Order Blocks & Breaker Blocks [TradingFinder] Signals + Alerts🔵 Introduction
Order Block and Breaker Block, are powerful tools in technical analysis. By understanding these concepts, traders can enhance their ability to predict potential price reversals and continuations, leading to more effective trading strategies.
Using historical price action, volume analysis, and candlestick patterns, traders can identify key areas where institutional activities influence market movements.
🟣 Demand Order Block and Supply Breaker Block
Demand Order Block : A Demand Order Block is formed when the price succeeds in breaking the previous high pivot.
Supply Breaker Block : A Supply Breaker Block is formed when the price succeeds in breaking the Demand Order Block. As a result, the Order Block changes its role and turns from the role of price support to resistance.
🟣 Supply Order Block and Demand Breaker Block
Supply Order Block : A Supply Order Block is formed when the price succeeds in breaking the previous low pivot.
Demand Breaker Block : A Demand Breaker Block is formed when the price succeeds in breaking the Supply Order Block. As a result, the Order Block changes its role and turns from the role of price resistance to support.
🔵 How to Use
🟣 Order Blocks (Supply and Demand)
Order blocks are zones where the likelihood of a price reversal is higher. In demand zones, buying opportunities arise, while in supply zones, selling opportunities can be explored.
The "Refinement" feature allows you to adjust the width of the order block to fit your trading strategy. There are two modes in the "Order Block Refine" feature: "Aggressive" and "Defensive." The primary difference between these modes is the width of the order block.
For risk-averse traders, the "Defensive" mode is ideal as it offers a lower loss limit and a higher reward-to-risk ratio.
Conversely, for traders who are willing to take more risks, the "Aggressive" mode is more suitable. This mode, with its wider order block width, caters to those who prefer entering trades at higher prices.
🟣 Breaker Blocks (Supply and Demand)
Trading based on breaker blocks is the same as order blocks and the price in these zones is likely to be reversed.
🔵 Setting
🟣 Global Setting
Pivot Period of Order Blocks Detector : Enter the desired pivot period to identify the Order Block.
Order Block Validity Period (Bar) : You can specify the maximum time the Order Block remains valid based on the number of candles from the origin.
Mitigation Level Order Block : Determining the basic level of a Order Block. When the price hits the basic level, the Order Block due to mitigation.
Mitigation Level Breaker Block : Determining the basic level of a Breaker Block. When the price hits the basic level, the Breaker Block due to mitigation.
Switching Colors Theme Mode : Three modes "Off", "Light" and "Dark" are included in this parameter. "Light" mode is for color adjustment for use in "Light Mode".
"Dark" mode is for color adjustment for use in "Dark Mode" and "Off" mode turns off the color adjustment function and the input color to the function is the same as the output color.
🟣 Order Block Display
Show All Order Block : If it is turned off, only the last Order Block will be displayed.
Demand Main Order Block : Show or not show and specify color.
Demand Sub (Propulsion & BoS Origin) Order Block : Show or not show and specify color.
Supply Main Order Block : Show or not show and specify color.
Supply Sub (Propulsion & BoS Origin) Order Block : Show or not show and specify color.
🟣 Breaker Block Display
Show All Breaker Block : If it is turned off, only the last Breaker Block will be displayed.
Demand Main Breaker Block : Show or not show and specify color.
Demand Sub (Propulsion & BoS Origin) Breaker Block : Show or not show and specify color.
Supply Main Breaker Block : Show or not show and specify color.
Supply Sub (Propulsion & BoS Origin) Breaker Block : Show or not show and specify color.
🟣 Order Block Refinement
Refine Order Blocks : Enable or disable the refinement feature. Mode selection.
🟣 Alert
Alert Name : The name of the alert you receive.
Alert Demand OB Mitigation :
On / Off
Alert Demand BB Mitigation :
On / Off
Alert Supply OB Mitigation :
On / Off
Alert Supply BB Mitigation :
On / Off
Message Frequency :
This string parameter defines the announcement frequency. Choices include: "All" (activates the alert every time the function is called), "Once Per Bar" (activates the alert only on the first call within the bar), and "Once Per Bar Close" (the alert is activated only by a call at the last script execution of the real-time bar upon closing). The default setting is "Once per Bar".
Show Alert Time by Time Zone :
The date, hour, and minute you receive in alert messages can be based on any time zone you choose. For example, if you want New York time, you should enter "UTC-4". This input is set to the time zone "UTC" by default.
Display More Info :
Displays information about the price range of the order blocks (Zone Price) and the date, hour, and minute under "Display More Info".
If you do not want this information to appear in the received message along with the alert, you should set it to "Off".
In den Scripts nach "BOS" suchen
Protected Highs & Lows [TFO]This indicator presents an alternative approach to identify Market Structure. The logic used is derived from learning material created by @DaveTeaches
When quantifying Market Structure, it is common to use fractal highs and lows to identify "significant" swing pivots. When price closes through these pivots, we may identify a Market Structure Shift (MSS) for reversals or a Break of Structure (BOS) for continuations. The main difference with this "protected" logic is in how we determine the pivots/levels that are utilized to determine a valid MSS or BOS.
Nonetheless, the significance of our swing pivots is still governed by the input Pivot Strength parameter, which requires valid swing pivots to be compared to this many bars to the left and right of them. This is an optional parameter as it is traditionally set to 1 by default.
When identifying a BOS: When price closes below a valid swing low, we look back from the current bar to find the highest high that was made in that range. This becomes our protected high; similarly, when price closes above a valid swing high, we look back from the current bar to find the lowest low that was made in that range, which then becomes our protected low.
Note these valid highs and lows are the first swing pivots created after a MSS/BOS. For example, when price makes a bullish BOS/MSS and then trades away, a swing high is formed. This first swing high is what needs to be traded through to see a valid BOS.
When identifying a MSS: If the current trend is bearish and we're looking for a bullish reversal, we would need price to close above the most recent protected high. When this happens, we still look back to find the lowest low that was created in that range, and make that our new protected low. Likewise when looking for a bearish reversal, price would need to close below the most recent protected low, which would then give us a new protected high as a result (the highest point in that range).
The Trend Candles option allows users to easily visualize the current state of Market Structure with bullish and bearish colors. Users may also show BOS and MSS labels if desired.
Show Protected Highs & Lows will annotate the protected highs and lows, just note that the labels themselves are plotted in the past due to the lookback function required to identify them.
Lastly, the Show Protected Trail option will draw a line to essentially indicate a trailing stop-like line to denote the most recent protected low (if bullish) or protected high (if bearish).
I am simply a student of Dave's concepts, so please feel free to leave feedback if you are familiar with his concepts and have suggestions/improvements.
Market Structure [TFO]The purpose of this indicator is to provide a simple approach to Market Structure. When price is closing over swing highs, we may categorize that as bullish structure; and when price is closing below swing lows, we may categorize that as bearish structure.
We can easily find swing highs and lows via the following built-in Pine Script functions:
ta.pivothigh()
ta.pivotlow()
We can pass in our Pivot Strength parameter to determine the size/significance of these pivots. The lowest value of 1 will validate a swing high when a given high is larger than that of 1 bar to the left and right of it. A pivot strength of 3, for example, would validate a swing high only when a high is larger than that of the 3 bars to the left and right of it, making it much more selective.
In any case, we can simply track the most recent swing highs and lows and check for when price through them. Enabling the Show Pivots option will mark all the swing highs and lows that are being considered for future structure breaks.
If the trend is bearish and we begin closing over swing highs, that would mark a Market Structure Shift (MSS). If the trend is already bullish and we are closing over swing highs, that would mark a Break of Structure (BOS), and vice versa for bearish conditions. MSS essentially signifies reversals in Market Structure while BOS signifies continuations.
Users may also create alerts for Any/Bull/Bear BOS or MSS. Simply create a new alert, select this indicator, and select the desired BOS or MSS criteria.
ICT Concepts [LuxAlgo]The ICT Concepts indicator regroups core concepts highlighted by trader and educator "The Inner Circle Trader" (ICT) into an all-in-one toolkit. Features include Market Structure (MSS & BOS), Order Blocks, Imbalances, Buyside/Sellside Liquidity, Displacements, ICT Killzones, and New Week/Day Opening Gaps.
🔶 SETTINGS
🔹 Mode
When Present is selected, only data of the latest 500 bars are used/visualized, except for NWOG/NDOG
🔹 Market Structure
Enable/disable Market Structure.
Length: will set the lookback period/sensitivity.
In Present Mode only the latest Market Structure trend will be shown, while in Historical Mode, previous trends will be shown as well:
You can toggle MSS/BOS separately and change the colors:
🔹 Displacement
Enable/disable Displacement.
🔹 Volume Imbalance
Enable/disable Volume Imbalance.
# Visible VI's: sets the amount of visible Volume Imbalances (max 100), color setting is placed at the side.
🔹 Order Blocks
Enable/disable Order Blocks.
Swing Lookback: Lookback period used for the detection of the swing points used to create order blocks.
Show Last Bullish OB: Number of the most recent bullish order/breaker blocks to display on the chart.
Show Last Bearish OB: Number of the most recent bearish order/breaker blocks to display on the chart.
Color settings.
Show Historical Polarity Changes: Allows users to see labels indicating where a swing high/low previously occurred within a breaker block.
Use Candle Body: Allows users to use candle bodies as order block areas instead of the full candle range.
Change in Order Blocks style:
🔹 Liquidity
Enable/disable Liquidity.
Margin: sets the sensitivity, 2 points are fairly equal when:
'point 1' < 'point 2' + (10 bar Average True Range / (10 / margin)) and
'point 1' > 'point 2' - (10 bar Average True Range / (10 / margin))
# Visible Liq. boxes: sets the amount of visible Liquidity boxes (max 50), this amount is for Sellside and Buyside boxes separately.
Colour settings.
Change in Liquidity style:
🔹 Fair Value Gaps
Enable/disable FVG's.
Balance Price Range: this is the overlap of latest bullish and bearish Fair Value Gaps.
By disabling Balance Price Range only FVGs will be shown.
Options: Choose whether you wish to see FVG or Implied Fair Value Gaps (this will impact Balance Price Range as well)
# Visible FVG's: sets the amount of visible FVG's (max 20, in the same direction).
Color settings.
Change in FVG style:
🔹 NWOG/NDOG
Enable/disable NWOG; color settings; amount of NWOG shown (max 50).
Enable/disable NDOG ; color settings; amount of NDOG shown (max 50).
🔹 Fibonacci
This tool connects the 2 most recent bullish/bearish (if applicable) features of your choice, provided they are enabled.
3 examples (FVG, BPR, OB):
Extend lines -> Enabled (example OB):
🔹 Killzones
Enable/disable all or the ones you need.
Time settings are coded in the corresponding time zones.
