Volume Sampled Supertrend [BackQuant]Volume Sampled Supertrend
A Supertrend that runs on a volume sampled price series instead of fixed time. New synthetic bars are only created after sufficient traded activity, which filters out low participation noise and makes the trend much easier to read and model.
Original Script Link
This indicator is built on top of my volume sampling engine. See the base implementation here:
Why Volume Sampling
Traditional charts print a bar every N minutes regardless of how active the tape is. During quiet periods you accumulate many small, low information bars that add noise and whipsaws to downstream signals.
Volume sampling replaces the clock with participation. A new synthetic bar is created only when a pre-set amount of volume accumulates (or, in Dollar Bars mode, when pricevolume reaches a dollar threshold). The result is a non-uniform time series that stretches in busy regimes and compresses in quiet regimes. This naturally:
filters dead time by skipping low volume chop;
standardizes the information content per bar, improving comparability across regimes;
stabilizes volatility estimates used inside banded indicators;
gives trend and breakout logic cleaner state transitions with fewer micro flips.
What this tool does
It builds a synthetic OHLCV stream from volume based buckets and then applies a Supertrend to that synthetic price. You are effectively running Supertrend on a participation clock rather than a wall clock.
Core Features
Sampling Engine - Choose Volume buckets or Dollar Bars . Thresholds can be dynamic from a rolling mean or median, or fixed by the user.
Synthetic Candles - Plots the volume sampled OHLC candles so you can visually compare against regular time candles.
Supertrend on Synthetic Price - ATR bands and direction are computed on the sampled series, not on time bars.
Adaptive Coloring - Candle colors can reflect side, intensity by volume, or a neutral scheme.
Research Panels - Table shows total samples, current bucket fill, threshold, bars-per-sample, and synthetic return stats.
Alerts - Long and Short triggers on Supertrend direction flips for the synthetic series.
How it works
Sampling
Pick Sampling Method = Volume or Dollar Bars.
Set the dynamic threshold via Rolling Lookback and Filter (Mean or Median), or enable Use Fixed and type a constant.
The script accumulates volume (or pricevolume) each time bar. When the bucket reaches the threshold, it finalizes one or more synthetic candles and resets accumulation.
Each synthetic candle stores its own OHLCV and is appended to the synthetic series used for all downstream logic.
Supertrend on the sampled stream
Choose Supertrend Source (Open, High, Low, Close, HLC3, HL2, OHLC4, HLCC4) derived from the synthetic candle.
Compute ATR over the synthetic series with ATR Period , then form upperBand = src + factorATR and lowerBand = src - factorATR .
Apply classic trailing band and direction rules to produce Supertrend and trend state.
Because bars only come when there is sufficient participation, band touches and flips tend to align with meaningful pushes, not idle prints.
Reading the display
Synthetic Volume Bars - The non-uniform candles that represent equal information buckets. Expect more candles during active sessions and fewer during lulls.
Volume Sampled Supertrend - The main line. Green when Trend is 1, red when Trend is -1.
Markers - Small dots appear when a new synthetic sample is created, useful for aligning activity cycles.
Time Bars Overlay (optional) - Plot regular time candles to compare how the synthetic stream compresses quiet chop.
Settings you will use most
Data Settings
Sampling Method - Volume or Dollar Bars.
Rolling Lookback and Filter - Controls the dynamic threshold. Median is robust to outliers, Mean is smoother.
Use Fixed and Fixed Threshold - Force a constant bucket size for consistent sampling across regimes.
Max Stored Samples - Ring buffer limit for performance.
Indicator Settings
SMA over last N samples - A moving average computed on the synthetic close series. Can be hidden for a cleaner layout.
Supertrend Source - Price field from the synthetic candle.
ATR Period and Factor - Standard Supertrend controls applied on the synthetic series.
Visuals and UI
Show Synthetic Bars - Turn synthetic candles on or off.
Candle Color Mode - Green/Red, Volume Intensity, Neutral, or Adaptive.
Mark new samples - Puts a dot when a bucket closes.
Show Time Bars - Overlay regular candles for comparison.
Paint candles according to Trend - Colors chart candles using current synthetic Supertrend direction.
Line Width , Colors , and Stats Table toggles.
Some workflow notes:
Trend Following
Set Sampling Method = Volume, Filter = Median, and a reasonable Rolling Lookback so busy regimes produce more samples.
Trade in the direction of the Volume Sampled Supertrend. Because flips require real participation, you tend to avoid micro whipsaws seen on time bars.
Use the synthetic SMA as a bias rail and trailing reference for partials or re-entries.
Breakout and Continuation
Watch for rapid clustering of new sample markers and a clean flip of the synthetic Supertrend.
The compression of quiet time and expansion in busy bursts often makes breakouts more legible than on uniform time charts.
Mean Reversion
In instruments that oscillate, faded moves against the synthetic Supertrend are easier to time when the bucket cadence slows and Supertrend flattens.
Combine with the synthetic SMA and return statistics in the table for sizing and expectation setting.
Stats table (top right)
Method and Total Samples - Sampling regime and current synthetic history length.
Current Vol or Dollar and Threshold - Live bucket fill versus the trigger.
Bars in Bucket and Avg Bars per Sample - How much time data each synthetic bar tends to compress.
Avg Return and Return StdDev - Simple research metrics over synthetic close-to-close changes.
Why this reduces noise
Time based bars treat a 5 minute print with 1 percent of average participation the same as one with 300 percent. Volume sampling equalizes bar information content. By advancing the bar only when sufficient activity occurs, you skip low quality intervals that add variance but little signal. For banded systems like Supertrend, this often means fewer false flips and cleaner runs.
Notes and tips
Use Dollar Bars on assets where nominal price varies widely over time or across symbols.
Median filter can resist single burst outliers when setting dynamic thresholds.
If you need a stable research baseline, set Use Fixed and keep the threshold constant across tests.
Enable Show Time Bars occasionally to sanity check what the synthetic stream is compressing or stretching.
Link again for reference
Original Volume Based Sampling engine:
Bottom line
When you let participation set the clock, your Supertrend reacts to meaningful flow instead of idle prints. The result is a cleaner state machine, fewer micro whipsaws, and a trend read that respects when the market is actually trading.
Indicator
Cumulative Volume Delta Profile and Heatmap [BackQuant]Cumulative Volume Delta Profile and Heatmap
A multi-view CVD workstation that measures buying vs selling pressure, renders a price-aligned CVD profile with Point of Control, paints an optional heatmap of delta intensity, and detects classical CVD divergences using pivot logic. Built for reading who is in control, where participation clustered, and when effort is failing to produce result.
What is CVD
Cumulative Volume Delta accumulates the difference between aggressive buys and aggressive sells over time. When CVD rises, buyers are lifting the offer more than sellers are hitting the bid. When CVD falls, the opposite is true. Plotting CVD alongside price helps you judge whether price moves are supported by real participation or are running on fumes.
Core Features
Visual Analysis Components
CVD Columns - Plot of cumulative delta, colored by side, for quick read of participation bias.
CVD Profile - Price-aligned histogram of CVD accumulation using user-set bins. Shows where net initiative clustered.
Split Buy and Sell CVD - Optional two-sided profile that separates positive and negative CVD into distinct wings.
POC - Point of Control - The price level with the highest absolute CVD accumulation, labeled and line-marked.
Heatmap - Semi-transparent blocks behind price that encode CVD intensity across the last N bars.
Divergence Engine - Pivot-based detection of Bearish and Bullish CVD divergences with optional lines and labels.
Stats Panel - Top level metrics: Total CVD, Buy and Sell totals with percentages, Delta Ratio, and current POC price.
How it works
Delta source and sampling
You select an Anchor Timeframe that defines the higher time aggregation for reading the trend of CVD.
The script pulls lower timeframe volume delta and aggregates it to the anchor window. You can let it auto-select the lower timeframe or force a custom one.
CVD is then accumulated bar by bar to form a running total. This plot shows the direction and persistence of initiative.
Profile construction
The recent price range is split into Profile Granularity bins.
As price traverses a bin, the current delta contribution is added to that bin.
If Split Buy and Sell CVD is enabled, positive CVD goes to the right wing and negative CVD to the left wing.
Widths are scaled by each side’s maximum so you can compare distribution shape at a glance.
The Point of Control is the bin with the highest absolute CVD. This marks where initiative concentrated the most.
Heatmap
For each bin, the script computes intensity as absolute CVD relative to the maximum bin value.
Color is derived from the side in control in that bin and shaded by intensity.
Heatmap Length sets how far back the panels extend, highlighting recurring participation zones.
Divergence model
You define pivot sensitivity with Pivot Left and Right .
Bearish divergence triggers when price confirms a higher high while CVD fails to make a higher high within a configurable Delta Tolerance .
Bullish divergence triggers when price confirms a lower low while CVD fails to make a lower low.
On trigger, optional link lines and labels are drawn at the pivots for immediate context.
Key Settings
Delta Source
Anchor Timeframe - Higher TF for the CVD narrative.
Custom Lower TF and Lower Timeframe - Force the sampling TF if desired.
Pivot Logic
Pivot Left and Right - Bars to each side for swing confirmation.
Delta Tolerance - Small allowance to avoid near-miss false positives.
CVD Profile
Show CVD Profile - Toggle profile rendering.
Split Buy and Sell CVD - Two-sided profile for clearer side attribution.
Show Heatmap - Project intensity panels behind price.
Show POC and POC Color - Mark the dominant CVD node.
Profile Granularity - Number of bins across the visible price range.
Profile Offset and Profile Width - Position and scale the profile.
Profile Position - Right, Left, or Current bar alignment.
Visuals
Bullish Div Color and Bearish Div Color - Colors for divergence artifacts.
Show Divergence Lines and Labels - Visualize pivots and annotations.
Plot CVD - Column plot of total CVD.
Show Statistics and Position - Toggle and place the summary table.
Reading the display
CVD columns
Rising CVD confirms buyers are in control. Falling CVD confirms sellers.
Flat or choppy CVD during wide price moves hints at passive or exhausted participation.
CVD profile wings
Thick right wing near a price zone implies heavy buy initiative accumulated there.
Thick left wing implies heavy sell initiative.
POC marks the strongest initiative node. Expect reactions on first touch and rotations around this level when the tape is balanced.
Heatmap
Brighter blocks indicate stronger historical net initiative at that price.
Stacked bright bands form CVD high volume nodes. These often behave like magnets or shelves for future trade.
Divergences
Bearish - Price prints a higher high while CVD fails to do so. Effort is not producing result. Potential fade or pause.
Bullish - Price prints a lower low while CVD fails to do so. Capitulation lacks initiative. Potential bounce or reversal.
Stats panel
Total CVD - Net initiative over the window.
Buy and Sell volume with percentages - Side composition.
Delta Ratio - Buy over Sell. Values above 1 favor buyers, below 1 favor sellers.
POC Price - Current control node for plan and risk.
Workflows
Trend following
Choose an Anchor Timeframe that matches your holding period.
Trade in the direction of CVD slope while price holds above a bullish POC or below a bearish POC.
Use pullbacks to CVD nodes on your profile as entry locations.
Trend weakens when price makes new highs but CVD stalls, or new lows while CVD recovers.
Mean reversion
Look for divergences at or near prior CVD nodes, especially the POC.
Fade tests into thick wings when the side that dominated there now fails to push CVD further.
Target rotations back toward the POC or the opposite wing edge.
Liquidity and execution map
Treat strong wings and heatmap bands as probable passive interest zones.
Expect pauses, partial fills, or flips at these shelves.
Stops make sense beyond the far edge of the active wing supporting your idea.
Alerts included
CVD Bearish Divergence and CVD Bullish Divergence.
Price Cross Above POC and Price Cross Below POC.
Extreme Buy Imbalance and Extreme Sell Imbalance from Delta Ratio.
CVD Turn Bullish and CVD Turn Bearish when net CVD crosses zero.
Price Near POC proximity alert.
Best practices
Use a higher Anchor Timeframe to stabilize the CVD story and a sensible Profile Granularity so wings are readable without clutter.
Keep Split mode on when you want to separate initiative attribution. Turn it off when you prefer a single net profile.
Tune Pivot Left and Right by instrument to avoid overfitting. Larger values find swing divergences. Smaller values find micro fades.
If volume is thin or synthetic for the symbol, CVD will be less reliable. The script will warn if volume is zero.
Trading applications
Context - Confirm or question breakouts with CVD slope.
Location - Build entries at CVD nodes and POC.
Timing - Use divergence and POC crosses for triggers.
Risk - Place stops beyond the opposite wing or outside the POC shelf.
Important notes and limits
This is a price and volume based study. It does not access off-book or venue-level order flow.
CVD profiles are built from the data available on your chart and the chosen lower timeframe sampling.
Like all volume tools, readings can distort during roll periods, holidays, or feed anomalies. Validate on your instrument.
Technical notes
Delta is aggregated from a lower timeframe into an Anchor Timeframe narrative.
Profile bins update in real time. Splitting by side scales each wing independently so both are readable in the same panel.
Divergences are confirmed using standard pivot definitions with user-set tolerances.
All profile drawing uses fixed X offsets so panels and POC do not swim when you scroll.
Quick start
Anchor Timeframe = Daily for intraday context.
Split Buy and Sell CVD = On.
Profile Granularity = 100 to 200, Profile Position = Right, Width to taste.
Pivot Left and Right around 8 to 12 to start, then adapt.
Turn on Heatmap for a fast map of interest bands.
Bottom line
CVD tells you who is doing the lifting. The profile shows where they did it. Divergences tell you when effort stops paying. Put them together and you get a clear read on control, location, and timing for both trend and mean reversion.
Indicator
VWAP Deviation Oscillator [BackQuant]VWAP Deviation Oscillator
Introduction
The VWAP Deviation Oscillator turns VWAP context into a clean, tradeable oscillator that works across assets and sessions. It adapts to your workflow with four VWAP regimes plus two rolling modes, and three deviation metrics: Percent, Absolute, and Z-Score. Colored zones, optional standard deviation rails, and flexible plot styles make it fast to read for both trend following and mean reversion.
What it does
This tool measures how far price is from a chosen VWAP and expresses that gap as an oscillator. You can view the deviation as raw price units, percent, or standardized Z-Score. The plot can be a histogram or a line with optional fills and sigma bands, so you can quickly spot polarity shifts, overbought and oversold conditions, and strength of extension.
VWAP modes track a session VWAP that resets (4H, Daily, Weekly) or a rolling VWAP that updates continuously over a fixed number of bars or days.
Deviation modes let you choose the lens: Percent, Absolute, or Z-Score. Each highlights different aspects of stretch and mean pressure.
Visual encoding uses a 10-zone color palette to grade the magnitude of deviation on both sides of zero.
Volatility guards compute mode-specific sigma so thresholds are stable even when volatility compresses.
Why this works
VWAP is a high signal anchor used by institutions to gauge fair participation. Deviations around VWAP cluster in regimes: mild oscillations within a band, decisive pushes that signal imbalance, and standardized extremes that often precede either continuation or snapback. Expressing that distance as a single time series adds clarity: bias is the oscillator’s sign, risk context is its magnitude, and regime is the way it behaves around sigma lines.
How to use it
Trend following
Favor the side of the zero line. Bullish when the oscillator is above zero and making higher swing highs. Bearish when below zero and making lower swing lows. Use +1 sigma and +2 sigma in your mode as strength tiers. Pullbacks that hold above zero in uptrends, or below zero in downtrends, are often continuation entries.
Mean reversion
Fade stretched readings when structure supports it. Look for tests of +2 sigma to +3 sigma that fail to progress and roll back toward zero, or the mirror on the downside. Z-Score mode is best when you want standardized gates across assets. Percent mode is intuitive for intraday scalps where a given percent stretch tends to mean revert.
Session playbook
Use Daily or Weekly VWAP for intraday or swing context. Rolling modes help when the asset lacks clean session boundaries or when you want a continuous anchor that adapts to liquidity shifts.
Key settings
VWAP computation
VWAP Mode = 4 Hours, Daily, Weekly, Rolling (Bars), Rolling (Days). Session modes reset the VWAP when a new session begins. Rolling modes compute VWAP over a fixed trailing window.
Rolling (Lookback: Bars) controls the trailing bar count when using Rolling (Bars).
Rolling (Lookback: Days) converts days to bars at runtime and uses that trailing span.
Use Close instead of HLC3 switches the price reference. HLC3 is smoother. Close makes the anchor track settlement more tightly.
Deviation measurement
Deviation Mode
Percent : 100 * (Price / VWAP - 1). Good for uniform scaling across instruments.
Absolute : Price - VWAP. Good when price units themselves matter.
Z-Score : Standardizes the absolute residual by its own mean and standard deviation over Z/Std Window . Ideal for cross-asset comparability and regime studies.
Z/Std Window sets the mean and standard deviation window for Z-Score mode.
Volatility controls
Percent Mode Volatility Lookback estimates sigma for percent deviations.
Absolute Mode Volatility Lookback estimates sigma for absolute deviations.
Minimum Sigma Guard (pct pts) prevents the percent sigma from collapsing to near zero in extremely quiet markets.
Visualization
Plot Type = Histogram or Line. Histogram emphasizes impulse and polarity changes. Line emphasizes trend waves and divergences.
Positive Color / Negative Color define the palette for line mode. Histogram uses a 10-bucket gradient automatically.
Show Standard Deviations plots symmetric rails at ±1, ±2, ±3 sigma in the current mode’s units.
Fill Line Oscillator and Fill Opacity add a soft bias band around zero for line mode.
Line Width affects both the oscillator and the sigma rails.
Reading the zones
The oscillator’s color and height map deviation to nine graded buckets on each side of zero, with deeper greens above and deeper reds below. In Percent and Absolute modes, those buckets are scaled by their mode-specific sigma. In Z-Score mode the bucket edges are fixed at 0.5, 1.0, 2.0, and 2.8.
0 to +1 sigma weak positive bias, usually rotational.
+1 to +2 sigma constructive impulse. Pullbacks that hold above zero often continue.
+2 to +3 sigma strong expansion. Watch for either trend continuation or exhaustion tells.
Beyond +3 sigma statistical extreme. Requires structure to avoid fading too soon.
Mirror logic applies on the negative side.
Suggested workflows
Trend continuation checklist
Pick a session VWAP that matches your timeframe, for example Daily for intraday or Weekly for position trades.
Wait for the oscillator to hold the correct side of zero and for a sequence of higher swing lows in the oscillator (uptrend) or lower swing highs (downtrend).
Buy pullbacks that stabilize between zero and +1 sigma in an uptrend. Sell rallies that stabilize between zero and -1 sigma in a downtrend.
Use the next sigma band or a prior price swing as your target reference.
Mean reversion checklist
Switch to Z-Score mode for standardized thresholds.
Identify tests of ±2 sigma to ±3 sigma that fail to extend while price meets support or resistance.
Enter on a polarity change through the prior histogram bar or a small hook in line mode.
Fade back to zero or to the opposite inner band, then reassess.
