Gainz Algo ProEngulfing Pattern Scanner with RSI Filter
This indicator identifies high-probability engulfing patterns using multiple confirmation filters including candle stability, RSI divergence, and price momentum over a specified period.
═══ INDICATOR LOGIC ═══
BUY Signal Generated When:
• Bullish engulfing pattern forms
• Candle stability exceeds threshold (body/wick ratio)
• RSI is below oversold threshold
• Price has decreased over the delta period
• Bar is confirmed (no repainting)
SELL Signal Generated When:
• Bearish engulfing pattern forms
• Candle stability exceeds threshold
• RSI is above overbought threshold
• Price has increased over the delta period
• Bar is confirmed (no repainting)
═══ KEY FEATURES ═══
• Candle Stability Index (0-1): Filters out unstable/noisy candles
• RSI Index (0-100): Confirms momentum conditions
• Candle Delta Length: Defines lookback period for price movement
• Disable Repeating Signals: Removes consecutive same-direction signals
• Multiple visual styles: Text bubbles, triangles, or arrows
• Customizable colors and label sizes
• Built-in alert conditions
═══ INPUT PARAMETERS ═══
Candle Stability Index (0.5 default): Higher values require more decisive candles
RSI Index (50 default): Threshold for overbought/oversold conditions
Candle Delta Length (5 default): Bars to measure price change
Label customization: Size, style, and colors
═══ HOW TO USE ═══
1. Add indicator to chart
2. Adjust technical parameters based on market volatility
3. Set visual preferences for signal display
4. Create alerts using the built-in conditions
5. Higher Candle Stability = fewer but higher quality signals
6. Lower RSI Index = more conservative entry points
═══ BEST PRACTICES ═══
• Use on higher timeframes (4H+) for swing trading
• Combine with support/resistance for confluence
• Test parameters on historical data before live trading
• Consider market conditions when adjusting filters
• All timeframes
Algotrading
Quantum Flux Universal Strategy Summary in one paragraph
Quantum Flux Universal is a regime switching strategy for stocks, ETFs, index futures, major FX pairs, and liquid crypto on intraday and swing timeframes. It helps you act only when the normalized core signal and its guide agree on direction. It is original because the engine fuses three adaptive drivers into the smoothing gains itself. Directional intensity is measured with binary entropy, path efficiency shapes trend quality, and a volatility squash preserves contrast. Add it to a clean chart, watch the polarity lane and background, and trade from positive or negative alignment. For conservative workflows use on bar close in the alert settings when you add alerts in a later version.
Scope and intent
• Markets. Large cap equities and ETFs. Index futures. Major FX pairs. Liquid crypto
• Timeframes. One minute to daily
• Default demo used in the publication. QQQ on one hour
• Purpose. Provide a robust and portable way to detect when momentum and confirmation align, while dampening chop and preserving turns
• Limits. This is a strategy. Orders are simulated on standard candles only
Originality and usefulness
• Unique concept or fusion. The novelty sits in the gain map. Instead of gating separate indicators, the model mixes three drivers into the adaptive gains that power two one pole filters. Directional entropy measures how one sided recent movement has been. Kaufman style path efficiency scores how direct the path has been. A volatility squash stabilizes step size. The drivers are blended into the gains with visible inputs for strength, windows, and clamps.
• What failure mode it addresses. False starts in chop and whipsaw after fast spikes. Efficiency and the squash reduce over reaction in noise.
• Testability. Every component has an input. You can lengthen or shorten each window and change the normalization mode. The polarity plot and background provide a direct readout of state.
• Portable yardstick. The core is normalized with three options. Z score, percent rank mapped to a symmetric range, and MAD based Z score. Clamp bounds define the effective unit so context transfers across symbols.
Method overview in plain language
The strategy computes two smoothed tracks from the chart price source. The fast track and the slow track use gains that are not fixed. Each gain is modulated by three drivers. A driver for directional intensity, a driver for path efficiency, and a driver for volatility. The difference between the fast and the slow tracks forms the raw flux. A small phase assist reduces lag by subtracting a portion of the delayed value. The flux is then normalized. A guide line is an EMA of a small lead on the flux. When the flux and its guide are both above zero, the polarity is positive. When both are below zero, the polarity is negative. Polarity changes create the trade direction.
Base measures
• Return basis. The step is the change in the chosen price source. Its absolute value feeds the volatility estimate. Mean absolute step over the window gives a stable scale.
• Efficiency basis. The ratio of net move to the sum of absolute step over the window gives a value between zero and one. High values mean trend quality. Low values mean chop.
• Intensity basis. The fraction of up moves over the window plugs into binary entropy. Intensity is one minus entropy, which maps to zero in uncertainty and one in very one sided moves.
Components
• Directional Intensity. Measures how one sided recent bars have been. Smoothed with RMA. More intensity increases the gain and makes the fast and slow tracks react sooner.
• Path Efficiency. Measures the straightness of the price path. A gamma input shapes the curve so you can make trend quality count more or less. Higher efficiency lifts the gain in clean trends.
• Volatility Squash. Normalizes the absolute step with Z score then pushes it through an arctangent squash. This caps the effect of spikes so they do not dominate the response.
• Normalizer. Three modes. Z score for familiar units, percent rank for a robust monotone map to a symmetric range, and MAD based Z for outlier resistance.
• Guide Line. EMA of the flux with a small lead term that counteracts lag without heavy overshoot.
Fusion rule
• Weighted sum of the three drivers with fixed weights visible in the code comments. Intensity has fifty percent weight. Efficiency thirty percent. Volatility twenty percent.
• The blend power input scales the driver mix. Zero means fixed spans. One means full driver control.
• Minimum and maximum gain clamps bound the adaptive gain. This protects stability in quiet or violent regimes.
Signal rule
• Long suggestion appears when flux and guide are both above zero. That sets polarity to plus one.
• Short suggestion appears when flux and guide are both below zero. That sets polarity to minus one.
• When polarity flips from plus to minus, the strategy closes any long and enters a short.
• When flux crosses above the guide, the strategy closes any short.
What you will see on the chart
• White polarity plot around the zero line
• A dotted reference line at zero named Zen
• Green background tint for positive polarity and red background tint for negative polarity
• Strategy long and short markers placed by the TradingView engine at entry and at close conditions
• No table in this version to keep the visual clean and portable
Inputs with guidance
Setup
• Price source. Default ohlc4. Stable for noisy symbols.
• Fast span. Typical range 6 to 24. Raising it slows the fast track and can reduce churn. Lowering it makes entries more reactive.
• Slow span. Typical range 20 to 60. Raising it lengthens the baseline horizon. Lowering it brings the slow track closer to price.
Logic
• Guide span. Typical range 4 to 12. A small guide smooths without eating turns.
• Blend power. Typical range 0.25 to 0.85. Raising it lets the drivers modulate gains more. Lowering it pushes behavior toward fixed EMA style smoothing.
• Vol window. Typical range 20 to 80. Larger values calm the volatility driver. Smaller values adapt faster in intraday work.
• Efficiency window. Typical range 10 to 60. Larger values focus on smoother trends. Smaller values react faster but accept more noise.
• Efficiency gamma. Typical range 0.8 to 2.0. Above one increases contrast between clean trends and chop. Below one flattens the curve.
• Min alpha multiplier. Typical range 0.30 to 0.80. Lower values increase smoothing when the mix is weak.
• Max alpha multiplier. Typical range 1.2 to 3.0. Higher values shorten smoothing when the mix is strong.
• Normalization window. Typical range 100 to 300. Larger values reduce drift in the baseline.
• Normalization mode. Z score, percent rank, or MAD Z. Use MAD Z for outlier heavy symbols.
• Clamp level. Typical range 2.0 to 4.0. Lower clamps reduce the influence of extreme runs.
Filters
• Efficiency filter is implicit in the gain map. Raising efficiency gamma and the efficiency window increases the preference for clean trends.
• Micro versus macro relation is handled by the fast and slow spans. Increase separation for swing, reduce for scalping.
• Location filter is not included in v1.0. If you need distance gates from a reference such as VWAP or a moving mean, add them before publication of a new version.
Alerts
• This version does not include alertcondition lines to keep the core minimal. If you prefer alerts, add names Long Polarity Up, Short Polarity Down, Exit Short on Flux Cross Up in a later version and select on bar close for conservative workflows.
Strategy has been currently adapted for the QQQ asset with 30/60min timeframe.
For other assets may require new optimization
Properties visible in this publication
• Initial capital 25000
• Base currency Default
• Default order size method percent of equity with value 5
• Pyramiding 1
• Commission 0.05 percent
• Slippage 10 ticks
• Process orders on close ON
• Bar magnifier ON
• Recalculate after order is filled OFF
• Calc on every tick OFF
Honest limitations and failure modes
• Past results do not guarantee future outcomes
• Economic releases, circuit breakers, and thin books can break the assumptions behind intensity and efficiency
• Gap heavy symbols may benefit from the MAD Z normalization
• Very quiet regimes can reduce signal contrast. Use longer windows or higher guide span to stabilize context
• Session time is the exchange time of the chart
• If both stop and target can be hit in one bar, tie handling would matter. This strategy has no fixed stops or targets. It uses polarity flips for exits. If you add stops later, declare the preference
Open source reuse and credits
• None beyond public domain building blocks and Pine built ins such as EMA, SMA, standard deviation, RMA, and percent rank
• Method and fusion are original in construction and disclosure
Legal
Education and research only. Not investment advice. You are responsible for your decisions. Test on historical data and in simulation before any live use. Use realistic costs.
Strategy add on block
Strategy notice
Orders are simulated by the TradingView engine on standard candles. No request.security() calls are used.
Entries and exits
• Entry logic. Enter long when both the normalized flux and its guide line are above zero. Enter short when both are below zero
• Exit logic. When polarity flips from plus to minus, close any long and open a short. When the flux crosses above the guide line, close any short
• Risk model. No initial stop or target in v1.0. The model is a regime flipper. You can add a stop or trail in later versions if needed
• Tie handling. Not applicable in this version because there are no fixed stops or targets
Position sizing
• Percent of equity in the Properties panel. Five percent is the default for examples. Risk per trade should not exceed five to ten percent of equity. One to two percent is a common choice
Properties used on the published chart
• Initial capital 25000
• Base currency Default
• Default order size percent of equity with value 5
• Pyramiding 1
• Commission 0.05 percent
• Slippage 10 ticks
• Process orders on close ON
• Bar magnifier ON
• Recalculate after order is filled OFF
• Calc on every tick OFF
Dataset and sample size
• Test window Jan 2, 2014 to Oct 16, 2025 on QQQ one hour
• Trade count in sample 324 on the example chart
Release notes template for future updates
Version 1.1.
• Add alertcondition lines for long, short, and exit short
• Add optional table with component readouts
• Add optional stop model with a distance unit expressed as ATR or a percent of price
Notes. Backward compatibility Yes. Inputs migrated Yes.
Kz GC1! ORBStrategy that trades breakouts on GC1! futures on the 5min timeframe. It also works on MGC1! for lower drawdown and to manage Apex and Top Step accounts with the lower risk.
Risk Disclaimer:
Past results as well as strategy tester reports do not indicate future performance. Guarantees do not exist in trading. By using this strategy you risk losing all your money.
Important:
It trades all days that markets are open. Set times may be seen on settings. Trades multiple times a day sometimes.
It works on the 5 and 15min timeframe only. Results are better on 5min timeframe.
The settings are optimized already for GC1! on the 5min timeframe.
How it works:
Every trading day it measures the range of the first 15min candle of pre-selected hours. As soon as price closes above or below on the 15min timeframe, it will trade the breakout targeting a set risk to reward ratio. SL on the opposite side of the range.
Settings:
Hourly Trading Hours: These are the times that worked best for this strategy. All boxes should be checked for best results. Excluded times were when it performed bad which is why those times have been left out.
ORB Formation Period: This setting determines on which timeframe candle has to close outside the opening range box in order to take a trade. It's set to 15min by default because this is what worked by far the best
Entry Type: Entries are immediate instead of waiting for a pull back to enter on a limit order.
Limit Orders: If enabled, the strategy will place a pending order x points from the current price, instead of a market order. Immediate orders are enabled by default for a better performance. Important: It doesn't actually place a limit order. The strategy will just wait for a pullback and then enter with a market order. It's more like a hidden limit order.
Limit Order Offset Points: If you have limit orders enabled, this setting determines how many points from the current price the limit order will be placed.
FVG Detection Type: How fast it detects the fair value gaps. Standard detection over immediate had better performance
Risk Type: You can chose either between Fixed USD Amount, Risk per Trade in % or Fixed Contract Size. By default it's set to fixed contract size.
Risk Amount (USD or Contracts): This setting is to set how many USD or how many contracts you want to risk per trade. Make sure to check which risk type you have selected before you choose the risk amount.
Take Profit Multiplier: This is simply the total SL size in points multiplied by x.
Example: If you put 2, you get a 2:1 Risk to Reward Ratio. By Default it's set to 2.5 because this gave the best results in backtests.
Stop Loss Padding: This is simply the buffer in points that is added to the SL for safety reasons. If you have it on 0, the SL will be at the exact price of the opposite side of the range. By default it's set to 5 because this is what delivered the best results in backtests.
Stop Loss Placement: This determines where the stop loss gets placed for the order. It has been set to ORB Range by default as this delivered the best results.
Max Trades Per Hour: This allows the user to decide how many trades are taken an hour. 1 is been set to default for best results
Visual Settings: Check boxes to show orb range, FVG's, Entry points, and trade visualization boxes.
Backtest Settings:
For the backtest the commissions were set to 1.29USD per contract and .35USD for micros which is the highest amount Tradovate charges Margin was not accounted for because typically on prop accounts you can use way more contracts than you need for the extremely low max drawdown. Margin would be important on personal accounts but even there typically it's not an issue at all especially because this strategy runs on the 15min timeframe so it won't use a lot of contracts anyways.
Why the source code is hidden:
The source code is hidden because I invested a lot of time and money into developing this strategy and optimizing it with paid 3rd party software.
Trendlines Breakouts Pro V1.2 - 4TP [Wukong Algo]Trendlines Breakouts Pro
Trading method “High Tight Trendline Breakout”. This is a simple but effective and flexible method that can support many other methods such as: support and resistance, supply and demand, volume profile...
Automatically connect TradingView and MetaTrader 5 (MT5) for automatic trading and order management via PineConnector
The system includes a risk management grid including the levels: Stop Loss (SL), Break-even (BE), Trail Trigger, Trailing Stop, TP1 (1/4), TP2 (2/4), TP3 (3/4), TP4 (4/4). This grid helps you easily monitor and manage orders on TradingView in parallel with automatic order management on MT5.
Focus on tight capital and risk management, reduce emotion and stress when trading
Suitable for all markets: Forex, Gold, Crypto, Stocks, as long as you use MT5 and TradingView
If you do not need to trade automatically via MT5, the Trendlines Breakokuts Pro can also be used as an effective indicator in visual order management on TradingView charts, helps maintain discipline and good trading psychology (less Stress or FOMO)
Trendlines Breakouts Pro System User Guide
Step 1 - Draw trendline AB. Just click to select 2 points A, B on the chart
This is a straight line at the border of a chart pattern or support/resistance zone on the chart that you determine has high potential when it is broken, the price will have strong momentum and you will enter the order (Entry). The trendline AB can be a diagonal line or a horizontal line.