🔶 USAGE
By default, the indicator displays each feature relevant to the most recent price variations in order to avoid clutter on the chart & to provide a very similar experience to how a user would contruct ICT Concepts by hand.
Users can use the historical mode in the settings to see historical market structure/imbalances. The ICT Concepts indicator has various use cases, below we outline many examples of how a trader could find usage of the features together.
In the above image we can see price took out Sellside liquidity, filled two bearish FVGs, a market structure shift, which then led to a clean retest of a bullish FVG as a clean setup to target the order block above.
Price then fills the OB which creates a breaker level as seen in yellow.
Broken OBs can be useful for a trader using the ICT Concepts indicator as it marks a level where orders have now been filled, indicating a solidified level that has proved itself as an area of liquidity. In the image above we can see a trade setup using a broken bearish OB as a potential entry level.
We can see the New Week Opening Gap (NWOG) above was an optimal level to target considering price may tend to fill / react off of these levels according to ICT.
In the next image above, we have another example of various use cases where the ICT Concepts indicator hypothetically allow traders to find key levels & find optimal entry points using market structure.
In the image above we can see a bearish Market Structure Shift (MSS) is confirmed, indicating a potential trade setup for targeting the Balanced Price Range imbalance (BPR) below with a stop loss above the buyside liquidity.
Although what we are demonstrating here is a hindsight example, it shows the potential usage this toolkit gives you for creating trading plans based on ICT Concepts.
Same chart but playing out the history further we can see directly after price came down to the Sellside liquidity & swept below it...
Then by enabling IFVGs in the settings, we can see the IFVG retests alongside the Sellside & Buyside liquidity acting in confluence.
Which allows us to see a great bullish structure in the market with various key levels for potential entries.
Here we can see a potential bullish setup as price has taken out a previous Sellside liquidity zone and is now retesting a NWOG + Volume Imbalance.
Users also have the option to display Fibonacci retracements based on market structure, order blocks, and imbalance areas, which can help place limit/stop orders more effectively as well as finding optimal points of interest beyond what the primary ICT Concepts features can generate for a trader.
In the above image we can see the Fibonacci extension was selected to be based on the NWOG giving us some upside levels above the buyside liquidity.
🔶 DETAILS
Each feature within the ICT Concepts indicator is described in the sub sections below.
🔹 Market Structure
Market structure labels are constructed from price breaking a prior swing point. This allows a user to determine the current market trend based on the price action.
There are two types of Market Structure labels included:
Market Structure Shift (MSS)
Break Of Structure (BOS)
A MSS occurs when price breaks a swing low in an uptrend or a swing high in a downtrend, highlighting a potential reversal. This is often labeled as "CHoCH", but ICT specifies it as MSS.
On the other hand, BOS labels occur when price breaks a swing high in an uptrend or a swing low in a downtrend. The occurrence of these particular swing points is caused by retracements (inducements) that highlights liquidity hunting in lower timeframes.
🔹 Order Blocks
More significant market participants (institutions) with the ability of placing large orders in the market will generally place a sequence of individual trades spread out in time. This is referred as executing what is called a "meta-order".
Order blocks highlight the area where potential meta-orders are executed. Bullish order blocks are located near local bottoms in an uptrend while bearish order blocks are located near local tops in a downtrend.
When price mitigates (breaks out) an order block, a breaker block is confirmed. We can eventually expect price to trade back to this breaker block offering a new trade opportunity.
🔹 Buyside & Sellside Liquidity
Buyside / Sellside liquidity levels highlight price levels where market participants might place limit/stop orders.
Buyside liquidity levels will regroup the stoploss orders of short traders as well as limit orders of long traders, while Sellside liquidity levels will regroup the stoploss orders of long traders as well as limit orders of short traders.
These levels can play different roles. More informed market participants might view these levels as source of liquidity, and once liquidity over a specific level is reduced it will be found in another area.
🔹 Imbalances
Imbalances highlight disparities between the bid/ask, these can also be defined as inefficiencies, which would suggest that not all available information is reflected by the price and would as such provide potential trading opportunities.
It is common for price to "rebalance" and seek to come back to a previous imbalance area.
ICT highlights multiple imbalance formations:
Fair Value Gaps: A three candle formation where the candle shadows adjacent to the central candle do not overlap, this highlights a gap area.
Implied Fair Value Gaps: Unlike the fair value gap the implied fair value gap has candle shadows adjacent to the central candle overlapping. The gap area is constructed from the average between the respective shadow and the nearest extremity of their candle body.
Balanced Price Range: Balanced price ranges occur when a fair value gap overlaps a previous fair value gap, with the overlapping area resulting in the imbalance area.
Volume Imbalance: Volume imbalances highlight gaps between the opening price and closing price with existing trading activity (the low/high overlap the previous high/low).
Opening Gap: Unlike volume imbalances opening gaps highlight areas with no trading activity. The low/high does not reach previous high/low, highlighting a "void" area.
🔹 Displacement
Displacements are scenarios where price forms successive candles of the same sentiment (bullish/bearish) with large bodies and short shadows.
These can more technically be identified by positive auto correlation (a close to open change is more likely to be followed by a change of the same sign) as well as volatility clustering (large changes are followed by large changes).
Displacements can be the cause for the formation of imbalances as well as market structure, these can be caused by the full execution of a meta order.
🔹 Kill Zones
Killzones represent different time intervals that aims at offering optimal trade entries. Killzones include:
- New York Killzone (7:9 ET)
- London Open Killzone (2:5 ET)
- London Close Killzone (10:12 ET)
- Asian Killzone (20:00 ET)
🔶 Conclusion & Supplementary Material
This script aims to emulate how a trader would draw each of the covered features on their chart in the most precise representation to how it's actually taught by ICT directly.
There are many parallels between ICT Concepts and Smart Money Concepts that we released in 2022 which has a more general & simpler usage:
ICT Concepts, however, is more specifically aligned toward the community's interpretation of how to analyze price 'based on ICT', rather than displaying features to have a more classic interpretation for a technical analyst.
Zendog V3 backtest DCA bot 3commasMAJOR UPDATE:
- Update to Pinescript v5
- MAJOR refactor for the logic of how orders are placed. BO order is placed when the condition is first encountered and we are not in a deal.
The extra SO orders (if based on price movement) are all placed on the next candle after BO order, instead of each being placed one after another.
Take profit (if percentage) and Stop loss are placed on the first candle after BO order because if BO and TP are on the same candle TV does not execute properly.
These changes should improve strategy accuracy when multiple prices are hit by the same candle.
- NEW FEATURE: Support to Stop deal using an external indicator (i.e. stop long deal when RSI > 80)
- NEW FEATURE: Support to trigger Safety orders using an external indicator (i.e. trigger each additional SO when RSI < 10, regardless of price movement)
The price movement logic may be implemented in the indicator that plots start / end signals. The SO size is calculated using the configuration of steps.
- NEW FEATURE: Safety order command for 3commas bot. This is implemented using Add funds in the quote currency (for pair BTCUSDT the quote currency is USDT)
The SO size is calculated using the configuration of steps, for exact order size (and price) use the built-in Steps table.
- NEW FEATURE: Addition of extra columns to the steps table: Required price for TP, Required % change for TP, Required % change for BEP (Breakeven point)
- Update to steps table to remove prices when Safety orders are not based on % price change
- The code is opensource. I will not be able to sustain merges for the script, but feel free to use and develop your own version and ping me on discord to review them
and maybe include in the original script
MAGIC TRADER RANGE BOX 2.0//@version=6
indicator("MAGIC TRADER RANGE BOX 2.0", overlay=false
// ===== PARAMÈTRES =====
rangeLen = input.int(20, "Longueur Range H1", minval=5)
atrLen = input.int(14, "ATR H1")
atrFactor = input.float(1.0, "Facteur ATR", step=0.1)
maLen = input.int(20, "MA H1")
slopeLimit = input.float(0.05, "Tolérance direction", step=0.01)
// 🎨 STYLE BOÎTE
boxColor = input.color(color.gray, "Couleur de la boîte")
opacity = input.int(85, "Opacité (0-100)", minval=0, maxval=100)
borderColor = input.color(color.gray, "Couleur du contour")
// ===== DONNÉES H1 =====
= request.security(
syminfo.tickerid,
"60",
)
h1HH = request.security(syminfo.tickerid, "60", ta.highest(high, rangeLen))
h1LL = request.security(syminfo.tickerid, "60", ta.lowest(low, rangeLen))
h1ATR = request.security(syminfo.tickerid, "60", ta.atr(atrLen))
h1MA = request.security(syminfo.tickerid, "60", ta.sma(close, maLen))
h1Slope = math.abs(h1MA - h1MA )
// ===== CONDITIONS RANGE H1 =====
lowVol = (h1HH - h1LL) < h1ATR * atrFactor
noDir = h1Slope < slopeLimit
isH1Range = lowVol and noDir
// ===== BOÎTE =====
var box h1Box = na
if isH1Range and na(h1Box)
h1Box := box.new(
left = bar_index,
right = bar_index,
top = h1HH,
bottom = h1LL,
bgcolor = color.new(boxColor, opacity),
border_color = borderColor
)
if isH1Range and not na(h1Box)
box.set_right(h1Box, bar_index)
box.set_top(h1Box, h1HH)
box.set_bottom(h1Box, h1LL)
if not isH1Range and not na(h1Box)
h1Box := na
// ===== ALERTES =====
alertcondition(isH1Range,
title="Range H1 détecté",
message="📦 RANGE H1 détecté sur {{ticker}}")
alertcondition(close > h1HH,
title="Breakout H1 Haussier",
message="🚀 Breakout HAUSSIER du range H1 sur {{ticker}}")
alertcondition(close < h1LL,
title="Breakout H1 Baissier",
message="🔻 Breakout BAISSIER du range H1 sur {{ticker}}")
Apex Adaptive Trend Navigator [Pineify]Apex Adaptive Trend Navigator
The Apex Adaptive Trend Navigator is a comprehensive trend-following indicator that combines adaptive moving average technology, dynamic volatility bands, and market structure analysis into a single, cohesive trading tool. Designed for traders who want to identify trend direction with precision while filtering out market noise, this indicator adapts its sensitivity based on real-time market efficiency calculations.
Key Features
Adaptive Moving Average with efficiency-based smoothing factor
Dynamic ATR-based volatility bands that expand and contract with market conditions
Market Structure detection including BOS (Break of Structure) and CHoCH (Change of Character)
Real-time performance dashboard displaying trend status and efficiency metrics
Color-coded cloud visualization for intuitive trend identification
How It Works
The core of this indicator is built on an Adaptive Moving Average that uses a unique efficiency-based calculation method inspired by the Kaufman Adaptive Moving Average (KAMA) and TRAMA concepts. The efficiency ratio measures the directional movement of price relative to total price movement over the lookback period:
Efficiency = |Price Change over N periods| / Sum of |Individual Bar Changes|
This ratio ranges from 0 to 1, where values closer to 1 indicate a strong trending market with minimal noise, and values closer to 0 indicate choppy, sideways conditions. The smoothing factor is then squared to penalize noisy markets more aggressively, causing the adaptive line to flatten during consolidation and respond quickly during strong trends.