Notes on the three modes
Percent is easy to reason about when you care about proportional stretch. It is well suited to intraday and multi-asset dashboards.
Absolute tracks cash distance from VWAP. This is useful when instruments have tight ticks and you plan risk in price units.
Z-Score standardizes the residual and is best for quant studies, cross-asset comparisons, and threshold research that must be scale invariant.
What the alerts can tell you
Polarity changes at zero can mark the start or end of a leg.
Crosses of ±1 sigma identify overbought or oversold in the current mode’s units.
Zone changes signal an upgrade or downgrade in deviation strength.
Troubleshooting and edge cases
If your instrument has long flat periods, keep Minimum Sigma Guard above zero in Percent mode so the rails do not vanish.
In Rolling modes, very short windows will respond quickly but can whip around. Session modes smooth this by resetting at well known boundaries.
If Z-Score looks erratic, increase Z/Std Window to stabilize the estimate of mean and sigma for the residual.
Final thoughts
VWAP is the anchor. The deviation oscillator is the narrative. By separating bias, magnitude, and regime into a simple stream you can execute faster and review cleaner. Pick the VWAP mode that matches your horizon, choose the deviation lens that matches your risk framework, and let the color graded zones guide your decisions.
Indicator
Volume Based Sampling [BackQuant]Volume Based Sampling
What this does
This indicator converts the usual time-based stream of candles into an event-based stream of “synthetic” bars that are created only when enough trading activity has occurred . You choose the activity definition:
Volume bars : create a new synthetic bar whenever the cumulative number of shares/contracts traded reaches a threshold.
Dollar bars : create a new synthetic bar whenever the cumulative traded dollar value (price × volume) reaches a threshold.
The script then keeps an internal ledger of these synthetic opens, highs, lows, closes, and volumes, and can display them as candles, plot a moving average calculated over the synthetic closes, mark each time a new sample is formed, and optionally overlay the native time-bars for comparison.
Why event-based sampling matters
Markets do not release information on a clock: activity clusters during news, opens/closes, and liquidity shocks. Event-based bars normalize for that heteroskedastic arrival of information: during active periods you get more bars (finer resolution); during quiet periods you get fewer bars (coarser resolution). Research shows this can reduce microstructure pathologies and produce series that are closer to i.i.d. and more suitable for statistical modeling and ML. In particular:
Volume and dollar bars are a common event-time alternative to time bars in quantitative research and are discussed extensively in Advances in Financial Machine Learning (AFML). These bars aim to homogenize information flow by sampling on traded size or value rather than elapsed seconds.
The Volume Clock perspective models market activity in “volume time,” showing that many intraday phenomena (volatility, liquidity shocks) are better explained when time is measured by traded volume instead of seconds.
Related market microstructure work on flow toxicity and liquidity highlights that the risk dealers face is tied to information intensity of order flow, again arguing for activity-based clocks.
How the indicator works (plain English)
Choose your bucket type
Volume : accumulate volume until it meets a threshold.
Dollar Bars : accumulate close × volume until it meets a dollar threshold.
Pick the threshold rule
Dynamic threshold : by default, the script computes a rolling statistic (mean or median) of recent activity to set the next bucket size. This adapts bar size to changing conditions (e.g., busier sessions produce more frequent synthetic bars).
Fixed threshold : optionally override with a constant target (e.g., exactly 100,000 contracts per synthetic bar, or $5,000,000 per dollar bar).
Build the synthetic bar
While a bucket fills, the script tracks:
o_s: first price of the bucket (synthetic open)
h_s: running maximum price (synthetic high)
l_s: running minimum price (synthetic low)
c_s: last price seen (synthetic close)
v_s: cumulative native volume inside the bucket
d_samples: number of native bars consumed to complete the bucket (a proxy for “how fast” the threshold filled)
Emit a new sample
Once the bucket meets/exceeds the threshold, a new synthetic bar is finalized and stored. If overflow occurs (e.g., a single native bar pushes you past the threshold by a lot), the code will emit multiple synthetic samples to account for the extra activity.
Maintain a rolling history efficiently
A ring buffer can overwrite the oldest samples when you hit your Max Stored Samples cap, keeping memory usage stable.
Compute synthetic-space statistics
The script computes an SMA over the last N synthetic closes and basic descriptors like average bars per synthetic sample, mean and standard deviation of synthetic returns, and more. These are all in event time , not clock time.
Inputs and options you will actually use
Data Settings
Sampling Method : Volume or Dollar Bars.
Rolling Lookback : window used to estimate the dynamic threshold from recent activity.
Filter : Mean or Median for the dynamic threshold. Median is more robust to spikes.
Use Fixed? / Fixed Threshold : override dynamic sizing with a constant target.
Max Stored Samples : cap on synthetic history to keep performance snappy.
Use Ring Buffer : turn on to recycle storage when at capacity.
Indicator Settings
SMA over last N samples : moving average in synthetic space . Because its index is sample count, not minutes, it adapts naturally: more updates in busy regimes, fewer in quiet regimes.
Visuals
Show Synthetic Bars : plot the synthetic OHLC candles.
Candle Color Mode :
Green/Red: directional close vs open
Volume Intensity: opacity scales with synthetic size
Neutral: single color
Adaptive: graded by how large the bucket was relative to threshold
Mark new samples : drop a small marker whenever a new synthetic bar prints.
Comparison & Research
Show Time Bars : overlay the native time-based candles to visually compare how the two sampling schemes differ.
How to read it, step by step
Turn on “Synthetic Bars” and optionally overlay “Time Bars.” You will see that during high-activity bursts, synthetic bars print much faster than time bars.
Watch the synthetic SMA . Crosses in synthetic space can be more meaningful because each update represents a roughly comparable amount of traded information.
Use the “Avg Bars per Sample” in the info table as a regime signal. Falling average bars per sample means activity is clustering, often coincident with higher realized volatility.
Try Dollar Bars when price varies a lot but share count does not; they normalize by dollar risk taken in each sample. Volume Bars are ideal when share count is a better proxy for information flow in your instrument.
Quant finance background and citations
Event time vs. clock time : Easley, López de Prado, and O’Hara advocate measuring intraday phenomena on a volume clock to better align sampling with information arrival. This framing helps explain volatility bursts and liquidity droughts and motivates volume-based bars.
Flow toxicity and dealer risk : The same authors show how adverse selection risk changes with the intensity and informativeness of order flow, further supporting activity-based clocks for modeling and risk management.
AFML framework : In Advances in Financial Machine Learning , event-driven bars such as volume, dollar, and imbalance bars are presented as superior sampling units for many ML tasks, yielding more stationary features and fewer microstructure distortions than fixed time bars. ( Alpaca )
Practical use cases
1) Regime-aware moving averages
The synthetic SMA in event time is not fooled by quiet periods: if nothing of consequence trades, it barely updates. This can make trend filters less sensitive to calendar drift and more sensitive to true participation.
2) Breakout logic on “equal-information” samples
The script exposes simple alerts such as breakout above/below the synthetic SMA . Because each bar approximates a constant amount of activity, breakouts are conditioned on comparable informational mass, not arbitrary time buckets.
3) Volatility-adaptive backtests
If you use synthetic bars as your base data stream, most signal rules become self-paced : entry and exit opportunities accelerate in fast markets and slow down in quiet regimes, which often improves the realism of slippage and fill modeling in research pipelines (pair this indicator with strategy code downstream).
4) Regime diagnostics
Avg Bars per Sample trending down: activity is dense; expect larger realized ranges.
Return StdDev (synthetic) rising: noise or trend acceleration in event time; re-tune risk.
Interpreting the info panel
Method : your sampling choice and current threshold.
Total Samples : how many synthetic bars have been formed.
Current Vol/Dollar : how much of the next bucket is already filled.
Bars in Bucket : native bars consumed so far in the current bucket.
Avg Bars/Sample : lower means higher trading intensity.
Avg Return / Return StdDev : return stats computed over synthetic closes .
Research directions you can build from here
Imbalance and run bars
Extend beyond pure volume or dollar thresholds to imbalance bars that trigger on directional order flow imbalance (e.g., buy volume minus sell volume), as discussed in the AFML ecosystem. These often further homogenize distributional properties used in ML. alpaca.markets
Volume-time indicators
Re-compute classical indicators (RSI, MACD, Bollinger) on the synthetic stream. The premise is that signals are updated by traded information , not seconds, which may stabilize indicator behavior in heteroskedastic regimes.
Liquidity and toxicity overlays
Combine synthetic bars with proxies of flow toxicity to anticipate spread widening or volatility clustering. For instance, tag synthetic bars that surpass multiples of the threshold and test whether subsequent realized volatility is elevated.
Dollar-risk parity sampling for portfolios
Use dollar bars to align samples across assets by notional risk, enabling cleaner cross-asset features and comparability in multi-asset models (e.g., correlation studies, regime clustering). AFML discusses the benefits of event-driven sampling for cross-sectional ML feature engineering.
Microstructure feature set
Compute duration in native bars per synthetic sample , range per sample , and volume multiple of threshold as inputs to state classifiers or regime HMMs . These features are inherently activity-aware and often predictive of short-horizon volatility and trend persistence per the event-time literature. ( Alpaca )
Tips for clean usage
Start with dynamic thresholds using Median over a sensible lookback to avoid outlier distortion, then move to Fixed thresholds when you know your instrument’s typical activity scale.
Compare time bars vs synthetic bars side by side to develop intuition for how your market “breathes” in activity time.
Keep Max Stored Samples reasonable for performance; the ring buffer avoids memory creep while preserving a rolling window of research-grade data.
Indicator
Volume Percentile Supertrend [BackQuant]Volume Percentile Supertrend
A volatility and participation aware Supertrend that automatically widens or tightens its bands based on where current volume sits inside its recent distribution. The goal is simple: fewer whipsaws when activity surges, faster reaction when the tape is quiet.
What it does
Calculates a standard Supertrend framework from an ATR on a volume weighted price source.
Measures current volume against its recent percentile and converts that context into a dynamic ATR multiplier.
Widens bands when volume is unusually high to reduce chop. Tightens bands when volume is unusually low to catch turns earlier.
Paints candles, draws the active Supertrend line and optional bands, and prints clear Long and Short signal markers.
Why volume percentile
Fixed ATR multipliers assume all bars are equal. They are not. When participation spikes, price swings expand and a static band gets sliced.
Percentiles place the current bar inside a recent distribution. If volume is in the top slice, the Supertrend allows more room. If volume is in the bottom slice, it expects smaller noise and tightens.
This keeps the same playbook usable across busy sessions and sleepy ones without constant manual retuning.
How it works
Volume distribution - A rolling window computes the Pth percentile of volume. Above that is flagged as high volume. A lower reference percentile marks quiet bars.
Dynamic multiplier - Start from a Base Multiplier. If bar is high volume, scale it up by a function of volume-to-average and a Sensitivity knob. If bar is low volume, scale it down. Smooth the result with an EMA to avoid jitter.
VWMA source - The price input for bands is a short volume weighted moving average of close. Heavy prints matter more.
ATR envelope - Compute ATR on your length. UpperBasic = VWMA + Multiplier x ATR. LowerBasic = VWMA - Multiplier x ATR.
Trailing logic - The final lines trail price so they only move in a direction that preserves Supertrend behavior. This prevents sudden flips from transient pokes.
Direction and signals - Direction flips when price crosses through the relevant trailing line. SupertrendLong and SupertrendShort mark those flips. The plotted Supertrend is the active trailing side.
Inputs and what they change
Volume Lookback - Window for percentile and average. Larger window = stabler percentile, smaller = snappier.
Volume Percentile Level - Threshold that defines high volume. Example 70 means top 30 percent of recent bars are treated as high activity.
Volume Sensitivity - Gain from volume ratio to the dynamic multiplier. Higher = bands expand more when volume spikes.
VWMA Source Length - Smoothing of the volume weighted price source for the bands.
ATR Length - Standard ATR window. Larger = slower, smaller = quicker.
Base Multiplier - Core band width before volume adjustment. Think of this as your neutral volatility setting.
Multiplier Smoothing - EMA on the dynamic multiplier. Reduces back and forth changes when volume oscillates around the threshold.
Show Supertrend on chart - Toggles the active line.
Show Upper Lower Bands - Draws both sides even when inactive. Good for context.
Paint candles according to Trend - Colors bars by trend direction.
Show Long and Short Signals - Prints 𝕃 and 𝕊 markers at flips.
Colors - Choose your long and short palette.
Reading the plot
Supertrend line - Thick line that hugs price from above in downtrends and from below in uptrends. Its distance breathes with volume.
Bands - Optional upper and lower rails. Useful to see the inactive side and judge how wide the envelope is right now.
Signals - 𝕃 prints when the trend flips long. 𝕊 prints when the trend flips short.
Candle colors - Quick bias read at a glance when painting is enabled.
Typical workflows
Trend following - Use 𝕃 flips to initiate longs and ride while bars remain colored long and price respects the lower trailing line. Mirror for shorts with 𝕊 and the upper trailing line. During high volume phases the line will give more room, which helps stay in the move.
Pullback adds - In an established trend, shallow tags toward the active line after a high volume expansion can be add points. The dynamic envelope adjusts to the session so your add distance is not fixed to a stale volatility regime.
Mean reversion filter - In quiet tape the multiplier contracts and flips come earlier. If you prefer fading, watch for quick toggles around the bands when volume percentile remains low. In high volume, avoid fading into the widened line unless you have other strong reasons.
Notes on behavior
High volume bar: the percentile gate opens, volRatio > 1 powers up the multiplier through the Sensitivity lever, bands widen, fewer false flips.
Low volume bar: multiplier contracts, bands tighten, flips can happen earlier which is useful when you want to catch regime changes in quiet conditions.
Smoothing matters: both the price source (VWMA) and the multiplier are smoothed to keep structure readable while still adapting.
Quick checklist
If you see frequent chop and today feels busy: check that volume is above your percentile. Wider bands are expected. Consider letting the trend prove itself against the expanded line before acting.
If everything feels slow and you want earlier entries: percentile likely marks low volume, so bands tighten and 𝕃 or 𝕊 can appear sooner.
If you want more or fewer flips overall: adjust Base Multiplier first. If you want more reaction specifically tied to volume surges: raise Volume Sensitivity. If the envelope breathes too fast: raise Multiplier Smoothing.
What the signals mean
SupertrendLong - Direction changed from non-long to long. 𝕃 marker prints. The active line switches to support below price.
SupertrendShort - Direction changed from non-short to short. 𝕊 marker prints. The active line switches to resistance above price.
Trend color - Bars painted long or short help validate context for entries and management.
Summary
Volume Percentile Supertrend adapts the classic Supertrend to the day you are trading. Volume percentile sets the mood, sensitivity translates it into dynamic band width, and smoothing keeps it clean. The result is a single plot that aims to stay conservative when the tape is loud and act decisively when it is quiet, without you having to constantly retune settings.
Indicator
Options Max Pain Calculator [BackQuant]Options Max Pain Calculator
A visualization tool that models option expiry dynamics by calculating "max pain" levels, displaying synthetic open interest curves, gamma exposure profiles, and pin-risk zones to help identify where market makers have the least payout exposure.
What is Max Pain?
Max Pain is the theoretical expiration price where the total dollar value of outstanding options would be minimized. At this price level, option holders collectively experience maximum losses while option writers (typically market makers) have minimal payout obligations. This creates a natural gravitational pull as expiration approaches.
Core Features
Visual Analysis Components:
Max Pain Line: Horizontal line showing the calculated minimum pain level
Strike Level Grid: Major support and resistance levels at key option strikes
Pin Zone: Highlighted area around max pain where price may gravitate
Pain Heatmap: Color-coded visualization showing pain distribution across prices
Gamma Exposure Profile: Bar chart displaying net gamma at each strike level
Real-time Dashboard: Summary statistics and risk metrics
Synthetic Market Modeling**
Since Pine Script cannot access live options data, the indicator creates realistic synthetic open interest distributions based on configurable market parameters including volume patterns, put/call ratios, and market maker positioning.
How It Works
Strike Generation:
The tool creates a grid of option strikes centered around the current price. You can control the range, density, and whether strikes snap to realistic market increments.
Open Interest Modeling:
Using your inputs for average volume, put/call ratios, and market maker behavior, the indicator generates synthetic open interest that mirrors real market dynamics:
Higher volume at-the-money with decay as strikes move further out
Adjustable put/call bias to reflect current market sentiment
Market maker inventory effects and typical short-gamma positioning
Weekly options boost for near-term expirations
Pain Calculation:
For each potential expiry price, the tool calculates total option payouts:
Call options contribute pain when finishing in-the-money
Put options contribute pain when finishing in-the-money
The strike with minimum total pain becomes the Max Pain level
Gamma Analysis:
Net gamma exposure is calculated at each strike using standard option pricing models, showing where hedging flows may be most intense. Positive gamma creates price support while negative gamma can amplify moves.
Key Settings
Basic Configuration:
Number of Strikes: Controls grid density (recommended: 15-25)
Days to Expiration: Time until option expiry
Strike Range: Price range around current level (recommended: 8-15%)
Strike Increment: Spacing between strikes
Market Parameters:
Average Daily Volume: Baseline for synthetic open interest
Put/Call Volume Ratio: Market sentiment bias (>1.0 = bearish, <1.0 = bullish) It does not work if set to 1.0
Implied Volatility: Current option volatility estimate
Market Maker Factors: Dealer positioning and hedging intensity
Display Options:
Model Complexity: Simple (line only), Standard (+ zones), Advanced (+ heatmap/gamma)
Visual Elements: Toggle individual components on/off
Theme: Dark/Light mode
Update Frequency: Real-time or daily calculation
Reading the Display
Dashboard Table (Top Right):
Current Price vs Max Pain Level
Distance to Pain: Percentage gap (smaller = higher pin risk)
Pin Risk Assessment: HIGH/MEDIUM/LOW based on proximity and time
Days to Expiry and Strike Count
Model complexity level
Visual Elements:
Red Line: Max Pain level where payout is minimized
Colored Zone: Pin risk area around max pain
Dotted Lines: Major strike levels (green = support, orange = resistance)
Color Bar: Pain heatmap (blue = high pain, red = low pain/max pain zones)
Horizontal Bars: Gamma exposure (green = positive, red = negative)
Yellow Dotted Line: Gamma flip level where hedging behavior changes
Trading Applications
Expiration Pinning:
When price is near max pain with limited time remaining, there's increased probability of gravitating toward that level as market makers hedge their positions.
Support and Resistance:
High open interest strikes often act as magnets, with max pain representing the strongest gravitational pull.
Volatility Expectations:
Above gamma flip: Expect dampened volatility (long gamma environment)
Below gamma flip: Expect amplified moves (short gamma environment)
Risk Assessment:
The pin risk indicator helps gauge likelihood of price manipulation near expiry, with HIGH risk suggesting potential range-bound action.