Step 2 - Entry Window: Set the time allowed for transactions
You can choose the earliest and latest time allowed for trading signals, called Entry Window. This means that the system will not allow trading outside the Entry Window. This option allows you to manage trading times as you wish, avoiding bad times for trading such as sideways, choppy, high volatility, news
Step 3 - Set up the input parameters for trading
You choose the direction you want to wait for trading: Wait Long (Buy), Wait Short (Sell), Turn Off, Hidden
You enter the ID of your PineConnector account if you want to trade automatically from TradingView to MT5
You enter the order parameters: Lotsize per order, Stop Loss (SL%), BE(%), Trail Trigger (%), TP1(%), TP2(%), TP3(%), TP4(%)
You enter the safe filter parameters for Entry: max distance from entry to swing high/low, max distance from entry to trendline's breakpoint C, max entries per trendlines
See more details in the screenshots
Step 4 - Set up automatic trading from TradingView via MT5
If you do not need automatic trading in MT5, skip this step. Entry signals and risk management grids will still be displayed on the TradingView chart for you to see, but there is no connection and automatic trading signal shooting and automatic order management from TradingView to MT5 via PineConnector.
We need to create an Alert in TradingView and attach it to this Indicator so that the Alert's trading signals are transmitted via MetaTrader 5 (MT5) via PineConnector.
When trading, you need to turn on 3 software at the same time to be able to connect to each other to operate: TradingView, MetaTrader 5 (MT5), PineConnector
See more details in the screenshots
Step 5 - Complete setup, and wait for trading signals
You have completed the setup steps for the Indicator, ready when there is a trading signal
You do not need to sit in front of the screen all day if you do not want. The system has been set up to execute and manage orders automatically.
Of course, sometimes you should still check your transaction status, in case of unexpected problems such as lost internet connection.
If you still have questions about this Indicator, please email tuanwukongvn@gmail.com for support.
TrendIsYourFriend Strategy (SPY,IWM,VYM,XLK,SPXL,BTC,GOLD,VT...)Personal disclaimer
Don’t trust this strategy. Don’t trust any other model either just because of its author or a backtest curve. Overfitting is an easy trap, and beginners often fall into it. This script isn’t meant to impress you. It’s meant to survive reality. If it does, maybe it will raise questions and you’ll remember it.
Legal disclaimer
Educational purposes only. Not financial advice. Past performance is not indicative of future results.
Strategy description
Long-only, trend-based logic with two entry types (trend continuation or excess-move reversion), dynamic stop-losses, and a VIX filter to avoid turbulent markets.
Minimal number of parameters with enough trades to support robustness.
For backtest, each trade is sized at $10,000 flat (no compounding, to focus on raw model quality and the regularity of its results over time).
Fees = $0 (neutral choice, as brokers differ).
Slippage = $0, deliberate choice: most entries occur on higher timeframes, and some assets start their history on charts at very low prices, which would otherwise distort results.
What makes this script original
Beyond a classical trend calculation, both excess-move entries and dynamic stop-loss exits also rely on trend logic. Except for the VIX filter, everything comes from trend functions, with very few parameters.
Pre-configurations are fixed in the code, allowing sincere performance tracking across a dozen cases over the medium to long term.
Allowed
SPY (ARCA) — 2-hour chart: S&P 500 ETF, most liquid equity benchmark
IWM (ARCA) — Daily chart: Russell 2000 ETF, US small caps
VYM (ARCA) — Daily chart: Vanguard High Dividend Yield ETF
XLK (ARCA) — Daily chart: Technology Select Sector SPDR
SPXL (ARCA) — Daily chart: 3× leveraged S&P 500 ETF
BTCUSD (COINBASE) — 4-hour chart: Bitcoin vs USD
GOLD (TVC) — Daily chart: Gold spot price
VT (ARCA) — Daily chart: Vanguard Total World Stock ETF
PG (NYSE) — Daily chart: Procter & Gamble Co.
CQQQ (ARCA) — Daily chart: Invesco China Technology ETF
EWC (ARCA) — Daily chart: iShares MSCI Canada ETF
EWJ (ARCA) — Daily chart: iShares MSCI Japan ETF
How to use and form an opinion on it
Works only on the pairs above.
Feel free to modify the input parameters (slippage, fees, order size, margins, …) to see how the model behaves under your own conditions
Compare it with a simple Buy & Hold (requires an order size of 100% equity).
You may also want to look at its time-in-market — the share of time your capital is actually at risk.
Finally, let me INSIST on this : let it run live for months before forming an opinion!
Share your thoughts in the comments 🚀 if you’d like to discuss its live performance.
Auto Levels & Smart Money [ #Algo ] Pro : Smart Levels is Smart Trades 🏆
"Auto Levels & Smart Money Pro" indicator is specially designed for day traders, pull-back / reverse trend traders / scalpers & trend analysts. This indicator plots the key smart levels , which will be automatically drawn at the session's start or during the session, if specific input is selected.
🔶 Usage and Settings :
A :
⇓ ( *refer 📷 image ) ⇓
B :
⇓ ( *refer 📷 images ) ⇓
🔷 Features :
a : automated smart levels with #algo compatibility.
b : plots auto SHADOW candle levels Zones ( smart money concept ).
c : ▄▀ RENKO Emulator engine ( plots Non-repaintable #renko data as a line chart ).
d : session 1st candle's High, Low & 50% levels ( irrespective of chart time-frame ).
e : 1-hour High & Low levels of specific candle, ( from the drop-down menu ), for any global market symbols or crypto.
f : previous Day / Week / Month, chart High & Low.
g : pivot point levels of the Daily, Weekly & Monthly charts.
h : 2 class types of ⏰ alerts ( only signals or algo execution ).
i : auto RENKO box size (ATR-based) table for 30 symbols.
j : auto processes " daylight saving time 🌓" data and plots accordingly.
💠Note: "For key smart levels, it processes data from a customized time frame, which is not available for the *free Trading View subscription users , and requires a premium plan." By this indicator, you have an edge over the paid subscription plan users and can automatically plot the shadow candle levels and Non-repaintable RENKO emulator for the current chart on the free Trading View Plan at any time frame .
⬇ Take a deep dive 👁️🗨️ into the Smart levels trading Basic Demonstration ⬇
▄▀ 1: "RENKO Emulator Engine" ⭐ , plots a noiseless chart for easy Top/Bottom set-up analysis. 10 types of 💼 asset classes options available in the drop-down menu.
LTP is tagged to current RSI ➕ volatility color change for instant decisions.
⇓ ( *refer 📷 image ) ⇓
🟣 2: "Shadow Candle Levels and Zones" will be drawn at the start of the session (which will project shadow candle levels of the previous day), and it comes with a zone. which specifies the Supply and Demand Zone area. *Shadow levels can be drawn for the NSE & BSE: Index/Futures/Options/Equity and MCX: Commodity/FNO market only.
⇓ ( *refer 📷 image ) ⇓https://www.tradingview.com/x/SIskBm77/
🟠 3: plots "Session first candle High, low, and 50%" levels ( irrespective of chart time-frame ), which a very important levels for an intraday trader with add-on levels of Previous Day, Week & Month High and Low levels.
⇓ ( *refer 📷 image ) ⇓
🔵 4: plots "Hourly chart candle" High & Low levels for the specific candles, selected from the drop-down menu with Pivot Points levels of Daily, Weekly, Monthly chart.
Note: The drop-down menu gives a manual selection of the hour candles for all "🌐 Crypto / XAU-USD / Forex / USA".
ex: "2nd hr" will give the session's First hour candle "High & Low" level.
⇓ ( *refer 📷 image ) ⇓
🔲 5: "Auto RENKO box size" ( ATR based ) : This indicator is specially designed for 'Renko' trading enthusiasts, where the Box size of the ' Renko chart ' for intraday or swing trading, ( ATR based ) , automatically calculated for the selected ( editable ) symbols in the table.
⇓ ( *refer 📷 image ) ⇓
*NOTE :
Table symbols are for NSE/BSE/USA.
Symbols are Non-editable (fixed).
Table Symbols for MCX only.
Table Symbols for XAU & 🌐CRYTO.
⏰ 6: "Alert functions."
⇓ ( *refer 📷 image ) ⇓
◻ : Total 8 signal alerts can be possible in a Single alert.
◻ : Total 12 #algo alerts , ( must ✔ tick the Consent check box for algo and alerts execution/trigger ).
💹 Modified moving average line. Includes data from both the exponential and simple moving average.
This Indicator will work like a Trading System . It is different from other indicators, which give Signals only. This script is designed to be tailored to your personal trading style by combining components to create your own comprehensive strategy . The synergy between the components is key to its usefulness.
It focuses on the key Smart Levels and gives you an Extra edge over others.
✅ HOW TO GET ACCESS :
You can see the Author's instructions to get instant access to this indicator & our premium suite. If you like any of my Invite-Only indicators, let me know!
⚠ RISK DISCLAIMER :
All content provided by "TradeWithKeshhav" is for informational & educational purposes only.
It does not constitute any financial advice or a solicitation to buy or sell any securities of any type. All investments / trading involve risks. Past performance does not guarantee future results / returns.
Regards :
TradeWithKeshhav & team
Happy trading and investing!
Apex Edge – Wolfe Wave HunterApex Edge – Wolfe Wave Hunter
The modern Wolfe Wave, rebuilt for the algo era
This isn’t just another Wolfe Wave indicator. Classic Wolfe detection is rigid, outdated, and rarely tradable. Apex Edge – Wolfe Wave Hunter re-engineers the pattern into a modern, SMC-driven model that adapts to today’s liquidity-dominated markets. It’s not about drawing pretty shapes – it’s about extracting precision entries with asymmetric risk-to-reward potential.
🔎 What it does
Automatic Wolfe Wave Detection
Identifies bullish and bearish Wolfe Wave structures using pivot-based logic, symmetry filters, and slope tolerances.
Channel Glow Zones
Highlights the Wolfe channel and projects it forward into the future (bars are user-defined). This allows you to see the full potential of the trade before price even begins its move.
Stop Loss (SL) & Entry Arrow
At the completion of Wave 5, the algo prints a Stop Loss line and a tiny entry arrow (green for bullish, red for bearish). but the colours can be changed in user settings. This is the “execution point” — where the Wolfe setup becomes tradable.
Target Projection Lines
TP1 (EPA): Derived from the traditional 1–4 line projection.
TP2 (1.272 Fib): Optional secondary profit target.
TP3 (1.618 Fib): Optional extended target for large runners.
All TP lines extend into the future, so you can track them as price evolves.
Volume Confirmation (optional)
A relative volume filter ensures Wave 5 is formed with meaningful market participation before a setup is confirmed.
Alerts (ready out of the box)
Custom alerts can be fired whenever a bullish or bearish Wolfe Wave is confirmed. No need to babysit the charts — let the script notify you.
⚙️ Customisation & User Control
Every trader’s market and style is different. That’s why Wolfe Wave Hunter is fully customisable:
Arrow Colours & Size
Works on both light and dark charts. Choose your own bullish/bearish entry arrow colours for maximum visibility.
Tolerance Levels
Adjust symmetry and slope tolerance to refine how strict the channel rules are.
Tighter settings = fewer but cleaner zones.
Looser settings = more frequent setups, but with slightly lower structural quality.
Channel Glow Projection
Define how many bars forward the channel is drawn. This controls how far into the future your Wolfe zones are extended.
Stop Loss Line Length
Keep the SL visible without it extending infinitely across your chart.
Take Profit Line Colors
Each TP projection can be styled to your preference, allowing you to clearly separate TP1, TP2, and TP3.
This isn’t a one-size-fits-all tool. You can shape Wolfe detection logic to match the pairs, timeframes, and market conditions you trade most.
🚀 Why it’s different
Classic Wolfe waves are rare — this script adapts the model into something practical and tradeable in modern markets.
Liquidity-aligned — many setups align with structural sweeps of Wave 3 liquidity before driving into profit.
Entry built-in — most Wolfe scripts only draw the structure. Wolfe Wave Hunter gives you a precise entry point, SL, and projected TPs.
Backtest-friendly — you’ll quickly discover which assets respect Wolfe waves and which don’t, creating your own high-probability Wolfe watchlist.
⚠️ Limitations & Disclaimer
Not all markets respect Wolfe Waves. Some FX pairs, metals, and indices respect the structure beautifully; others do not. Backtest and create your own shortlist.
No guaranteed sweeps. Many entries occur after a liquidity sweep of Wave 3, but not all. The algo is designed to detect Wolfe completion, not enforce textbook liquidity rules.
Probabilistic, not predictive. Wolfe setups don’t win every time. Always use risk management.
High-RR focus. This is not a high-frequency tool. It’s designed for precision, asymmetric setups where risk is small and reward potential is large.
✅ The Bottom Line
Apex Edge – Wolfe Wave Hunter is a modern reimagination of the Wolfe Wave. It blends structural geometry, liquidity dynamics, and algo-driven execution into a single tool that:
Detects the pattern automatically
Provides SL, entry, and TP levels
Offers alerts for hands-off trading
Allows deep customisation for different markets
When it hits, it delivers outstanding risk-to-reward. Backtest, refine your tolerances, and build your watchlist of assets where Wolfe structures consistently pay.
This isn’t just Wolfe detection — it’s Wolfe trading, rebuilt for the modern trader.
Developer Notes - As always with the Apex Edge Brand, user feedback and recommendations will always be respected. Simply drop us a message with your comments and we will endeavour to address your needs in future version updates.
Extremum Range MA Crossover Strategy1. Principle of Work & Strategy Logic ⚙️📈
Main idea: The strategy tries to catch the moment of a breakout from a price consolidation range (flat) and the start of a new trend. It combines two key elements:
Moving Average (MA) 📉: Acts as a dynamic support/resistance level and trend filter.
Range Extremes (Range High/Low) 🔺🔻: Define the borders of the recent price channel or consolidation.
The strategy does not attempt to catch absolute tops and bottoms. Instead, it enters an already formed move after the breakout, expecting continuation.
Type: Trend-following, momentum-based.
Timeframes: Works on different TFs (H1, H4, D), but best suited for H4 and higher, where breakouts are more meaningful.
2. Justification of Indicators & Settings ⚙️
A. Moving Average (MA) 📊
Why used: Core of the strategy. It smooths price fluctuations and helps define the trend. The price (via extremes) must cross the MA → signals a potential trend shift or strengthening.
Parameters:
maLength = 20: Default length (≈ one trading month, 20-21 days). Good balance between sensitivity & smoothing.
Lower TF → reduce (10–14).
Higher TF → increase (50).
maSource: Defines price source (default = Close). Alternatives (HL2, HLC3) → smoother, less noisy MA.
maType: Default = EMA (Exponential MA).
Why EMA? Faster reaction to recent price changes vs SMA → useful for breakout strategies.
Other options:
SMA 🟦 – classic, slowest.
WMA 🟨 – weights recent data stronger.
HMA 🟩 – near-zero lag, but “nervous,” more false signals.
DEMA/TEMA 🟧 – even faster & more sensitive than EMA.
VWMA 🔊 – volume-weighted.
ZLEMA ⏱ – reduced lag.
👉 Choice = tradeoff between speed of reaction & false signals.
B. Range Extremes (Previous High/Low) 📏
Why used: Define borders of recent trading range.
prevHigh = local resistance.
prevLow = local support.
Break of these levels on close = trigger.
Parameters:
lookbackPeriod = 5: Searches for highest high / lowest low of last 5 candles. Very recent range.