The Dynamic Volatility Bands are calculated using the Average True Range (ATR) multiplied by a user-defined factor. These bands create a channel around the adaptive moving average, helping traders visualize the current volatility regime and potential support/resistance zones.
Trading Ideas and Insights
When price stays above the adaptive line with the bullish cloud forming, consider this a confirmation of uptrend strength
The efficiency percentage in the dashboard indicates trend quality - higher values suggest more reliable trends
Watch for price interactions with the upper and lower bands as potential reversal or continuation zones
A flat adaptive line indicates consolidation - wait for a clear directional break before entering trades
How Multiple Indicators Work Together
This indicator integrates three complementary analytical approaches:
The Adaptive Moving Average serves as the trend backbone, providing a dynamic centerline that automatically adjusts to market conditions. Unlike fixed-period moving averages, it reduces lag during trends while minimizing whipsaws during ranging markets.
The ATR Volatility Bands work in conjunction with the adaptive MA to create a volatility envelope. When the adaptive line is trending and price remains within the cloud (between the MA and outer band), this confirms trend strength. Price breaking through the opposite band may signal exhaustion or reversal.
The Market Structure Analysis using swing point detection adds a Smart Money Concepts (SMC) layer. BOS signals indicate trend continuation when price breaks previous swing highs in uptrends or swing lows in downtrends. CHoCH signals warn of potential reversals when the structure shifts against the prevailing trend.
Unique Aspects
The squared efficiency factor creates a non-linear response that dramatically reduces noise sensitivity
Cloud fills only appear on the trend side, providing clear visual distinction between bullish and bearish regimes
The integrated dashboard eliminates the need to switch between multiple indicators for trend assessment
Pivot-based swing detection ensures accurate market structure identification
How to Use
Add the indicator to your chart and adjust the Lookback Period based on your trading timeframe (shorter for scalping, longer for swing trading)
Monitor the cloud color - green clouds indicate bullish conditions, red clouds indicate bearish conditions
Use the efficiency reading in the dashboard to gauge trend reliability before entering positions
Consider entries when price pulls back to the adaptive line during strong trends (high efficiency)
Use the volatility bands as dynamic take-profit or stop-loss reference levels
Customization
Lookback Period : Controls the sensitivity of trend detection and swing point identification (default: 20)
Volatility Multiplier : Adjusts the width of the ATR bands (default: 2.0)
Show Market Structure : Toggle visibility of BOS and CHoCH labels
Show Performance Dashboard : Toggle the trend status table
Color Settings : Customize bullish, bearish, and neutral colors to match your chart theme
Conclusion
The Apex Adaptive Trend Navigator offers traders a sophisticated yet intuitive approach to trend analysis. By combining adaptive smoothing technology with volatility measurement and market structure concepts, it provides multiple layers of confirmation for trading decisions. Whether you are a day trader seeking quick trend identification or a swing trader looking for reliable trend-following signals, this indicator adapts to your market conditions and trading style. The efficiency-based calculations ensure you always know not just the trend direction, but also the quality and reliability of that trend.
Adaptive Market Wave TheoryAdaptive Market Wave Theory
🌊 CORE INNOVATION: PROBABILISTIC PHASE DETECTION WITH MULTI-AGENT CONSENSUS
Adaptive Market Wave Theory (AMWT) represents a fundamental paradigm shift in how traders approach market phase identification. Rather than counting waves subjectively or drawing static breakout levels, AMWT treats the market as a hidden state machine —using Hidden Markov Models, multi-agent consensus systems, and reinforcement learning algorithms to quantify what traditional methods leave to interpretation.
The Wave Analysis Problem:
Traditional wave counting methodologies (Elliott Wave, harmonic patterns, ABC corrections) share fatal weaknesses that AMWT directly addresses:
1. Non-Falsifiability : Invalid wave counts can always be "recounted" or "adjusted." If your Wave 3 fails, it becomes "Wave 3 of a larger degree" or "actually Wave C." There's no objective failure condition.
2. Observer Bias : Two expert wave analysts examining the same chart routinely reach different conclusions. This isn't a feature—it's a fundamental methodology flaw.
3. No Confidence Measure : Traditional analysis says "This IS Wave 3." But with what probability? 51%? 95%? The binary nature prevents proper position sizing and risk management.
4. Static Rules : Fixed Fibonacci ratios and wave guidelines cannot adapt to changing market regimes. What worked in 2019 may fail in 2024.
5. No Accountability : Wave methodologies rarely track their own performance. There's no feedback loop to improve.
The AMWT Solution:
AMWT addresses each limitation through rigorous mathematical frameworks borrowed from speech recognition, machine learning, and reinforcement learning:
• Non-Falsifiability → Hard Invalidation : Wave hypotheses die permanently when price violates calculated invalidation levels. No recounting allowed.
• Observer Bias → Multi-Agent Consensus : Three independent analytical agents must agree. Single-methodology bias is eliminated.
• No Confidence → Probabilistic States : Every market state has a calculated probability from Hidden Markov Model inference. "72% probability of impulse state" replaces "This is Wave 3."
• Static Rules → Adaptive Learning : Thompson Sampling multi-armed bandits learn which agents perform best in current conditions. The system adapts in real-time.
• No Accountability → Performance Tracking : Comprehensive statistics track every signal's outcome. The system knows its own performance.
The Core Insight:
"Traditional wave analysis asks 'What count is this?' AMWT asks 'What is the probability we are in an impulsive state, with what confidence, confirmed by how many independent methodologies, and anchored to what liquidity event?'"
🔬 THEORETICAL FOUNDATION: HIDDEN MARKOV MODELS
Why Hidden Markov Models?
Markets exist in hidden states that we cannot directly observe—only their effects on price are visible. When the market is in an "impulse up" state, we see rising prices, expanding volume, and trending indicators. But we don't observe the state itself—we infer it from observables.
This is precisely the problem Hidden Markov Models (HMMs) solve. Originally developed for speech recognition (inferring words from sound waves), HMMs excel at estimating hidden states from noisy observations.
HMM Components:
1. Hidden States (S) : The unobservable market conditions
2. Observations (O) : What we can measure (price, volume, indicators)
3. Transition Matrix (A) : Probability of moving between states
4. Emission Matrix (B) : Probability of observations given each state
5. Initial Distribution (π) : Starting state probabilities
AMWT's Six Market States:
State 0: IMPULSE_UP
• Definition: Strong bullish momentum with high participation
• Observable Signatures: Rising prices, expanding volume, RSI >60, price above upper Bollinger Band, MACD histogram positive and rising
• Typical Duration: 5-20 bars depending on timeframe
• What It Means: Institutional buying pressure, trend acceleration phase
State 1: IMPULSE_DN
• Definition: Strong bearish momentum with high participation
• Observable Signatures: Falling prices, expanding volume, RSI <40, price below lower Bollinger Band, MACD histogram negative and falling
• Typical Duration: 5-20 bars (often shorter than bullish impulses—markets fall faster)
• What It Means: Institutional selling pressure, panic or distribution acceleration
State 2: CORRECTION
• Definition: Counter-trend consolidation with declining momentum
• Observable Signatures: Sideways or mild counter-trend movement, contracting volume, RSI returning toward 50, Bollinger Bands narrowing
• Typical Duration: 8-30 bars
• What It Means: Profit-taking, digestion of prior move, potential accumulation for next leg
State 3: ACCUMULATION
• Definition: Base-building near lows where informed participants absorb supply
• Observable Signatures: Price near recent lows but not making new lows, volume spikes on up bars, RSI showing positive divergence, tight range
• Typical Duration: 15-50 bars
• What It Means: Smart money buying from weak hands, preparing for markup phase
State 4: DISTRIBUTION
• Definition: Top-forming near highs where informed participants distribute holdings
• Observable Signatures: Price near recent highs but struggling to advance, volume spikes on down bars, RSI showing negative divergence, widening range
• Typical Duration: 15-50 bars
• What It Means: Smart money selling to late buyers, preparing for markdown phase
State 5: TRANSITION
• Definition: Regime change period with mixed signals and elevated uncertainty
• Observable Signatures: Conflicting indicators, whipsaw price action, no clear momentum, high volatility without direction
• Typical Duration: 5-15 bars
• What It Means: Market deciding next direction, dangerous for directional trades
The Transition Matrix:
The transition matrix A captures the probability of moving from one state to another. AMWT initializes with empirically-derived values then updates online:
From/To IMP_UP IMP_DN CORR ACCUM DIST TRANS
IMP_UP 0.70 0.02 0.20 0.02 0.04 0.02
IMP_DN 0.02 0.70 0.20 0.04 0.02 0.02
CORR 0.15 0.15 0.50 0.10 0.10 0.00
ACCUM 0.30 0.05 0.15 0.40 0.05 0.05
DIST 0.05 0.30 0.15 0.05 0.40 0.05
TRANS 0.20 0.20 0.20 0.15 0.15 0.10
Key Insights from Transition Probabilities:
• Impulse states are sticky (70% self-transition): Once trending, markets tend to continue
• Corrections can transition to either impulse direction (15% each): The next move after correction is uncertain
• Accumulation strongly favors IMP_UP transition (30%): Base-building leads to rallies
• Distribution strongly favors IMP_DN transition (30%): Topping leads to declines
The Viterbi Algorithm:
Given a sequence of observations, how do we find the most likely state sequence? This is the Viterbi algorithm—dynamic programming to find the optimal path through the state space.
Mathematical Formulation:
δ_t(j) = max_i × B_j(O_t)
Where:
δ_t(j) = probability of most likely path ending in state j at time t
A_ij = transition probability from state i to state j
B_j(O_t) = emission probability of observation O_t given state j
AMWT Implementation:
AMWT runs Viterbi over a rolling window (default 50 bars), computing the most likely state sequence and extracting:
• Current state estimate
• State confidence (probability of current state vs alternatives)
• State sequence for pattern detection
Online Learning (Baum-Welch Adaptation):
Unlike static HMMs, AMWT continuously updates its transition and emission matrices based on observed market behavior:
f_onlineUpdateHMM(prev_state, curr_state, observation, decay) =>
// Update transition matrix
A *= decay
A += (1.0 - decay)
// Renormalize row
// Update emission matrix
B *= decay
B += (1.0 - decay)
// Renormalize row
The decay parameter (default 0.85) controls adaptation speed:
• Higher decay (0.95): Slower adaptation, more stable, better for consistent markets
• Lower decay (0.80): Faster adaptation, more reactive, better for regime changes
Why This Matters for Trading:
Traditional indicators give you a number (RSI = 72). AMWT gives you a probabilistic state assessment :
"There is a 78% probability we are in IMPULSE_UP state, with 15% probability of CORRECTION and 7% distributed among other states. The transition matrix suggests 70% chance of remaining in IMPULSE_UP next bar, 20% chance of transitioning to CORRECTION."