Best Practices
Setup Recommendations
Start with Model Complexity set to "Standard"
Use realistic strike ranges (8-12% for most assets)
Set put/call ratio based on current market sentiment
Adjust implied volatility to match current levels
Interpretation Guidelines:
Small distance to pain + short time = high pin probability
Large gamma bars indicate key hedging levels to monitor
Heatmap intensity shows strength of pain concentration
Multiple nearby strikes can create wider pin zones
Update Strategy:
Use "Daily" updates for cleaner visuals during trading hours
Switch to "Every Bar" for real-time analysis near expiration
Monitor changes in max pain level as new options activity emerges
Important Disclaimers
This is a modeling tool using synthetic data, not live market information. While the calculations are mathematically sound and the modeling realistic, actual market dynamics involve numerous factors not captured in any single indicator.
Max pain represents theoretical minimum payout levels and suggests where natural market forces may create gravitational pull, but it does not guarantee price movement or predict exact expiration levels. Market gaps, news events, and changing volatility can override these dynamics.
Use this tool as additional context for your analysis, not as a standalone trading signal. The synthetic nature of the data makes it most valuable for understanding market structure and potential zones of interest rather than precise price prediction.
Technical Notes
The indicator uses established option pricing principles with simplified implementations optimized for Pine Script performance. Gamma calculations use standard financial models while pain calculations follow the industry-standard definition of minimized option payouts.
All visual elements use fixed positioning to prevent movement when scrolling charts, and the tool includes performance optimizations to handle real-time calculation without timeout errors. Indicator
Pairs Trading Scanner [BackQuant]Pairs Trading Scanner
What it is
This scanner analyzes the relationship between your chart symbol and a chosen pair symbol in real time. It builds a normalized “spread” between them, tracks how tightly they move together (correlation), converts the spread into a Z-Score (how far from typical it is), and then prints clear LONG / SHORT / EXIT prompts plus an at-a-glance dashboard with the numbers that matter.
Why pairs at all?
Markets co-move. When two assets are statistically related, their relationship (the spread) tends to oscillate around a mean.
Pairs trading doesn’t require calling overall market direction you trade the relative mispricing between two instruments.
This scanner gives you a robust, visual way to find those dislocations, size their significance, and structure the trade.
How it works (plain English)
Step 1 Pick a partner: Select the Pair Symbol to compare against your chart symbol. The tool fetches synchronized prices for both.
Step 2 Build a spread: Choose a Spread Method that defines “relative value” (e.g., Log Spread, Price Ratio, Return Difference, Price Difference). Each lens highlights a different flavor of divergence.
Step 3 Validate relationship: A rolling Correlation checks if the pair is moving together enough to be tradable. If correlation is weak, the scanner stands down.
Step 4 Standardize & score: The spread is normalized (mean & variability over a lookback) to form a Z-Score . Large absolute Z means “stretched,” small means “near fair.”
Step 5 Signals: When the Z-Score crosses user-defined thresholds with sufficient correlation , entries print:
LONG = long chart symbol / short pair symbol,
SHORT = short chart symbol / long pair symbol,
EXIT = mean reversion into the exit zone or correlation failure.
Core concepts (the three pillars)
Spread Method Your definition of “distance” between the two series.
Guidance:
Log Spread: Focuses on proportional differences; robust when prices live on different scales.
Price Ratio: Classic relative value; good when you care about “X per Y.”
Return Difference: Emphasizes recent performance gaps; nimble for momentum-to-mean plays.
Price Difference: Straight subtraction; intuitive for similar-scale assets (e.g., two ETFs).
Correlation A rolling score of co-movement. The scanner requires it to be above your Min Correlation before acting, so you’re not trading random divergence.
Z-Score “How abnormal is today’s spread?” Positive = chart richer than pair; negative = cheaper. Thresholds define entries/exits with transparent, statistical context.
What you’ll see on the chart
Correlation plot (blue line) with a dashed Min Correlation guide. Above the line = green zone for signals; below = hands off.
Z-Score plot (white line) with colored, dashed Entry bands and dotted Exit bands. Zero line for mean.
Normalized spread (yellow) for a quick “shape read” of recent divergence swings.
Signal markers :
LONG (green label) when Z < –Entry and corr OK,
SHORT (red label) when Z > +Entry and corr OK,
EXIT (gray label) when Z returns inside the Exit band or correlation drops below the floor.
Background tint for active state (faint green for long-spread stance, faint red for short-spread stance).
The two built-in dashboards
Statistics Table (top-right)
Pair Symbol Your chosen partner.
Correlation Live value vs. your minimum.
Z-Score How stretched the spread is now.
Current / Pair Prices Real-time anchors.
Signal State NEUTRAL / LONG / SHORT.
Price Ratio Context for ratio-style setups.
Analysis Table (bottom-right)
Avg Correlation Typical co-movement level over your window.
Max |Z| The recent extremes of dislocation.
Spread Volatility How “lively” the spread has been.
Trade Signal A human-readable prompt (e.g., “LONG A / SHORT B” or “NO TRADE” / “LOW CORRELATION”).
Risk Level LOW / MEDIUM / HIGH based on current stretch (absolute Z).
Signals logic (plain English)
Entry (LONG): The spread is unusually negative (chart cheaper vs pair) and correlation is healthy. Expect mean reversion upward in the spread: long chart, short pair.
Entry (SHORT): The spread is unusually positive (chart richer vs pair) and correlation is healthy. Expect mean reversion downward in the spread: short chart, long pair.
Exit: The spread relaxes back toward normal (inside your exit band), or correlation deteriorates (relationship no longer trusted).
A quick, repeatable workflow
1) Choose your pair in context (same sector/theme or known macro link). Think: “Do these two plausibly co-move?”
2) Pick a spread lens that matches your narrative (ratio for relative value, returns for short-term performance gaps, etc.).
3) Confirm correlation is above your floor no corr, no trade.
4) Wait for a stretch (Z beyond Entry band) and a printed LONG / SHORT .
5) Manage to the mean (EXIT band) or correlation failure; let the scanners’ state/labels keep you honest.
Settings that matter (and why)
Spread Method Defines the “mispricing” you care about.
Correlation Period Longer = steadier regime read, shorter = snappier to regime change.
Z-Score Period The window that defines “normal” for the spread; it sets the yardstick.
Use Percentage Returns Normalizes series when using return-based logic; keep on for mixed-scale assets.
Entry / Exit Thresholds Set your stretch and your target reversion zone. Wider entries = rarer but stronger signals.
Minimum Correlation The gatekeeper. Raising it favors quality over quantity.
Choosing pairs (practical cheat sheet)
Same family: two index ETFs, two oil-linked names, two gold miners, two L1 tokens.
Hedge & proxy: stock vs. sector ETF, BTC vs. BTC index, WTI vs. energy ETF.
Cross-venue or cross-listing: instruments that are functionally the same exposure but price differently intraday.
Reading the cues like a pro
Divergence shape: The yellow normalized spread helps you see rhythm fast spike and snap-back versus slow grind.
Corr-first discipline: Don’t fight the “Min Correlation” line. Good pairs trading starts with a relationship you can trust.
Exit humility: When Z re-centers, let the EXIT do its job. The edge is the journey to the mean, not overstaying it.
Frequently asked (quick answers)
“Long/Short means what exactly?”
LONG = long the chart symbol and short the pair symbol.
SHORT = short the chart symbol and long the pair symbol.
“Do I need same price scales?” No. The spread methods normalize in different ways; choose the one that fits your use case (log/ratio are great for mixed scales).
“What if correlation falls mid-trade?” The scanner will neutralize the state and print EXIT . Relationship first; trade second.
Field notes & patterns
Snap-back days: After a one-sided session, return-difference spreads often flag cleaner intraday mean reversions.
Macro rotations: Ratio spreads shine during sector re-weights (e.g., value vs. growth ETFs); look for steady corr + elevated |Z|.
Event bleed-through: If one symbol reacts to news and its partner lags, Z often flags a high-quality, short-horizon re-centering.
Display controls at a glance
Show Statistics Table Live state & key numbers, top-right.
Show Analysis Table Context/risk read, bottom-right.
Show Correlation / Spread / Z-Score Toggle the sub-charts you want visible.
Show Entry/Exit Signals Turn markers on/off as needed.
Coloring Adjust Long/Short/Neutral and correlation line colors to match your theme.
Alerts (ready to route to your workflow)
Pairs Long Entry Z falls through the long threshold with correlation above minimum.
Pairs Short Entry Z rises through the short threshold with correlation above minimum.
Pairs Trade Exit Z returns to neutral or the relationship fails your correlation floor.
Correlation Breakdown Rolling correlation crosses your minimum; relationship caution.
Final notes
The scanner is designed to keep you systematic: require relationship (correlation), quantify dislocation (Z-Score), act when stretched, stand down when it normalizes or the relationship degrades. It’s a full, visual loop for relative-value trading that stays out of your way when it should and gets loud only when the numbers line up.
Indicator
Dynamic Stop Loss Optimizer [BackQuant]Dynamic Stop Loss Optimizer
Overview
Stop placement decides expectancy. This tool gives you three professional-grade, adaptive stop engines, ATR, Volatility, and Hybrid. So your exits scale with current conditions instead of guessing fixed ticks. It trails intelligently, redraws as the market evolves, and annotates the chart with clean labels/lines and a compact stats table. Pick the engine that fits the trade, or switch on the fly.
What it does
Calculates three adaptive stops in real time (ATR-based, Volatility-based, and Hybrid) and keeps them trailed as price makes progress.
Shows exactly where your risk lives with on-chart levels, color-coded markers (long/short), and precise “Risk %” labels at the current bar.
Surfaces context you actually use - current ATR, daily volatility, selected method, and the live stop level—in a tidy, movable table.
Fires alerts on stop hits so you can automate exits or journal outcomes without staring at the screen.
Why it matters
Adaptive risk control: Stops expand in fast tape and tighten in quiet tape. You’re not punished for volatility; you’re aligned with it.
Consistency across assets: The same playbook works whether you’re trading indexes, FX, crypto, or equities, because the engine normalizes to each symbol’s behavior.
Cleaner decision-making: One chart shows your entry idea and its invalidation in the same breath. If price trespasses, you know it instantly.
The three methods (choose your engine)
1) ATR Based “Structure-aware” distance
This classic approach keys off Average True Range to set a stop just beyond typical bar-to-bar excursion. It adapts smoothly to changing ranges and respects swing structure.
Use when: you want a steady, intuitive buffer that tracks trend legs without hugging price.
See it in action:
2) Volatility Based “Behavior-aware” distance
This engine derives stop distance from current return volatility (annualized, then scaled back down to the session). It reacts to regime shifts quickly and normalizes risk across symbols with very different prices.
Use when: you want the stop to breathe with realized volatility and respond faster to heat-ups/cool-downs.
See it in action:
3) Hybrid “Best of both worlds”
The Hybrid blends the ATR and Volatility distances into one consensus level, then trails it intelligently. You get the structural common sense of ATR and the regime sensitivity of Vol.
Use when: you want robust, all-weather behavior without micromanaging inputs.
See it in action:
How it trails
Longs: The stop ratchets up with favorable movement and holds its ground on shallow pullbacks. If price closes back into the risk zone, the level refreshes to the newest valid distance.
Shorts: Mirror logic ratchets down with trend, resists noise, and refreshes if price reclaims the zone.
Hybrid trailing: Uses the blended distance and the same “no give-backs” principle to keep gains protected as structure builds.
Reading the chart
Markers: Circles = ATR stops, Crosses = Vol stops, Diamonds = Hybrid. Colors indicate long (red level under price) vs short (green level above price).
Lines: The latest active stop is extended with a dashed line so you can see it at a glance.
Labels: “Long SL / Short SL” shows the exact price and current risk % from the last close no math required.
Table: ATR value, Daily Vol %, your chosen Method, the Current SL, and Risk %—all in one compact block that you can pin top-left/right/center.
Quick workflow
Define the idea: Long or Short, and which engine fits the tape (ATR, Vol, or Hybrid).
Place and trail: Let the optimizer print the level; trail automatically as the move develops.
Manage outcomes: If the line is tagged, you’re out clean. If it holds, you’ve contained heat while giving the trade room to work.
Inputs you’ll actually touch
Calculation Settings
ATR Length / Multiplier: Controls the “structural” cushion.
Volatility Length / Multiplier: Controls the “behavioral” cushion.
Trading Days: 252 or 365 to keep the volatility math aligned with the asset’s trading calendar.
Stop Loss Method
ATR Based | Volatility Based | Hybrid : Switch engines instantly to fit the trade.
Position Type
Long | Short | Both : Show only what you need for the current strategy.
Visual Settings
Show ATR / Vol / Hybrid Stops: Toggle families on/off.
Show Labels: Print price + Risk % at the live stop.
Table Position: Park the metrics where you like.
Coloring
Long/Short/Hybrid colors: Set a palette that matches your theme and stands out on your background.
Practical patterns to watch
Trend-pullback continuation: The stop ratchets behind higher lows (long) or lower highs (short). If price tests the level and rejects, that’s your risk-defined continuation cue.
Break-and-run: After a clean break, the Hybrid will usually sit slightly wider than pure Vol, use it to avoid getting shaken on the first retest.
Range compression: When the ATR and Vol distances converge, the table will show small Risk %. That’s your green light to size up with the same dollar risk, or keep it conservative if you expect expansion.
Alerts
Long Stop Loss Hit : Notifies when price crosses below the live long stop.
Short Stop Loss Hit : Notifies when price crosses above the live short stop.
Why this feels “set-and-serious”
You get a single look that answers three questions in real time: “Where’s my line in the sand?”, “How much heat am I taking right now?”, and “Is this distance appropriate for current conditions?” With ATR, Vol, and Hybrid in one tool, you can run the exact same playbook across symbols and regimes while keeping your chart clean and your risk explicit.
Indicator
PolyFilter [BackQuant]PolyFilter
A flexible, low-lag trend filter with three smoothing engines—optimized for clean bias, fewer whipsaws, and clear alerting.
What it does
PolyFilter draws a single “intelligent” baseline that adapts to price while suppressing noise. You choose the engine— Fractional MA , Ehlers 2-Pole Super Smoother , or a Multi-Kernel blend . The line can color itself by slope (trend) or by position vs price (above/below), and you get four ready-made alerts for flips and crosses.
What it plots
PolyFilter line — your smoothed trend baseline (width set by “Line Width”).
Optional candle & background coloring — choose: color by trend slope or by whether price is above/below the filter.
Signal markers — Arrows with L/S when the slope flips or when price crosses the line (if you enable shapes/alerts).
How the three engines differ
Fractional MA (experimental) — A power-law weighting of past bars (heavier focus on the most recent samples without throwing away history). The Adaptation Speed acts like the “fraction” exponent (default 0.618). Lower values lean more on recent bars; higher values spread weight further back.
Ehlers 2-Pole Super Smoother — Classic low-lag IIR smoother that aggressively reduces high-frequency noise while preserving turns. Great default when you want a steady, responsive baseline with minimal parameter fuss.
Multi-Kernel — A 70/30 blend of a Gaussian window and an exponential kernel. The Gaussian contributes smooth structure; the exponential adds a hint of responsiveness. Useful for assets that oscillate but still trend.
Reading the colors
Trend mode (default) — Line & candles turn green while the filter is rising (signal > signal ) and red while it’s falling.
Above/Below mode — Line & candles reflect price’s position relative to the filter: green when price > filter, red when price < filter. This is handy if you treat the filter like a dynamic “fair value” or bias line.
Inputs you’ll actually use
Calculation Settings
Price Source — Default HLC/3. Switch to Close for stricter trend, or HLC3/HL2 to soften single-print spikes.
Filter Length — Window/period for all engines. Shorter = snappier turns; longer = smoother line.
Adaptation Speed — Only affects Fractional MA . Lower it for faster, more local weighting; raise it for smoother, more global weighting.
Filter Type — Pick one of: Fractional MA, Ehlers 2-Pole, Multi-Kernel.
UI & Plotting
Color based off… — Choose Trend (slope) or > or < Close (position vs price).
Long/Short Colors — Customize bull/bear hues to your theme.
Show Filter Line / Paint candles / Color background — Visual toggles for the line, bars, and backdrop.
Line Width — Make the filter stand out (2–3 works well on most charts).
Signals & Alerts
PolyFilter Trend Up — Slope flips upward (the filter crosses above its prior value). Good for early continuation entries or stop-tightening on shorts.
PolyFilter Trend Down — Slope flips downward. Often used to scale out longs or rotate bias.
PolyFilter Above Price — The filter line crosses up through price (filter > price). This can confirm that mean has “caught up” after a pullback.
PolyFilter Below Price — The filter line crosses down through price (filter < price). Useful to confirm momentum loss on bounces.
Quick starts (suggested presets)
Intraday (5–15m, crypto or indices) — Ehlers 2-Pole, Length 55–80. Trend coloring ON, candle paint ON. Look for pullbacks to a rising filter; avoid fading a falling one.
Swing (1H–4H) — Multi-Kernel, Length 80–120. Background color OFF (cleaner), candle paint ON. Add a higher-TF confirmation (e.g., 4H filter rising when you trade 1H).
Range-prone FX — Fractional MA, Length 70–100, Adaptation ~0.55–0.70. Consider Above/Below mode to trade mean reversion to the line with a strict risk cap.
How to use it in practice
Bias line — Trade in the direction of the filter slope; stand aside when it flattens and color chops back and forth.
Dynamic support/resistance — Treat the line as a moving value area. In trends, entries often appear on shallow tags of the line with structure confluence.
Regime switch — When the filter flips and holds color for several bars, tighten stops on the opposing side and look for first pullback in the new color.
Stacking filters — Many users run PolyFilter on the active chart and a slower instance (longer length) on a higher timeframe as a “macro bias” guardrail.
Tuning tips
If you see too many flips, lengthen the filter or switch to Multi-Kernel.
If turns feel late, shorten the filter or try Ehlers 2-Pole for lower lag.
On thin or very noisy symbols, prefer HLC3 as the source and longer lengths.
Performance note: very large lengths increase computation time for the Multi-Kernel and Fractional engines. Start moderate and scale up only if needed.
Summary
PolyFilter gives you a single, trustworthy baseline that you can read at a glance—either as a pure trend line (slope coloring) or as a dynamic “above/below fair value” reference. Pick the engine that matches your market’s personality, set a sensible length, and let the color and alerts guide bias, entries on pullbacks, and risk on reversals.
Indicator
Kalman Adjusted Average True Range [BackQuant]Kalman Adjusted Average True Range
A volatility-aware trend baseline that fuses a Kalman price estimate with ATR “rails” to create a smooth, adaptive guide for entries, exits, and trailing risk.
Built on my original Kalman
This indicator is based on my original Kalman Price Filter:
That core smoother is used here to estimate the “true” price path, then blended with ATR to control step size and react proportionally to market noise.
What it plots
Kalman ATR Line the main baseline that turns up/down with the filtered trend.
Optional Moving Average of the Kalman ATR a secondary line for confluence (SMA/Hull/EMA/WMA/DEMA/RMA/LINREG/ALMA).