Higher value (10–20) → wider, stronger ranges but rarer signals.
3. Entry & Exit Rules 🎯
Long signals (BUY) 🟢📈
Condition (longCondition): Previous Low crosses MA from below upwards.
→ Price bounced from the bottom & strong enough to push range border above MA.
Execution: Auto-close short (if any) → open long.
Short signals (SELL) 🔴📉
Condition (shortCondition): Previous High crosses MA from above downwards.
→ Price rejected from the top, upper border failed above MA.
Execution: Auto-close long (if any) → open short.
Exit conditions 🚪
Exit Long (exitLongCondition): Close below prevLow.
→ Uptrend likely ended, range shifts down.
Exit Short (exitShortCondition): Close above prevHigh.
→ Downtrend likely ended, range shifts up.
⚠️ Important: Exit = only on candle close beyond extremes (not just wick).
4. Trading Settings ⚒️
overlay = true → indicators shown on chart.
initial_capital = 10000 💵.
default_qty_type = strategy.cash, default_qty_value = 100 → trades fixed $100 per order (not lots). Can switch to % of equity.
commission_type = strategy.commission.percent, commission_value = 0.1 → default broker fee = 0.1%. Adjust for your broker!
slippage = 3 → slippage = 3 ticks. Adjust to asset liquidity.
currency = USD.
margin_long = 100, margin_short = 100 → no leverage (100% margin).
5. Visualization on Chart 📊
The strategy draws 3 lines:
🔵 MA line (thickness 2).
🔴 Previous High (last N candles).
🟢 Previous Low (last N candles).
Also: entry/exit arrows & equity curve shown in backtest.
Disclaimer ⚠️📌
Risk Warning: This description & code are for educational purposes only. Not financial advice. Trading (Forex, Stocks, Crypto) carries high risk and may lead to full capital loss. You trade at your own risk.
Testing: Always backtest & demo test first. Past results ≠ future profits.
Responsibility: Author of this strategy & description is not responsible for your trading decisions or losses.
Siege Gold Strategy-1m
Siege Gold Strategy - An Advanced Trading Strategy with a Multi-Confirmation System
This powerful indicator is designed to help you base your trading decisions on solid foundations. Thanks to its advanced algorithms and multi-confirmation mechanism, it helps you understand market trends more clearly.
Key Features
Trend Pivot Points: Instantly identify trend reversals and potential support/resistance levels with intelligent pivot points that react to real-time price movements. This allows you to analyze the trend's strength and direction more accurately.
Relative Strength Index (RSI) Integration: We combine the classic overbought/oversold signals of the RSI with our custom strategies to generate more reliable and filtered signals. This integration minimizes false signals.
"Range" Module: This feature measures the volatility range the price is currently in, showing whether the market is consolidating or moving. This helps reduce the risk of making incorrect trades in sideways markets.
Who Is This For?
Traders who follow trend-following strategies.
Anyone who wants to automatically determine support and resistance levels.
Those looking for a multi-confirmation mechanism instead of relying on a single indicator.
Anyone who wants to generate more reliable trading signals.
This strategy can be used in the XAUUSD pair, as well as in crypto and forex markets. To use this strategy more accurately, we encourage you to watch a few videos. It's important to remember that every instrument and indicator setting yields different results, and we cannot guarantee that you will make a profit.
BE-Fib Channel 2 Sided Trading█ Overview:
"BE-Fib Channel 2 Sided Trading" indicator is built with the thought of 2 profound setups named "Cup & Handle (C&H)" and "Fibonacci Channel Trading (FCT)" with the context of "day trading" or with a minimum holding period.
█ Similarities, Day Trading Context & Error Patterns:
While the known fact is that both C&H and FCT provide setups with lesser risk with bigger returns, they both share the similar "Base Pattern".
Note: Inverse of the above Image shall switch the setups between long vs short.
Since the indicator is designed for smaller time-frame candles, there may be instances where the "base pattern" does not visually resemble a Cup & Handle (C&H) pattern. However, patterns are validated using pivot points. The points labeled "A" and "C" can be equal or slightly slanted. Settings of the Indicator allows traders a flexibility to control the angle of these points to spot the strategies according to set conditions. Therefore, understanding the nuances of these patterns is crucial for effective decision-making.
█ 2 Sided Edge: FCT suggests to take trade closer to the yellow line to get better RR ratio. this leaves a small chance of doubt as to; what if price is intended to break the Yellow line thereby activating the C&H.
Wait for the confirmation is a Big FOMO with a compromised RR.
Hence, This indicator is designed to handle both the patterns based on the strength, FIFO and pattern occurring delay.
█ How to Use this Indicator:
Step 1: Enable the Show Sample Sensitivity option to understand the angle of yellow line shown in the sample image. By enabling this option, On the last bar you shall see 4 lines being plotted depicting the max angle which is acceptable for both long and short trades.
Note: Angle can be controlled via setting "Sensitivity".
Higher Sensitivity --> Higher Setup identification --> can lead to failed setups due to 2 sided trading.
Lower Sensitivity --> Lower Setup identification --> can increase the changes of being right.
Step 2: Adjust the look back & look forward periods which shall be used for identifying patterns.
Note: Smaller values can lead to more setups being identified but can hamper the performance of the indicator while increasing the chances of failures. larger values identifies more significant setup but leads to more waiting period thereby compromising on the RR.
Step 3: Adjust the Base Range.
Note: Smaller values can lead to more setups being identified but can hamper the performance of the indicator while increasing the chances of failures. larger values identifies more significant setup but leads to more Risk on play.
Step 4: set the Entry level for FCT & Set the SL for Both FCT & C&H and Target Reward ratio for C&H.
█ Features of Indicator & How it works:
1. Patterns are being identified using Pivot Points method.
2. Tracks & validates both the setups simultaneously on every candle and traded one at a time based on FIFO, New setups found in-between, Defined Entry Levels while on wait for the other pattern to get activated.
3. Alerts added for trade events.
4. FCT setups are generally traded with trailed SL level and increasing Target level on every completed bar. while C&H has the standard SL & TP level with no Trail SL option.
DISCLAIMER: No sharing, copying, reselling, modifying, or any other forms of use are authorized for our documents, script / strategy, and the information published with them. This informational planning script / strategy is strictly for individual use and educational purposes only. This is not financial or investment advice. Investments are always made at your own risk and are based on your personal judgement. I am not responsible for any losses you may incur. Please invest wisely.
Happy to receive suggestions and feedback in order to improve the performance of the indicator better.
PnL Bubble [%] | Fractalyst1. What's the indicator purpose?
The PnL Bubble indicator transforms your strategy's trade PnL percentages into an interactive bubble chart with professional-grade statistics and performance analytics. It helps traders quickly assess system profitability, understand win/loss distribution patterns, identify outliers, and make data-driven strategy improvements.
How does it work?
Think of this indicator as a visual report card for your trading performance. Here's what it does:
What You See
Colorful Bubbles: Each bubble represents one of your trades
Blue/Cyan bubbles = Winning trades (you made money)
Red bubbles = Losing trades (you lost money)
Bigger bubbles = Bigger wins or losses
Smaller bubbles = Smaller wins or losses
How It Organizes Your Trades:
Like a Photo Album: Instead of showing all your trades at once (which would be messy), it shows them in "pages" of 500 trades each:
Page 1: Your first 500 trades
Page 2: Trades 501-1000
Page 3: Trades 1001-1500, etc.
What the Numbers Tell You:
Average Win: How much money you typically make on winning trades
Average Loss: How much money you typically lose on losing trades
Expected Value (EV): Whether your trading system makes money over time
Positive EV = Your system is profitable long-term
Negative EV = Your system loses money long-term
Payoff Ratio (R): How your average win compares to your average loss
R > 1 = Your wins are bigger than your losses
R < 1 = Your losses are bigger than your wins
Why This Matters:
At a Glance: You can instantly see if you're a profitable trader or not
Pattern Recognition: Spot if you have more big wins than big losses
Performance Tracking: Watch how your trading improves over time
Realistic Expectations: Understand what "average" performance looks like for your system
The Cool Visual Effects:
Animation: The bubbles glow and shimmer to make the chart more engaging
Highlighting: Your biggest wins and losses get extra attention with special effects
Tooltips: hover any bubble to see details about that specific trade.
What are the underlying calculations?
The indicator processes trade PnL data using a dual-matrix architecture for optimal performance:
Dual-Matrix System:
• Display Matrix (display_matrix): Bounded to 500 trades for rendering performance
• Statistics Matrix (stats_matrix): Unbounded storage for complete statistical accuracy
Trade Classification & Aggregation:
// Separate wins, losses, and break-even trades
if val > 0.0
pos_sum += val // Sum winning trades
pos_count += 1 // Count winning trades
else if val < 0.0
neg_sum += val // Sum losing trades
neg_count += 1 // Count losing trades
else
zero_count += 1 // Count break-even trades
Statistical Averages:
avg_win = pos_count > 0 ? pos_sum / pos_count : na
avg_loss = neg_count > 0 ? math.abs(neg_sum) / neg_count : na
Win/Loss Rates:
total_obs = pos_count + neg_count + zero_count
win_rate = pos_count / total_obs
loss_rate = neg_count / total_obs
Expected Value (EV):
ev_value = (avg_win × win_rate) - (avg_loss × loss_rate)
Payoff Ratio (R):
R = avg_win ÷ |avg_loss|
Contribution Analysis:
ev_pos_contrib = avg_win × win_rate // Positive EV contribution
ev_neg_contrib = avg_loss × loss_rate // Negative EV contribution
How to integrate with any trading strategy?
Equity Change Tracking Method:
//@version=6
strategy("Your Strategy with Equity Change Export", overlay=true)
float prev_trade_equity = na
float equity_change_pct = na
if barstate.isconfirmed and na(prev_trade_equity)
prev_trade_equity := strategy.equity
trade_just_closed = strategy.closedtrades != strategy.closedtrades
if trade_just_closed and not na(prev_trade_equity)
current_equity = strategy.equity
equity_change_pct := ((current_equity - prev_trade_equity) / prev_trade_equity) * 100
prev_trade_equity := current_equity
else
equity_change_pct := na
plot(equity_change_pct, "Equity Change %", display=display.data_window)
Integration Steps:
1. Add equity tracking code to your strategy
2. Load both strategy and PnL Bubble indicator on the same chart
3. In bubble indicator settings, select your strategy's equity tracking output as data source
4. Configure visualization preferences (colors, effects, page navigation)
How does the pagination system work?
The indicator uses an intelligent pagination system to handle large trade datasets efficiently:
Page Organization:
• Page 1: Trades 1-500 (most recent)
• Page 2: Trades 501-1000
• Page 3: Trades 1001-1500
• Page N: Trades to
Example: With 1,500 trades total (3 pages available):
• User selects Page 1: Shows trades 1-500
• User selects Page 4: Automatically falls back to Page 3 (trades 1001-1500)
5. Understanding the Visual Elements
Bubble Visualization:
• Color Coding: Cyan/blue gradients for wins, red gradients for losses
• Size Mapping: Bubble size proportional to trade magnitude (larger = bigger P&L)
• Priority Rendering: Largest trades displayed first to ensure visibility
• Gradient Effects: Color intensity increases with trade magnitude within each category
Interactive Tooltips:
Each bubble displays quantitative trade information:
tooltip_text = outcome + " | PnL: " + pnl_str +
" Date: " + date_str + " " + time_str +
" Trade #" + str.tostring(trade_number) + " (Page " + str.tostring(active_page) + ")" +
" Rank: " + str.tostring(rank) + " of " + str.tostring(n_display_rows) +
" Percentile: " + str.tostring(percentile, "#.#") + "%" +
" Magnitude: " + str.tostring(magnitude_pct, "#.#") + "%"
Example Tooltip:
Win | PnL: +2.45%
Date: 2024.03.15 14:30
Trade #1,247 (Page 3)
Rank: 5 of 347
Percentile: 98.6%
Magnitude: 85.2%
Reference Lines & Statistics:
• Average Win Line: Horizontal reference showing typical winning trade size
• Average Loss Line: Horizontal reference showing typical losing trade size
• Zero Line: Threshold separating wins from losses
• Statistical Labels: EV, R-Ratio, and contribution analysis displayed on chart
What do the statistical metrics mean?