This enables:
• Position sizing by confidence : 90% confidence = full size; 60% confidence = half size
• Risk management by transition probability : High correction probability = tighten stops
• Strategy selection by state : IMPULSE = trend-follow; CORRECTION = wait; ACCUMULATION = scale in
🎰 THE 3-BANDIT CONSENSUS SYSTEM
The Multi-Agent Philosophy:
No single analytical methodology works in all market conditions. Trend-following excels in trending markets but gets chopped in ranges. Mean-reversion excels in ranges but gets crushed in trends. Structure-based analysis works when structure is clear but fails in chaotic markets.
AMWT's solution: employ three independent agents , each analyzing the market from a different perspective, then use Thompson Sampling to learn which agents perform best in current conditions.
Agent 1: TREND AGENT
Philosophy : Markets trend. Follow the trend until it ends.
Analytical Components:
• EMA Alignment: EMA8 > EMA21 > EMA50 (bullish) or inverse (bearish)
• MACD Histogram: Direction and rate of change
• Price Momentum: Close relative to ATR-normalized movement
• VWAP Position: Price above/below volume-weighted average price
Signal Generation:
Strong Bull: EMA aligned bull AND MACD histogram > 0 AND momentum > 0.3 AND close > VWAP
→ Signal: +1 (Long), Confidence: 0.75 + |momentum| × 0.4
Moderate Bull: EMA stack bull AND MACD rising AND momentum > 0.1
→ Signal: +1 (Long), Confidence: 0.65 + |momentum| × 0.3
Strong Bear: EMA aligned bear AND MACD histogram < 0 AND momentum < -0.3 AND close < VWAP
→ Signal: -1 (Short), Confidence: 0.75 + |momentum| × 0.4
Moderate Bear: EMA stack bear AND MACD falling AND momentum < -0.1
→ Signal: -1 (Short), Confidence: 0.65 + |momentum| × 0.3
When Trend Agent Excels:
• Trend days (IB extension >1.5x)
• Post-breakout continuation
• Institutional accumulation/distribution phases
When Trend Agent Fails:
• Range-bound markets (ADX <20)
• Chop zones after volatility spikes
• Reversal days at major levels
Agent 2: REVERSION AGENT
Philosophy: Markets revert to mean. Extreme readings reverse.
Analytical Components:
• Bollinger Band Position: Distance from bands, percent B
• RSI Extremes: Overbought (>70) and oversold (<30)
• Stochastic: %K/%D crossovers at extremes
• Band Squeeze: Bollinger Band width contraction
Signal Generation:
Oversold Bounce: BB %B < 0.20 AND RSI < 35 AND Stochastic < 25
→ Signal: +1 (Long), Confidence: 0.70 + (30 - RSI) × 0.01
Overbought Fade: BB %B > 0.80 AND RSI > 65 AND Stochastic > 75
→ Signal: -1 (Short), Confidence: 0.70 + (RSI - 70) × 0.01
Squeeze Fire Bull: Band squeeze ending AND close > upper band
→ Signal: +1 (Long), Confidence: 0.65
Squeeze Fire Bear: Band squeeze ending AND close < lower band
→ Signal: -1 (Short), Confidence: 0.65
When Reversion Agent Excels:
• Rotation days (price stays within IB)
• Range-bound consolidation
• After extended moves without pullback
When Reversion Agent Fails:
• Strong trend days (RSI can stay overbought for days)
• Breakout moves
• News-driven directional moves
Agent 3: STRUCTURE AGENT
Philosophy: Market structure reveals institutional intent. Follow the smart money.
Analytical Components:
• Break of Structure (BOS): Price breaks prior swing high/low
• Change of Character (CHOCH): First break against prevailing trend
• Higher Highs/Higher Lows: Bullish structure
• Lower Highs/Lower Lows: Bearish structure
• Liquidity Sweeps: Stop runs that reverse
Signal Generation:
BOS Bull: Price breaks above prior swing high with momentum
→ Signal: +1 (Long), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bull: First higher low after downtrend, breaking structure
→ Signal: +1 (Long), Confidence: 0.75
BOS Bear: Price breaks below prior swing low with momentum
→ Signal: -1 (Short), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bear: First lower high after uptrend, breaking structure
→ Signal: -1 (Short), Confidence: 0.75
Liquidity Sweep Long: Price sweeps below swing low then reverses strongly
→ Signal: +1 (Long), Confidence: 0.80
Liquidity Sweep Short: Price sweeps above swing high then reverses strongly
→ Signal: -1 (Short), Confidence: 0.80
When Structure Agent Excels:
• After liquidity grabs (stop runs)
• At major swing points
• During institutional accumulation/distribution
When Structure Agent Fails:
• Choppy, structureless markets
• During news events (structure becomes noise)
• Very low timeframes (noise overwhelms structure)
Thompson Sampling: The Bandit Algorithm
With three agents giving potentially different signals, how do we decide which to trust? This is the multi-armed bandit problem —balancing exploitation (using what works) with exploration (testing alternatives).
Thompson Sampling Solution:
Each agent maintains a Beta distribution representing its success/failure history:
Agent success rate modeled as Beta(α, β)
Where:
α = number of successful signals + 1
β = number of failed signals + 1
On Each Bar:
1. Sample from each agent's Beta distribution
2. Weight agent signals by sampled probabilities
3. Combine weighted signals into consensus
4. Update α/β based on trade outcomes
Mathematical Implementation:
// Beta sampling via Gamma ratio method
f_beta_sample(alpha, beta) =>
g1 = f_gamma_sample(alpha)
g2 = f_gamma_sample(beta)
g1 / (g1 + g2)
// Thompson Sampling selection
for each agent:
sampled_prob = f_beta_sample(agent.alpha, agent.beta)
weight = sampled_prob / sum(all_sampled_probs)
consensus += agent.signal × agent.confidence × weight
Why Thompson Sampling?
• Automatic Exploration : Agents with few samples get occasional chances (high variance in Beta distribution)
• Bayesian Optimal : Mathematically proven optimal solution to exploration-exploitation tradeoff
• Uncertainty-Aware : Small sample size = more exploration; large sample size = more exploitation
• Self-Correcting : Poor performers naturally get lower weights over time
Example Evolution:
Day 1 (Initial):
Trend Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Reversion Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Structure Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
After 50 Signals:
Trend Agent: Beta(28,23) → samples ~0.55 (moderate confidence)
Reversion Agent: Beta(18,33) → samples ~0.35 (underperforming)
Structure Agent: Beta(32,19) → samples ~0.63 (outperforming)
Result: Structure Agent now receives highest weight in consensus
Consensus Requirements by Mode:
Aggressive Mode:
• Minimum 1/3 agents agreeing
• Consensus threshold: 45%
• Use case: More signals, higher risk tolerance
Balanced Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 55%
• Use case: Standard trading
Conservative Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 65%
• Use case: Higher quality, fewer signals
Institutional Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 75%
• Additional: Session quality >0.65, mode adjustment +0.10
• Use case: Highest quality signals only
🌀 INTELLIGENT CHOP DETECTION ENGINE
The Chop Problem:
Most trading losses occur not from being wrong about direction, but from trading in conditions where direction doesn't exist . Choppy, range-bound markets generate false signals from every methodology—trend-following, mean-reversion, and structure-based alike.
AMWT's chop detection engine identifies these low-probability environments before signals fire, preventing the most damaging trades.
Five-Factor Chop Analysis:
Factor 1: ADX Component (25% weight)
ADX (Average Directional Index) measures trend strength regardless of direction.
ADX < 15: Very weak trend (high chop score)
ADX 15-20: Weak trend (moderate chop score)
ADX 20-25: Developing trend (low chop score)
ADX > 25: Strong trend (minimal chop score)
adx_chop = (i_adxThreshold - adx_val) / i_adxThreshold × 100
Why ADX Works: ADX synthesizes +DI and -DI movements. Low ADX means price is moving but not directionally—the definition of chop.
Factor 2: Choppiness Index (25% weight)
The Choppiness Index measures price efficiency using the ratio of ATR sum to price range:
CI = 100 × LOG10(SUM(ATR, n) / (Highest - Lowest)) / LOG10(n)
CI > 61.8: Choppy (range-bound, inefficient movement)
CI < 38.2: Trending (directional, efficient movement)
CI 38.2-61.8: Transitional
chop_idx_score = (ci_val - 38.2) / (61.8 - 38.2) × 100
Why Choppiness Index Works: In trending markets, price covers distance efficiently (low ATR sum relative to range). In choppy markets, price oscillates wildly but goes nowhere (high ATR sum relative to range).
Factor 3: Range Compression (20% weight)
Compares recent range to longer-term range, detecting volatility squeezes:
recent_range = Highest(20) - Lowest(20)
longer_range = Highest(50) - Lowest(50)
compression = 1 - (recent_range / longer_range)
compression > 0.5: Strong squeeze (potential breakout imminent)
compression < 0.2: No compression (normal volatility)
range_compression_score = compression × 100
Why Range Compression Matters: Compression precedes expansion. High compression = market coiling, preparing for move. Signals during compression often fail because the breakout hasn't occurred yet.
Factor 4: Channel Position (15% weight)
Tracks price position within the macro channel:
channel_position = (close - channel_low) / (channel_high - channel_low)
position 0.4-0.6: Center of channel (indecision zone)
position <0.2 or >0.8: Near extremes (potential reversal or breakout)
channel_chop = abs(0.5 - channel_position) < 0.15 ? high_score : low_score
Why Channel Position Matters: Price in the middle of a range is in "no man's land"—equally likely to go either direction. Signals in the channel center have lower probability.
Factor 5: Volume Quality (15% weight)
Assesses volume relative to average:
vol_ratio = volume / SMA(volume, 20)
vol_ratio < 0.7: Low volume (lack of conviction)
vol_ratio 0.7-1.3: Normal volume
vol_ratio > 1.3: High volume (conviction present)
volume_chop = vol_ratio < 0.8 ? (1 - vol_ratio) × 100 : 0
Why Volume Quality Matters: Low volume moves lack institutional participation. These moves are more likely to reverse or stall.
Combined Chop Intensity:
chopIntensity = (adx_chop × 0.25) + (chop_idx_score × 0.25) +
(range_compression_score × 0.20) + (channel_chop × 0.15) +
(volume_chop × i_volumeChopWeight × 0.15)
Regime Classifications:
Based on chop intensity and component analysis:
• Strong Trend (0-20%): ADX >30, clear directional momentum, trade aggressively
• Trending (20-35%): ADX >20, moderate directional bias, trade normally
• Transitioning (35-50%): Mixed signals, regime change possible, reduce size
• Mid-Range (50-60%): Price trapped in channel center, avoid new positions
• Ranging (60-70%): Low ADX, price oscillating within bounds, fade extremes only
• Compression (70-80%): Volatility squeeze, expansion imminent, wait for breakout
• Strong Chop (80-100%): Multiple chop factors aligned, avoid trading entirely
Signal Suppression:
When chop intensity exceeds the configurable threshold (default 80%), signals are suppressed entirely. The dashboard displays "⚠️ CHOP ZONE" with the current regime classification.