Candle Coloring (optional) paint bars by the baseline’s current direction.
Why combine Kalman + ATR?
Kalman reduces measurement noise and produces a stable path without the lag of heavy MAs.
ATR rails scale the baseline’s step to current volatility, so it’s calm in chop and more responsive in expansion.
The result is a single, intelligible line you can trade around: slope-up = constructive; slope-down = caution.
How it works (plain English)
Each bar, the Kalman filter updates an internal state (tunable via Process Noise , Measurement Noise , and Filter Order ) to estimate the underlying price.
An ATR band (Period × Factor) defines the allowed per-bar adjustment. The baseline cannot “jump” beyond those rails in one step.
A direction flip is detected when the baseline’s slope changes sign (upturn/downturn), and alerts are provided for both.
Typical uses
Trend confirmation Trade in the baseline’s direction; avoid fading a firmly rising/falling line.
Pullback timing Look for entries when price mean-reverts toward a rising baseline (or exits on tags of a falling one).
Trailing risk Use the baseline as a dynamic guide; many traders set stops a small buffer beyond it (e.g., a fraction of ATR).
Confluence Enable the MA overlay of the Kalman ATR; alignment (baseline above its MA and rising) supports continuation.
Inputs & what they do
Calculation
Kalman Price Source which price the filter tracks (Close by default).
Process Noise how quickly the filter can adapt. Higher = more responsive (but choppier).
Measurement Noise how much you distrust raw price. Higher = smoother (but slower to turn).
Filter Order (N) depth of the internal state array. Higher = slightly steadier behavior.
Kalman ATR
Period ATR lookback. Shorter = snappier; longer = steadier.
Factor scales the allowed step per bar. Larger factors permit faster drift; smaller factors clamp movement.
Confluence (optional)
MA Type & Period compute an MA on the Kalman ATR line , not on price.
Sigma (ALMA) if ALMA is selected, this input controls the curve’s shape. (Ignored for other MA types.)
Visuals
Plot Kalman ATR toggle the main line.
Paint Candles color bars by up/down slope.
Colors choose long/short hues.
Signals & alerts
Trend Up baseline turns upward (slope crosses above 0).
Alert: “Kalman ATR Trend Up”
Trend Down baseline turns downward (slope crosses below 0).
Alert: “Kalman ATR Trend Down”
These are state flips , not “price crossovers,” so you avoid many one-bar head-fakes.
How to start (fast presets)
Swing (daily/4H) ATR Period 7–14, Factor 0.5–0.8, Process Noise 0.02–0.05, Measurement Noise 2–4, N = 3–5.
Intraday (5–15m) ATR Period 5–7, Factor 0.6–1.0, Process Noise 0.05–0.10, Measurement Noise 2–3, N = 3–5.
Slow assets / FX raise Measurement Noise or ATR Period for calmer lines; drop Factor if the baseline feels too jumpy.
Reading the line
Rising & curving upward momentum building; consider long bias until a clear downturn.
Flat & choppy regime uncertainty; many traders stand aside or tighten risk.
Falling & accelerating distribution lower; short bias until a clean upturn.
Practical playbook
Continuation entries After a Trend Up alert, wait for a minor pullback toward the baseline; enter on evidence the line keeps rising.
Exit/reduce If long and the baseline flattens then turns down, trim or exit; reverse logic for shorts.
Filters Add a higher-timeframe check (e.g., only take longs when the daily Kalman ATR is rising).
Stops Place stops just beyond the baseline (e.g., baseline − x% ATR for longs) to avoid “tag & reverse” noise.
Notes
This is a guide to state and momentum, not a guarantee. Combine with your process (structure, volume, time-of-day) for decisions.
Settings are asset/timeframe dependent; start with the presets and nudge Process/Measurement Noise until the baseline “feels right” for your market.
Summary
Kalman ATR takes the noise-reduction of a Kalman price estimate and couples it with volatility-scaled movement to produce a clean, adaptive baseline. If you liked the original Kalman Price Filter (), this is its trend-trading cousin purpose-built for cleaner state flips, intuitive trailing, and confluence with your existing
Indicator
Key Levels: Daily, Weekly, Monthly [BackQuant]Key Levels: Daily, Weekly, Monthly
Map the market’s “memory” in one glance—yesterday’s range, this week’s chosen day high/low, and D/W/M opens—then auto-clean levels once they break.
What it does
This tool plots three families of high-signal reference lines and keeps them tidy as price evolves:
Chosen Day High/Low (per week) — Pick a weekday (e.g., Monday). For each past week, the script records that day’s session high and low and projects them forward for a configurable number of bars. These act like “memory levels” that price often revisits.
Daily / Weekly / Monthly Opens — Plots the opening price of each new day, week, and month with separate styling. These opens frequently behave like magnets/flip lines intraday and anchors for regime on higher timeframes.
Auto-pruning — When price breaks a stored level, the script can automatically remove it to reduce clutter and refocus you on still-active lines. See: (broken levels removed).
Why these levels matter
Liquidity pockets — Prior day’s high/low and the daily open concentrate stops and pending orders. Mapping them quickly reveals likely sweep or fade zones. Example: previous day highs + daily open highlighting liquidity:
Context & regime — Monthly opens frame macro bias; trading above a rising cluster of monthly opens vs. below gives a clean top-down read. Example: monthly-only “macro outlook” view:
Cleaner charts — Auto-remove broken lines so you focus on what still matters right now.
What it plots (at a glance)
Past Chosen Day High/Low for up to N prior weeks (your choice), extended right.
Current Daily Open , Weekly Open , and Monthly Open , each with its own color, label, and forward extension.
Optional short labels (e.g., “Mon High”) or full labels (with week/month info).
How breaks are detected & cleaned
You control both the evidence and the timing of a “break”:
Break uses — Choose Close (more conservative) or Wick (more sensitive).
Inclusive? — If enabled, equality counts (≥ high or ≤ low). If disabled, you need a strict cross.
Allow intraday breaks? — If on, a level can break during the tracked day; if off, the script only counts breaks after the session completes.
Remove Broken Levels — When a break is confirmed, the line/label is deleted automatically. (See the demo: )
Quick start
Pick a Day of Week to Track (e.g., Monday).
Set how many weeks back to show (e.g., 8–10).
Choose how far to extend each family (bars to the right for chosen-day H/L and D/W/M opens).
Decide if a break uses Close or Wick , and whether equality counts.
Toggle Remove Broken Levels to keep the chart clean automatically.
Tips by use-case
Intraday bias — Watch the Daily Open as a magnet/flip. If price gaps above and holds, pullbacks to the daily open often decide direction. Pair with last day’s high/low for sweep→reversal or true breakout cues. See:
Weekly structure — Track the week’s chosen day (e.g., Monday) high/low across prior weeks. If price stalls near a cluster of old “Monday Highs,” look for sweep/reject patterns or continuation on reclaim.
Macro regime — Hide daily/weekly lines and keep only Monthly Opens to read bigger cycles at a glance (BTC/crypto especially). Example:
Customization
Use wicks or bodies for highs/lows (wicks capture extremes; bodies are stricter).
Line style & thickness — solid/dashed/dotted, width 1–5, plus global transparency.
Labels — Abbreviated (“Mon High”, “D Open”) or full (month/week/day info).
Color scheme — Separate colors for highs, lows, and each of D/W/M opens.
Capacity controls — Set how many daily/weekly/monthly opens and how many weeks of chosen-day H/L to keep visible.
What’s under the hood
On your selected weekday, the script records that session’s true high and true low (using wicks or body-based extremes—your choice), then projects a horizontal line forward for the next bars.
At each new day/week/month , it records the opening price and projects that line forward as well.
Each bar, the script checks your “break” rules; once broken, lines/labels are removed if auto-cleaning is on.
Everything updates in real time; past levels don’t repaint after the session finishes.
Recommended presets
Day trading — Weeks back: 6–10; extend D/W opens: 50–100 bars; Break uses: Close ; Inclusive: off; Auto-remove: on.
Swing — Fewer daily opens, more weekly opens (2–6), and 8–12 weeks of chosen-day H/L.
Macro — Show only Monthly Opens (1–6 months), dashed style, thicker lines for clarity.
Reading the examples
Broken lines disappear — decluttering in action:
Macro outlook — monthly opens as cycle rails:
Liquidity map — previous day highs + daily open:
Final note
These are not “signals”—they’re reference points that many participants watch. By standardising how you draw them and automatically clearing the ones that no longer matter, you turn a noisy chart into a focused map: where liquidity likely sits, where price memory lives, and which lines are still in play.
Indicator
Quantile Regression Bands [BackQuant]Quantile Regression Bands
Tail-aware trend channeling built from quantiles of real errors, not just standard deviations.
What it does
This indicator fits a simple linear trend over a rolling lookback and then measures how price has actually deviated from that trend during the window. It then places two pairs of bands at user-chosen quantiles of those deviations (inner and outer). Because bands are based on empirical quantiles rather than a symmetric standard deviation, they adapt to skewed and fat-tailed behaviour and often hug price better in trending or asymmetric markets.
Why “quantile” bands instead of Bollinger-style bands?
Bollinger Bands assume a (roughly) symmetric spread around the mean; quantiles don’t—upper and lower bands can sit at different distances if the error distribution is skewed.
Quantiles are robust to outliers; a single shock won’t inflate the bands for many bars.
You can choose tails precisely (e.g., 1%/99% or 5%/95%) to match your risk appetite.
How it works (intuitive)
Center line — a rolling linear regression approximates the local trend.
Residuals — for each bar in the lookback, the indicator looks at the gap between actual price and where the line “expected” price to be.
Quantiles — those gaps are sorted; you select which percentiles become your inner/outer offsets.
Bands — the chosen quantile offsets are added to the current end of the regression line to draw parallel support/resistance rails.
Smoothing — a light EMA can be applied to reduce jitter in the line and bands.
What you see
Center (linear regression) line (optional).
Inner quantile bands (e.g., 25th/75th) with optional translucent fill.
Outer quantile bands (e.g., 1st/99th) with a multi-step gradient to visualise “tail zones.”
Optional bar coloring: bars trend-colored by whether price is rising above or falling below the center line.
Alerts when price crosses the outer bands (upper or lower).
How to read it
Trend & drift — the slope of the center line is your local trend. Persistent closes on the same side of the center line indicate directional drift.
Pullbacks — tags of the inner band often mark routine pullbacks within trend. Reaction back to the center line can be used for continuation entries/partials.
Tails & squeezes — outer-band touches highlight statistically rare excursions for the chosen window. Frequent outer-band activity can signal regime change or volatility expansion.
Asymmetry — if the upper band sits much further from the center than the lower (or vice versa), recent behaviour has been skewed. Trade management can be adjusted accordingly (e.g., wider take-profit upslope than downslope).
A simple trend interpretation can be derived from the bar colouring
Good use-cases
Volatility-aware mean reversion — fade moves into outer bands back toward the center when trend is flat.
Trend participation — buy pullbacks to the inner band above a rising center; flip logic for shorts below a falling center.
Risk framing — set dynamic stops/targets at quantile rails so position sizing respects recent tail behaviour rather than fixed ticks.
Inputs (quick guide)
Source — price input used for the fit (default: close).
Lookback Length — bars in the regression window and residual sample. Longer = smoother, slower bands; shorter = tighter, more reactive.
Inner/Outer Quantiles (τ) — choose your “typical” vs “tail” levels (e.g., 0.25/0.75 inner, 0.01/0.99 outer).
Show toggles — independently toggle center line, inner bands, outer bands, and their fills.
Colors & transparency — customize band and fill appearance; gradient shading highlights the tail zone.
Band Smoothing Length — small EMA on lines to reduce stair-step artefacts without meaningfully changing levels.
Bar Coloring — optional trend tint from the center line’s momentum.
Practical settings
Swing trading — Length 75–150; inner τ = 0.25/0.75, outer τ = 0.05/0.95.
Intraday — Length 50–100 for liquid futures/FX; consider 0.20/0.80 inner and 0.02/0.98 outer in high-vol assets.
Crypto — Because of fat tails, try slightly wider outers (0.01/0.99) and keep smoothing at 2–4 to tame weekend jumps.
Signal ideas
Continuation — in an uptrend, look for pullback into the lower inner band with a close back above the center as a timing cue.
Exhaustion probe — in ranges, first touch of an outer band followed by a rejection candle back inside the inner band often precedes mean-reversion swings.
Regime shift — repeated closes beyond an outer band or a sharp re-tilt in the center line can mark a new trend phase; adjust tactics (stop-following along the opposite inner band).
Alerts included
“Price Crosses Upper Outer Band” — potential overextension or breakout risk.
“Price Crosses Lower Outer Band” — potential capitulation or breakdown risk.
Notes
The fit and quantiles are computed on a fixed rolling window and do not repaint; bands update as the window moves forward.
Quantiles are based on the recent distribution; if conditions change abruptly, expect band widths and skew to adapt over the next few bars.
Parameter choices directly shape behaviour: longer windows favour stability, tighter inner quantiles increase touch frequency, and extreme outer quantiles highlight only the rarest moves.
Final thought
Quantile bands answer a simple question: “How unusual is this move given the current trend and the way price has been missing it lately?” By scoring that question with real, distribution-aware limits rather than one-size-fits-all volatility you get cleaner pullback zones in trends, more honest “extreme” tags in ranges, and a framework for risk that matches the market’s recent personality.
Indicator
Deadband Hysteresis Supertrend [BackQuant]Deadband Hysteresis Supertrend
A two-stage trend tool that first filters price with a deadband baseline, then runs a Supertrend around that baseline with optional flip hysteresis and ATR-based adverse exits.
What this is
A hybrid of two ideas:
Deadband Hysteresis Baseline that only advances when price pulls far enough from the baseline to matter. This suppresses micro noise and gives you a stable centerline.
Supertrend bands wrapped around that baseline instead of raw price. Flips are further gated by an extra margin so side changes are more deliberate.
The goal is fewer whipsaws in chop and clearer regime identification during trends.
How it works (high level)
Deadband step — compute a per-bar “deadband” size from one of four modes: ATR, Percent of price, Ticks, or Points. If price deviates from the baseline by more than this amount, move the baseline forward by a fraction of the excess. If not, hold the line.
Centered Supertrend — build upper and lower bands around the baseline using ATR and a user factor. Track the usual trailing logic that tightens a band while price moves in its favor.
Flip hysteresis — require price to exceed the active band by an extra flip offset × ATR before switching sides. This adds stickiness at the boundary.
Adverse exit — once a side is taken, trigger an exit if price moves against the entry by K × ATR .
If you would like to check out the filter by itself:
What it plots
DBHF baseline (optional) as a smooth centerline.
DBHF Supertrend as the active trailing band.
Candle coloring by trend side for quick read.
Signal markers 𝕃 and 𝕊 at flips plus ✖ on adverse exits.
Inputs that matter
Price Source — series being filtered. Close is typical. HL2 or HLC3 can be steadier.
Deadband mode — ATR, Percent, Ticks, or Points. This defines the “it’s big enough to matter” zone.
ATR Length / Mult (DBHF) — only used when mode = ATR. Larger values widen the do-nothing zone.
Percent / Ticks / Points — alternatives to ATR; pick what fits your market’s convention.
Enter Mult — scales the deadband you must clear before the baseline moves. Increase to filter more noise.
Response — fraction of the excess applied to baseline movement. Higher responds faster; lower is smoother.
Supertrend ATR Period & Factor — traditional band size controls; higher factor widens and flips less often.
Flip Offset ATR — extra ATR buffer required to flip. Useful in choppy regimes.
Adverse Stop K·ATR — per-trade danger brake that forces an exit if price moves K×ATR against entry.
UI — toggle baseline, supertrend, signals, and bar painting; choose long and short colors.
How to read it
Green regime — candles painted long and the Supertrend running below price. Pullbacks toward the baseline that fail to breach the opposite band often resume higher.
Red regime — candles painted short and the Supertrend running above price. Rallies that cannot reclaim the band may roll over.
Frequent side swaps — reduce sensitivity by increasing Enter Mult, using ATR mode, raising the Supertrend factor, or adding Flip Offset ATR.
Use cases
Bias filter — allow entries only in the direction of the current side. Use your preferred triggers inside that bias.
Trailing logic — treat the active band as a dynamic stop. If the side flips or an adverse K·ATR exit prints, reduce or close exposure.
Regime map — on higher timeframes, the combination baseline + band produces a clean up vs down template for allocation decisions.
Tuning guidance
Fast markets — ATR deadband, modest Enter Mult (0.8–1.2), response 0.2–0.35, Supertrend factor 1.7–2.2, small Flip Offset (0.2–0.5 ATR).
Choppy ranges — widen deadband or raise Enter Mult, lower response, and add more Flip Offset so flips require stronger evidence.
Slow trends — longer ATR periods and higher Supertrend factor to keep you on side longer; use a conservative adverse K.
Included alerts
DBHF ST Long — side flips to long.
DBHF ST Short — side flips to short.
Adverse Exit Long / Short — K·ATR stop triggers against the current side.
Strengths
Deadbanded baseline reduces micro whipsaws before Supertrend logic even begins.
Flip hysteresis adds a second layer of confirmation at the boundary.
Optional adverse ATR stop provides a uniform risk cut across assets and regimes.
Clear visuals and minimal parameters to adjust for symbol behavior.
Putting it together
Think of this tool as two decisions layered into one view. The deadband baseline answers “does this move even count,” then the Supertrend wrapped around that baseline answers “if it counts, which side should I be on and where do I flip.” When both parts agree you tend to stay on the correct side of a trend for longer, and when they disagree you get an early warning that conditions are changing.
When the baseline bends and price cannot reclaim the opposite band , momentum is usually continuing. Pullbacks into the baseline that stall before the far band often resolve in trend.
When the baseline flattens and the bands compress , expect indecision. Use the Flip Offset ATR to avoid reacting to the first feint. Wait for a clean band breach with follow through.
When an adverse K·ATR exit prints while the side has not flipped , treat it as a risk event rather than a full regime change. Many users cut size, re-enter only if the side reasserts, and let the next flip confirm a new trend.
Final thoughts
Deadband Hysteresis Supertrend is best read as a regime lens. The baseline defines your tolerance for noise, the bands define your trailing structure, and the flip offset plus adverse ATR stop define how forgiving or strict you want to be at the boundary. On strong trends it helps you hold through shallow shakeouts. In choppy conditions it encourages patience until price does something meaningful. Start with settings that reflect the cadence of your market, observe how often flips occur, then nudge the deadband and flip offset until the tool spends most of its time describing the move you care about rather than the noise in between. Indicator
Theil-Sen Line Filter [BackQuant]Theil-Sen Line Filter
A robust, median-slope baseline that tracks price while resisting outliers. Designed for the chart pane as a clean, adaptive reference line with optional candle coloring and slope-flip alerts.