Expected Value (EV):
Represents the mathematical expectation per trade in percentage terms
EV = (Average Win × Win Rate) - (Average Loss × Loss Rate)
Interpretation:
• EV > 0: Profitable system with positive mathematical expectation
• EV = 0: Break-even system, profitability depends on execution
• EV < 0: Unprofitable system with negative mathematical expectation
Example: EV = +0.34% means you expect +0.34% profit per trade on average
Payoff Ratio (R):
Quantifies the risk-reward relationship of your trading system
R = Average Win ÷ |Average Loss|
Interpretation:
• R > 1.0: Wins are larger than losses on average (favorable risk-reward)
• R = 1.0: Wins and losses are equal in magnitude
• R < 1.0: Losses are larger than wins on average (unfavorable risk-reward)
Example: R = 1.5 means your average win is 50% larger than your average loss
Contribution Analysis (Σ):
Breaks down the components of expected value
Positive Contribution (Σ+) = Average Win × Win Rate
Negative Contribution (Σ-) = Average Loss × Loss Rate
Purpose:
• Shows how much wins contribute to overall expectancy
• Shows how much losses detract from overall expectancy
• Net EV = Σ+ - Σ- (Expected Value per trade)
Example: Σ+: 1.23% means wins contribute +1.23% to expectancy
Example: Σ-: -0.89% means losses drag expectancy by -0.89%
Win/Loss Rates:
Win Rate = Count(Wins) ÷ Total Trades
Loss Rate = Count(Losses) ÷ Total Trades
Shows the probability of winning vs losing trades
Higher win rates don't guarantee profitability if average losses exceed average wins
7. Demo Mode & Synthetic Data Generation
When using built-in sources (close, open, etc.), the indicator generates realistic demo trades for testing:
if isBuiltInSource(source_data)
// Generate random trade outcomes with realistic distribution
u_sign = prand(float(time), float(bar_index))
if u_sign < 0.5
v_push := -1.0 // Loss trade
else
// Skewed distribution favoring smaller wins (realistic)
u_mag = prand(float(time) + 9876.543, float(bar_index) + 321.0)
k = 8.0 // Skewness factor
t = math.pow(u_mag, k)
v_push := 2.5 + t * 8.0 // Win trade
Demo Characteristics:
• Realistic win/loss distribution mimicking actual trading patterns
• Skewed distribution favoring smaller wins over large wins
• Deterministic randomness for consistent demo results
• Includes jitter effects to prevent visual overlap
8. Performance Limitations & Optimizations
Display Constraints:
points_count = 500 // Maximum 500 dots per page for optimal performance
Pine Script v6 Limits:
• Label Count: Maximum 500 labels per indicator
• Line Count: Maximum 100 lines per indicator
• Box Count: Maximum 50 boxes per indicator
• Matrix Size: Efficient memory management with dual-matrix system
Optimization Strategies:
• Pagination System: Handle unlimited trades through 500-trade pages
• Priority Rendering: Largest trades displayed first for maximum visibility
• Dual-Matrix Architecture: Separate display (bounded) from statistics (unbounded)
• Smart Fallback: Automatic page clamping prevents empty displays
Impact & Workarounds:
• Visual Limitation: Only 500 trades visible per page
• Statistical Accuracy: Complete dataset used for all calculations
• Navigation: Use page input to browse through entire trade history
• Performance: Smooth operation even with thousands of trades
9. Statistical Accuracy Guarantees
Data Integrity:
• Complete Dataset: Statistics matrix stores ALL trades without limit
• Proper Aggregation: Separate tracking of wins, losses, and break-even trades
• Mathematical Precision: Pine Script v6's enhanced floating-point calculations
• Dual-Matrix System: Display limitations don't affect statistical accuracy
Calculation Validation:
// Verified formulas match standard trading mathematics
avg_win = pos_sum / pos_count // Standard average calculation
win_rate = pos_count / total_obs // Standard probability calculation
ev_value = (avg_win * win_rate) - (avg_loss * loss_rate) // Standard EV formula
Accuracy Features:
• Mathematical Correctness: Formulas follow established trading statistics
• Data Preservation: Complete dataset maintained for all calculations
• Precision Handling: Proper rounding and boundary condition management
• Real-Time Updates: Statistics recalculated on every new trade
10. Advanced Technical Features
Real-Time Animation Engine:
// Shimmer effects with sine wave modulation
offset = math.sin(shimmer_t + phase) * amp
// Dynamic transparency with organic flicker
new_transp = math.min(flicker_limit, math.max(-flicker_limit, cur_transp + dir * flicker_step))
• Sine Wave Shimmer: Dynamic glowing effects on bubbles
• Organic Flicker: Random transparency variations for natural feel
• Extreme Value Highlighting: Special visual treatment for outliers
• Smooth Animations: Tick-based updates for fluid motion
Magnitude-Based Priority Rendering:
// Sort trades by magnitude for optimal visual hierarchy
sort_indices_by_magnitude(values_mat)
• Largest First: Most important trades always visible
• Intelligent Sorting: Custom bubble sort algorithm for trade prioritization
• Performance Optimized: Efficient sorting for real-time updates
• Visual Hierarchy: Ensures critical trades never get hidden
Professional Tooltip System:
• Quantitative Data: Pure numerical information without interpretative language
• Contextual Ranking: Shows trade position within page dataset
• Percentile Analysis: Performance ranking as percentage
• Magnitude Scaling: Relative size compared to page maximum
• Professional Format: Clean, data-focused presentation
11. Quick Start Guide
Step 1: Add Indicator
• Search for "PnL Bubble | Fractalyst" in TradingView indicators
• Add to your chart (works on any timeframe)
Step 2: Configure Data Source
• Demo Mode: Leave source as "close" to see synthetic trading data
• Strategy Mode: Select your strategy's PnL% output as data source
Step 3: Customize Visualization
• Colors: Set positive (cyan), negative (red), and neutral colors
• Page Navigation: Use "Trade Page" input to browse trade history
• Visual Effects: Built-in shimmer and animation effects are enabled by default
Step 4: Analyze Performance
• Study bubble patterns for win/loss distribution
• Review statistical metrics: EV, R-Ratio, Win Rate
• Use tooltips for detailed trade analysis
• Navigate pages to explore full trade history
Step 5: Optimize Strategy
• Identify outlier trades (largest bubbles)
• Analyze risk-reward profile through R-Ratio
• Monitor Expected Value for system profitability
• Use contribution analysis to understand win/loss impact
12. Why Choose PnL Bubble Indicator?
Unique Advantages:
• Advanced Pagination: Handle unlimited trades with smart fallback system
• Dual-Matrix Architecture: Perfect balance of performance and accuracy
• Professional Statistics: Institution-grade metrics with complete data integrity
• Real-Time Animation: Dynamic visual effects for engaging analysis
• Quantitative Tooltips: Pure numerical data without subjective interpretations
• Priority Rendering: Intelligent magnitude-based display ensures critical trades are always visible
Technical Excellence:
• Built with Pine Script v6 for maximum performance and modern features
• Optimized algorithms for smooth operation with large datasets
• Complete statistical accuracy despite display optimizations
• Professional-grade calculations matching institutional trading analytics
Practical Benefits:
• Instantly identify system profitability through visual patterns
• Spot outlier trades and risk management issues
• Understand true risk-reward profile of your strategies
• Make data-driven decisions for strategy optimization
• Professional presentation suitable for performance reporting
Disclaimer & Risk Considerations:
Important: Historical performance metrics, including positive Expected Value (EV), do not guarantee future trading success. Statistical measures are derived from finite sample data and subject to inherent limitations:
• Sample Bias: Historical data may not represent future market conditions or regime changes
• Ergodicity Assumption: Markets are non-stationary; past statistical relationships may break down
• Survivorship Bias: Strategies showing positive historical EV may fail during different market cycles
• Parameter Instability: Optimal parameters identified in backtesting often degrade in forward testing
• Transaction Cost Evolution: Slippage, spreads, and commission structures change over time
• Behavioral Factors: Live trading introduces psychological elements absent in backtesting
• Black Swan Events: Extreme market events can invalidate statistical assumptions instantaneously
Follow-up Buy / Sell Volume Pressure at Supply / Demand Zones█ Overview:
BE-Volume Footprint & Pressure Candles, is an indicator which is preliminarily designed to analyze the supply and demand patterns based on Rally Base Rally (RBR), Drop Base Drop (DBD), Drop Base Rally (DBR) & Rally Base Drop (RBD) concepts in conjunction to volume pressure. Understanding these concepts are crucial. Let's break down why the "Base" is you Best friend in this context.
Commonness in RBR, DBD, DBR, RBD patterns ?
There is an impulse price movement at first, be it rally (price moving up) or the Drop (price moving down), followed by a period of consolidation which is referred as "BASE" and later with another impulse move of price (Rally or Drop).
Why is the Base Important
1. Market Balance: Base represents a balance between buyers and sellers. This is where decisions are made.
2. Confirmation: It confirms the strength of previous impulse move which has happened.
Base & the Liquidity Play:
Supply & Demand Zone predict the presence of all large orders within the limits of the Base Zone. Price is expected to return to the zone to fill the unfilled orders placed by large players.
For the price to move in the intended direction Liquidity plays the major role. hence indicator aims to help traders in identifying those zones where liquidity exists and the volume pressure helps in confirming that liquidity is making its play.
Bottom pane in the below snapshots is a visual representation of Buyers volume pressure (Green Line & the Green filled area) making the price move upwards vs Sellers volume pressure (Red Line & the Red filled area) making the price move downwards.
Top pane in the below snapshots is a visual representation on the pattern identification (Blue marked zone & the Blue line referred as Liquidity level)
Bullish Pressure On Buy Liquidity:
Bearish Pressure On Sell Liquidity:
█ How It Works:
1. Indicator computes technical & mathematical operations such as ATR, delta of Highs & Lows of the candle and Candle ranges to identify the patterns and marks the liquidity lines accordingly.
2. Indicator then waits for price to return to the liquidity levels and checks if Directional volume pressure to flow-in while the prices hover near the Liquidity zones.
3. Once the Volume pressure is evident, loop in to the ride.
█ When It wont Work:
When there no sufficient Liquidity or sustained Opposite volume pressure, trades are expected to fail.
█ Limitations:
Works only on the scripts which has volume info. Relays on LTF candles to determine intra-bar volumes. Hence, Use on TF greater than 1 min and lesser than 15 min.
█ Indicator Features:
1. StrictEntries: employs' tighter rules (rather most significant setups) on the directional volume pressure applied for the price to move. If unchecked, liberal rules applied on the directional volume pressure leading to more setups being identified.
2. Setup Confirmation period: Indicates Waiting period to analyze the directional volume pressure. Early (lesser wait period) is Risky and Late (longer wait period) is too late for the
ride. Find the quant based on the accuracy of the setup provided in the bottom right table.
3. Algo Enabled with Place Holders:
Indicator is equipped with algo alerts, supported with necessary placeholders to trade any instrument like stock, options etc.
Accepted PlaceHolders (Case Sensitive!!)
1. {{ticker}}-->InstrumentName
2. {{datetime}}-->Date & Time Of Order Placement
3. {{close}}-->LTP Price of Script
4. {{TD}}-->Current Level:
Note: Negative Numbers for Short Setup
5. {{EN}} {{SL}} {{TGT}} {{T1}} {{T2}} --> Trade Levels
6. {{Qty}} {{Qty*x}} --> Qty -> Trade Qty mapped in Settings. Replace x with actual number of your choice for the multiplier
7. {{BS}}-->Based on the Direction of Trade Output shall be with B or S (B == Long Trade & S == Short Trade)
8. {{BUYSELL}}-->Based on the Direction of Trade Output shall be with BUY or SELL (BUY == Long Trade & SELL == Short Trade)
9. {{IBUYSELL}}-->Based on the Direction of Trade Output shall be with BUY or SELL (BUY == SHORT Trade & SELL == LONG Trade)
Dynamic Alerts:
10. { {100R0} }-->Dynamic Place Holder 100 Refers to Strike Difference and Zero refers to ATM
11. { {100R-1} }-->Dynamic Place Holder 100 Refers to Strike Difference and -1 refers to
ATM - 100 strike
12. { {50R2} }-->Dynamic Place Holder 50 Refers to Strike Difference and 2 refers to
ATM + (2 * 50 = 100) strike
13. { {"ddMMyy", 0} }-->Dynamically Picks today date in the specified format.
14. { {"ddMMyy", n} }-->replace n with actual number of your choice to Pick date post today date in the specified format.
15. { {"ddMMyy", "MON"} }-->dynamically pick Monday date (coming Monday, if today is not Monday)
Note. for the 2nd Param-->you can choose to specify either Number OR any letter from =>
16. {{CEPE}} {{ICEPE}} {{CP}} {{ICP}} -> Dynamic Option Side CE or C refers to Calls and PE or P refers to Puts. If "I" is used in PlaceHolder text, On long entries PUTs shall be used
Indicator is equipped with customizable Trade & Risk management settings like multiple Take profit levels, Trailing SL.
Signalgo Strategy ISignalgo Strategy I: Technical Overview
Signalgo Strategy I is a systematically engineered TradingView strategy script designed to automate, test, and manage trend-following trades using multi-timeframe price/volume logic, volatility-based targets, and multi-layered exit management. This summary covers its operational structure, user inputs, entry and exit methodology, unique technical features, and practical application.
Core Logic and Workflow
Multi-Timeframe Data Synthesis
User-Defined Timeframe: The user chooses a timeframe (e.g., 1H, 4H, 1D, etc.), on which all strategy signals are based.
Cross-Timeframe Inputs: The strategy imports closing price, volume, and Average True Range (ATR) for the selected interval, independently from the chart’s native timeframe, enabling robust multi-timeframe analysis.
Price Change & Volume Ratio: It calculates the percent change of price per bar and computes a volume ratio by comparing current volume to its 20-bar moving average—enabling detection of true “event” moves vs. normal market noise.
Hype Filtering
Anti-Hype Mechanism: An entry is automatically filtered out if abnormal high volume occurs without corresponding price movement, commonly observed during manipulation or announcement periods. This helps isolate genuine market-driven momentum.
User Inputs
Select Timeframe: Choose which interval drives signal generation.
Backtest Start Date: Specify from which date historical signals are included in the strategy (for precise backtests).
Take-Profit/Stop-Loss Configuration: Internally, risk levels are set as multiples of ATR and allow for three discrete profit targets.
Entry Logic
Trade Signal Criteria:
Price change magnitude in the current bar must exceed a fixed sensitivity threshold.
Volume for the bar must be significantly elevated compared to average, indicating meaningful participation.
Anti-hype check must not be triggered.
Bullish/Bearish Determination: If all conditions are met and price change direction is positive, a long signal triggers. If negative, a short signal triggers.
Signal Debouncing: Ensures a signal triggers only when a new condition emerges, avoiding duplicate entries on flat or choppy bars.
State Management: The script tracks whether an active long or short is open to avoid overlapping entries and to facilitate clean reversals.
Exit Strategy
Take-Profits: Three distinct profit targets (TP1, TP2, TP3) are calculated as fixed multiples of the ATR-based stop loss, adapting dynamically to volatility.
Reversals: If a buy signal appears while a short is open (or vice versa), the existing trade is closed and reversed in a single step.
Time-Based Exit: If, 49 bars after entry, the trade is in-profit but hasn’t reached TP1, it exits to avoid stagnation risk.
Adverse Move Exit: The position is force-closed if it suffers a 10% reversal from entry, acting as a catastrophic stop.
Visual Feedback: Each TP/SL/exit is plotted as a clear, color-coded line on the chart; no hidden logic is used.
Alerts: Built-in TradingView alert conditions allow automated notification for both entries and strategic exits.
Distinguishing Features vs. Traditional MA Strategies
Event-Based, Not Just Slope-Based: While classic moving average strategies enter trades on MA crossovers or slope changes, Signalgo Strategy I demands high-magnitude price and volume confirmation on the chosen timeframe.
Volume Filtering: Very few MA strategies independently filter for meaningful volume spikes.
Real Market Event Focus: The anti-hype filter differentiates organic market trends from manipulated “high-volume, no-move” sessions.
Three-Layer Exit Logic: Instead of a single trailing stop or fixed RR, this script manages three profit targets, time-based closures, and hard adverse thresholds.
Multi-Timeframe, Not Chart-Dependent: The “main” analytical interval can be set independently from the current chart, allowing for in-depth cross-timeframe backtests and system runs.
Reversal Handling: Automatic handling of signal reversals closes and flips positions precisely, reducing slippage and manual error.
Persistent State Tracking: Maintains variables tracking entry price, trade status, and target/stop levels independently of chart context.
Trading Application
Strategy Sandbox: Designed for robust backtesting, allowing users to simulate performance across historical data for any major asset or interval.
Active Risk Management: Trades are consistently managed for both fixed interval “stall” and significant loss, not just via trailing stops or fixed-day closes.
Alert Driven: Can power algorithmic trading bots or notify discretionary traders the moment a qualifying market event occurs.
Script_Algo - ORB Strategy with Filters🔍 Core Concept: This strategy combines three powerful technical analysis tools: Range Breakout, the SuperTrend indicator, and a volume filter. Additionally, it features precise customization of the number of candles used to construct the breakout range, enabling optimized performance for specific assets.
🎯 How It Works:
The strategy defines a trading range at the beginning of the trading session based on a selected number of candles.
It waits for a breakout above the upper or below the lower boundary of this range, requiring a candle close.
It filters signals using the SuperTrend indicator for trend confirmation.
It utilizes trading volume to filter out false breakouts.
⚡ Strategy Features
📈 Entry Points:
Long: Candle close above the upper range boundary + SuperTrend confirmation
Short: Candle close below the lower range boundary + SuperTrend confirmation
🛡️ Risk Management:
Stop-Loss: Set at the opposite range boundary.
Take-Profit: Calculated based on a risk/reward ratio (3:1 by default).
Position Size: 10 contracts (configurable).
⚠️ IMPORTANT SETTINGS
🕐 Time Parameters:
Set the correct time and time zone!
❕ATTENTION: The strategy works ONLY with correct time settings! Set the time corresponding to your location and trading session.
📊 This strategy is optimized for trading TESLA stock!
Parameters are tailored to TESLA's volatility, and trading volumes are adequate for signal filtering. Trading time corresponds to the American session.
📈 If you look at the backtesting results, you can see that the strategy could potentially have generated about 70 percent profit on Tesla stock over six months on 5m timeframe. However, this does not guarantee that results will be repeated in the future; remain vigilant.