Chop Box Visualization:
When chop is detected, AMWT draws a semi-transparent box on the chart showing the chop zone. This visual reminder helps traders avoid entering positions during unfavorable conditions.
💧 LIQUIDITY ANCHORING SYSTEM
The Liquidity Concept:
Markets move from liquidity pool to liquidity pool. Stop losses cluster at predictable locations—below swing lows (buy stops become sell orders when triggered) and above swing highs (sell stops become buy orders when triggered). Institutions know where these clusters are and often engineer moves to trigger them before reversing.
AMWT identifies and tracks these liquidity events, using them as anchors for signal confidence.
Liquidity Event Types:
Type 1: Volume Spikes
Definition: Volume > SMA(volume, 20) × i_volThreshold (default 2.8x)
Interpretation: Sudden volume surge indicates institutional activity
• Near swing low + reversal: Likely accumulation
• Near swing high + reversal: Likely distribution
• With continuation: Institutional conviction in direction
Type 2: Stop Runs (Liquidity Sweeps)
Definition: Price briefly exceeds swing high/low then reverses within N bars
Detection:
• Price breaks above recent swing high (triggering buy stops)
• Then closes back below that high within 3 bars
• Signal: Bullish stop run complete, reversal likely
Or inverse for bearish:
• Price breaks below recent swing low (triggering sell stops)
• Then closes back above that low within 3 bars
• Signal: Bearish stop run complete, reversal likely
Type 3: Absorption Events
Definition: High volume with small candle body
Detection:
• Volume > 2x average
• Candle body < 30% of candle range
• Interpretation: Large orders being filled without moving price
• Implication: Accumulation (at lows) or distribution (at highs)
Type 4: BSL/SSL Pools (Buy-Side/Sell-Side Liquidity)
BSL (Buy-Side Liquidity):
• Cluster of swing highs within ATR proximity
• Stop losses from shorts sit above these highs
• Breaking BSL triggers short covering (fuel for rally)
SSL (Sell-Side Liquidity):
• Cluster of swing lows within ATR proximity
• Stop losses from longs sit below these lows
• Breaking SSL triggers long liquidation (fuel for decline)
Liquidity Pool Mapping:
AMWT continuously scans for and maps liquidity pools:
// Detect swing highs/lows using pivot function
swing_high = ta.pivothigh(high, 5, 5)
swing_low = ta.pivotlow(low, 5, 5)
// Track recent swing points
if not na(swing_high)
bsl_levels.push(swing_high)
if not na(swing_low)
ssl_levels.push(swing_low)
// Display on chart with labels
Confluence Scoring Integration:
When signals fire near identified liquidity events, confluence scoring increases:
• Signal near volume spike: +10% confidence
• Signal after liquidity sweep: +15% confidence
• Signal at BSL/SSL pool: +10% confidence
• Signal aligned with absorption zone: +10% confidence
Why Liquidity Anchoring Matters:
Signals "in a vacuum" have lower probability than signals anchored to institutional activity. A long signal after a liquidity sweep below swing lows has trapped shorts providing fuel. A long signal in the middle of nowhere has no such catalyst.
📊 SIGNAL GRADING SYSTEM
The Quality Problem:
Not all signals are created equal. A signal with 6/6 factors aligned is fundamentally different from a signal with 3/6 factors aligned. Traditional indicators treat them the same. AMWT grades every signal based on confluence.
Confluence Components (100 points total):
1. Bandit Consensus Strength (25 points)
consensus_str = weighted average of agent confidences
score = consensus_str × 25
Example:
Trend Agent: +1 signal, 0.80 confidence, 0.35 weight
Reversion Agent: 0 signal, 0.50 confidence, 0.25 weight
Structure Agent: +1 signal, 0.75 confidence, 0.40 weight
Weighted consensus = (0.80×0.35 + 0×0.25 + 0.75×0.40) / (0.35 + 0.40) = 0.77
Score = 0.77 × 25 = 19.25 points
2. HMM State Confidence (15 points)
score = hmm_confidence × 15
Example:
HMM reports 82% probability of IMPULSE_UP
Score = 0.82 × 15 = 12.3 points
3. Session Quality (15 points)
Session quality varies by time:
• London/NY Overlap: 1.0 (15 points)
• New York Session: 0.95 (14.25 points)
• London Session: 0.70 (10.5 points)
• Asian Session: 0.40 (6 points)
• Off-Hours: 0.30 (4.5 points)
• Weekend: 0.10 (1.5 points)
4. Energy/Participation (10 points)
energy = (realized_vol / avg_vol) × 0.4 + (range / ATR) × 0.35 + (volume / avg_volume) × 0.25
score = min(energy, 1.0) × 10
5. Volume Confirmation (10 points)
if volume > SMA(volume, 20) × 1.5:
score = 10
else if volume > SMA(volume, 20):
score = 5
else:
score = 0
6. Structure Alignment (10 points)
For long signals:
• Bullish structure (HH + HL): 10 points
• Higher low only: 6 points
• Neutral structure: 3 points
• Bearish structure: 0 points
Inverse for short signals
7. Trend Alignment (10 points)
For long signals:
• Price > EMA21 > EMA50: 10 points
• Price > EMA21: 6 points
• Neutral: 3 points
• Against trend: 0 points
8. Entry Trigger Quality (5 points)
• Strong trigger (multiple confirmations): 5 points
• Moderate trigger (single confirmation): 3 points
• Weak trigger (marginal): 1 point
Grade Scale:
Total Score → Grade
85-100 → A+ (Exceptional—all factors aligned)
70-84 → A (Strong—high probability)
55-69 → B (Acceptable—proceed with caution)
Below 55 → C (Marginal—filtered by default)
Grade-Based Signal Brightness:
Signal arrows on the chart have transparency based on grade:
• A+: Full brightness (alpha = 0)
• A: Slight fade (alpha = 15)
• B: Moderate fade (alpha = 35)
• C: Significant fade (alpha = 55)
This visual hierarchy helps traders instantly identify signal quality.
Minimum Grade Filter:
Configurable filter (default: C) sets the minimum grade for signal display:
• Set to "A" for only highest-quality signals
• Set to "B" for moderate selectivity
• Set to "C" for all signals (maximum quantity)
🕐 SESSION INTELLIGENCE
Why Sessions Matter:
Markets behave differently at different times. The London open is fundamentally different from the Asian lunch hour. AMWT incorporates session-aware logic to optimize signal quality.
Session Definitions:
Asian Session (18:00-03:00 ET)
• Characteristics: Lower volatility, range-bound tendency, fewer institutional participants
• Quality Score: 0.40 (40% of peak quality)
• Strategy Implications: Fade extremes, expect ranges, smaller position sizes
• Best For: Mean-reversion setups, accumulation/distribution identification
London Session (03:00-12:00 ET)
• Characteristics: European institutional activity, volatility pickup, trend initiation
• Quality Score: 0.70 (70% of peak quality)
• Strategy Implications: Watch for trend development, breakouts more reliable
• Best For: Initial trend identification, structure breaks
New York Session (08:00-17:00 ET)
• Characteristics: Highest liquidity, US institutional activity, major moves
• Quality Score: 0.95 (95% of peak quality)
• Strategy Implications: Best environment for directional trades
• Best For: Trend continuation, momentum plays
London/NY Overlap (08:00-12:00 ET)
• Characteristics: Peak liquidity, both European and US participants active
• Quality Score: 1.0 (100%—maximum quality)
• Strategy Implications: Highest probability for successful breakouts and trends
• Best For: All signal types—this is prime time
Off-Hours
• Characteristics: Thin liquidity, erratic price action, gaps possible
• Quality Score: 0.30 (30% of peak quality)
• Strategy Implications: Avoid new positions, wider stops if holding
• Best For: Waiting
Smart Weekend Detection:
AMWT properly handles the Sunday evening futures open:
// Traditional (broken):
isWeekend = dayofweek == saturday OR dayofweek == sunday
// AMWT (correct):
anySessionActive = not na(asianTime) or not na(londonTime) or not na(nyTime)
isWeekend = calendarWeekend AND NOT anySessionActive
This ensures Sunday 6pm ET (when futures open) correctly shows "Asian Session" rather than "Weekend."
Session Transition Boosts:
Certain session transitions create trading opportunities:
• Asian → London transition: +15% confidence boost (volatility expansion likely)
• London → Overlap transition: +20% confidence boost (peak liquidity approaching)
• Overlap → NY-only transition: -10% confidence adjustment (liquidity declining)
• Any → Off-Hours transition: Signal suppression recommended
📈 TRADE MANAGEMENT SYSTEM
The Signal Spam Problem:
Many indicators generate signal after signal, creating confusion and overtrading. AMWT implements a complete trade lifecycle management system that prevents signal spam and tracks performance.
Trade Lock Mechanism:
Once a signal fires, the system enters a "trade lock" state:
Trade Lock Duration: Configurable (default 30 bars)
Early Exit Conditions:
• TP3 hit (full target reached)
• Stop Loss hit (trade failed)
• Lock expiration (time-based exit)
During lock:
• No new signals of same type displayed
• Opposite signals can override (reversal)
• Trade status tracked in dashboard
Target Levels:
Each signal generates three profit targets based on ATR:
TP1 (Conservative Target)
• Default: 1.0 × ATR
• Purpose: Quick partial profit, reduce risk
• Action: Take 30-40% off position, move stop to breakeven
TP2 (Standard Target)
• Default: 2.5 × ATR
• Purpose: Main profit target
• Action: Take 40-50% off position, trail stop
TP3 (Extended Target)
• Default: 5.0 × ATR
• Purpose: Runner target for trend days
• Action: Close remaining position or continue trailing
Stop Loss:
• Default: 1.9 × ATR from entry
• Purpose: Define maximum risk
• Placement: Below recent swing low (longs) or above recent swing high (shorts)
Invalidation Level:
Beyond stop loss, AMWT calculates an "invalidation" level where the wave hypothesis dies:
invalidation = entry - (ATR × INVALIDATION_MULT × 1.5)
If price reaches invalidation, the current market interpretation is wrong—not just the trade.
Visual Trade Management:
During active trades, AMWT displays:
• Entry arrow with grade label (▲A+, ▼B, etc.)