What this is
A trend filter that estimates the underlying slope of price using a Theil-Sen style median of past slopes, then advances a baseline by a controlled fraction of that slope each bar. The result is a smooth line that reacts to real directional change while staying calm through noise, gaps, and single-bar shocks.
Why Theil-Sen
Classical moving averages are sensitive to outliers and shape changes. Ordinary least squares is sensitive to large residuals. The Theil-Sen idea replaces a single fragile estimate with the median of many simple slopes, which is statistically robust and less influenced by a few extreme bars. That makes the baseline steadier in choppy conditions and cleaner around regime turns.
What it plots
Filtered baseline that advances by a fraction of the robust slope each bar.
Optional candle coloring by baseline slope sign for quick trend read.
Alerts when the baseline slope turns up or down.
How it behaves (high level)
Looks back over a fixed window and forms many “current vs past” bar-to-bar slopes.
Takes the median of those slopes to get a robust estimate for the bar.
Optionally caps the magnitude of that per-bar slope so a single volatile bar cannot yank the line.
Moves the baseline forward by a user-controlled fraction of the estimated slope. Lower fractions are smoother. Higher fractions are more responsive.
Inputs and what they do
Price Source — the series the filter tracks. Typical is close; HL2 or HLC3 can be smoother.
Window Length — how many bars to consider for slopes. Larger windows are steadier and slower. Smaller windows are quicker and noisier.
Response — fraction of the estimated slope applied each bar. 1.00 follows the robust slope closely; values below 1.00 dampen moves.
Slope Cap Mode — optional guardrail on each bar’s slope:
None — no cap.
ATR — cap scales with recent true range.
Percent — cap scales with price level.
Points — fixed absolute cap in price points.
ATR Length / Mult, Cap Percent, Cap Points — tune the chosen cap mode’s size.
UI Settings — show or hide the line, paint candles by slope, choose long and short colors.
How to read it
Up-slope baseline and green candles indicate a rising robust trend. Pullbacks that do not flip the slope often resolve in trend direction.
Down-slope baseline and red candles indicate a falling robust trend. Bounces against the slope are lower-probability until proven otherwise.
Flat or frequent flips suggest a range. Increase window length or decrease response if you want fewer whipsaws in sideways markets.
Use cases
Bias filter — only take longs when slope is up, shorts when slope is down. It is a simple way to gate faster setups.
Stop or trail reference — use the line as a trailing guide. If price closes beyond the line and the slope flips, consider reducing exposure.
Regime detector — widen the window on higher timeframes to define major up vs down regimes for asset rotation or risk toggles.
Noise control — enable a cap mode in very volatile symbols to retain the line’s continuity through event bars.
Tuning guidance
Quick swing trading — shorter window, higher response, optionally add a percent cap to keep it stable on large moves.
Position trading — longer window, moderate response. ATR cap tends to scale well across cycles.
Low-liquidity or gappy charts — prefer longer window and a points or ATR cap. That reduces jumpiness around discontinuities.
Alerts included
Theil-Sen Up Slope — baseline’s one-bar change crosses above zero.
Theil-Sen Down Slope — baseline’s one-bar change crosses below zero.
Strengths
Robust to outliers through median-based slope estimation.
Continuously advances with price rather than re-anchoring, which reduces lag at turns.
User-selectable slope caps to tame shock bars without over-smoothing everything.
Minimal visuals with optional candle painting for fast regime recognition.
Notes
This is a filter, not a trading system. It does not account for execution, spreads, or gaps. Pair it with entry logic, risk management, and higher-timeframe context if you plan to use it for decisions.
Indicator
Deadband Hysteresis Filter [BackQuant]Deadband Hysteresis Filter
What this is
This tool builds a “debounced” price baseline that ignores small fluctuations and only reacts when price meaningfully departs from its recent path. It uses a deadband to define how much deviation matters and a hysteresis scheme to avoid rapid flip-flops around the decision boundary. The baseline’s slope provides a simple trend cue, used to color candles and to trigger up and down alerts.
Why deadband and hysteresis help
They filter micro noise so the baseline does not react to every tiny tick.
They stabilize state changes. Hysteresis means the rule to start moving is stricter than the rule to keep holding, which reduces whipsaw.
They produce a stepped, readable path that advances during sustained moves and stays flat during chop.
How it works (conceptual)
At each bar the script maintains a running baseline dbhf and compares it to the input price p .
Compute a base threshold baseTau using the selected mode (ATR, Percent, Ticks, or Points).
Build an enter band tauEnter = baseTau × Enter Mult and an exit band tauExit = baseTau × Exit Mult where typically Exit Mult < Enter Mult .
Let diff = p − dbhf .
If diff > +tauEnter , raise the baseline by response × (diff − tauEnter) .
If diff < −tauEnter , lower the baseline by response × (diff + tauEnter) .
Otherwise, hold the prior value.
Trend state is derived from slope: dbhf > dbhf → up trend, dbhf < dbhf → down trend.
Inputs and what they control
Threshold mode
ATR — baseTau = ATR(atrLen) × atrMult . Adapts to volatility. Useful when regimes change.
Percent — baseTau = |price| × pctThresh% . Scale-free across symbols of different prices.
Ticks — baseTau = syminfo.mintick × tickThresh . Good for futures where tick size matters.
Points — baseTau = ptsThresh . Fixed distance in price units.
Band multipliers and response
Enter Mult — outer band. Price must travel at least this far from the baseline before an update occurs. Larger values reject more noise but increase lag.
Exit Mult — inner band for hysteresis. Keep this smaller than Enter Mult to create a hold zone that resists small re-entries.
Response — step size when outside the enter band. Higher response tracks faster; lower response is smoother.
UI settings
Show Filtered Price — plots the baseline on price.
Paint candles — colors bars by the filtered slope using your long/short colors.
How it can be used
Trend qualifier — take entries only in the direction of the baseline slope and skip trades against it.
Debounced crossovers — use the baseline as a stabilized surrogate for price in moving-average or channel crossover rules.
Trailing logic — trail stops a small distance beyond the baseline so small pullbacks do not eject the trade.
Session aware filtering — widen Enter Mult or switch to ATR mode for volatile sessions; tighten in quiet sessions.
Parameter interactions and tuning
Enter Mult vs Response — both govern sensitivity. If you see too many flips, increase Enter Mult or reduce Response. If turns feel late, do the opposite.
Exit Mult — widening the gap between Enter and Exit expands the hold zone and reduces oscillation around the threshold.
Mode choice — ATR adapts automatically; Percent keeps behavior consistent across instruments; Ticks or Points are useful when you think in fixed increments.
Timeframe coupling — on higher timeframes you can often lower Enter Mult or raise Response because raw noise is already reduced.
Concrete starter recipes
General purpose — ATR mode, atrLen=14 , atrMult=1.0–1.5 , Enter=1.0 , Exit=0.5 , Response=0.20 . Balanced noise rejection and lag.
Choppy range filter — ATR mode, increase atrMult to 2.0, keep Response≈0.15 . Stronger suppression of micro-moves.
Fast intraday — Percent mode, pctThresh=0.1–0.3 , Enter=1.0 , Exit=0.4–0.6 , Response=0.30–0.40 . Quicker turns for scalping.
Futures ticks — Ticks mode, set tickThresh to a few spreads beyond typical noise; start with Enter=1.0 , Exit=0.5 , Response=0.25 .
Strengths
Clear, explainable logic with an explicit noise budget.
Multiple threshold modes so the same tool fits equities, futures, and crypto.
Built-in hysteresis that reduces flip-flop near the boundary.
Slope-based coloring and alerts that make state changes obvious in real time.
Limitations and notes
All filters add lag. Larger thresholds and smaller response trade faster reaction for fewer false turns.
Fixed Points or Ticks can under- or over-filter when volatility regime shifts. ATR adapts, but will also expand bands during spikes.
On extremely choppy symbols, even a well tuned band will step frequently. Widen Enter Mult or reduce Response if needed.
This is a chart study. It does not include commissions, slippage, funding, or gap risks.
Alerts
DBHF Up Slope — baseline turns from down to up on the latest bar.
DBHF Down Slope — baseline turns from up to down on the latest bar.
Implementation details worth knowing
Initialization sets the baseline to the first observed price to avoid a cold-start jump.
Slope is evaluated bar-to-bar. The up and down alerts check for a change of slope rather than raw price crossings.
Candle colors and the baseline plot share the same long/short palette with transparency applied to the line.
Practical workflow
Pick a mode that matches how you think about distance. ATR for volatility aware, Percent for scale-free, Ticks or Points for fixed increments.
Tune Enter Mult until the number of flips feels appropriate for your timeframe.
Set Exit Mult clearly below Enter Mult to create a real hold zone.
Adjust Response last to control “how fast” the baseline chases price once it decides to move.
Final thoughts
Deadband plus hysteresis gives you a principled way to “only care when it matters.” With a sensible threshold and response, the filter yields a stable, low-chop trend cue you can use directly for bias or plug into your own entries, exits, and risk rules. Indicator
Martingale Strategy Simulator [BackQuant]Martingale Strategy Simulator
Purpose
This indicator lets you study how a martingale-style position sizing rule interacts with a simple long or short trading signal. It computes an equity curve from bar-to-bar returns, adapts position size after losing streaks, caps exposure at a user limit, and summarizes risk with portfolio metrics. An optional Monte Carlo module projects possible future equity paths from your realized daily returns.
What a martingale is
A martingale sizing rule increases stake after losses and resets after a win. In its classical form from gambling, you double the bet after each loss so that a single win recovers all prior losses plus one unit of profit. In markets there is no fixed “even-money” payout and returns are multiplicative, so an exact recovery guarantee does not exist. The core idea is unchanged:
Lose one leg → increase next position size
Lose again → increase again
Win → reset to the base size
The expectation of your strategy still depends on the signal’s edge. Sizing does not create positive expectancy on its own. A martingale raises variance and tail risk by concentrating more capital as a losing streak develops.
What it plots
Equity – simulated portfolio equity including compounding
Buy & Hold – equity from holding the chart symbol for context
Optional helpers – last trade outcome, current streak length, current allocation fraction
Optional diagnostics – daily portfolio return, rolling drawdown, metrics table
Optional Monte Carlo probability cone – p5, p16, p50, p84, p95 aggregate bands
Model assumptions
Bar-close execution with no slippage or commissions
Shorting allowed and frictionless
No margin interest, borrow fees, or position limits
No intrabar moves or gaps within a bar (returns are close-to-close)
Sizing applies to equity fraction only and is capped by your setting
All results are hypothetical and for education only.
How the simulator applies it
1) Directional signal
You pick a simple directional rule that produces +1 for long or −1 for short each bar. Options include 100 HMA slope, RSI above or below 50, EMA or SMA crosses, CCI and other oscillators, ATR move, BB basis, and more. The stance is evaluated bar by bar. When the stance flips, the current trade ends and the next one starts.
2) Sizing after losses and wins
Position size is a fraction of equity:
Initial allocation – the starting fraction, for example 0.15 means 15 percent of equity
Increase after loss – multiply the next allocation by your factor after a losing leg, for example 2.00 to double
Reset after win – return to the initial allocation
Max allocation cap – hard ceiling to prevent runaway growth
At a high level the size after k consecutive losses is
alloc(k) = min( cap , base × factor^k ) .
In practice the simulator changes size only when a leg ends and its PnL is known.
3) Equity update
Let r_t = close_t / close_{t-1} − 1 be the symbol’s bar return, d_{t−1} ∈ {+1, −1} the prior bar stance, and a_{t−1} the prior bar allocation fraction. The simulator compounds:
eq_t = eq_{t−1} × (1 + a_{t−1} × d_{t−1} × r_t) .
This is bar-based and avoids intrabar lookahead. Costs, slippage, and borrowing costs are not modeled.
Why traders experiment with martingale sizing
Mean-reversion contexts – if the signal often snaps back after a string of losses, adding size near the tail of a move can pull the average entry closer to the turn
Behavioral or microstructure edges – some rules have modest edge but frequent small whipsaws; size escalation may shorten time-to-recovery when the edge manifests
Exploration and stress testing – studying the relationship between streaks, caps, and drawdowns is instructive even if you do not deploy martingale sizing live
Why martingale is dangerous
Martingale concentrates capital when the strategy is performing worst. The main risks are structural, not cosmetic:
Loss streaks are inevitable – even with a 55 percent win rate you should expect multi-loss runs. The probability of at least one k-loss streak in N trades rises quickly with N.
Size explodes geometrically – with factor 2.0 and base 10 percent, the sequence is 10, 20, 40, 80, 100 (capped) after five losses. Without a strict cap, required size becomes infeasible.
No fixed payout – in gambling, one win at even odds resets PnL. In markets, there is no guaranteed bounce nor fixed profit multiple. Trends can extend and gaps can skip levels.
Correlation of losses – losses cluster in trends and in volatility bursts. A martingale tends to be largest just when volatility is highest.
Margin and liquidity constraints – leverage limits, margin calls, position limits, and widening spreads can force liquidation before a mean reversion occurs.
Fat tails and regime shifts – assumptions of independent, Gaussian returns can understate tail risk. Structural breaks can keep the signal wrong for much longer than expected.
The simulator exposes these dynamics in the equity curve, Max Drawdown, VaR and CVaR, and via Monte Carlo sketches of forward uncertainty.
Interpreting losing streaks with numbers
A rough intuition: if your per-trade win probability is p and loss probability is q=1−p , the chance of a specific run of k consecutive losses is q^k . Over many trades, the chance that at least one k-loss run occurs grows with the number of opportunities. As a sanity check:
If p=0.55 , then q=0.45 . A 6-loss run has probability q^6 ≈ 0.008 on any six-trade window. Across hundreds of trades, a 6 to 8-loss run is not rare.
If your size factor is 1.5 and your base is 10 percent, after 8 losses the requested size is 10% × 1.5^8 ≈ 25.6% . With factor 2.0 it would try to be 10% × 2^8 = 256% but your cap will stop it. The equity curve will still wear the compounded drawdown from the sequence that led to the cap.
This is why the cap setting is central. It does not remove tail risk, but it prevents the sizing rule from demanding impossible positions
Note: The p and q math is illustrative. In live data the win rate and distribution can drift over time, so real streaks can be longer or shorter than the simple q^k intuition suggests..
Using the simulator productively
Parameter studies
Start with conservative settings. Increase one element at a time and watch how the equity, Max Drawdown, and CVaR respond.
Initial allocation – lower base reduces volatility and drawdowns across the board
Increase factor – set modestly above 1.0 if you want the effect at all; doubling is aggressive
Max cap – the most important brake; many users keep it between 20 and 50 percent
Signal selection
Keep sizing fixed and rotate signals to see how streak patterns differ. Trend-following signals tend to produce long wrong-way streaks in choppy ranges. Mean-reversion signals do the opposite. Martingale sizing interacts very differently with each.
Diagnostics to watch
Use the built-in metrics to quantify risk:
Max Drawdown – worst peak-to-trough equity loss
Sharpe and Sortino – volatility and downside-adjusted return
VaR 95 percent and CVaR – tail risk measures from the realized distribution
Alpha and Beta – relationship to your chosen benchmark
If you would like to check out the original performance metrics script with multiple assets with a better explanation on all metrics please see
Monte Carlo exploration
When enabled, the forecast draws many synthetic paths from your realized daily returns:
Choose a horizon and a number of runs
Review the bands: p5 to p95 for a wide risk envelope; p16 to p84 for a narrower range; p50 as the median path
Use the table to read the expected return over the horizon and the tail outcomes
Remember it is a sketch based on your recent distribution, not a predictor
Concrete examples
Example A: Modest martingale
Base 10 percent, factor 1.25, cap 40 percent, RSI>50 signal. You will see small escalations on 2 to 4 loss runs and frequent resets. The equity curve usually remains smooth unless the signal enters a prolonged wrong-way regime. Max DD may rise moderately versus fixed sizing.
Example B: Aggressive martingale
Base 15 percent, factor 2.0, cap 60 percent, EMA cross signal. The curve can look stellar during favorable regimes, then a single extended streak pushes allocation to the cap, and a few more losses drive deep drawdown. CVaR and Max DD jump sharply. This is a textbook case of high tail risk.
Strengths
Bar-by-bar, transparent computation of equity from stance and size
Explicit handling of wins, losses, streaks, and caps
Portable signal inputs so you can A–B test ideas quickly
Risk diagnostics and forward uncertainty visualization in one place
Example, Rolling Max Drawdown
Limitations and important notes
Martingale sizing can escalate drawdowns rapidly. The cap limits position size but not the possibility of extended adverse runs.
No commissions, slippage, margin interest, borrow costs, or liquidity limits are modeled.
Signals are evaluated on closes. Real execution and fills will differ.
Monte Carlo assumes independent draws from your recent return distribution. Markets often have serial correlation, fat tails, and regime changes.
All results are hypothetical. Use this as an educational tool, not a production risk engine.
Practical tips
Prefer gentle factors such as 1.1 to 1.3. Doubling is usually excessive outside of toy examples.
Keep a strict cap. Many users cap between 20 and 40 percent of equity per leg.
Stress test with different start dates and subperiods. Long flat or trending regimes are where martingale weaknesses appear.
Compare to an anti-martingale (increase after wins, cut after losses) to understand the other side of the trade-off.
If you deploy sizing live, add external guardrails such as a daily loss cut, volatility filters, and a global max drawdown stop.
Settings recap
Backtest start date and initial capital
Initial allocation, increase-after-loss factor, max allocation cap
Signal source selector
Trading days per year and risk-free rate
Benchmark symbol for Alpha and Beta
UI toggles for equity, buy and hold, labels, metrics, PnL, and drawdown
Monte Carlo controls for enable, runs, horizon, and result table
Final thoughts
A martingale is not a free lunch. It is a way to tilt capital allocation toward losing streaks. If the signal has a real edge and mean reversion is common, careful and capped escalation can reduce time-to-recovery. If the signal lacks edge or regimes shift, the same rule can magnify losses at the worst possible moment. This simulator makes those trade-offs visible so you can calibrate parameters, understand tail risk, and decide whether the approach belongs anywhere in your research workflow.
Indicator
Adaptive Valuation [BackQuant]Adaptive Valuation
What this is
A composite, zero-centered oscillator that standardizes several classic indicators and blends them into one “valuation” line. It computes RSI, CCI, Demarker, and the Price Zone Oscillator, converts each to a rolling z-score, then forms a weighted average. Optional smoothing, dynamic overbought and oversold bands, and an on-chart table make the inputs and the final score easy to inspect.
How it works
Components
• RSI with its own lookback.
• CCI with its own lookback.
• DM (Demarker) with its own lookback.
• PZO (Price Zone Oscillator) with its own lookback.
Standardization via z-score
Each component is transformed using a rolling z-score over lookback bars:
z = (value − mean) ÷ stdev , where the mean is an EMA and the stdev is rolling.
This puts all inputs on a comparable scale measured in standard deviations.