⚠️ For other assets, the following is required:
Testing and parameter optimization
Adjustment of time intervals and the number of candles forming the range
Calibration of stop-loss and take-profit levels
⚠️ Limitations and Drawbacks
🔗 Automation Constraints:
❌ Cannot be directly connected via Webhook to CFD brokers!
Additional IT solutions are required for automation, thus only manual trading based on signals is possible.
📉 Risk Management:
Do not risk more than 2-3% of your account per trade.
Test on historical data before live use.
Start with a demo account.
💪 Strategy Advantages
✅ Combined approach – multiple signal filters
✅ Clear entry and exit rules
✅ Visual signals on the chart
✅ Volume-based false breakout filtering
✅ Automatic position management
🎯 Usage Recommendations
Always test the strategy on historical data.
Start with small trading volumes.
Ensure time settings are correct.
Adapt parameters to current market volatility.
Use only for stocks – futures and Forex require adaptation.
📚 Suitable Timeframes - M1-M15
Only highly liquid stocks
🍀 I wish all subscribers good luck in trading and steady profits!
📈 May your charts move in the right direction!
⚠️ Remember: Trading involves risk. Do not invest money you cannot afford to lose!
Script_Algo - High Low Range MA Crossover Strategy🎯 Core Concept
This strategy uses modified moving averages crossover, built on maximum and minimum prices, to determine entry and exit points in the market. A key advantage of this strategy is that it avoids most false signals in trendless conditions, which is characteristic of traditional moving average crossover strategies. This makes it possible to improve the risk/reward ratio and, consequently, the strategy's profitability.
📊 How the Strategy Works
Main Mechanism
The strategy builds 4 moving averages:
Two senior MAs (on high and low) with a longer period
Two junior MAs (on high and low) with a shorter period
Buy signal 🟢: when the junior MA of lows crosses above the senior MA of highs
Sell signal 🔴: when the junior MA of highs crosses below the senior MA of lows
As seen on the chart, it was potentially possible to make 9X on the WIFUSDT cryptocurrency pair in just a year and a half. However, be careful—such results may not necessarily be repeated in the future.
Special Feature
Position closing priority ❗: if an opposite signal arrives while a position is open, the strategy first closes the current position and only then opens a new one
⚙️ Indicator Settings
Available Moving Average Types
EMA - Exponential MA
SMA - Simple MA
SSMA - Smoothed MA
WMA - Weighted MA
VWMA - Volume Weighted MA
RMA - Adaptive MA
DEMA - Double EMA
TEMA - Triple EMA
Adjustable Parameters
Senior MA Length - period for long-term moving averages
Junior MA Length - period for short-term moving averages
✅ Advantages of the Strategy
🛡️ False Signal Protection - using two pairs of modified MAs reduces the number of false entries
🔄 Configuration Flexibility - ability to choose MA type and calculation periods
⚡ Automatic Switching - the strategy automatically closes the current position when receiving an opposite signal
📈 Visual Clarity - all MAs are displayed on the chart in different colors
⚠️ Disadvantages and Risks
📉 Signal Lag - like all MA-based strategies, it may provide delayed signals during sharp movements
🔁 Frequent Switching - in sideways markets, it may lead to multiple consecutive position openings/closings
📊 Requires Optimization - optimal parameters need to be selected for different instruments and timeframes
💡 Usage Recommendations
Backtest - test the strategy's performance on historical data
Optimize Parameters - select MA periods suitable for the specific trading instrument
Use Filters - add additional filters to confirm signals
Manage Risks - always use stop-loss and take-profit orders.
You can safely connect to the exchange via webhook and enjoy trading.
Good luck and profits to everyone!!
Script_Algo - Fibo Correction Strategy🔹 Core Concept
The strategy is built on combining Fibonacci retracement levels, candlestick pattern confirmation, and trend filtering for trade selection. It performs well on the 1-hour timeframe across many cryptocurrency pairs. Particularly on LINKUSDT over the past year and a half, despite the not very optimal 1:1 risk/reward ratio.
The logic is simple: after a strong impulse move, the price often retraces to key Fibonacci levels (specifically, the 61.8% level). If a confirming candlestick (pattern) appears at this moment, the strategy looks for an entry in the direction of the main trend.
🔹 Indicators Used in the Strategy
ATR (Average True Range) — Used to calculate the stop-loss and take-profit levels.
EMA (9 and 21) — Additional moving averages for assessing the direction of movement (not directly used in entry conditions, but the logic can be expanded to include them).
SMA (Trend Filter, 20 by default) — The trend direction filter. Trades are only opened in its direction.
Fibonacci Levels — The 61.8% retracement level is calculated based on the high and low of the previous candle.
🔹 Entry Conditions
🟢 Long (Buy):
Previous Candle:
Must be green (close higher than open).
Must have a body not smaller than a specified minimum.
The upper wick must not exceed 30% of the body size.
→ This filters out "weak" or "indecisive" candles.
Current Candle:
Price touches or breaches the Fibonacci 61.8% retracement level from the previous range.
Closes above this level.
Closes above the Trend Filter (SMA) line.
A position is opened only if there are no other open trades at the moment.
🔴 Short (Sell):
Previous Candle:
Must be red (close lower than open).
Must have a body not smaller than a specified minimum.
The lower wick must not exceed 30% of the body size.
Current Candle:
Price touches or breaches the Fibonacci 61.8% retracement level from the previous range.
Closes below this level.
Closes below the Trend Filter (SMA) line.
A trade is opened only if there are no other open positions.
🔹 Risk Management
Stop-Loss = ATR × multiplier (default is 5).
Take-Profit = ATR × the same multiplier.
Thus, the default risk/reward ratio is 1:1, but it can be easily adjusted by changing the coefficient. Although, strangely enough, this ratio has shown the best results on some assets on the 1-hour timeframe.
🔹 Chart Visualization
Fibonacci level for Long — Green line with circles.
Fibonacci level for Short — Red line with circles.
Trend Filter line (SMA) — Blue.
🔹 Strengths of the Strategy
✅ Utilizes a proven market pattern — retracement to the 61.8% level.
✅ Further filters entries using trend and candlestick patterns.
✅ Simple, transparent logic that is easy to expand (e.g., adding other Fib levels, an EMA filter, etc.).
🔹 Limitations
⚠️ Performs better in trending markets; can generate false signals during ranging (sideways) conditions.
⚠️ The fixed 1:1 risk/reward ratio is not always optimal and could be refined.
⚠️ Performance depends on the selected timeframe and ATR parameters.
📌 Summary:
The strategy seeks corrective entries in the direction of the trend, confirmed by candlestick patterns. It is versatile and can be applied to forex pairs, cryptocurrencies, and stocks.
⚠️ Not financial advice. Pay close attention to risk management to avoid blowing your account. The strategy is not repainting — I have personally verified it through real testing — but it may not necessarily replicate the same results in the future, as the market is constantly changing. Test it, profit, and good luck to everyone!
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain?
This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions.
How does a Markov Chain work, and how should I read this spheres-and-arrows diagram?
Think of three weather modes: Sunny, Rainy, Cloudy.
Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow).
The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy).
Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step.
Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny.
Now relabel the spheres to markets: Bull, Bear, Neutral.
Spheres: market regimes (uptrend, downtrend, range).
Self‑loop: tendency for the current regime to continue on the next bar.
Arrows: the most common next regime if a switch happens.
How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear).
What states and transitions are shown?
The three market states visualized are:
Bullish (Bull): Upward or strong-market regime.
Bearish (Bear): Downward or weak-market regime.
Neutral: Sideways or range-bound regime.
Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull).
How does the regime detection system work?
You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.).
Values are statistically normalized (Z-scored) over a configurable lookback period.
The normalized outputs are classified into Bull, Bear, or Neutral zones.
If using three indicators, their regime signals are averaged and smoothed for robustness.
How are transition probabilities calculated?
On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions.
The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear).
Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time.
What is unique about the visualization?
3D animated spheres represent each regime and change visually when active.
Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows.
Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed.
All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format.
Can I use custom indicators for regime classification?
Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement.
What does the “Lookback Period” control?
Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt.
How is this different from a Hidden Markov Model (HMM)?
It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust.
How is this Markov Chain different from a Hidden Markov Model (HMM)?
Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable.
Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training.
A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data.
Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune.
HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data.
How does the indicator avoid “repainting” or look-ahead bias?
All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation.
Are there practical tuning tips?
Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability.
Use custom indicators if your asset has unique regime drivers.
Watch for rapid changes in transition probabilities as early warning of a possible regime shift.
Who is this indicator for?
Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models.
Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting:
The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear).
Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions.
Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies.
It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes.
The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting.
Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis.
What's the mathematical foundation behind this?
The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM).
How to use this indicator programmatically?
The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading.
Step-by-Step Integration with Pine Script (input.source)
Add the Markov Chain indicator to your chart.
This must be done first, since your custom script will "pull" the bias signal from the indicator's plot.
In your strategy, create an input using input.source()
Example:
//@version=5
strategy("MC Bias Strategy Example")
mcBias = input.source(close, "MC Bias Source")
After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot).
Use the bias in your trading logic
Example (long only on Bull, flat otherwise):
if mcBias == 1
strategy.entry("Long", strategy.long)
else
strategy.close("Long")
For more advanced workflows, combine mcBias with additional filters or trailing stops.
How does this work behind-the-scenes?
TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source).
The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.).
This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code.
Integrating 45+ Indicators with Your Markov Chain — How & Why
The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots.
// Enhanced Custom Indicators Export - 45 Technical Indicators
// Comprehensive technical analysis suite for advanced market regime detection
//@version=6
indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500)
// |----- Input Parameters -----| //
momentum_group = "Momentum Indicators"
trend_group = "Trend Indicators"
volume_group = "Volume Indicators"
volatility_group = "Volatility Indicators"
oscillator_group = "Oscillator Indicators"
display_group = "Display Settings"
// Common lengths
length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group)
length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group)
length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group)
// Display options
show_table = input.bool(true, "Show Values Table", group=display_group)
table_size = input.string("Small", "Table Size", options= , group=display_group)
// |----- MOMENTUM INDICATORS (15 indicators) -----| //
// 1. RSI (Relative Strength Index)
rsi_14 = ta.rsi(close, length_14)
rsi_centered = rsi_14 - 50
// 2. Stochastic Oscillator
stoch_k = ta.stoch(close, high, low, length_14)
stoch_d = ta.sma(stoch_k, 3)
stoch_centered = stoch_k - 50
// 3. Williams %R
williams_r = ta.stoch(close, high, low, length_14) - 100
// 4. MACD (Moving Average Convergence Divergence)
= ta.macd(close, 12, 26, 9)
// 5. Momentum (Rate of Change)
momentum = ta.mom(close, length_14)
momentum_pct = (momentum / close ) * 100
// 6. Rate of Change (ROC)
roc = ta.roc(close, length_14)
// 7. Commodity Channel Index (CCI)
cci = ta.cci(close, length_20)
// 8. Money Flow Index (MFI)
mfi = ta.mfi(close, length_14)
mfi_centered = mfi - 50
// 9. Awesome Oscillator (AO)
ao = ta.sma(hl2, 5) - ta.sma(hl2, 34)
// 10. Accelerator Oscillator (AC)
ac = ao - ta.sma(ao, 5)
// 11. Chande Momentum Oscillator (CMO)
cmo = ta.cmo(close, length_14)
// 12. Detrended Price Oscillator (DPO)
dpo = close - ta.sma(close, length_20)
// 13. Price Oscillator (PPO)
ppo = ta.sma(close, 12) - ta.sma(close, 26)
ppo_pct = (ppo / ta.sma(close, 26)) * 100
// 14. TRIX
trix_ema1 = ta.ema(close, length_14)
trix_ema2 = ta.ema(trix_ema1, length_14)
trix_ema3 = ta.ema(trix_ema2, length_14)
trix = ta.roc(trix_ema3, 1) * 10000
// 15. Klinger Oscillator
klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55)
// 16. Fisher Transform
fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25
fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2))
// 17. Stochastic RSI
stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14)
stoch_rsi_centered = stoch_rsi - 50
// 18. Relative Vigor Index (RVI)
rvi_num = ta.swma(close - open)
rvi_den = ta.swma(high - low)
rvi = rvi_den != 0 ? rvi_num / rvi_den : 0
// 19. Balance of Power (BOP)
bop = (close - open) / (high - low)
// |----- TREND INDICATORS (10 indicators) -----| //
// 20. Simple Moving Average Momentum
sma_20 = ta.sma(close, length_20)
sma_momentum = ((close - sma_20) / sma_20) * 100
// 21. Exponential Moving Average Momentum
ema_20 = ta.ema(close, length_20)
ema_momentum = ((close - ema_20) / ema_20) * 100
// 22. Parabolic SAR
sar = ta.sar(0.02, 0.02, 0.2)
sar_trend = close > sar ? 1 : -1
// 23. Linear Regression Slope
lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1)
// 24. Moving Average Convergence (MAC)
mac = ta.sma(close, 10) - ta.sma(close, 30)
// 25. Trend Intensity Index (TII)
tii_sum = 0.0
for i = 1 to length_20
tii_sum += close > close ? 1 : 0
tii = (tii_sum / length_20) * 100
// 26. Ichimoku Cloud Components
ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2
ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2
ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1
// 27. MESA Adaptive Moving Average (MAMA)
mama_alpha = 2.0 / (length_20 + 1)
mama = ta.ema(close, length_20)
mama_momentum = ((close - mama) / mama) * 100
// 28. Zero Lag Exponential Moving Average (ZLEMA)
zlema_lag = math.round((length_20 - 1) / 2)
zlema_data = close + (close - close )
zlema = ta.ema(zlema_data, length_20)
zlema_momentum = ((close - zlema) / zlema) * 100
// |----- VOLUME INDICATORS (6 indicators) -----| //
// 29. On-Balance Volume (OBV)
obv = ta.obv
// 30. Volume Rate of Change (VROC)
vroc = ta.roc(volume, length_14)
// 31. Price Volume Trend (PVT)
pvt = ta.pvt
// 32. Negative Volume Index (NVI)
nvi = 0.0
nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi
// 33. Positive Volume Index (PVI)
pvi = 0.0
pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi
// 34. Volume Oscillator
vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10)
// 35. Ease of Movement (EOM)
eom_distance = high - low
eom_box_height = volume / 1000000
eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0
eom_sma = ta.sma(eom, length_14)
// 36. Force Index
force_index = volume * (close - close )
force_index_sma = ta.sma(force_index, length_14)
// |----- VOLATILITY INDICATORS (10 indicators) -----| //
// 37. Average True Range (ATR)
atr = ta.atr(length_14)
atr_pct = (atr / close) * 100
// 38. Bollinger Bands Position
bb_basis = ta.sma(close, length_20)
bb_dev = 2.0 * ta.stdev(close, length_20)
bb_upper = bb_basis + bb_dev
bb_lower = bb_basis - bb_dev
bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0
bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0
// 39. Keltner Channels Position
kc_basis = ta.ema(close, length_20)
kc_range = ta.ema(ta.tr, length_20)
kc_upper = kc_basis + (2.0 * kc_range)
kc_lower = kc_basis - (2.0 * kc_range)
kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0
// 40. Donchian Channels Position
dc_upper = ta.highest(high, length_20)
dc_lower = ta.lowest(low, length_20)
dc_basis = (dc_upper + dc_lower) / 2
dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0
// 41. Standard Deviation
std_dev = ta.stdev(close, length_20)
std_dev_pct = (std_dev / close) * 100
// 42. Relative Volatility Index (RVI)
rvi_up = ta.stdev(close > close ? close : 0, length_14)
rvi_down = ta.stdev(close < close ? close : 0, length_14)
rvi_total = rvi_up + rvi_down
rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50
// 43. Historical Volatility
hv_returns = math.log(close / close )
hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100
// 44. Garman-Klass Volatility
gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open)
gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100
// 45. Parkinson Volatility
park_vol = math.log(high/low) * math.log(high/low)
parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100
// 46. Rogers-Satchell Volatility
rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open)
rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100
// |----- OSCILLATOR INDICATORS (5 indicators) -----| //
// 47. Elder Ray Index
elder_bull = high - ta.ema(close, 13)
elder_bear = low - ta.ema(close, 13)
elder_power = elder_bull + elder_bear
// 48. Schaff Trend Cycle (STC)
stc_macd = ta.ema(close, 23) - ta.ema(close, 50)
stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10)
stc_d = ta.ema(stc_k, 3)
stc = ta.stoch(stc_d, stc_d, stc_d, 10)
// 49. Coppock Curve
coppock_roc1 = ta.roc(close, 14)
coppock_roc2 = ta.roc(close, 11)
coppock = ta.wma(coppock_roc1 + coppock_roc2, 10)
// 50. Know Sure Thing (KST)
kst_roc1 = ta.roc(close, 10)
kst_roc2 = ta.roc(close, 15)
kst_roc3 = ta.roc(close, 20)
kst_roc4 = ta.roc(close, 30)
kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15)
// 51. Percentage Price Oscillator (PPO)
ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100
ppo_signal = ta.ema(ppo_line, 9)
ppo_histogram = ppo_line - ppo_signal
// |----- PLOT MAIN INDICATORS -----| //
// Plot key momentum indicators
plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1)
plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1)
plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1)
plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1)
plot(cci, title="05_CCI", color=color.green, linewidth=1)
// Plot trend indicators
plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1)
plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1)
plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1)
plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1)
plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1)
// Plot volatility indicators
plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1)
plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1)
plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1)
plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1)
plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1)
// Plot volume indicators
plot(vroc, title="16_VROC", color=color.blue, linewidth=1)
plot(eom_sma, title="17_EOM", color=color.red, linewidth=1)
plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1)
plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1)
plot(obv, title="20_OBV", color=color.purple, linewidth=1)
// Plot additional oscillators
plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1)
plot(cmo, title="22_CMO", color=color.maroon, linewidth=1)
plot(dpo, title="23_DPO", color=color.teal, linewidth=1)
plot(trix, title="24_TRIX", color=color.lime, linewidth=1)
plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1)
// Plot more momentum indicators
plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1)
plot(ac, title="27_AC", color=color.aqua, linewidth=1)
plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1)
plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1)
plot(klinger, title="30_Klinger", color=color.gray, linewidth=1)
// Plot trend continuation
plot(tii, title="31_TII", color=color.blue, linewidth=1)
plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1)
plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1)
plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1)
plot(bop, title="35_BOP", color=color.purple, linewidth=1)
// Plot volume continuation
plot(nvi, title="36_NVI", color=color.navy, linewidth=1)
plot(pvi, title="37_PVI", color=color.maroon, linewidth=1)
plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1)
plot(roc, title="39_ROC", color=color.lime, linewidth=1)
plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1)
// Plot volatility continuation
plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1)
plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1)
plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1)
plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1)
plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1)
// Plot final oscillators
plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1)
plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1)
plot(stc, title="48_STC", color=color.green, linewidth=1)
plot(coppock, title="49_Coppock", color=color.orange, linewidth=1)
plot(kst, title="50_KST", color=color.purple, linewidth=1)
// Plot final indicators
plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1)
plot(pvt, title="52_PVT", color=color.maroon, linewidth=1)
// |----- Reference Lines -----| //
hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1)
hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
// |----- Enhanced Information Table -----| //
if show_table and barstate.islast
table_position = position.top_right
table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal
var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray)
// Headers
table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
// Key Momentum Indicators
table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size)
table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size)
// Key Trend Indicators
table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90))
table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size)
// Key Volatility Indicators
table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90))
table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size)
// Key Volume Indicators
table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90))
table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size)
// Key Oscillators
table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size)
// Summary Statistics
table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70))
table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size)
regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray
regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL"
table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size)
This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator.