• TP1, TP2, TP3 horizontal lines in green
• Stop Loss line in red
• Invalidation line in orange (dashed)
• Progress indicator in dashboard
Persistent Execution Markers:
When targets or stops are hit, permanent markers appear:
• TP hit: Green dot with "TP1"/"TP2"/"TP3" label
• SL hit: Red dot with "SL" label
These persist on the chart for review and statistics.
💰 PERFORMANCE TRACKING & STATISTICS
Tracked Metrics:
• Total Trades: Count of all signals that entered trade lock
• Winning Trades: Signals where at least TP1 was reached before SL
• Losing Trades: Signals where SL was hit before any TP
• Win Rate: Winning / Total × 100%
• Total R Profit: Sum of R-multiples from winning trades
• Total R Loss: Sum of R-multiples from losing trades
• Net R: Total R Profit - Total R Loss
Currency Conversion System:
AMWT can display P&L in multiple formats:
R-Multiple (Default)
• Shows risk-normalized returns
• "Net P&L: +4.2R | 78 trades" means 4.2 times initial risk gained over 78 trades
• Best for comparing across different position sizes
Currency Conversion (USD/EUR/GBP/JPY/INR)
• Converts R-multiples to currency based on:
- Dollar Risk Per Trade (user input)
- Tick Value (user input)
- Selected currency
Example Configuration:
Dollar Risk Per Trade: $100
Display Currency: USD
If Net R = +4.2R
Display: Net P&L: +$420.00 | 78 trades
Ticks
• For futures traders who think in ticks
• Converts based on tick value input
Statistics Reset:
Two reset methods:
1. Toggle Reset
• Turn "Reset Statistics" toggle ON then OFF
• Clears all statistics immediately
2. Date-Based Reset
• Set "Reset After Date" (YYYY-MM-DD format)
• Only trades after this date are counted
• Useful for isolating recent performance
🎨 VISUAL FEATURES
Macro Channel:
Dynamic regression-based channel showing market boundaries:
• Upper/lower bounds calculated from swing pivot linear regression
• Adapts to current market structure
• Shows overall trend direction and potential reversal zones
Chop Boxes:
Semi-transparent overlay during high-chop periods:
• Purple/orange coloring indicates dangerous conditions
• Visual reminder to avoid new positions
Confluence Heat Zones:
Background shading indicating setup quality:
• Darker shading = higher confluence
• Lighter shading = lower confluence
• Helps identify optimal entry timing
EMA Ribbon:
Trend visualization via moving average fill:
• EMA 8/21/50 with gradient fill between
• Green fill when bullish aligned
• Red fill when bearish aligned
• Gray when neutral
Absorption Zone Boxes:
Marks potential accumulation/distribution areas:
• High volume + small body = absorption
• Boxes drawn at these levels
• Often act as support/resistance
Liquidity Pool Lines:
BSL/SSL levels with labels:
• Dashed lines at liquidity clusters
• "BSL" label above swing high clusters
• "SSL" label below swing low clusters
Six Professional Themes:
• Quantum: Deep purples and cyans (default)
• Cyberpunk: Neon pinks and blues
• Professional: Muted grays and greens
• Ocean: Blues and teals
• Matrix: Greens and blacks
• Ember: Oranges and reds
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Learning the System (Week 1)
Goal: Understand AMWT concepts and dashboard interpretation
Setup:
• Signal Mode: Balanced
• Display: All features enabled
• Grade Filter: C (see all signals)
Actions:
• Paper trade ONLY—no real money
• Observe HMM state transitions throughout the day
• Note when agents agree vs disagree
• Watch chop detection engage and disengage
• Track which grades produce winners vs losers
Key Learning Questions:
• How often do A+ signals win vs B signals? (Should see clear difference)
• Which agent tends to be right in current market? (Check dashboard)
• When does chop detection save you from bad trades?
• How do signals near liquidity events perform vs signals in vacuum?
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to your instrument and timeframe
Signal Mode Testing:
• Run 5 days on Aggressive mode (more signals)
• Run 5 days on Conservative mode (fewer signals)
• Compare: Which produces better risk-adjusted returns?
Grade Filter Testing:
• Track A+ only for 20 signals
• Track A and above for 20 signals
• Track B and above for 20 signals
• Compare win rates and expectancy
Chop Threshold Testing:
• Default (80%): Standard filtering
• Try 70%: More aggressive filtering
• Try 90%: Less filtering
• Which produces best results for your instrument?
Phase 3: Strategy Development (Weeks 3-4)
Goal: Develop personal trading rules based on system signals
Position Sizing by Grade:
• A+ grade: 100% position size
• A grade: 75% position size
• B grade: 50% position size
• C grade: 25% position size (or skip)
Session-Based Rules:
• London/NY Overlap: Take all A/A+ signals
• NY Session: Take all A+ signals, selective on A
• Asian Session: Only A+ signals with extra confirmation
• Off-Hours: No new positions
Chop Zone Rules:
• Chop >70%: Reduce position size 50%
• Chop >80%: No new positions
• Chop <50%: Full position size allowed
Phase 4: Live Micro-Sizing (Month 2)
Goal: Validate paper trading results with minimal risk
Setup:
• 10-20% of intended full position size
• Take ONLY A+ signals initially
• Follow trade management religiously
Tracking:
• Log every trade: Entry, Exit, Grade, HMM State, Chop Level, Agent Consensus
• Calculate: Win rate by grade, by session, by chop level
• Compare to paper trading (should be within 15%)
Red Flags:
• Win rate diverges significantly from paper trading: Execution issues
• Consistent losses during certain sessions: Adjust session rules
• Losses cluster when specific agent dominates: Review that agent's logic
Phase 5: Scaling Up (Months 3-6)
Goal: Gradually increase to full position size
Progression:
• Month 3: 25-40% size (if micro-sizing profitable)
• Month 4: 40-60% size
• Month 5: 60-80% size
• Month 6: 80-100% size
Scale-Up Requirements:
• Minimum 30 trades at current size
• Win rate ≥50%
• Net R positive
• No revenge trading incidents
• Emotional control maintained
💡 DEVELOPMENT INSIGHTS
Why HMM Over Simple Indicators:
Early versions used standard indicators (RSI >70 = overbought, etc.). Win rates hovered at 52-55%. The problem: indicators don't capture state. RSI can stay "overbought" for weeks in a strong trend.
The insight: markets exist in states, and state persistence matters more than indicator levels. Implementing HMM with state transition probabilities increased signal quality significantly. The system now knows not just "RSI is high" but "we're in IMPULSE_UP state with 70% probability of staying in IMPULSE_UP."
The Multi-Agent Evolution:
Original version used a single analytical methodology—trend-following. Performance was inconsistent: great in trends, destroyed in ranges. Added mean-reversion agent: now it was inconsistent the other way.
The breakthrough: use multiple agents and let the system learn which works . Thompson Sampling wasn't the first attempt—tried simple averaging, voting, even hard-coded regime switching. Thompson Sampling won because it's mathematically optimal and automatically adapts without manual regime detection.
Chop Detection Revelation:
Chop detection was added almost as an afterthought. "Let's filter out obviously bad conditions." Testing revealed it was the most impactful single feature. Filtering chop zones reduced losing trades by 35% while only reducing total signals by 20%. The insight: avoiding bad trades matters more than finding good ones.
Liquidity Anchoring Discovery:
Watched hundreds of trades. Noticed pattern: signals that fired after liquidity events (stop runs, volume spikes) had significantly higher win rates than signals in quiet markets. Implemented liquidity detection and anchoring. Win rate on liquidity-anchored signals: 68% vs 52% on non-anchored signals.
The Grade System Impact:
Early system had binary signals (fire or don't fire). Adding grading transformed it. Traders could finally match position size to signal quality. A+ signals deserved full size; C signals deserved caution. Just implementing grade-based sizing improved portfolio Sharpe ratio by 0.3.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What AMWT Is NOT:
• NOT a Holy Grail : No system wins every trade. AMWT improves probability, not certainty.
• NOT Fully Automated : AMWT provides signals and analysis; execution requires human judgment.
• NOT News-Proof : Exogenous shocks (FOMC surprises, geopolitical events) invalidate all technical analysis.
• NOT for Scalping : HMM state estimation needs time to develop. Sub-minute timeframes are not appropriate.
Core Assumptions:
1. Markets Have States : Assumes markets transition between identifiable regimes. Violation: Random walk markets with no regime structure.
2. States Are Inferable : Assumes observable indicators reveal hidden states. Violation: Market manipulation creating false signals.
3. History Informs Future : Assumes past agent performance predicts future performance. Violation: Regime changes that invalidate historical patterns.
4. Liquidity Events Matter : Assumes institutional activity creates predictable patterns. Violation: Markets with no institutional participation.
Performs Best On:
• Liquid Futures : ES, NQ, MNQ, MES, CL, GC
• Major Forex Pairs : EUR/USD, GBP/USD, USD/JPY
• Large-Cap Stocks : AAPL, MSFT, TSLA, NVDA (>$5B market cap)
• Liquid Crypto : BTC, ETH on major exchanges
Performs Poorly On:
• Illiquid Instruments : Low volume stocks, exotic pairs
• Very Low Timeframes : Sub-5-minute charts (noise overwhelms signal)
• Binary Event Days : Earnings, FDA approvals, court rulings
• Manipulated Markets : Penny stocks, low-cap altcoins
Known Weaknesses:
• Warmup Period : HMM needs ~50 bars to initialize properly. Early signals may be unreliable.
• Regime Change Lag : Thompson Sampling adapts over time, not instantly. Sudden regime changes may cause short-term underperformance.
• Complexity : More parameters than simple indicators. Requires understanding to use effectively.
⚠️ RISK DISCLOSURE
Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Adaptive Market Wave Theory, while based on rigorous mathematical frameworks including Hidden Markov Models and multi-armed bandit algorithms, does not guarantee profits and can result in significant losses.
AMWT's methodologies—HMM state estimation, Thompson Sampling agent selection, and confluence-based grading—have theoretical foundations but past performance is not indicative of future results.
Hidden Markov Model assumptions may not hold during:
• Major news events disrupting normal market behavior
• Flash crashes or circuit breaker events
• Low liquidity periods with erratic price action
• Algorithmic manipulation or spoofing
Multi-agent consensus assumes independent analytical perspectives provide edge. Market conditions change. Edges that existed historically can diminish or disappear.
Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively before risking capital. Start with micro position sizing.
Never risk more than you can afford to lose completely. Use proper position sizing. Implement stop losses without exception.
By using this indicator, you acknowledge these risks and accept full responsibility for all trading decisions and outcomes.
"Elliott Wave was a first-order approximation of market phase behavior. AMWT is the second—probabilistic, adaptive, and accountable."