Weighted blend
The z-scores are combined with user weights w_rsi, w_cci, w_dm, w_pzo to produce a single valuation series. If desired, it is then smoothed with a selected moving average (SMA, EMA, WMA, HMA, RMA, DEMA, TEMA, LINREG, ALMA, T3). ALMA’s sigma input shapes its curve.
Dynamic thresholds (optional)
Two ways to set overbought and oversold:
• Static : fixed levels at ob_thres and os_thres .
• Dynamic : ±k·σ bands, where σ is the rolling standard deviation of the valuation over dynLen .
Bands can be centered at zero or around the valuation’s rolling mean ( centerZero ).
Visualization and UI
• Zero line at 0 with gradient fill that darkens as the valuation moves away from 0.
• Optional plotting of band lines and background highlights when OB or OS is active.
• Optional candle and background coloring driven by the valuation.
• Summary table showing each component’s current z-score, the final score, and a compact status.
How it can be used
• Bias filter : treat crosses above 0 as bullish bias and below 0 as bearish bias.
• Mean-reversion context : look for exhaustion when the valuation enters the OB or OS region, then watch for exits from those regions or a return toward 0.
• Signal confirmation : use the final score to confirm setups from structure or price action.
• Adaptive banding : with dynamic thresholds, OB and OS adjust to prevailing variability rather than relying on fixed lines.
• Component tuning : change weights to emphasize trend (raise DM, reduce RSI/CCI) or range behavior (raise RSI/CCI, reduce DM). PZO can help in swing environments.
Why z-score blending helps
Indicators often live on different scales. Z-scoring places them on a common, unitless axis, so a one-sigma move in RSI has comparable influence to a one-sigma move in CCI. This reduces scale bias and allows transparent weighting. It also facilitates regime-aware thresholds because the dynamic bands scale with recent dispersion.
Inputs to know
• Component lookbacks : rsilb, ccilb, dmlb, pzolb control each raw signal.
• Standardization window : lookback sets the z-score memory. Longer smooths, shorter reacts.
• Weights : w_rsi, w_cci, w_dm, w_pzo determine each component’s influence.
• Smoothing : maType, smoothP, sig govern optional post-blend smoothing.
• Dynamic bands : dyn_thres, dynLen, thres_k, centerZero configure the adaptive OB/OS logic.
• UI : toggle the plot, table, candle coloring, and threshold lines.
Reading the plot
• Above 0 : composite pressure is positive.
• Below 0 : composite pressure is negative.
• OB region : valuation above the chosen OB line. Risk of mean reversion rises and momentum continuation needs evidence.
• OS region : mirror logic on the downside.
• Band exits : leaving OB or OS can serve as a normalization cue.
Strengths
• Normalizes heterogeneous signals into one interpretable series.
• Adjustable component weights to match instrument behavior.
• Dynamic thresholds adapt to changing volatility and drift.
• Transparent diagnostics from the on-chart table.
• Flexible smoothing choices, including ALMA and T3.
Limitations and cautions
• Z-scores assume a reasonably stationary window. Sharp regime shifts can make recent bands unrepresentative.
• Highly correlated components can overweight the same effect. Consider adjusting weights to avoid double counting.
• More smoothing adds lag. Less smoothing adds noise.
• Dynamic bands recalibrate with dynLen ; if set too short, bands may swing excessively. If too long, bands can be slow to adapt.
Practical tuning tips
• Trending symbols: increase w_dm , use a modest smoother like EMA or T3, and use centerZero dynamic bands.
• Choppy symbols: increase w_rsi and w_cci , consider ALMA with a higher sigma , and widen bands with a larger thres_k .
• Multiday swing charts: lengthen lookback and dynLen to stabilize the scale.
• Lower timeframes: shorten component lookbacks slightly and reduce smoothing to keep signals timely.
Alerts
• Enter and exit of Overbought and Oversold, based on the active band choice.
• Bullish and bearish zero crosses.
Use alerts as prompts to review context rather than as stand-alone trade commands.
Final Remarks
We created this to show people a different way of making indicators & trading.
You can process normal indicators in multiple ways to enhance or change the signal, especially with this you can utilise machine learning to optimise the weights, then trade accordingly.
All of the different components were selected to give some sort of signal, its made out of simple components yet is effective. As long as the user calibrates it to their Trading/ investing style you can find good results. Do not use anything standalone, ensure you are backtesting and creating a proper system. Indicator
VWAP For Loop [BackQuant]VWAP For Loop
What this tool does—in one sentence
A volume-weighted trend gauge that anchors VWAP to a calendar period (day/week/month/quarter/year) and then scores the persistence of that VWAP trend with a simple for-loop “breadth” count; the result is a clean, threshold-driven oscillator plus an optional VWAP overlay and alerts.
Plain-English overview
Instead of judging raw price alone, this indicator focuses on anchored VWAP —the market’s average price paid during your chosen institutional period. It then asks a simple question across a configurable set of lookback steps: “Is the current anchored VWAP higher than it was i bars ago—or lower?” Each “yes” adds +1, each “no” adds −1. Summing those answers creates a score that reflects how consistently the volume-weighted trend has been rising or falling. Extreme positive scores imply persistent, broad strength; deeply negative scores imply persistent weakness. Crossing predefined thresholds produces objective long/short events and color-coded context.
Under the hood
• Anchoring — VWAP using hlc3 × volume resets exactly when the selected period rolls:
Day → session change, Week → new week, Month → new month, Quarter/Year → calendar quarter/year.
• For-loop scoring — For lag steps i = , compare today’s VWAP to VWAP .
– If VWAP > VWAP , add +1.
– Else, add −1.
The final score ∈ , where N = (end − start + 1). With defaults (1→45), N = 45.
• Signal logic (stateful)
– Long when score > upper (e.g., > 40 with N = 45 → VWAP higher than ~89% of checked lags).
– Short on crossunder of lower (e.g., dropping below −10).
– A compact state variable ( out ) holds the current regime: +1 (long), −1 (short), otherwise unchanged. This “stickiness” avoids constant flipping between bars without sufficient evidence.
Why VWAP + a breadth score?
• VWAP aggregates both price and volume—where participants actually traded.
• The breadth-style count rewards consistency of the anchored trend, not one-off spikes.
• Thresholds give you binary structure when you need it (alerts, automation), without complex math.
What you’ll see on the chart
• Sub-pane oscillator — The for-loop score line, colored by regime (long/short/neutral).
• Main-pane VWAP (optional) — Even though the indicator runs off-chart, the anchored VWAP can be overlaid on price (toggle visibility and whether it inherits trend colors).
• Threshold guides — Horizontal lines for the long/short bands (toggle).
• Cosmetics — Optional candle painting and background shading by regime; adjustable line width and colors.
Input map (quick reference)
• VWAP Anchor Period — Day, Week, Month, Quarter, Year.
• Calculation Start/End — The for-loop lag window . With 1→45, you evaluate 45 comparisons.
• Long/Short Thresholds — Default upper=40, lower=−10 (asymmetric by design; see below).
• UI/Style — Show thresholds, paint candles, background color, line width, VWAP visibility and coloring, custom long/short colors.
Interpreting the score
• Near +N — Current anchored VWAP is above most historical VWAP checkpoints in the window → entrenched strength.
• Near −N — Current anchored VWAP is below most checkpoints → entrenched weakness.
• Between — Mixed, choppy, or transitioning regimes; use thresholds to avoid reacting to noise.
Why the asymmetric default thresholds?
• Long = score > upper (40) — Demands unusually broad upside persistence before declaring “long regime.”
• Short = crossunder lower (−10) — Triggers only on downward momentum events (a fresh breach), not merely being below −10. This combination tends to:
– Capture sustained uptrends only when they’re very strong.
– Flag downside turns as they occur, rather than waiting for an extreme negative breadth.
Tuning guide
Choose an anchor that matches your horizon
– Intraday scalps : Day anchor on intraday charts.
– Swing/position : Month or Quarter anchor on 1h/4h/D charts to capture institutional cycles.
Pick the for-loop window
– Larger N (bigger end) = stronger evidence requirement, smoother oscillator.
– Smaller N = faster, more reactive score.
Set achievable thresholds
– Ensure upper ≤ N and lower ≥ −N ; if N=30, an upper of 40 can never trigger.
– Symmetric setups (e.g., +20/−20) are fine if you want balanced behavior.
Match visuals to intent
– Enabling VWAP coloring lets you see regime directly on price.
– Background shading is useful for discretionary reading; turn it off for cleaner automation displays.
Playbook examples
• Trend confirmation with disciplined entries — On Month anchor, N=45, upper=38–42: when the long regime engages, use pullbacks toward anchored VWAP on the main pane for entries, with stops just beyond VWAP or a recent swing.
• Downside transition detection — Keep lower around −8…−12 and watch for crossunders; combine with price losing anchored VWAP to validate risk-off.
• Intraday bias filter — Day anchor on a 5–15m chart, N=20–30, upper ~ 16–20, lower ~ −6…−10. Only take longs while score is positive and above a midline you define (e.g., 0), and shorts only after a genuine crossunder.
Behavior around resets (important)
Anchored VWAP is hard-reset each period. Immediately after a reset, the series can be young and comparisons to pre-reset values may span two periods. If you prefer within-period evaluation only, choose end small enough not to bridge typical period length on your timeframe, or accept that the breadth test intentionally spans regimes.
Alerts included
• VWAP FL Long — Fires when the long condition is true (score > upper and not in short).
• VWAP FL Short — Fires on crossunder of the lower threshold (event-driven).
Messages include {{ticker}} and {{interval}} placeholders for routing.
Strengths
• Simple, transparent math — Easy to reason about and validate.
• Volume-aware by construction — Decisions reference VWAP, not just price.
• Robust to single-bar noise — Needs many lags to agree before flipping state (by design, via thresholds and the stateful output).
Limitations & cautions
• Threshold feasibility — If N < upper or |lower| > N, signals will never trigger; always cross-check N.
• Path dependence — The state variable persists until a new event; if you want frequent re-evaluation, lower thresholds or reduce N.
• Regime changes — Calendar resets can produce early ambiguity; expect a few bars for the breadth to mature.
• VWAP sensitivity to volume spikes — Large prints can tilt VWAP abruptly; that behavior is intentional in VWAP-based logic.
Suggested starting profiles
• Intraday trend bias : Anchor=Day, N=25 (1→25), upper=18–20, lower=−8, paint candles ON.
• Swing bias : Anchor=Month, N=45 (1→45), upper=38–42, lower=−10, VWAP coloring ON, background OFF.
• Balanced reactivity : Anchor=Week, N=30 (1→30), upper=20–22, lower=−10…−12, symmetric if desired.
Implementation notes
• The indicator runs in a separate pane (oscillator), but VWAP itself is drawn on price using forced overlay so you can see interactions (touches, reclaim/loss).
• HLC3 is used for VWAP price; that’s a common choice to dampen wick noise while still reflecting intrabar range.
• For-loop cap is kept modest (≤50) for performance and clarity.
How to use this responsibly
Treat the oscillator as a bias and persistence meter . Combine it with your entry framework (structure breaks, liquidity zones, higher-timeframe context) and risk controls. The design emphasizes clarity over complexity—its edge is in how strictly it demands agreement before declaring a regime, not in predicting specific turns.
Summary
VWAP For Loop distills the question “How broadly is the anchored, volume-weighted trend advancing or retreating?” into a single, thresholded score you can read at a glance, alert on, and color through your chart. With careful anchoring and thresholds sized to your window length, it becomes a pragmatic bias filter for both systematic and discretionary workflows. Indicator
Machine Learning BBPct [BackQuant]Machine Learning BBPct
What this is (in one line)
A Bollinger Band %B oscillator enhanced with a simplified K-Nearest Neighbors (KNN) pattern matcher. The model compares today’s context (volatility, momentum, volume, and position inside the bands) to similar situations in recent history and blends that historical consensus back into the raw %B to reduce noise and improve context awareness. It is informational and diagnostic—designed to describe market state, not to sell a trading system.
Background: %B in plain terms
Bollinger %B measures where price sits inside its dynamic envelope: 0 at the lower band, 1 at the upper band, ~ 0.5 near the basis (the moving average). Readings toward 1 indicate pressure near the envelope’s upper edge (often strength or stretch), while readings toward 0 indicate pressure near the lower edge (often weakness or stretch). Because bands adapt to volatility, %B is naturally comparable across regimes.
Why add (simplified) KNN?
Classic %B is reactive and can be whippy in fast regimes. The simplified KNN layer builds a “nearest-neighbor memory” of recent market states and asks: “When the market looked like this before, where did %B tend to be next bar?” It then blends that estimate with the current %B. Key ideas:
• Feature vector . Each bar is summarized by up to five normalized features:
– %B itself (normalized)
– Band width (volatility proxy)
– Price momentum (ROC)
– Volume momentum (ROC of volume)
– Price position within the bands
• Distance metric . Euclidean distance ranks the most similar recent bars.
• Prediction . Average the neighbors’ prior %B (lagged to avoid lookahead), inverse-weighted by distance.
• Blend . Linearly combine raw %B and KNN-predicted %B with a configurable weight; optional filtering then adapts to confidence.
This remains “simplified” KNN: no training/validation split, no KD-trees, no scaling beyond windowed min-max, and no probabilistic calibration.
How the script is organized (by input groups)
1) BBPct Settings
• Price Source – Which price to evaluate (%B is computed from this).
• Calculation Period – Lookback for SMA basis and standard deviation.
• Multiplier – Standard deviation width (e.g., 2.0).
• Apply Smoothing / Type / Length – Optional smoothing of the %B stream before ML (EMA, RMA, DEMA, TEMA, LINREG, HMA, etc.). Turning this off gives you the raw %B.
2) Thresholds
• Overbought/Oversold – Default 0.8 / 0.2 (inside ).
• Extreme OB/OS – Stricter zones (e.g., 0.95 / 0.05) to flag stretch conditions.
3) KNN Machine Learning
• Enable KNN – Switch between pure %B and hybrid.
• K (neighbors) – How many historical analogs to blend (default 8).
• Historical Period – Size of the search window for neighbors.
• ML Weight – Blend between raw %B and KNN estimate.
• Number of Features – Use 2–5 features; higher counts add context but raise the risk of overfitting in short windows.
4) Filtering
• Method – None, Adaptive, Kalman-style (first-order),
or Hull smoothing.
• Strength – How aggressively to smooth. “Adaptive” uses model confidence to modulate its alpha: higher confidence → stronger reliance on the ML estimate.
5) Performance Tracking
• Win-rate Period – Simple running score of past signal outcomes based on target/stop/time-out logic (informational, not a robust backtest).
• Early Entry Lookback – Horizon for forecasting a potential threshold cross.
• Profit Target / Stop Loss – Used only by the internal win-rate heuristic.
6) Self-Optimization
• Enable Self-Optimization – Lightweight, rolling comparison of a few canned settings (K = 8/14/21 via simple rules on %B extremes).
• Optimization Window & Stability Threshold – Governs how quickly preferred K changes and how sensitive the overfitting alarm is.
• Adaptive Thresholds – Adjust the OB/OS lines with volatility regime (ATR ratio), widening in calm markets and tightening in turbulent ones (bounded 0.7–0.9 and 0.1–0.3).
7) UI Settings
• Show Table / Zones / ML Prediction / Early Signals – Toggle informational overlays.
• Signal Line Width, Candle Painting, Colors – Visual preferences.
Step-by-step logic
A) Compute %B
Basis = SMA(source, len); dev = stdev(source, len) × multiplier; Upper/Lower = Basis ± dev.
%B = (price − Lower) / (Upper − Lower). Optional smoothing yields standardBB .
B) Build the feature vector
All features are min-max normalized over the KNN window so distances are in comparable units. Features include normalized %B, normalized band width, normalized price ROC, normalized volume ROC, and normalized position within bands. You can limit to the first N features (2–5).
C) Find nearest neighbors
For each bar inside the lookback window, compute the Euclidean distance between current features and that bar’s features. Sort by distance, keep the top K .
D) Predict and blend
Use inverse-distance weights (with a strong cap for near-zero distances) to average neighbors’ prior %B (lagged by one bar). This becomes the KNN estimate. Blend it with raw %B via the ML weight. A variance of neighbor %B around the prediction becomes an uncertainty proxy ; combined with a stability score (how long parameters remain unchanged), it forms mlConfidence ∈ . The Adaptive filter optionally transforms that confidence into a smoothing coefficient.
E) Adaptive thresholds
Volatility regime (ATR(14) divided by its 50-bar SMA) nudges OB/OS thresholds wider or narrower within fixed bounds. The aim: comparable extremeness across regimes.
F) Early entry heuristic
A tiny two-step slope/acceleration probe extrapolates finalBB forward a few bars. If it is on track to cross OB/OS soon (and slope/acceleration agree), it flags an EARLY_BUY/SELL candidate with an internal confidence score. This is explicitly a heuristic—use as an attention cue, not a signal by itself.
G) Informational win-rate
The script keeps a rolling array of trade outcomes derived from signal transitions + rudimentary exits (target/stop/time). The percentage shown is a rough diagnostic , not a validated backtest.
Outputs and visual language
• ML Bollinger %B (finalBB) – The main line after KNN blending and optional filtering.
• Gradient fill – Greenish tones above 0.5, reddish below, with intensity following distance from the midline.
• Adaptive zones – Overbought/oversold and extreme bands; shaded backgrounds appear at extremes.
• ML Prediction (dots) – The KNN estimate plotted as faint circles; becomes bright white when confidence > 0.7.
• Early arrows – Optional small triangles for approaching OB/OS.
• Candle painting – Light green above the midline, light red below (optional).
• Info panel – Current value, signal classification, ML confidence, optimized K, stability, volatility regime, adaptive thresholds, overfitting flag, early-entry status, and total signals processed.
Signal classification (informational)
The indicator does not fire trade commands; it labels state:
• STRONG_BUY / STRONG_SELL – finalBB beyond extreme OS/OB thresholds.
• BUY / SELL – finalBB beyond adaptive OS/OB.
• EARLY_BUY / EARLY_SELL – forecast suggests a near-term cross with decent internal confidence.
• NEUTRAL – between adaptive bands.
Alerts (what you can automate)
• Entering adaptive OB/OS and extreme OB/OS.
• Midline cross (0.5).
• Overfitting detected (frequent parameter flipping).
• Early signals when early confidence > 0.7.
These are purely descriptive triggers around the indicator’s state.
Practical interpretation
• Mean-reversion context – In range markets, adaptive OS/OB with ML smoothing can reduce whipsaws relative to raw %B.
• Trend context – In persistent trends, the KNN blend can keep finalBB nearer the mid/upper region during healthy pullbacks if history supports similar contexts.
• Regime awareness – Watch the volatility regime and adaptive thresholds. If thresholds compress (high vol), “OB/OS” comes sooner; if thresholds widen (calm), it takes more stretch to flag.
• Confidence as a weight – High mlConfidence implies neighbors agree; you may rely more on the ML curve. Low confidence argues for de-emphasizing ML and leaning on raw %B or other tools.