How to use this script with the Markov Chain for research and backtesting:
Add the Enhanced Indicator Export to your chart.
Every calculated indicator is available as an individual data stream.
Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option.
In the Markov Chain indicator’s settings, turn ON the custom indicator mode.
For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name.
This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering.
Backtest regime logic directly.
Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns.
The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration.
Export the regime bias for programmatic use.
As described above, use input.source() in your Pine Script strategy or system and link the bias output.
You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes.
With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you.
Optimize your signal generation by backtesting across a universe of meaningful indicator blends.
Enhance risk management with objective, real-time regime boundaries.
Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes.
Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic.
Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem.
References & Further Reading
Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182.
Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384.
Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg.
Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764.
Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC.
On Quantitative Finance and Markov Models:
Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press.
Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press.
TradingView Pine Script Documentation: www.tradingview.com
TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com
GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org
What makes this indicator original and unique?
- On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm.
- Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script.
- Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time.
- Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window.
- Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior.
Integrate your own signals (using input.source())
- In settings, choose the Source . This is powered by input.source() .
- Feed it price, an indicator you compute inside the script, or a custom composite series.
- The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time.
Credits:
Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community.
Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization.
Disclaimer
This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
Xcalibur Signals & Alerts [AlgoXcalibur]An advanced trend-following algorithm forged to empower retail traders with an edge.
Xcalibur Signals & Alerts is a sophisticated, multi-layered algorithm designed to consistently deliver real-time trend signals—without clutter or unnecessary complexity. The system combines refined trend-following logic with breakout detection, flat-market filtration, false signal failsafes, take profit cues, live alerts, and more — all in a visually simple, easy-to-use indicator built for all assets, timeframes, and market conditions.
🧠 Algorithm Logic
Xcalibur Signals & Alerts operates on a systematic framework that evaluates multiple technical dimensions in harmony—directional alignment, momentum confirmation, relative strength, volume bias, breakout detection, Fibonacci calculations, and more. Rather than reacting to isolated triggers, it filters every opportunity through a multi-layered confirmation engine. It doesn’t just react to every move—it evaluates them. This cohesive approach ensures that each signal results from aligned conditions—not arbitrary thresholds. By combining structural awareness with adaptive filtering, Xcalibur maintains clarity and consistency across a wide range of market environments—delivering actionable signals without unnecessary noise or lag.
⚙️ User-Adjustable Features
• Adjustable Sensitivity:
Choose from 5 pre-tuned Signal Trigger Settings and 3 dynamic Confirmation Filter Modes to tailor the system to your trading style, asset, and timeframe. Candle color reflects the active trigger condition, while an adaptive cyan line displays the selected Confirmation Filter—blocking signals until the filter threshold is crossed.
• Directional Stability Filter: When enabled, this filter uses mean-reversion calculations to determine directional bias and block unreliable signals during choppy, indecisive price action. A magenta line represents this filter threshold and provides higher-confidence signals during periods of low directional conviction.
• Pullback Allowance Filter:
When enabled, this unique filter uses Fibonacci ratios to deliberately block signals from temporary pullbacks during strong trend periods. A green (uptrend) or red (downtrend) line marks the active pullback allowance zone.
• False Signal Failsafe
:
Two selectable modes:
Simple — Cancels the signal if price breaks the signal candle’s high or low.
Advanced — Requires both a price break and opposing momentum confirmation.
When triggered, the system plots a white “X” signal, turns candles gray, disables the background color, sends an alert (if enabled), and enters standby mode until a valid trend condition re-emerges.
• Reaction Zones:
Identifies probable reversal or breakout zones based on recent price action patterns. A yellow line appears when active, with a yellow caution flag plotted if the price reaches this critical area.
• Take-Profit Cues
: Automatically detects potential trend exhaustion using price action structure and momentum shifts. When triggered, a visual “TP” marker is plotted—advising traders to manage profits or prepare for a possible reversal.
• Trailing Stop:
Plots a dynamic, percentage-based trailing stop or trailing take-profit using your selected input. Adjust it to suit your risk tolerance and asset.
• Multi-Timeframe Monitor
: Displays real-time trend direction across 1m, 2m, 5m, 15m, 1H, 4H, and 1D timeframes in a compact, easy-to-read table.
• Alert System
:
Receive desktop and/or mobile alerts for:
* New trend signals
* Failsafe triggers
* 9:00 AM Morning Greeting messages with auto re-arming confirmation
(Alerts are limited to 9:00 AM – 4:00 PM Eastern Time)
• SuperCandles
: Highlights strong momentum moves with a stunning and easily recognizable glow effect.
• Color-Coded Candles & Background
: Candles reflect the current trigger condition, while the background tint tracks the most recent trend—enhancing situational awareness.
*All input settings include tooltips to guide users through setup and interpretation.
⚔️ Not Just Another Signal Tool
Xcalibur Signals & Alerts was built from the ground up to empower retail traders with access to a cohesive, structured algorithmic system—one that reflects the kind of awareness, discipline, and market adaptability found in professional-grade algorithms.
This is not another oversensitive or under-responsive signal indicator that is limited to one specific type of market condition or trader. It does not utilize hyperactive triggers, rely on lagging crossover logic, or need infinitely adjustable and complex sensitivity settings. Instead of cluttered visuals to interpret, this indicator delivers a simple, easy-to-use tool—prioritizing clarity and usability without compromising on depth and sophistication.
Whether the market is trending, breaking out, or moving sideways, Xcalibur adapts—prioritizing trend stability, directional integrity, and visual clarity from one signal to the next.
⚠️ While the Xcalibur Signals & Alerts algorithm is immune to human emotion, you are not. Be mindful not to fall victim to costly emotions that can manipulate your judgment, and understand the unpredictable and complex nature of trading. No algorithm, strategy, or technique can deliver perfect accuracy, and Xcalibur Signals & Alerts is no exception. While AlgoXcalibur strives to be as accurate as possible, incorrect signals can and will occur. Xcalibur Signals & Alerts is a tool, not a guarantee. Users are fully responsible for making their own trading decisions, implementing proper risk management, and always trading responsibly.
🛡️ Wield Xcalibur as a standalone weapon or use it alongside other tools.
🔐 To get access or learn more, visit the Author’s Instructions section.
Order Blocks v2Order Blocks v2 – Smart OB Detection with Time & FVG Filters
Order Blocks v2 is an advanced tool designed to identify potential institutional footprints in the market by dynamically plotting bullish and bearish order blocks.
This indicator refines classic OB logic by combining:
Fractal-based break conditions
Time-level filtering (Power of 3)
Optional Fair Value Gap (FVG) confirmation
Real-time plotting and auto-invalidation
Perfect for traders using ICT, Smart Money, or algorithmic timing models like Hopplipka.
🧠 What the indicator does
Detects order blocks after break of bullish/bearish fractals
Supports 3-bar or 5-bar fractal structures
Allows OB detection based on close breaks or high/low breaks
Optionally confirms OBs only if followed by a Fair Value Gap within N candles
Filters OBs based on specific time levels (3, 7, 11, 14) — core anchors in many algorithmic models
Automatically deletes invalidated OBs once price closes through the zone
⚙️ How it works
The indicator:
Tracks local fractal highs/lows
Once a fractal is broken by price, it backtracks to identify the best OB candle (highest bullish or lowest bearish)
Validates the level by checking:
OB type logic (close or HL break)
Time stamp match with algorithmic time anchors (e.g. 3, 7, 11, 14 – known from the Power of 3 concept)
Optional FVG confirmation after OB
Plots OB zones as lines (body or wick-based) and removes them if invalidated by a candle close
This ensures traders see only valid, active levels — removing noise from broken or out-of-context zones.
🔧 Customization
Choose 3-bar or 5-bar fractals
OB detection type: close break or HL break
Enable/disable OBs only on times 3, 7, 11, 14 (Hopplipka style)
Optional: require nearby FVG for validation
Line style: solid, dashed, or dotted
Adjust OB length, width, color, and use body or wick for OB height
🚀 How to use it
Add the script to your chart
Choose your preferred OB detection mode and filters
Use plotted OB zones to:
Anticipate price rejections and reversals
Validate Smart Money or ICT-based entry zones
Align setups with algorithmic time sequences (3, 7, 11, 14)
Filter out invalid OBs automatically, keeping your chart clean
The tool is useful on any timeframe but performs best when combined with a liquidity-based or time-anchored trading model.
💡 What makes it original
Combines fractal logic with OB confirmation and time anchors
Implements time-based filtering inspired by Hopplipka’s interpretation of the "Power of 3"
Allows OB validation via optional FVG follow-up — rarely available in public indicators
Auto-cleans invalidated OBs to reduce clutter
Designed to reflect market structure logic used by institutions and algorithms
💬 Why it’s worth using
Order Blocks v2 simplifies one of the most nuanced parts of SMC: identifying clean and high-probability OBs.
It removes subjectivity, adds clear timing logic, and integrates optional confluence tools — like FVG.
For traders serious about algorithmic-level structure and clean setups, this tool delivers both logic and clarity.
⚠️ Important
This indicator:
Is not a signal generator or financial advice tool
Is intended for experienced traders using OB/SMC/time-based logic
Does not predict market direction — it provides visual structural levels only
BeeQuant - Hive Bars🔶 OVERVIEW
The "Hive Bars" indicator is a truly revolutionary analytical instrument, meticulously engineered to transcend the limitations of conventional price charting and unveil the profound, underlying essence of market dynamics. Imagine possessing a sophisticated visual engine that intelligently reconstructs raw price data into unique, dynamically consolidated "Hive Bars." These specialized constructs intuitively reveal the dominant market momentum and highlight high-conviction signals often obscured by the ubiquitous noise of traditional candlesticks. This indicator acts as a precision filter, illuminating exactly when pivotal shifts are occurring by coloring these reconstructed units with an adaptive, unparalleled accuracy. It is expertly crafted for the discerning trader seeking an undeniable analytical advantage, offering a fresh, meticulously refined perspective that enables the discernment of concealed patterns, fostering more decisive and confident trading actions. Crucially, "Hive Bars" now feature proactive, real-time alert capabilities, ensuring no critical market inflection point ever goes unnoticed.
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🧠 CONCEPTS
At its intellectual core, the "Hive Bars" indicator operates upon an advanced, proprietary framework that fundamentally reinterprets market data. It presents this refined information through its unique "Hive Bars"—specialized visual constructs that dynamically encapsulate the consolidated spirit and true directional bias of price action, delivering unparalleled clarity.
⬜ Smart Bar Reconstruction: Hive Bars don’t follow time, they follow the market. They are derived through a sophisticated, multi-faceted internal process that precisely captures the dominant price influence and momentum over variable periods. This structure adapts dynamically to changing conditions, letting you see the real pressure behind price moves with consistency that time-based candles can’t match. This proprietary reconstruction creates a new, inherently consistent, and highly focused visual narrative of underlying market flow, effectively stripping away extraneous "noise" and revealing the market's authentic directional intent.