Initial Public Release
Core Engine:
• True Hidden Markov Model with online Baum-Welch learning
• Viterbi algorithm for optimal state sequence decoding
• 6-state market regime classification
Agent System:
• 3-Bandit consensus (Trend, Reversion, Structure)
• Thompson Sampling with true Beta distribution sampling
• Adaptive weight learning based on performance
Signal Generation:
• Quality-based confluence grading (A+/A/B/C)
• Four signal modes (Aggressive/Balanced/Conservative/Institutional)
• Grade-based visual brightness
Chop Detection:
• 5-factor analysis (ADX, Choppiness Index, Range Compression, Channel Position, Volume)
• 7 regime classifications
• Configurable signal suppression threshold
Liquidity:
• Volume spike detection
• Stop run (liquidity sweep) identification
• BSL/SSL pool mapping
• Absorption zone detection
Trade Management:
• Trade lock with configurable duration
• TP1/TP2/TP3 targets
• ATR-based stop loss
• Persistent execution markers
Session Intelligence:
• Asian/London/NY/Overlap detection
• Smart weekend handling (Sunday futures open)
• Session quality scoring
Performance:
• Statistics tracking with reset functionality
• 7 currency display modes
• Win rate and Net R calculation
Visuals:
• Macro channel with linear regression
• Chop boxes
• EMA ribbon
• Liquidity pool lines
• 6 professional themes
Dashboards:
• Main Dashboard: Market State, Consensus, Trade Status, Statistics
📋 AMWT vs AMWT-PRO:
This version includes all core AMWT functionality:
✓ Full Hidden Markov Model state estimation
✓ 3-Bandit Thompson Sampling consensus system
✓ Complete 5-factor chop detection engine
✓ All four signal modes
✓ Full trade management with TP/SL tracking
✓ Main dashboard with complete statistics
✓ All visual features (channels, zones, pools)
✓ Identical signal generation to PRO
✓ Six professional themes
✓ Full alert system
The PRO version adds the AMWT Advisor panel—a secondary dashboard providing:
• Real-time Market Pulse situation assessment
• Agent Matrix visualization (individual agent votes)
• Structure analysis breakdown
• "Watch For" upcoming setups
• Action Command coaching
Both versions generate identical signals . The Advisor provides additional guidance for interpreting those signals.
Taking you to school. - Dskyz, Trade with probability. Trade with consensus. Trade with AMWT.
Market Structure [odnac]Overview
This indicator is a comprehensive tool designed for traders utilizing Smart Money Concepts (SMC) and Price Action. It automatically identifies and labels significant market structure shifts, specifically BOS (Break of Structure) and CHoCH (Change of Character), helping you stay on the right side of the trend.
Key Features
Dual Logic Modes (V1 & V2):
V1 (Fixed Pivot): Only utilizes confirmed pivot points. Ideal for conservative traders looking for major swing levels.
V2 (Dynamic Update): Automatically updates swing points to the actual highest high or lowest low between breaks. This provides a more fluid and accurate representation of price flow.
Smart Confirmation: Unlike basic pivot scripts, this indicator uses a multi-bar confirmation logic (checking candle polarity and close sequences) to filter out market noise and false pivots.
Automatic Trend Detection: The indicator tracks the current market bias (Bullish/Bearish) and visualizes it through customizable background colors or shapes.
Clear Visual Cues: * BOS: Indicates a continuation of the current trend.
CHoCH: Signals a potential trend reversal.
How to Use
Identify Trend Direction: Use the background coloring or the shapes at the bottom to quickly identify if the market is in a Bullish (Green) or Bearish (Red) phase.
Look for Structure Breaks: * When price breaks a previous high/low, the indicator will draw a line and label it as BOS if the trend continues, or CHoCH if the trend flips.
Customize for Your Assets: * For volatile assets like XLM or other cryptocurrencies, you can adjust the Swing Left/Right Bars inputs to filter for either micro-structures or macro-trends.
Input Settings
Version: Choose between V1 (Strict Pivots) and V2 (Dynamic Ranges).
Swing Left/Right Bars: Determines the sensitivity of high/low detection. Increase these values to find "stronger" structural points.
Trend Visualization: Toggle between Background fills, Shape markers at the bottom, or None for a cleaner look.
Show Swings: Toggle the visibility of the white circles marking confirmed pivot points.
Disclaimer
Market structure is a lagging indicator by nature as it requires confirmation. Always use this tool in conjunction with other technical analysis methods (Order Blocks, Fair Value Gaps, or Volume) for the best results.
Impulsive Trend Detector [dtAlgo]This advanced Pine Script indicator identifies and tracks impulsive price movements based on Break of Structure (BOS) and Change of Character (CHoCH) concepts from Smart Money trading methodology.
The indicator automatically detects pivot highs and lows, then monitors when price breaks these key levels to signal potential impulsive moves. BOS indicates continuation in the current trend direction, while CHoCH signals potential trend reversals. Each detected move is measured from the break point to the next opposing pivot, providing accurate percentage calculations that match TradingView's measuring tool.
Impulsive moves are categorized into four levels based on magnitude (Level 1: 5-10%, Level 2: 10-15%, Level 3: 15-20%, Level 4: 20%+), with color-coded visual labels and connecting lines displayed directly on the chart.
Comprehensive Session Analysis:
Track moves across 11 distinct trading sessions in Eastern Time: Pre-London/NY, London/NY overlap, NY (with Power Hour and End subdivisions), Sydney, Asia, Sake Time, Asia/London overlap, London, Weekend, and No Session periods.
Three Dynamic Tables provide:
Real-time statistics (bullish/bearish, BOS/CHoCH, levels)
Session breakdown with move counts and average percentages
Event log showing last 10 moves with date, day, session, direction, type, level, percentage, duration, and bar count
Perfect for Smart Money traders seeking data-driven insights into market structure behavior across global trading sessions.
Mirpapa_Lib_StructsLibrary "Mirpapa_Lib_Structs"
ICT 구조 변화 감지 라이브러리 (BOS, CHoCH, MSS, Sweep)
initStructState()
StructState 초기화
checkBOS(_trend, _currentClose, _lastHHPrice, _lastLLPrice)
BOS 체크 (추세 지속) - 종가 기준
Parameters:
_trend (string) : 현재 추세
_currentClose (float) : 현재 종가
_lastHHPrice (float) : 마지막 HH 가격
_lastLLPrice (float) : 마지막 LL 가격
Returns:
checkCHoCH(_trend, _currentClose, _lastHHPrice, _lastLLPrice)
CHoCH 체크 (추세 전환) - 종가 기준
Parameters:
_trend (string) : 현재 추세
_currentClose (float) : 현재 종가
_lastHHPrice (float) : 마지막 HH 가격
_lastLLPrice (float) : 마지막 LL 가격
Returns:
checkSweep(_currentHigh, _currentLow, _currentClose, _lastHHPrice, _lastLLPrice)
Sweep 체크 (유동성 수집)
Parameters:
_currentHigh (float) : 현재 고가
_currentLow (float) : 현재 저가
_currentClose (float) : 현재 종가
_lastHHPrice (float) : 마지막 HH 가격
_lastLLPrice (float) : 마지막 LL 가격
Returns:
checkMSS(_hadCHoCH, _chochDir, _currentHigh, _currentLow, _chochPrice)
MSS 체크 (CHoCH + 리테스트 확인)
Parameters:
_hadCHoCH (bool) : CHoCH 발생 여부
_chochDir (string) : CHoCH 방향
_currentHigh (float) : 현재 고가
_currentLow (float) : 현재 저가
_chochPrice (float) : CHoCH 발생 가격
Returns:
drawStructLabel(_price, _time, _type, _dir, _lblColor)
구조 변화 라벨 그리기
Parameters:
_price (float) : 가격
_time (int) : 시간
_type (string) : 구조 타입
_dir (string) : 방향
_lblColor (color) : 라벨 색상
drawStructLine(_price, _startTime, _endTime, _lineColor, _lineWidth)
구조 변화 라인 그리기
Parameters:
_price (float) : 가격
_startTime (int) : 시작 시간
_endTime (int) : 끝 시간
_lineColor (color) : 라인 색상
_lineWidth (int) : 라인 두께
StructType
구조 타입 상수
Fields:
BOS (series string)
CHOCH (series string)
MSS (series string)
SWEEP (series string)
TrendDir
추세 방향 상수
Fields:
UP (series string)
DOWN (series string)
NONE (series string)
StructState
구조 변화 상태
Fields:
_trend (series string) : 현재 추세 방향
_lastHHPrice (series float) : 마지막 HH 가격
_lastHHTime (series int) : 마지막 HH 시간
_lastLLPrice (series float) : 마지막 LL 가격
_lastLLTime (series int) : 마지막 LL 시간
SMC N-Gram Probability Matrix [PhenLabs]📊 SMC N-Gram Probability Matrix
Version: PineScript™ v6
📌 Description
The SMC N-Gram Probability Matrix applies computational linguistics methodology to Smart Money Concepts trading. By treating SMC patterns as a discrete “alphabet” and analyzing their sequential relationships through N-gram modeling, this indicator calculates the statistical probability of which pattern will appear next based on historical transitions.
Traditional SMC analysis is reactive—traders identify patterns after they form and then anticipate the next move. This indicator inverts that approach by building a transition probability matrix from up to 5,000 bars of pattern history, enabling traders to see which SMC formations most frequently follow their current market sequence.
The indicator detects and classifies 11 distinct SMC patterns including Fair Value Gaps, Order Blocks, Liquidity Sweeps, Break of Structure, and Change of Character in both bullish and bearish variants, then tracks how these patterns transition from one to another over time.
🚀 Points of Innovation
First indicator to apply N-gram sequence modeling from computational linguistics to SMC pattern analysis
Dynamic transition matrix rebuilds every 50 bars for adaptive probability calculations
Supports bigram (2), trigram (3), and quadgram (4) sequence lengths for varying analysis depth
Priority-based pattern classification ensures higher-significance patterns (CHoCH, BOS) take precedence
Configurable minimum occurrence threshold filters out statistically insignificant predictions
Real-time probability visualization with graphical confidence bars
🔧 Core Components
Pattern Alphabet System: 11 discrete SMC patterns encoded as integers for efficient matrix indexing and transition tracking
Swing Point Detection: Uses ta.pivothigh/pivotlow with configurable sensitivity for non-repainting structure identification
Transition Count Matrix: Flattened array storing occurrence counts for all possible pattern sequence transitions
Context Encoder: Converts N-gram pattern sequences into unique integer IDs for matrix lookup
Probability Calculator: Transforms raw transition counts into percentage probabilities for each possible next pattern
🔥 Key Features
Multi-Pattern SMC Detection: Simultaneously identifies FVGs, Order Blocks, Liquidity Sweeps, BOS, and CHoCH formations
Adjustable N-Gram Length: Choose between 2-4 pattern sequences to balance specificity against sample size
Flexible Lookback Range: Analyze anywhere from 100 to 5,000 historical bars for matrix construction
Pattern Toggle Controls: Enable or disable individual SMC pattern types to customize analysis focus
Probability Threshold Filtering: Set minimum occurrence requirements to ensure prediction reliability
Alert Integration: Built-in alert conditions trigger when high-probability predictions emerge
🎨 Visualization
Probability Table: Displays current pattern, recent sequence, sample count, and top N predicted patterns with percentage probabilities
Graphical Probability Bars: Visual bar representation (█░) showing relative probability strength at a glance
Chart Pattern Markers: Color-coded labels placed directly on price bars identifying detected SMC formations
Pattern Short Codes: Compact notation (F+, F-, O+, O-, L↑, L↓, B+, B-, C+, C-) for quick pattern identification
Customizable Table Position: Place probability display in any corner of your chart
📖 Usage Guidelines
N-Gram Configuration
N-Gram Length: Default 2, Range 2-4. Lower values provide more samples but less specificity. Higher values capture complex sequences but require more historical data.
