
• Candles : Displays the non-smooth IER and two moving averages of different lengths as candles .
The `open` and `close` of the candle are the longest and shortest length MAs of the IER respectively.
The `high` and `low` of the candle are the max and min of the IER, longest length MA of the IER, and shortest length MA of the IER respectively.
Colors for this display can be customized via the "Candles" portion of the "Visuals" section in the script settings.
• Circles : Displays three MAs of the IER as circles .
The color of each plot depends on the percent rank of the respective MA over the previous 100 bars.
Different colors are triggered when ranks are below 10%, between 10% and 50%, between 50% and 90%, and above 90%.
Colors for this display can be customized via the "Circles" portion of the "Visuals" section in the script settings.
With either display type, an optional information box can be displayed. This box shows the LTF that the script is using, the average number of lower timeframe bars per chart bar, and the number of chart bars that contain LTF data.
Specifying intrabar precision
Ten options are included in the script to control the number of intrabars used per chart bar for calculations. The greater the number of intrabars per chart bar, the fewer chart bars can be analyzed.
The first five options allow users to specify the approximate amount of chart bars to be covered:
• Least Precise (Most chart bars) : Covers all chart bars by dividing the current timeframe by four.
This ensures the highest level of intrabar precision while achieving complete coverage for the dataset.
• Less Precise (Some chart bars) & More Precise (Less chart bars) : These options calculate a stepped LTF in relation to the current chart's timeframe.
• Very precise (2min intrabars) : Uses the second highest quantity of intrabars possible with the 2min LTF.
• Most precise (1min intrabars) : Uses the maximum quantity of intrabars possible with the 1min LTF.
The stepped lower timeframe for "Less Precise" and "More Precise" options is calculated from the current chart's timeframe as follows:
Chart Timeframe Lower Timeframe
Less Precise More Precise
< 1hr 1min 1min
< 1D 15min 1min
< 1W 2hr 30min
> 1W 1D 60min
The last five options allow users to specify an approximate fixed number of intrabars to analyze per chart bar. The available choices are 12, 24, 50, 100, and 250. The script will calculate the LTF which most closely approximates the specified number of intrabars per chart bar. Keep in mind that due to factors such as the length of a ticker's sessions and rounding of the LTF, it is not always possible to produce the exact number specified. However, the script will do its best to get as close to the value as possible.
Specifying MA type
Seven MA types are included in the script for different averaging effects:
• Simple
• Exponential
• Wilder (RMA)
• Weighted
• Volume-Weighted
• Arnaud Legoux with `offset` and `sigma` set to 0.85 and 6 respectively.
• Hull
Weighting
This script includes the option to weight IER values based on the percent rank of absolute price changes on the current chart's timeframe over a specified period, which can be enabled by checking the "Weigh using relative close changes" option in the script settings. This places reduced emphasis on IER values from smaller changes, which may help to reduce noise in the output.
█ FOR Pine Script™ CODERS
• This script imports the recently published lower_ltf library for calculating intrabar statistics and the optimal lower timeframe in relation to the current chart's timeframe.
• This script uses the recently released request.security_lower_tf() Pine Script™ function discussed in this blog post .
It works differently from the usual request.security() in that it can only be used on LTFs, and it returns an array containing one value per intrabar.
This makes it much easier for programmers to access intrabar information.
• This script implements a new recommended best practice for tables which works faster and reduces memory consumption.
Using this new method, tables are declared only once with var , as usual. Then, on the first bar only, we use table.cell() to populate the table.
Finally, table.set_*() functions are used to update attributes of table cells on the last bar of the dataset.
This greatly reduces the resources required to render tables.
Look first. Then leap.