⬜ Multi-Layered Internal Analysis: A dynamic and live, adaptive line powers the core of Hive Bars. It recalibrates constantly, tracking market structure in real time. Every bar is formed in relation to this internal baseline, giving immediate context to price behavior. You choose the data that drives this line—open, close, high, low, or custom blends—to match your style.
⬜ Intelligent Bar Formation Sequences: Bars are created when the market speaks, not when the clock ticks. A built-in pattern engine reads the flow and waits for real structure to form. This allows the indicator to autonomously consolidate price action, presenting a cleaner, more coherent visualization of trend development as it truly unfolds, rather than fragmented snapshots based on time.
⬜ Visual Signal Precision: "Hive Bars" spring to life with an intuitively powerful coloring system. While primary colors (Green for upward bias, Red for downward bias) denote the prevailing market direction, the "Hive Bars" indicator introduces distinctively colored "Signal Hive Bars". These specialized bars emerge when the market price exhibits a particularly robust, high-conviction interaction with the adaptive internal baseline, standing out instantly and often mark key turning points or breakouts you want to act on.
⬜ Daily Reset Option: For intraday traders, there’s a reset feature that clears the internal build-up at the start of each new trading day. This ensures fresh, unbiased perspectives that are meticulously tailored to the distinct market dynamics and cyclic rhythms of the current trading day.
⬜ Adjustable Sensitivity: With Hive Smoothing, you’re in full control. This setting lets you fine-tune how sensitive the bars are to price movement. Want tighter, faster signals? Dial it down. Prefer broader, more filtered setups? Turn it up. You decide when a new Hive Bar forms—and when a Signal Bar confirms. It’s all based on how you trade and how your asset moves. No guesswork, no one-size-fits-all defaults. Hive Bars adapts to your strategy and trading style, not the other way around.
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✨ FEATURES
The "Hive Bars" indicator is equipped with a comprehensive suite of cutting-edge features, designed for unparalleled clarity, adaptive responsiveness, augmented analytical depth, seamless interoperability with your broader analytical toolkit, and proactive real-time notifications:
🔹Proprietary Hive Bar Reconstruction
Experience a uniquely advanced visual representation of price action that dynamically consolidates market data, leading to enhanced trend and momentum clarity that goes beyond standard charting and candlestick data.
🔹Customizable Internal Analysis Line
Gain precise control over the underlying adaptive baseline's calculation by selecting various internal price source options, ensuring its alignment with your specific analytical focus.
🔹 Smart Alerts for Key Events 🔔
Get notified in real time when:
◦ A new Hive Bar completes – signaling a fresh structural range reset
◦ A new Signal Hive Bar closes – identifying a potential overbought or oversold condition
Built-in alert conditions make it easy to stay ahead of shifts without watching every candle manually.
🔹Intelligent Bar Formation Sequencing
Diamond-shaped markers clearly indicate the start of the indicator's internal combination logic for enhanced visual understanding.
🔹High-Conviction "Signal Hive Bars" (Distinct Colors)
Receive specialized, uniquely colored visual alerts when Hive Bars exhibit strong, decisive movements relative to the adaptive baseline, indicating moments of heightened market conviction and potential opportunity.
🔹Session-Based Reconstruction
Opt for the "Daily New Start" to intelligently reset the indicator's perspective with each new trading day, providing fresh, session-aligned insights tailored for intraday precision.
🔹Unrivaled External Indicator Collaboration
A truly unique and powerful advantage of "Hive Bars" is its capability to seamlessly integrate and profoundly enhance the performance of other external indicators. By outputting clean, smoothed price data, it lets you feed a higher-quality source into tools like RSI, MACD, moving averages etc. Use close for indicators like RSI, and close for moving averages. The result is better clarity, fewer false signals, and a stronger edge across your setup. Hive Bars isn’t just an indicator, it’s an upgrade for everything you use.
🔹Non-Repainting Historical Integrity
Hive Bars never repaints. Each bar is locked in only after all internal conditions are fully met. This means you can trust every historical signal—it won’t shift or vanish after the fact. What you see in hindsight is exactly what was shown in real time.
🔹Universal Timeframe Compatibility
Whether you're scalping on the 1-minute chart or analyzing multi-month trends, Hive Bars delivers consistent, clean insights. Its architecture adapts to any timeframe without losing fidelity, making it a reliable tool for any strategy or style.
🔹Cross-Market Versatility
Hive Bars is engineered to perform with precision across all major markets—whether you're trading forex, commodities, stocks, or indices. Its adaptive logic automatically aligns with the unique volatility and structure of each asset class, delivering consistently reliable insights no matter where you trade.
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⚙️ USAGE
Integrating the "Hive Bars" indicator into your daily analytical regimen is an intuitive process that will profoundly enhance your ability to discern crucial market dynamics and spot high-conviction opportunities with unprecedented clarity:
💁 Effortless Application
Simply add the "Hive Bars" indicator to any chart within your TradingView platform. Note that it plots on a separate panel below your main price chart to provide its unique visual output without obscuring the primary price action.
📊 Strategic Calibration
Access the indicator's comprehensive settings panel to meticulously calibrate its powerful engines and unlock its full potential:
⚙ "Internal EMA Config"
Configure the internal adaptive baseline by choosing its source (e.g., CLOSE, HL/2) and its specific EMA length. This shapes the core reference point for the dynamic formation of the "Hive Bars."
🤖 "CONFIG Group"
Here, you decide if you want "Daily New Start" for session-based analytical resets (particularly beneficial for intraday strategies). The "Hive Smoothing" input allows you to control a further layer of consolidation for the "Hive Bars."
🟩🟥 "Color": Customize the appearance of both standard "Hive Bars" and "Signal Hive Bars" to suit your visual preferences, enhancing their immediate interpretability.
🧭 Empirical Exploration
Experimentation with these parameters is paramount. Dedicate time to exploring different combinations across various assets and timeframes to discover the optimal configuration that resonates with your unique trading methodology and the inherent volatility of the market being analyzed.
👀 Interpreting the Unveiled Market Reality: Once calibrated, the "Hive Bars" will present a strikingly clear and actionable picture of market dynamics:
+ Green/Red Hive Bars: These visually denote the consolidated directional bias of the market over the reconstructed period. A sustained sequence of Green "Hive Bars" suggests pervasive bullish pressure and an upward path of least resistance, while a series of Red "Hive Bars" indicates dominant bearish control and a clear downward momentum.
+ "Signal Hive Bars" (Distinct Colors): Pay close attention to these specially colored "Hive Bars." They signify critical moments where the reconstructed price action exhibits a particularly strong, high-conviction interaction with its adaptive internal baseline. These often precede or confirm significant market movements and serve as your clearest, most reliable visual triggers for potential shifts in market control.
⛓️ Intermittent Appearance: Observe that "Hive Bars" do not necessarily appear for every single native time unit of your chart. They are intelligently reconstructed and consolidated representations of price action, appearing only when specific internal conditions are met to present a coherent, high-impact view of distinct market phases.
🔗 Harnessing Advanced External Synergy: To unlock a new dimension of analytical power, profoundly enhance your existing indicator suite by integrating the output of "Hive Bars" as the data source for other external indicators. When adding or configuring indicators such as RSI, Stochastic Oscillators, various Moving Averages (EMA, SMA), or any other indicator that prompts for a 'source' input, you can now select the purified output of the "Hive Bars" as your desired data stream.
For oscillators (e.g., RSI, MACD), select the close or a similar relevant output from "Hive Bars" as your source. This allows the oscillator to react to the purified, consolidated momentum of the "Hive Bars" rather than the potentially noisy raw price data, leading to smoother and more meaningful oscillator signals.
For moving averages (e.g., EMA, SMA), utilize the close or other pertinent "Hive Bar" output as your source. This provides an exceptionally smooth, highly responsive, and less choppy average that precisely tracks the true underlying trend as identified by "Hive Bars." This unique capability allows for the construction of powerfully layered and synergistic trading strategies.
📢 Setting Up Proactive Alerts for Critical Events: Leverage the newly incorporated alert capabilities to maintain real-time awareness of pivotal market developments, even when not actively monitoring your charts.
You can now choose to be alerted specifically when a "New Hive Bar Closed" (signifying the definitive completion of a major market phase as identified by the indicator) or when a "New Signal Hive Bar Closed" (highlighting a high-conviction market event that warrants immediate attention due to its pronounced significance).
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⚠️ LIMITATIONS
While the "Hive Bars" indicator is an incredibly powerful and advanced tool for dissecting market dynamics, it is vital to understand its inherent design parameters and the prevailing platform-specific constraints for optimal and informed utilization:
👉 Visual Gaps in Plotting: Due to current platform limitations pertaining to custom candle plotting functionality, you may occasionally observe visual gaps or intermittent non-contiguous plotting between "Hive Bars" on the chart. They’re not missing data, but a result of strict plotting rules. A bar is only drawn when all internal conditions are met. This ensures accuracy, even if the chart shows some spacing.
👉 Complementary Tool: This indicator excels at providing high-conviction directional insights and identifying significant market phases. However, it is fundamentally designed as a sophisticated complementary tool to a broader trading strategy, not as a standalone, all-encompassing system. Its true power is unlocked when integrated with other analytical methods.
👉 Input Calibration Essential: The efficacy and depth of insights derived from the "Hive Bars" are highly dependent on the careful and thoughtful calibration of its input parameters, including the "Internal EMA Config," "Hive Smoothing" setting. Optimal results necessitate empirical user experimentation and fine-tuning to discover the configurations best suited for specific assets, analytical objectives, and market conditions.
👉 Exclusion of Auxiliary Data: The "Hive Bars" indicator's primary focus is exclusively on transforming and presenting price data. It does not natively incorporate other vital market information such as fundamental economic data, or news events. Integrating these additional analytical layers remains an essential aspect of constructing a truly comprehensive and robust trading strategy.
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🎯 CONCLUSION
The "Hive Bars" indicator offers an unparalleled, intuitively accessible, and highly adaptable framework for instantly grasping true price momentum and direction through its intelligent, non-repainting reconstruction of market data. By transforming chaotic raw data into strikingly clear, high-conviction "Hive Bars" and dynamic signals, and now with proactive alerts to highlight critical moments, it empowers you to cut through distractions and identify market currents with unprecedented ease. Think of it as a custom lens for the market. It filters out the clutter and shows you the real structure—bars formed not by time, but by intent. It's about seeing the unseen, with enhanced clarity and a deeper understanding of market forces, now with the power to supercharge all your other tools and keep you informed. No fluff. No hype. Just an edge you can actually see—and use.
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🚨 RISK DISCLAIMER
Engagement in financial market speculation inherently carries a substantial degree of inherent risk, and the potential for capital diminution, potentially exceeding initial deposits, is a pervasive and non-trivial consideration. All content, algorithmic tools, scripts, articles, and educational materials disseminated by "Hive Bars" are exclusively purposed for informational and pedagogical objectives, strictly for reference. Historical performance data, whether explicitly demonstrated or implicitly suggested, offers no infallible assurance or guarantee of future outcomes. Users bear sole and ultimate accountability for their individual trading decisions and are emphatically urged to meticulously assess their financial disposition, risk tolerance parameters, and conduct independent due diligence prior to engaging in any speculative activity.
BeeQuant - Hive Factra🔶 OVERVIEW
The "Hive Factra" is a groundbreaking analytical instrument designed to unveil the true essence of market movement, transforming complex price action into powerfully consolidated insights. Imagine having a specialized lens that intelligently reconstructs market periods into unique "Hive Factra Bars," revealing underlying momentum and high-conviction signals often obscured in traditional charts. This indicator cuts through the noise, showing you precisely when significant shifts are occurring by coloring these reconstructed bars with an adaptive precision. It's built for traders who seek unfiltered perspective that helps see hidden patterns and make more decisive moves.
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🧠 CONCEPTS
Markets move in impulses and compressions. Most trend indicators rely on single-frame slope logic, which often flips during minor pullbacks. Hive Factra takes a different route. At its core, the "Hive Factra" operates on a sophisticated framework that reinterprets market data, presenting it through its proprietary "Hive Factra Bars", unique visualizations that capture the consolidated spirit of price action.
⬜ The "Hive Factra" Reconstruction: Unlike standard candles, "Hive Factra Bars" are intelligently re-engineered representations of market activity. They are derived through a proprietary process that captures the dominant price influence over specific periods, presenting a clearer, more focused view of underlying momentum. These unique bars visually consolidate information, making the core directional bias immediately apparent.
⬜ The Adaptive Baseline: An internal, dynamic analysis line constantly adjusts to market flow, serving as a crucial reference point for the "Hive Factra Bars." This adaptive baseline provides real-time context, helping the indicator precisely determine the significance of each reconstructed bar's movement.
⬜ High-Conviction Coloring & Signal Bars: The "Factra Bars" come to life with a discerning coloring system. While they reflect the primary market direction (Green for upward bias, Red for downward bias), the "Hive Factra" introduces specialized "Signal Hive Bars" with distinct colors. These unique bars appear when the consolidated price action exhibits a particularly strong, high-conviction interaction with the adaptive baseline, acting as powerful visual alerts for moments of heightened significance.
⬜ Session-Aligned Insights: For intraday traders, the "Daily New Start" option provides a unique advantage. When enabled, the indicator can reset its internal reconstruction process with each new trading session, offering fresh, unbiased perspectives tailored to the day's distinct market dynamics.
⬜ Dynamic Sensitivity: A configurable "Offset" allows you to fine-tune the indicator's responsiveness and the thresholds for initiating these "Hive Factra Bars" and "Signal Hive Bars." This ensures the indicator aligns perfectly with your individual trading style and the volatility of the asset you're analyzing.
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✨ FEATURES
The "Hive Factra" is equipped with a suite of cutting-edge features, all meticulously designed for unparalleled clarity, adaptive responsiveness, and augmented analytical depth:
🔹 Proprietary Hive Factra Bars
Experience a unique visual representation of price action that consolidates market data for enhanced trend and momentum clarity.
🔹 Customizable Internal Analysis Line
Control the underlying adaptive baseline's calculation for precise alignment with market flow, utilizing various price source options.
🔹 High-Conviction "Signal Hive Bars" (Distinct Colors)
Receive specialized visual alerts when Factra Bars exhibit strong, decisive movements relative to the adaptive baseline, indicating moments of heightened market conviction.
🔹 Overbought/Oversold Visuals
Signal Hive Bars highlight areas of potential exhaustion, providing intuitive insight into stretched conditions
🔹 Session-Based Reconstruction
Opt for the "Daily New Start" to reset the indicator's perspective with each new trading day, providing fresh, session-aligned insights.
🔹 Dynamic Offset Control
Adjust the "Offset" parameter to fine-tune the sensitivity of the Factra Bar reconstruction and signal generation thresholds, tailoring the indicator to specific market conditions.
🔹 Non-Repainting Logic for Historical Reliability
Each "Hive Factra Bar" is plotted only when its internal reconstruction conditions are fully met and confirmed. This ensures that the historical display of Factra Bars does not repaint, providing a high degree of reliability and trust in past signals and visualizations.
🔹 Cross-Market Versatility
This indicator is engineered to perform with precision across all major markets—whether you're trading forex, commodities, stocks, or indices. Its adaptive logic automatically aligns with the unique volatility and structure of each asset class, delivering consistently reliable insights no matter where you trade.
🔹 Custom Range Start Marker
A subtle diamond-shaped symbol is plotted to indicate the start of the Hive Factra logic cycle. This marks the bar from which the internal price range begins accumulating until a new Hive Factra Bar is confirmed and displayed. Helps visualize the dynamic evaluation period used in Factra’s structural detection.