• Stability score – Rising stability indicates consistent parameter selection and fewer flips; dropping stability hints at a shifting backdrop.
Methodological notes
• Normalization uses rolling min-max over the KNN window. This is simple and scale-agnostic but sensitive to outliers; the distance metric will reflect that.
• Distance is unweighted Euclidean. If you raise featureCount, you increase dimensionality; consider keeping K larger and lookback ample to avoid sparse-neighbor artifacts.
• Lag handling intentionally uses neighbors’ previous %B for prediction to avoid lookahead bias.
• Self-optimization is deliberately modest: it only compares a few canned K/threshold choices using simple “did an extreme anticipate movement?” scoring, then enforces a stability regime and an overfitting guard. It is not a grid search or GA.
• Kalman option is a first-order recursive filter (fixed gain), not a full state-space estimator.
• Hull option derives a dynamic length from 1/strength; it is a convenience smoothing alternative.
Limitations and cautions
• Non-stationarity – Nearest neighbors from the recent window may not represent the future under structural breaks (policy shifts, liquidity shocks).
• Curse of dimensionality – Adding features without sufficient lookback can make genuine neighbors rare.
• Overfitting risk – The script includes a crude overfitting detector (frequent parameter flips) and will fall back to defaults when triggered, but this is only a guardrail.
• Win-rate display – The internal score is illustrative; it does not constitute a tradable backtest.
• Latency vs. smoothness – Smoothing and ML blending reduce noise but add lag; tune to your timeframe and objectives.
Tuning guide
• Short-term scalping – Lower len (10–14), slightly lower multiplier (1.8–2.0), small K (5–8), featureCount 3–4, Adaptive filter ON, moderate strength.
• Swing trading – len (20–30), multiplier ~2.0, K (8–14), featureCount 4–5, Adaptive thresholds ON, filter modest.
• Strong trends – Consider higher adaptive_upper/lower bounds (or let volatility regime do it), keep ML weight moderate so raw %B still reflects surges.
• Chop – Higher ML weight and stronger Adaptive filtering; accept lag in exchange for fewer false extremes.
How to use it responsibly
Treat this as a state descriptor and context filter. Pair it with your execution signals (structure breaks, volume footprints, higher-timeframe bias) and risk management. If mlConfidence is low or stability is falling, lean less on the ML line and more on raw %B or external confirmation.
Summary
Machine Learning BBPct augments a familiar oscillator with a transparent, simplified KNN memory of recent conditions. By blending neighbors’ behavior into %B and adapting thresholds to volatility regime—while exposing confidence, stability, and a plain early-entry heuristic—it provides an informational, probability-minded view of stretch and reversion that you can interpret alongside your own process. Indicator
Seasonality Monte Carlo Forecaster [BackQuant]Seasonality Monte Carlo Forecaster
Plain-English overview
This tool projects a cone of plausible future prices by combining two ideas that traders already use intuitively: seasonality and uncertainty. It watches how your market typically behaves around this calendar date, turns that seasonal tendency into a small daily “drift,” then runs many randomized price paths forward to estimate where price could land tomorrow, next week, or a month from now. The result is a probability cone with a clear expected path, plus optional overlays that show how past years tended to move from this point on the calendar. It is a planning tool, not a crystal ball: the goal is to quantify ranges and odds so you can size, place stops, set targets, and time entries with more realism.
What Monte Carlo is and why quants rely on it
• Definition . Monte Carlo simulation is a way to answer “what might happen next?” when there is randomness in the system. Instead of producing a single forecast, it generates thousands of alternate futures by repeatedly sampling random shocks and adding them to a model of how prices evolve.
• Why it is used . Markets are noisy. A single point forecast hides risk. Monte Carlo gives a distribution of outcomes so you can reason in probabilities: the median path, the 68% band, the 95% band, tail risks, and the chance of hitting a specific level within a horizon.
• Core strengths in quant finance .
– Path-dependent questions : “What is the probability we touch a stop before a target?” “What is the expected drawdown on the way to my objective?”
– Pricing and risk : Useful for path-dependent options, Value-at-Risk (VaR), expected shortfall (CVaR), stress paths, and scenario analysis when closed-form formulas are unrealistic.
– Planning under uncertainty : Portfolio construction and rebalancing rules can be tested against a cloud of plausible futures rather than a single guess.
• Why it fits trading workflows . It turns gut feel like “seasonality is supportive here” into quantitative ranges: “median path suggests +X% with a 68% band of ±Y%; stop at Z has only ~16% odds of being tagged in N days.”
How this indicator builds its probability cone
1) Seasonal pattern discovery
The script builds two day-of-year maps as new data arrives:
• A return map where each calendar day stores an exponentially smoothed average of that day’s log return (yesterday→today). The smoothing (90% old, 10% new) behaves like an EWMA, letting older seasons matter while adapting to new information.
• A volatility map that tracks the typical absolute return for the same calendar day.
It calculates the day-of-year carefully (with leap-year adjustment) and indexes into a 365-slot seasonal array so “March 18” is compared with past March 18ths. This becomes the seasonal bias that gently nudges simulations up or down on each forecast day.
2) Choice of randomness engine
You can pick how the future shocks are generated:
• Daily mode uses a Gaussian draw with the seasonal bias as the mean and a volatility that comes from realized returns, scaled down to avoid over-fitting. It relies on the Box–Muller transform internally to turn two uniform random numbers into one normal shock.
• Weekly mode uses bootstrap sampling from the seasonal return history (resampling actual historical daily drifts and then blending in a fraction of the seasonal bias). Bootstrapping is robust when the empirical distribution has asymmetry or fatter tails than a normal distribution.
Both modes seed their random draws deterministically per path and day, which makes plots reproducible bar-to-bar and avoids flickering bands.
3) Volatility scaling to current conditions
Markets do not always live in average volatility. The engine computes a simple volatility factor from ATR(20)/price and scales the simulated shocks up or down within sensible bounds (clamped between 0.5× and 2.0×). When the current regime is quiet, the cone narrows; when ranges expand, the cone widens. This prevents the classic mistake of projecting calm markets into a storm or vice versa.
4) Many futures, summarized by percentiles
The model generates a matrix of price paths (capped at 100 runs for performance inside PulseWire), each path stepping forward for your selected horizon. For each forecast day it sorts the simulated prices and pulls key percentiles:
• 5th and 95th → approximate 95% band (outer cone).
• 16th and 84th → approximate 68% band (inner cone).
• 50th → the median or “expected path.”
These are drawn as polylines so you can immediately see central tendency and dispersion.
5) A historical overlay (optional)
Turn on the overlay to sketch a dotted path of what a purely seasonal projection would look like for the next ~30 days using only the return map, no randomness. This is not a forecast; it is a visual reminder of the seasonal drift you are biasing toward.
Inputs you control and how to think about them
Monte Carlo Simulation
• Price Series for Calculation . The source series, typically close.
• Enable Probability Forecasts . Master switch for simulation and drawing.
• Simulation Iterations . Requested number of paths to run. Internally capped at 100 to protect performance, which is generally enough to estimate the percentiles for a trading chart. If you need ultra-smooth bands, shorten the horizon.
• Forecast Days Ahead . The length of the cone. Longer horizons dilute seasonal signal and widen uncertainty.
• Probability Bands . Draw all bands, just 95%, just 68%, or a custom level (display logic remains 68/95 internally; the custom number is for labeling and color choice).
• Pattern Resolution . Daily leans on day-of-year effects like “turn-of-month” or holiday patterns. Weekly biases toward day-of-week tendencies and bootstraps from history.
• Volatility Scaling . On by default so the cone respects today’s range context.
Plotting & UI
• Probability Cone . Plots the outer and inner percentile envelopes.
• Expected Path . Plots the median line through the cone.
• Historical Overlay . Dotted seasonal-only projection for context.
• Band Transparency/Colors . Customize primary (outer) and secondary (inner) band colors and the mean path color. Use higher transparency for cleaner charts.
What appears on your chart
• A cone starting at the most recent bar, fanning outward. The outer lines are the ~95% band; the inner lines are the ~68% band.
• A median path (default blue) running through the center of the cone.
• An info panel on the final historical bar that summarizes simulation count, forecast days, number of seasonal patterns learned, the current day-of-year, expected percentage return to the median, and the approximate 95% half-range in percent.
• Optional historical seasonal path drawn as dotted segments for the next 30 bars.
How to use it in trading
1) Position sizing and stop logic
The cone translates “volatility plus seasonality” into distances.
• Put stops outside the inner band if you want only ~16% odds of a stop-out due to noise before your thesis can play.
• Size positions so that a test of the inner band is survivable and a test of the outer band is rare but acceptable.
• If your target sits inside the 68% band at your horizon, the payoff is likely modest; outside the 68% but inside the 95% can justify “one-good-push” trades; beyond the 95% band is a low-probability flyer—consider scaling plans or optionality.
2) Entry timing with seasonal bias
When the median path slopes up from this calendar date and the cone is relatively narrow, a pullback toward the lower inner band can be a high-quality entry with a tight invalidation. If the median slopes down, fade rallies toward the upper band or step aside if it clashes with your system.
3) Target selection
Project your time horizon to N bars ahead, then pick targets around the median or the opposite inner band depending on your style. You can also anchor dynamic take-profits to the moving median as new bars arrive.
4) Scenario planning & “what-ifs”
Before events, glance at the cone: if the 95% band already spans a huge range, trade smaller, expect whips, and avoid placing stops at obvious band edges. If the cone is unusually tight, consider breakout tactics and be ready to add if volatility expands beyond the inner band with follow-through.
5) Options and vol tactics
• When the cone is tight : Prefer long gamma structures (debit spreads) only if you expect a regime shift; otherwise premium selling may dominate.
• When the cone is wide : Debit structures benefit from range; credit spreads need wider wings or smaller size. Align with your separate IV metrics.
Reading the probability cone like a pro
• Cone slope = seasonal drift. Upward slope means the calendar has historically favored positive drift from this date, downward slope the opposite.
• Cone width = regime volatility. A widening fan tells you that uncertainty grows fast; a narrow cone says the market typically stays contained.
• Mean vs. price gap . If spot trades well above the median path and the upper band, mean-reversion risk is high. If spot presses the lower inner band in an up-sloping cone, you are in the “buy fear” zone.
• Touches and pierces . Touching the inner band is common noise; piercing it with momentum signals potential regime change; the outer band should be rare and often brings snap-backs unless there is a structural catalyst.
Methodological notes (what the code actually does)
• Log returns are used for additivity and better statistical behavior: sim_ret is applied via exp(sim_ret) to evolve price.
• Seasonal arrays are updated online with EWMA (90/10) so the model keeps learning as each bar arrives.
• Leap years are handled; indexing still normalizes into a 365-slot map so the seasonal pattern remains stable.
• Gaussian engine (Daily mode) centers shocks on the seasonal bias with a conservative standard deviation.
• Bootstrap engine (Weekly mode) resamples from observed seasonal returns and adds a fraction of the bias, which captures skew and fat tails better.
• Volatility adjustment multiplies each daily shock by a factor derived from ATR(20)/price, clamped between 0.5 and 2.0 to avoid extreme cones.
• Performance guardrails : simulations are capped at 100 paths; the probability cone uses polylines (no heavy fills) and only draws on the last confirmed bar to keep charts responsive.
• Prerequisite data : at least ~30 seasonal entries are required before the model will draw a cone; otherwise it waits for more history.
Strengths and limitations
• Strengths :
– Probabilistic thinking replaces single-point guessing.
– Seasonality adds a small but meaningful directional bias that many markets exhibit.
– Volatility scaling adapts to the current regime so the cone stays realistic.
• Limitations :
– Seasonality can break around structural changes, policy shifts, or one-off events.
– The number of paths is performance-limited; percentile estimates are good for trading, not for academic precision.
– The model assumes tomorrow’s randomness resembles recent randomness; if regime shifts violently, the cone will lag until the EWMA adapts.
– Holidays and missing sessions can thin the seasonal sample for some assets; be cautious with very short histories.
Tuning guide
• Horizon : 10–20 bars for tactical trades; 30+ for swing planning when you care more about broad ranges than precise targets.
• Iterations : The default 100 is enough for stable 5/16/50/84/95 percentiles. If you crave smoother lines, shorten the horizon or run on higher timeframes.
• Daily vs. Weekly : Daily for equities and crypto where month-end and turn-of-month effects matter; Weekly for futures and FX where day-of-week behavior is strong.
• Volatility scaling : Keep it on. Turn off only when you intentionally want a “pure seasonality” cone unaffected by current turbulence.
Workflow examples
• Swing continuation : Cone slopes up, price pulls into the lower inner band, your system fires. Enter near the band, stop just outside the outer line for the next 3–5 bars, target near the median or the opposite inner band.
• Fade extremes : Cone is flat or down, price gaps to the upper outer band on news, then stalls. Favor mean-reversion toward the median, size small if volatility scaling is elevated.
• Event play : Before CPI or earnings on a proxy index, check cone width. If the inner band is already wide, cut size or prefer options structures that benefit from range.
Good habits
• Pair the cone with your entry engine (breakout, pullback, order flow). Let Monte Carlo do range math; let your system do signal quality.
• Do not anchor blindly to the median; recalc after each bar. When the cone’s slope flips or width jumps, the plan should adapt.
• Validate seasonality for your symbol and timeframe; not every market has strong calendar effects.
Summary
The Seasonality Monte Carlo Forecaster wraps institutional risk planning into a single overlay: a data-driven seasonal drift, realistic volatility scaling, and a probabilistic cone that answers “where could we be, with what odds?” within your trading horizon. Use it to place stops where randomness is less likely to take you out, to set targets aligned with realistic travel, and to size positions with confidence born from distributions rather than hunches. It will not predict the future, but it will keep your decisions anchored to probabilities—the language markets actually speak. Indicator
Dip Hunter [BackQuant]Dip Hunter
What this tool does in plain language
Dip Hunter is a pullback detector designed to find high quality buy-the-dip opportunities inside healthy trends and to avoid random knife catches. It watches for a quick drop from a recent high, checks that the drop happened with meaningful participation and volatility, verifies short-term weakness inside a larger uptrend, then scores the setup and paints the chart so you can act with confidence. It also draws clean entry lines, provides a meter that shows dip strength at a glance, and ships with alerts that match common execution workflows.
How Dip Hunter thinks
It defines a recent swing reference, measures how far price has dipped off that high, and only looks at candidates that meet your minimum percentage drop.
It confirms the dip with real activity by requiring a volume spike and a volatility spike.
It checks structure with two EMAs. Price should be weak in the short term while the larger context remains constructive.
It optionally requires a higher-timeframe trend to be up so you focus on pullbacks in trending markets.
It bundles those checks into a score and shows you the score on the candles and on a gradient meter.
When everything lines up it paints a green triangle below the bar, shades the background, and (if you wish) draws a horizontal entry line at your chosen level.
Inputs and what they mean
Dip Hunter Settings
• Vol Lookback and Vol Spike : The script computes an average volume over the lookback window and flags a spike when current volume is a multiple of that average. A multiplier of 2.0 means today’s volume must be at least double the average. This helps filter noise and focuses on dips that other traders actually traded.
• Fast EMA and Slow EMA : Short-term and medium-term structure references. A dip is more credible if price closes below the fast EMA while the fast EMA is still below the slow EMA during the pullback. That is classic corrective behavior inside a larger trend.
• Price Smooth : Optional smoothing length for price-derived series. Use this if you trade very noisy assets or low timeframes.
• Volatility Len and Vol Spike (volatility) : The script checks both standard deviation and true range against their own averages. If either expands beyond your multiplier the market confirms the move with range.
• Dip % and Lookback Bars : The engine finds the highest high over the lookback window, then computes the percentage drawdown from that high to the current close. Only dips larger than your threshold qualify.
Trend Filter
• Enable Trend Filter : When on, Dip Hunter will only trigger if the market is in an uptrend.
• Trend EMA Period : The longer EMA that defines the session’s backbone trend.
• Minimum Trend Strength : A small positive slope requirement. In practice this means the trend EMA should be rising, and price should be above it. You can raise the value to be more selective.
Entries
• Show Entry Lines : Draws a horizontal guide from the signal bar for a fixed number of bars. Great for limit orders, scaling, or re-tests.
• Line Length (bars) : How far the entry guide extends.
• Min Gap (bars) : Suppresses new entry lines if another dip fired recently. Prevents clutter during choppy sequences.
• Entry Price : Choose the line level. “Low” anchors at the signal candle’s low. “Close” anchors at the signal close. “Dip % Level” anchors at the theoretical level defined by recent_high × (1 − dip%). This lets you work resting orders at a consistent discount.
Heat / Meter
• Color Bars by Score : Colors each candle using a red→white→green gradient. Red is overheated, green is prime dip territory, white is neutral.
• Show Meter Table : Adds a compact gradient strip with a pointer that tracks the current score.
• Meter Cells and Meter Position : Resolution and placement of the meter.
UI Settings
• Show Dip Signals : Plots green triangles under qualifying bars and tints the background very lightly.
• Show EMAs : Plots fast, slow, and the trend EMA (if the trend filter is enabled).
• Bullish, Bearish, Neutral colors : Theme controls for shapes, fills, and bar painting.
Core calculations explained simply
Recent high and dip percent
The script finds the highest high over Lookback Bars , calls it “recent high,” then calculates:
dip% = (recent_high − close) ÷ recent_high × 100.
If dip% is larger than Dip % , condition one passes.
Volume confirmation
It computes a simple moving average of volume over Vol Lookback . If current volume ÷ average volume > Vol Spike , we have a participation spike. It also checks 5-bar ROC of volume. If ROC > 50 the spike is forceful. This gets an extra score point.
Volatility confirmation
Two independent checks:
• Standard deviation of closes vs its own average.
• True range vs ATR.
If either expands beyond Vol Spike (volatility) the move has range. This prevents false triggers from quiet drifts.
Short-term structure
Price should close below the Fast EMA and the fast EMA should be below the Slow EMA at the moment of the dip. That is the anatomy of a pullback rather than a full breakdown.
Macro trend context (optional)
When Enable Trend Filter is on, the Trend EMA must be rising and price must be above it. The logic prefers “micro weakness inside macro strength” which is the highest probability pattern for buying dips.
Signal formation
A valid dip requires:
• dip% > threshold
• volume spike true
• volatility spike true
• close below fast EMA
• fast EMA below slow EMA
If the trend filter is enabled, a rising trend EMA with price above it is also required. When all true, the triangle prints, the background tints, and optional entry lines are drawn.
Scoring and visuals
Binary checks into a continuous score
Each component contributes to a score between 0 and 1. The script then rescales to a centered range (−50 to +50).
• Low or negative scores imply “overheated” conditions and are shaded toward red.
• High positive scores imply “ripe for a dip buy” conditions and are shaded toward green.
• The gradient meter repeats the same logic, with a pointer so you can read the state quickly.
Bar coloring
If you enable “Color Bars by Score,” each candle inherits the gradient. This makes sequences obvious. Red clusters warn you not to buy. White means neutral. Increasing green suggests the pullback is maturing.