🔹 Smart Alerts for Key Events
Get notified in real time when:
◦ A new Hive Factra Bar completes – signaling a fresh structural range reset
◦ A new Signal Hive Bar closes – identifying a potential overbought or oversold condition
Built-in alert conditions make it easy to stay ahead of shifts without watching every candle manually.
🔹 Universal Timeframe Compatibility: The "Hive Factra" is meticulously engineered to perform flawlessly across all timeframes, from rapid intraday charts to long-term weekly and monthly views. This universal compatibility ensures you receive consistent, high-quality insights regardless of your analytical horizon.
🔹 Unrivaled External Indicator Collaboration: A truly unique advantage of the "Hive Factra" is its capability to seamlessly integrate and enhance the performance of other external indicators. Its meticulously processed output, can serve as a highly purified and consolidated 'source' for indicators that accept such inputs (e.g., RSI, StochRSI, moving averages), which allows for more insightful data stream into your favorite indicators, potentially unlocking new levels of responsiveness and signal accuracy for your entire analytical setup.
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⚙️ USAGE
Integrating the "Hive Factra" into your daily analytical regimen is intuitive and will profoundly enhance your ability to discern crucial market dynamics and spot high-conviction opportunities:
💁 Effortless Application
Simply add the "Hive Factra" indicator to any chart within your TradingView platform. Note that it plots on a separate panel below your main price chart to provide its unique visual output without obscuring price.
📊 Tailored Calibration: Access the indicator's settings to unlock its full potential:
⚙ "Internal EMA Config"
Configure the internal adaptive baseline by choosing its source (e.g., Close, HL/2) and length. This shapes the core reference point for the Factra Bars.
⚙ "Hive Factra"
Decide if you want "Daily New Start" for session-based analysis and choose the "Source" type for how the Factra Bars are built.
🤖 "Offset"
Experiment with the "Offset" percentage to adjust the sensitivity of the Factra Bar's reconstruction. A smaller offset will make the Factra Bars appear more frequently, while a larger one will highlight only more significant movements.
🟩🟥 Green/Red Hive Factra Bars
These indicate the consolidated directional bias of the market over the reconstructed period. A sequence of Green bars suggests sustained bullish pressure, while Red bars point to dominant bearish control.
🚀 "Signal Hive Bars" (Unique Colors)
Pay close attention to these specially colored Hive Factra Bars. They signify moments where the reconstructed price action exhibits a high-conviction interaction with its adaptive baseline, often preceding or confirming significant market moves. These are your clearest signals for potential shifts.
✨ Appearance of Hive Factra Bars
Notice that these Bars do not necessarily appear for every single time unit. They intelligently reconstruct and consolidate price action, appearing only when conditions align to present a coherent, high-impact view of market phases.
🪢 Harnessing External Synergy
To unlock a new dimension of analysis, consider integrating "Hive Factra" as the data source for other indicators:
1. When adding indicators like RSI, StochRSI, or others that prompt for a 'source' input, you can select the "Hive Factra" as the input.
2. For oscillators (e.g., RSI, Stochastic), choose the close or similar output from "Hive Factra" as your source. This allows the oscillator to react to the purified, consolidated momentum of the Factra Bars rather than raw price.
For moving averages (e.g., EMA, SMA), use the close or other relevant Factra Bar output as your source. This provides an exceptionally smooth and responsive average that tracks the true underlying trend.
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⚠️ LIMITATIONS
While the "Hive Factra" is an incredibly powerful tool for dissecting market dynamics, it's vital to understand its design parameters for optimal use. It does not attempt to front-run reversals or predict market turns. Instead, it focuses on framing price behavior so traders can react with context.
👉 Visual Gaps in Plotting: Due to Tradingview platform limitations with custom candle plotting functionality, you may observe visual gaps between "Hive Factra Bars" on the chart. This occurs because the indicator only plots a Hive Factra Bar when its internal conditions for reconstruction are fully met, and there isn't an 'offset' parameter for custom candles to bridge these visual discontinuities. Importantly, this behavior ensures that each plotted Factra Bar is confirmed and does not repaint, providing reliable historical analysis.
👉 Reconstructed Data, Not Raw Price: It's crucial to remember that "Hive Factra Bars" are not traditional candles. They are a derived visualization that intelligently consolidates price data.
👉 Complementary Tool: This indicator excels at providing high-conviction directional insights and identifying significant market phases. However, it is designed as a sophisticated complement to a broader trading strategy, not a standalone system.
👉 Input Calibration Essential: The effectiveness of the "Hive Factra" is highly dependent on careful calibration of its input parameters, especially the "Offset" and internal EMA settings. Optimal results require user experimentation to find settings best suited for specific assets and timeframes.
👉 Exclusion of Auxiliary Data: The "Hive Factra" focuses solely on transforming price data. It does not incorporate other vital market information such as trading volume, market breadth, or fundamental news. Integrating these additional analytical layers remains essential for a comprehensive trading strategy.
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🎯 CONCLUSION
The "Hive Factra" offers an unparalleled, intuitive, and highly adaptable framework for instantly grasping true price momentum and direction through its intelligent reconstruction of market data. By transforming chaotic raw data into strikingly clear, high-conviction "Factra Bars" and dynamic signals, it empowers you to cut through distractions and identify critical market currents with ease. Its revolutionary capability for seamless collaboration with external indicators (like RSI, EMA, etc., by using its purified output as their source) means you can elevate the performance of your entire analytical suite to new levels of precision and clarity. Seamlessly integrate this advanced visual tool within your analytical framework to gain a sharper, more confident perspective, and elevate your strategic decision-making in the markets. It's about seeing the unseen, with enhanced clarity and a deeper understanding of market forces, now with the power to supercharge all your other tools.
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🚨 RISK DISCLAIMER
Engagement in financial market speculation inherently carries a substantial degree of inherent risk, and the potential for capital diminution, potentially exceeding initial deposits, is a pervasive and non-trivial consideration. All content, algorithmic tools, scripts, articles, and educational materials disseminated by "Hive Factra" are exclusively purposed for informational and pedagogical objectives, strictly for reference. Historical performance data, whether explicitly demonstrated or implicitly suggested, offers no infallible assurance or guarantee of future outcomes. Users bear sole and ultimate accountability for their individual trading decisions and are emphatically urged to meticulously assess their financial disposition, risk tolerance parameters, and conduct independent due diligence prior to engaging in any speculative market activity.
Aftershock Playbook: Stock Earnings Drift EngineStrategy type
Event-driven post-earnings momentum engine (long/short) built for single-stock charts or ADRs that publish quarterly results.
What it does
Detects the exact earnings bar (request.earnings, lookahead_off).
Scores the surprise and launches a position on that candle’s close.
Tracks PnL: if the first leg closes green, the engine automatically re-enters on the very next bar, milking residual drift.
Blocks mid-cycle trades after a loss until the next earnings release—keeping the risk contained to one cycle.
Think of it as a sniper that fires on the earnings pop, reloads once if the shot lands, then goes silent until the next report.
Core signal inputs
Component Default Purpose
EPS Surprise % +0 % / –5 % Minimum positive / negative shock to trigger longs/shorts.
Reverse signals? Off Quick flip for mean-reversion experiments.
Time Risk Mgt. Off Optional hard exit after 45 calendar days (auto-scaled to any TF).
Risk engine
ATR-based stop (ATR × 2 by default, editable).
Bar time stop (15-min → Daily: Have to select the bar value ).
No pyramiding beyond the built-in “double-tap”.
All positions sized as % of equity via Strategy Properties.
Visual aids
Yellow triangle marks the earnings bar.
Diagnostics table (top-right) shows last Actual, Estimate, and Surprise %.
Status-line tool-tips on every input.
Default inputs
Setting Value
Positive surprise ≥ 0 %
Negative surprise ≤ –5 %
ATR stop × 2
ATR length 50
Hold horizon 350 ( 1h timeframe chart bars)
Back-test properties
Initial capital 10 000
Order size 5 % of equity
Pyramiding 1 (internal re-entry only)
Commission 0.03 %
Slippage 5 ticks
Fills Bar magnifier ✔ · On bar close ✔ · Standard OHLC ✔
How to use
Add the script to any earnings-driven stock (AAPL, MSFT, TSLA…).
Turn on Time Risk Management if you want stricter risk management
Back-test different ATR multipliers to fit the stock’s volatility.
Sync commission & slippage with your broker before forward-testing.
Important notes
Works on every timeframe from 15 min to 1 D. Sweet spot around 30min/1h
All request.earnings() & request.security() calls use lookahead_off—zero repaint.
The “double-tap” re-entry occurs once per winning cycle to avoid drift-chasing loops.
Historical stats ≠ future performance. Size positions responsibly.
BeeQuant - Hive Smoothing Average🔶 OVERVIEW
The "Hive Smoothing Average" is your gateway to crystal-clear market insights, a truly advanced tool that cuts through confusing price "noise" to reveal the true underlying trend. Imagine having a panoramic view of the market's true direction, unclouded by minor ups and downs. This powerful indicator dynamically filters out market distractions, presenting you with a highly refined line that not only shows you the genuine path of price but also changes color. It’s built for traders who demand clarity and want to confidently spot opportunities that others might miss in messy charts.
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🧠 CONCEPTS
At its heart, the "Hive Smoothing Average" employs a sophisticated multi-stage processing system to transform raw price data into an incredibly smooth and responsive smoothed moving average line. It's designed to give you an unparalleled view of market direction and momentum.
⬜ Synthesizes multiple smoothing layers to deliver a balanced representation of underlying price action.
⬜ Offers enhanced visual consistency by filtering volatility distortion without delay-based lag.
⬜ Presents color-coded transitions and signal markers to aid in directional conviction and structural flow.
⬜ Embeds a modular smoothing core adaptable across market environments and asset classes.
Hive Smoothing Average doesn't forecast, it refines. It provides a more coherent view of price evolution, allowing for higher-confidence discretion and more robust strategy overlays.
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✨ FEATURES
Hive Smoothing Average is loaded with flexibility and visual power to enhance your decision-making:
🔹Customizable Smoothing
Tailor the indicator’s core behavior using a wide range of smoothing algorithms — from classic to advanced — to match your trading tempo and asset dynamics.
🔹 Intelligent Color Feedback
The line color dynamically shifts to reflect meaningful trend transitions, offering at-a-glance clarity without crowding your chart.
🔹 Trend Signal Markers
Built-in arrow markers highlight potential transitions in price momentum, acting as subtle nudges to investigate further.
🔹 Multi-Timeframe Ready
Designed to operate cleanly across all timeframes, from scalping micro-trends to monitoring macro cycles.
🔹 External Source Collaboration
Hive Smoothing Average includes two flexible input channels that can seamlessly connect with other indicators on your chart.
🔹 Adaptive Bands
A powerful enhancement to the Hive framework, the optional Standard Deviation Bands add dynamic context to price behavior by highlighting how far price is moving relative to its recent average volatility.
Length: Controls the lookback period for volatility calculation.
Lower values (e.g., 20 – 50) make the bands highly reactive Higher values (e.g., 200 – 500) smooth out the bands (classic envelope systems )
These bands offer valuable visual cues for both volatility expansion and mean reversion potential, especially when combined with Hive’s core candle coloration logic.
🔹Non-Repainting Logic for Historical Reliability
Each "Hive Smoothing Average" is plotted only when its internal reconstruction conditions are fully met and confirmed. This ensures that the historical display of Hive Smoothing Average does not repaint, providing a high degree of reliability and trust in past signals and visualizations.
🔹Cross-Market Versatility
This indicator is engineered to perform with precision across all major markets—whether you're trading forex, commodities, stocks, or indices. Its adaptive logic automatically aligns with the unique volatility and structure of each asset class, delivering consistently reliable insights no matter where you trade.
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⚙️ USAGE
Getting started with Hive Smoothing Average is seamless and intuitive:
✨ Apply to Any Chart
Simply add the indicator to any asset or timeframe and see immediate transformation in chart clarity.
💹 Source Data Flexibility
Choose your preferred price data source for each smoothing stage (e.g., Close, Open, High, Low), providing complete control over the input feeding the sophisticated smoothing algorithms.
🛠️ Adjust Smoothing Behavior
Choose your preferred initial and final smoothing types (EMA, HMA, ALMA, etc.), and tweak lengths for desired responsiveness or smoothness.
📐 Use Bands for Confluence
Enable the Bands mode to visualize dynamic zones around your smoothed price. Useful for breakout validation and fade zones.
🟩 Green Smoother Line
Indicates strengthening bullish bias and upward progression.
🟥 Red Smoother Line
Suggests weakening or shifting trend toward bearish territory.
📈 Arrow Signals
Upward or downward triangles appear when directional bias changes — confirming subtle pivots in trend behavior.
🎯 Offset Adjustment
Fine-tune the visual positioning of the smoothed line and bands on your chart with a convenient "Offset" input.
📏 Lookback Filter
Activate the “Lookback Filter” setting to remove weaker signals based on custom historical logic. By checking recent candle behavior, it filters out low-quality transitions and only keeps strong, confirmed shifts — helping you avoid noise and stay focused on reliable breakouts.
Experiment with settings based on your trading timeframe. Short-term traders may prefer fast-reactive configurations, while swing or positional traders can explore higher-period smoothings for structural signals.
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⚠️ LIMITATIONS
While Hive Smoothing Average delivers incredible trend clarity, it’s essential to use it within its designed purpose:
👉 Visual Clarity, Not Trade Calls: This tool enhances visibility of market behavior, not automatic signals. Use it as a trusted lens — not a standalone system.
👉 Reactive, Not Predictive: Hive Smoothing Average responds to price action with refined smoothing. It is not a forecasting model.
👉 Config-Sensitive Output: Different smoothing setups can produce different levels of sensitivity or delay. Calibration matters — explore what fits your asset and style.
👉 Focuses on Price Action Only: It does not integrate volume, fundamentals, or external market influences. It’s engineered purely for price structure refinement.
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🎯 CONCLUSION
Hive Smoothing Average provides a high-performance, low-noise framework to view price with remarkable clarity. With its adaptive smoothing layers, bands support, and intelligent signal markers, it becomes a powerful tool to enhance your trend confidence and charting efficiency. By furnishing immediate, data-driven feedback on the market's core momentum and signaling critical turning points, it profoundly empowers traders to rapidly ascertain nascent market shifts and identify pivotal directional changes. Seamlessly integrate this sophisticated visual tool within your pre-existing technical analysis architecture to acquire a sharper, more insightful perspective, and fundamentally elevate your strategic acumen, optimizing your decision-making processes to a degree previously unattainable. It's about experiencing the market's true rhythm.
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🚨 RISK DISCLAIMER
Engagement in financial market speculation inherently carries a substantial degree of inherent risk, and the potential for capital diminution, potentially exceeding initial deposits, is a pervasive and non-trivial consideration. All content, algorithmic tools, scripts, articles, and educational materials disseminated by "Hive Smoothing Average" are exclusively purposed for informational and pedagogical objectives, strictly for reference. Historical performance data, whether explicitly demonstrated or implicitly suggested, offers no infallible assurance or guarantee of future outcomes. Users bear sole and ultimate accountability for their individual trading decisions and are emphatically urged to meticulously assess their financial disposition, risk tolerance parameters, and conduct independent due diligence prior to engaging in any speculative market activity.