EMAs and the trend EMA
• Fast EMA turns down relative to the slow EMA inside the pullback.
• Trend EMA stays rising and above price once the dip exhausts, which is your cue to focus on long setups rather than bottom fishing in downtrends.
Entry lines
When a fresh signal fires and no other signal happened within Min Gap (bars) , the indicator draws a horizontal level for Line Length bars. Use these lines for limit entries at the low, at the close, or at the defined dip-percent level. This keeps your plan consistent across instruments.
Alerts and what they mean
• Market Overheated : Score is deeply negative. Do not chase. Wait for green.
• Close To A Dip : Score has reached a healthy level but the full signal did not trigger yet. Prepare orders.
• Dip Confirmed : First bar of a fresh validated dip. This is the most direct entry alert.
• Dip Active : The dip condition remains valid. You can scale in on re-tests.
• Dip Fading : Score crosses below 0.5 from above. Momentum of the setup is fading. Tighten stops or take partials.
• Trend Blocked Signal : All dip conditions passed but the trend filter is offside. Either reduce risk or skip, depending on your plan.
How to trade with Dip Hunter
Classic pullback in uptrend
Turn on the trend filter.
Watch for a Dip Confirmed alert with green triangle.
Use the entry line at “Dip % Level” to stage a limit order. This keeps your entries consistent across assets and timeframes.
Initial stop under the signal bar’s low or under the next lower EMA band.
First target at prior swing high, second target at a multiple of risk.
If you use partials, trail the remainder under the fast EMA once price reclaims it.
Aggressive intraday scalps
Lower Dip % and Lookback Bars so you catch shallow flags.
Keep Vol Spike meaningful so you only trade when participation appears.
Take quick partials when price reclaims the fast EMA, then exit on Dip Fading if momentum stalls.
Counter-trend probes
Disable the trend filter if you intentionally hunt reflex bounces in downtrends.
Require strong volume and volatility confirmation.
Use smaller size and faster targets. The meter should move quickly from red toward white and then green. If it does not, step aside.
Risk management templates
Stops
• Conservative: below the entry line minus a small buffer or below the signal bar’s low.
• Structural: below the slow EMA if you aim for swing continuation.
• Time stop: if price does not reclaim the fast EMA within N bars, exit.
Position sizing
Use the distance between the entry line and your structural stop to size consistently. The script’s entry lines make this distance obvious.
Scaling
• Scale at the entry line first touch.
• Add only if the meter stays green and price reclaims the fast EMA.
• Stop adding on a Dip Fading alert.
Tuning guide by market and timeframe
Equities daily
• Dip %: 1.5 to 3.0
• Lookback Bars: 5 to 10
• Vol Spike: 1.5 to 2.5
• Volatility Len: 14 to 20
• Trend EMA: 100 or 200
• Keep trend filter on for a cleaner list.
Futures and FX intraday
• Dip %: 0.4 to 1.2
• Lookback Bars: 3 to 7
• Vol Spike: 1.8 to 3.0
• Volatility Len: 10 to 14
• Use Min Gap to avoid clusters during news.
Crypto
• Dip %: 3.0 to 6.0 for majors on higher timeframes, lower on 15m to 1h
• Lookback Bars: 5 to 12
• Vol Spike: 1.8 to 3.0
• ATR and stdev checks help in erratic sessions.
Reading the chart at a glance
• Green triangle below the bar: a validated dip.
• Light green background: the current bar meets the full condition.
• Bar gradient: red is overheated, white is neutral, green is dip-friendly.
• EMAs: fast below slow during the pullback, then reclaim fast EMA on the bounce for quality continuation.
• Trend EMA: a rising spine when the filter is on.
• Entry line: a fixed level to anchor orders and risk.
• Meter pointer: right side toward “Dip” means conditions are maturing.
Why this combination reduces false positives
Any single criterion will trigger too often. Dip Hunter demands a dip off a recent high plus a volume surge plus a volatility expansion plus corrective EMA structure. Optional trend alignment pushes odds further in your favor. The score and meter visualize how many of these boxes you are actually ticking, which is more reliable than a binary dot.
Limitations and practical tips
• Thin or illiquid symbols can spoof volume spikes. Use larger Vol Lookback or raise Vol Spike .
• Sideways markets will show frequent small dips. Increase Dip % or keep the trend filter on.
• News candles can blow through entry lines. Widen stops or skip around known events.
• If you see many back-to-back triangles, raise Min Gap to keep only the best setups.
Quick setup recipes
• Clean swing trader: Trend filter on, Dip % 2.0 to 3.0, Vol Spike 2.0, Volatility Len 14, Fast 20 EMA, Slow 50 EMA, Trend 100 EMA.
• Fast intraday scalper: Trend filter off, Dip % 0.7 to 1.0, Vol Spike 2.5, Volatility Len 10, Fast 9 EMA, Slow 21 EMA, Min Gap 10 bars.
• Crypto swing: Trend filter on, Dip % 4.0, Vol Spike 2.0, Volatility Len 14, Fast 20 EMA, Slow 50 EMA, Trend 200 EMA.
Summary
Dip Hunter is a focused pullback engine. It quantifies a real dip off a recent high, validates it with volume and volatility expansion, enforces corrective structure with EMAs, and optionally restricts signals to an uptrend. The score, bar gradient, and meter make reading conditions instant. Entry lines and alerts turn that read into an executable plan. Tune the thresholds to your market and timeframe, then let the tool keep you patient in red, selective in white, and decisive in green. Indicator
Time-Decaying Percentile Oscillator [BackQuant]Time-Decaying Percentile Oscillator
1. Big-picture idea
Traditional percentile or stochastic oscillators treat every bar in the look-back window as equally important. That is fine when markets are slow, but if volatility regime changes quickly yesterday’s print should matter more than last month’s. The Time-Decaying Percentile Oscillator attempts to fix that blind spot by assigning an adjustable weight to every past price before it is ranked. The result is a percentile score that “breathes” with market tempo much faster to flag new extremes yet still smooth enough to ignore random noise.
2. What the script actually does
Build a weight curve
• You pick a look-back length (default 28 bars).
• You decide whether weights fall Linearly , Exponentially , by Power-law or Logarithmically .
• A decay factor (lower = faster fade) shapes how quickly the oldest price loses influence.
• The array is normalised so all weights still sum to 1.
Rank prices by weighted mass
• Every close in the window is paired with its weight.
• The pairs are sorted from low to high.
• The cumulative weight is walked until it equals your chosen percentile level (default 50 = median).
• That price becomes the Time-Decayed Percentile .
Find dispersion with robust statistics
• Instead of a fragile standard deviation the script measures weighted Median-Absolute-Deviation about the new percentile.
• You multiply that deviation by the Deviation Multiplier slider (default 1.0) to get a non-parametric volatility band.
Build an adaptive channel
• Upper band = percentile + (multiplier × deviation)
• Lower band = percentile – (multiplier × deviation)
Normalise into a 0-100 oscillator
• The current close is mapped inside that band:
0 = lower band, 50 = centre, 100 = upper band.
• If the channel squeezes, tiny moves still travel the full scale; if volatility explodes, it automatically widens.
Optional smoothing
• A second-stage moving average (EMA, SMA, DEMA, TEMA, etc.) tames the jitter.
• Length 22 EMA by default—change it to tune reaction speed.
Threshold logic
• Upper Threshold 70 and Lower Threshold 30 separate standard overbought/oversold states.
• Extreme bands 85 and 15 paint background heat when aggressive fade or breakout trades might trigger.
Divergence engine
• Looks back twenty bars.
• Flags Bullish divergence when price makes a lower low but oscillator refuses to confirm (value < 40).
• Flags Bearish divergence when price prints a higher high but oscillator stalls (value > 60).
3. Component walk-through
• Source – Any price series. Close by default, switch to typical price or custom OHLC4 for futures spreads.
• Look-back Period – How many bars to rank. Short = faster, long = slower.
• Base Percentile Level – 50 shows relative position around the median; set to 25 / 75 for quartile tracking or 90 / 10 for extreme tails.
• Deviation Multiplier – Higher values widen the dynamic channel, lowering whipsaw but delaying signals.
• Decay Settings
– Type decides the curve shape. Exponential (default 1.16) mimics EMA logic.
– Factor < 1 shrinks influence faster; > 1 spreads influence flatter.
– Toggle Enable Time Decay off to compare with classic equal-weight stochastic.
• Smoothing Block – Choose one of seven MA flavours plus length.
• Thresholds – Overbought / Oversold / Extreme levels. Push them out when working on very mean-reverting assets like FX; pull them in for trend monsters like crypto.
• Display toggles – Show or hide threshold lines, extreme filler zones, bar colouring, divergence labels.
• Colours – Bullish green, bearish red, neutral grey. Every gradient step is automatically blended to generate a heat map across the 0-100 range.
4. How to read the chart
• Oscillator creeping above 70 = market auctioning near the top of its adaptive range.
• Fast poke above 85 with no follow-through = exhaustion fade candidate.
• Slow grind that lives above 70 for many bars = valid bullish trend, not a fade.
• Cross back through 50 shows balance has shifted; treat it like a micro trend change.
• Divergence arrows add extra confidence when you already see two-bar reversal candles at range extremes.
• Background shading (semi-transparent red / green) warns of extreme states and throttles your position size.
5. Practical trading playbook
Mean-reversion scalps
1. Wait for oscillator to reach your desired OB/ OS levels
2. Check the slope of the smoothing MA—if it is flattening the squeeze is mature.
3. Look for a one- or two-bar reversal pattern.
4. Enter against the move; first target = midline 50, second target = opposite threshold.
5. Stop loss just beyond the extreme band.
Trend continuation pullbacks
1. Identify a clean directional trend on the price chart.
2. During the trend, TDP will oscillate between midline and extreme of that side.
3. Buy dips when oscillator hits OS levels, and the same for OB levels & shorting
4. Exit when oscillator re-tags the same-side extreme or prints divergence.
Volatility regime filter
• Use the Enable Time Decay switch as a regime test.
• If equal-weight oscillator and decayed oscillator diverge widely, market is entering a new volatility regime—tighten stops and trade smaller.
Divergence confirmation for other indicators
• Pair TDP divergence arrows with MACD histogram or RSI to filter false positives.
• The weighted nature means TDP often spots divergence a bar or two earlier than standard RSI.
Swing breakout strategy
1. During consolidation, band width compresses and oscillator oscillates around 50.
2. Watch for sudden expansion where oscillator blasts through extreme bands and stays pinned.
3. Enter with momentum in breakout direction; trail stop behind upper or lower band as it re-expands.
6. Customising decay mathematics
Linear – Each older bar loses the same fixed amount of influence. Intuitive and stable; good for slow swing charts.
Exponential – Influence halves every “decay factor” steps. Mirrors EMA thinking and is fastest to react.
Power-law – Mid-history bars keep more authority than exponential but oldest data still fades. Handy for commodities where seasonality matters.
Logarithmic – The gentlest curve; weight drops sharply at first then levels off. Mimics how traders remember dramatic moves for weeks but forget ordinary noise quickly.
Turn decay off to verify the tool’s added value; most users never switch back.
7. Alert catalogue
• TD Overbought / TD Oversold – Cross of regular thresholds.
• TD Extreme OB / OS – Breach of danger zones.
• TD Bullish / Bearish Divergence – High-probability reversal watch.
• TD Midline Cross – Momentum shift that often precedes a window where trend-following systems perform.
8. Visual hygiene tips
• If you already plot price on a dark background pick Bullish Color and Bearish Color default; change to pastel tones for light themes.
• Hide threshold lines after you memorise the zones to declutter scalping layouts.
• Overlay mode set to false so the oscillator lives in its own panel; keep height about 30 % of screen for best resolution.
9. Final notes
Time-Decaying Percentile Oscillator marries robust statistical ranking, adaptive dispersion and decay-aware weighting into a simple oscillator. It respects both recent order-flow shocks and historical context, offers granular control over responsiveness and ships with divergence and alert plumbing out of the box. Bolt it onto your price action framework, trend-following system or volatility mean-reversion playbook and see how much sooner it recognises genuine extremes compared to legacy oscillators.
Backtest thoroughly, experiment with decay curves on each asset class and remember: in trading, timing beats timidity but patience beats impulse. May this tool help you find that edge. Indicator
Recession Warning Model [BackQuant]Recession Warning Model
Overview
The Recession Warning Model (RWM) is a Pine Script® indicator designed to estimate the probability of an economic recession by integrating multiple macroeconomic, market sentiment, and labor market indicators. It combines over a dozen data series into a transparent, adaptive, and actionable tool for traders, portfolio managers, and researchers. The model provides customizable complexity levels, display modes, and data processing options to accommodate various analytical requirements while ensuring robustness through dynamic weighting and regime-aware adjustments.
Purpose
The RWM fulfills the need for a concise yet comprehensive tool to monitor recession risk. Unlike approaches relying on a single metric, such as yield-curve inversion, or extensive economic reports, it consolidates multiple data sources into a single probability output. The model identifies active indicators, their confidence levels, and the current economic regime, enabling users to anticipate downturns and adjust strategies accordingly.
Core Features
- Indicator Families : Incorporates 13 indicators across five categories: Yield, Labor, Sentiment, Production, and Financial Stress.
- Dynamic Weighting : Adjusts indicator weights based on recent predictive accuracy, constrained within user-defined boundaries.
- Leading and Coincident Split : Separates early-warning (leading) and confirmatory (coincident) signals, with adjustable weighting (default 60/40 mix).
- Economic Regime Sensitivity : Modulates output sensitivity based on market conditions (Expansion, Late-Cycle, Stress, Crisis), using a composite of VIX, yield-curve, financial conditions, and credit spreads.
- Display Options : Supports four modes—Probability (0-100%), Binary (four risk bins), Lead/Coincident, and Ensemble (blended probability).
- Confidence Intervals : Reflects model stability, widening during high volatility or conflicting signals.
- Alerts : Configurable thresholds (Watch, Caution, Warning, Alert) with persistence filters to minimize false signals.
- Data Export : Enables CSV output for probabilities, signals, and regimes, facilitating external analysis in Python or R.
Model Complexity Levels
Users can select from four tiers to balance simplicity and depth:
1. Essential : Focuses on three core indicators—yield-curve spread, jobless claims, and unemployment change—for minimalistic monitoring.
2. Standard : Expands to nine indicators, adding consumer confidence, PMI, VIX, S&P 500 trend, money supply vs. GDP, and the Sahm Rule.
3. Professional : Includes all 13 indicators, incorporating financial conditions, credit spreads, JOLTS vacancies, and wage growth.
4. Research : Unlocks all indicators plus experimental settings for advanced users.
Key Indicators
Below is a summary of the 13 indicators, their data sources, and economic significance:
- Yield-Curve Spread : Difference between 10-year and 3-month Treasury yields. Negative spreads signal banking sector stress.
- Jobless Claims : Four-week moving average of unemployment claims. Sustained increases indicate rising layoffs.
- Unemployment Change : Three-month change in unemployment rate. Sharp rises often precede recessions.
- Sahm Rule : Triggers when unemployment rises 0.5% above its 12-month low, a reliable recession indicator.
- Consumer Confidence : University of Michigan survey. Declines reflect household pessimism, impacting spending.
- PMI : Purchasing Managers’ Index. Values below 50 indicate manufacturing contraction.
- VIX : CBOE Volatility Index. Elevated levels suggest market anticipation of economic distress.
- S&P 500 Growth : Weekly moving average trend. Declines reduce wealth effects, curbing consumption.
- M2 + GDP Trend : Monitors money supply and real GDP. Simultaneous declines signal credit contraction.
- NFCI : Chicago Fed’s National Financial Conditions Index. Positive values indicate tighter conditions.
- Credit Spreads : Proxy for corporate bond spreads using 10-year vs. 2-year Treasury yields. Widening spreads reflect stress.
- JOLTS Vacancies : Job openings data. Significant drops precede hiring slowdowns.
- Wage Growth : Year-over-year change in average hourly earnings. Late-cycle spikes often signal economic overheating.
Data Processing
- Rate of Change (ROC) : Optionally applied to capture momentum in data series (default: 21-bar period).
- Z-Score Normalization : Standardizes indicators to a common scale (default: 252-bar lookback).
- Smoothing : Applies a short moving average to final signals (default: 5-bar period) to reduce noise.
- Binary Signals : Generated for each indicator (e.g., yield-curve inverted or PMI below 50) based on thresholds or Z-score deviations.
Probability Calculation
1. Each indicator’s binary signal is weighted according to user settings or dynamic performance.
2. Weights are normalized to sum to 100% across active indicators.
3. Leading and coincident signals are aggregated separately (if split mode is enabled) and combined using the specified mix.
4. The probability is adjusted by a regime multiplier, amplifying risk during Stress or Crisis regimes.
5. Optional smoothing ensures stable outputs.
Display and Visualization
- Probability Mode : Plots a continuous 0-100% recession probability with color gradients and confidence bands.
- Binary Mode : Categorizes risk into four levels (Minimal, Watch, Caution, Alert) for simplified dashboards.
- Lead/Coincident Mode : Displays leading and coincident probabilities separately to track signal divergence.
- Ensemble Mode : Averages traditional and split probabilities for a balanced view.
- Regime Background : Color-coded overlays (green for Expansion, orange for Late-Cycle, amber for Stress, red for Crisis).
- Analytics Table : Optional dashboard showing probability, confidence, regime, and top indicator statuses.
Practical Applications
- Asset Allocation : Adjust equity or bond exposures based on sustained probability increases.
- Risk Management : Hedge portfolios with VIX futures or options during regime shifts to Stress or Crisis.
- Sector Rotation : Shift toward defensive sectors when coincident signals rise above 50%.
- Trading Filters : Disable short-term strategies during high-risk regimes.
- Event Timing : Scale positions ahead of high-impact data releases when probability and VIX are elevated.
Configuration Guidelines
- Enable ROC and Z-score for consistent indicator comparison unless raw data is preferred.
- Use dynamic weighting with at least one economic cycle of data for optimal performance.
- Monitor stress composite scores above 80 alongside probabilities above 70 for critical risk signals.
- Adjust adaptation speed (default: 0.1) to 0.2 during Crisis regimes for faster indicator prioritization.
- Combine RWM with complementary tools (e.g., liquidity metrics) for intraday or short-term trading.
Limitations
- Macro indicators lag intraday market moves, making RWM better suited for strategic rather than tactical trading.
- Historical data availability may constrain dynamic weighting on shorter timeframes.
- Model accuracy depends on the quality and timeliness of economic data feeds.
Final Note
The Recession Warning Model provides a disciplined framework for monitoring economic downturn risks. By integrating diverse indicators with transparent weighting and regime-aware adjustments, it empowers users to make informed decisions in portfolio management, risk hedging, or macroeconomic research. Regular review of model outputs alongside market-specific tools ensures its effective application across varying market conditions. Indicator