Matrix Lookback Bars: Default 500, Range 100-5000. More bars increase statistical significance but may include outdated market behavior.
Min Occurrences for Prediction: Default 2, Range 1-10. Higher values filter noise but may reduce prediction availability.
SMC Detection Settings
Swing Detection Length: Default 5, Range 2-20. Controls pivot sensitivity for structure analysis.
FVG Minimum Size: Default 0.1%, Range 0.01-2.0%. Filters insignificant gaps.
Order Block Lookback: Default 10, Range 3-30. Bars to search for OB formations.
Liquidity Sweep Threshold: Default 0.3%, Range 0.05-1.0%. Minimum wick extension beyond swing points.
Display Settings
Show Probability Table: Toggle the probability matrix display on/off.
Show Top N Probabilities: Default 5, Range 3-10. Number of predicted patterns to display.
Show SMC Markers: Toggle on-chart pattern labels.
✅ Best Use Cases
Anticipating continuation or reversal patterns after liquidity sweeps
Identifying high-probability BOS/CHoCH sequences for trend trading
Filtering FVG and Order Block signals based on historical follow-through rates
Building confluence by comparing predicted patterns with other technical analysis
Studying how SMC patterns typically sequence on specific instruments or timeframes
⚠️ Limitations
Predictions are based solely on historical pattern frequency and do not account for fundamental factors
Low sample counts produce unreliable probabilities—always check the Samples display
Market regime changes can invalidate historical transition patterns
The indicator requires sufficient historical data to build meaningful probability matrices
Pattern detection uses standardized parameters that may not capture all institutional activity
💡 What Makes This Unique
Linguistic Modeling Applied to Markets: Treats SMC patterns like words in a language, analyzing how they “flow” together
Quantified Pattern Relationships: Transforms subjective SMC analysis into objective probability percentages
Adaptive Learning: Matrix rebuilds periodically to incorporate recent pattern behavior
Comprehensive SMC Coverage: Tracks all major Smart Money Concepts in a unified probability framework
🔬 How It Works
1. Pattern Detection Phase
Each bar is analyzed for SMC formations using configurable detection parameters
A priority hierarchy assigns the most significant pattern when multiple detections occur
2. Sequence Encoding Phase
Detected patterns are stored in a rolling history buffer of recent classifications
The current N-gram context is encoded into a unique integer identifier
3. Matrix Construction Phase
Historical pattern sequences are iterated to count transition occurrences
Each context-to-next-pattern transition increments the appropriate matrix cell
4. Probability Calculation Phase
Current context ID retrieves corresponding transition counts from the matrix
Raw counts are converted to percentages based on total context occurrences
5. Visualization Phase
Probabilities are sorted and the top N predictions are displayed in the table
Chart markers identify the current detected pattern for visual reference
💡 Note:
This indicator performs best when used as a confluence tool alongside traditional SMC analysis. The probability predictions highlight statistically common pattern sequences but should not be used as standalone trading signals. Always verify predictions against price action context, higher timeframe structure, and your overall trading plan. Monitor the sample count to ensure predictions are based on adequate historical data.
SMC + OB + FVG + Reversal + UT Bot + Hull Suite – by Fatich.id🎯 7 INTEGRATED SYSTEMS:
✓ Mxwll Suite (SMC + Auto Fibs + CHoCH/BOS)
✓ UT Bot (Trend Signals + Label Management)
✓ Hull Suite (Momentum Analysis)
✓ LuxAlgo FVG (Fair Value Gaps)
✓ LuxAlgo Order Blocks (Volume Pivots) ⭐ NEW
✓ Three Bar Reversal (Pattern Recognition)
✓ Reversal Signals (Momentum Count Style)
⚡ KEY FEATURES:
• Smart Money Structure (CHoCH/BOS/I-CHoCH/I-BoS)
• Auto Fibonacci (10 customizable levels)
• Order Block Detection (Auto mitigation)
• Fair Value Gap Tracking
• Session Highlights (NY/London/Asia)
• Volume Activity Dashboard
• Multi-Timeframe Support
• Clean Label Management
🎨 PERFECT FOR:
• Smart Money Concept Traders
• Order Flow & Liquidity Analysis
• Support/Resistance Trading
• Trend Following & Reversals
• Multi-Timeframe Analysis
💡 RECOMMENDED SETTINGS:
Clean Charts: OB Count 3, UT Signals 3, FVG 5
Detailed Analysis: OB Count 5-10, All Signals
Scalping: Low sensitivity, Hull 20-30
Swing Trading: High sensitivity, Hull 55-100
Мой скриптinputs:
window(1),
type(0), // 0: close, 1: high low, 2: fractals up down, 3: new fractals
persistent(False),
exittype(1),
nbars(160),
adxthres(40),
nstop(3000);
vars:
currentSwingLow(0),
currentSwingHigh(0),
trailStructureValid(false),
downFractal(0),
upFractal(0),
breakStructureHigh(0),
breakStructureLow(0),
BoS_H(0),
BoS_L(0),
Regime(0),
Last_BoS_L(0),
Last_BoS_H(0),
PeakfilterX(false);
BoS(window,persistent,type,Bos_H,BoS_L,upFractal,downFractal,breakStructureHigh,breakStructureLow);
//BOS Regime
If BoS_H <> 0 then begin
Regime = 1; // Bullish
Last_BoS_H = BoS_H ;
end;
If BoS_L <> 0 Then begin
Regime = -1; // Bearish
Last_BoS_L = BoS_L ;
end;
//Entry Logic: if we are in BoS regime then wait for break swing to entry
if ADX(5) of data2 < adxthres then begin
if time>900 and Regime = 1 and EntriesToday(date)= 0 and Last_BoS_H upFractal then buy next bar at market;
end;
if time>900 and EntriesToday(date)= 0 and Regime = -1 and Last_BoS_L>downFractal then
begin
if close < downFractal then sellshort next bar at market;
end;
end;
// Exits: nbars or stoploss or at the end of the day
if marketposition <> 0 and barssinceentry >nbars then begin
sell next bar at market;
buytocover next bar at market;
end;
setstoploss(nstop);
setexitonclose;
Мой скриптinputs:
window(1),
type(0), // 0: close, 1: high low, 2: fractals up down, 3: new fractals
persistent(False),
exittype(1),
nbars(160),
adxthres(40),
nstop(3000);
vars:
currentSwingLow(0),
currentSwingHigh(0),
trailStructureValid(false),
downFractal(0),
upFractal(0),
breakStructureHigh(0),
breakStructureLow(0),
BoS_H(0),
BoS_L(0),
Regime(0),
Last_BoS_L(0),
Last_BoS_H(0),
PeakfilterX(false);
BoS(window,persistent,type,Bos_H,BoS_L,upFractal,downFractal,breakStructureHigh,breakStructureLow);
//BOS Regime
If BoS_H <> 0 then begin
Regime = 1; // Bullish
Last_BoS_H = BoS_H ;
end;
If BoS_L <> 0 Then begin
Regime = -1; // Bearish
Last_BoS_L = BoS_L ;
end;
//Entry Logic: if we are in BoS regime then wait for break swing to entry
if ADX(5) of data2 < adxthres then begin
if time>900 and Regime = 1 and EntriesToday(date)= 0 and Last_BoS_H upFractal then buy next bar at market;
end;
if time>900 and EntriesToday(date)= 0 and Regime = -1 and Last_BoS_L>downFractal then
begin
if close < downFractal then sellshort next bar at market;
end;
end;
// Exits: nbars or stoploss or at the end of the day
if marketposition <> 0 and barssinceentry >nbars then begin
sell next bar at market;
buytocover next bar at market;
end;
setstoploss(nstop);
setexitonclose;
Smart Money Toolkit - PD Engine Bias Map [KedArc Quant]Description
Smart Money is an advanced multi-layer Smart Money Concepts framework that automatically detects structure shifts, premium-discount zones, and institutional order flow.
It is built around the PD Engine, which calculates the midpoint of the most recent market swing and dynamically determines BUY or SELL bias based on where current price trades relative to that equilibrium. This toolkit visualizes structure, order blocks, and bias context in one clean map, giving traders an institutional-grade view without unnecessary signal clutter.
Why It Is Unique
- All CHoCH, BOS, Order Block, FVG, and PD logic are coded from scratch.
- Uses true equilibrium (50 percent PD midpoint) for dynamic bias.
- Optimized for stability and non-repainting behavior.
- Designed for clarity with minimal, performance-safe visuals.
Entry and Exit Logic (Discretionary Framework)
- This toolkit is not a signal generator. It provides market context that guides discretionary trading.
BUY Bias (Discount Zone)
- Price trades below PD Mid: the market is in discount.
- Wait for a bullish CHoCH or reaction from a demand OB or FVG before buying.
- Target 1 = PD Mid. Target 2 = next opposite OB or FVG.
SELL Bias (Premium Zone)
- Price trades above PD Mid: the market is in premium.
- Wait for a bearish CHoCH or reaction from a supply OB or FVG before shorting.
- Target 1 = PD Mid. Target 2 = next opposite OB or FVG.
Institutional concept sequence: Bias → Structure Shift → Confirmation → Execution.
Input Configuration
Swing Sensitivity - Determines how far back to identify HH and LL pivots.
OB / FVG Detection - Toggles visual Order Block or Fair Value Gap zones.
PD Engine - Shows PD midpoint line, zone shading, and bias table.
Multi-TF Bias Sync - Optionally reads a higher timeframe bias for confirmation.
Color Themes - Switch between light, dark, or institutional palettes.
Formula / Logic Summary
Concept Formula
PD Mid (Equilibrium) (Recent Swing High + Recent Swing Low) / 2
BUY Bias close < PD Mid
SELL Bias close > PD Mid
CHoCH / BOS Pivot-based structure reversal: HH→LL or LL→HH
Order Block Last bullish or bearish candle before displacement.
FVG Gap between prior candle high/low and next candle range.
These formulas follow the structure used in institutional Smart Money Concepts.
How It Helps Traders
- Shows institutional premium and discount zones visually.
- Defines clear directional bias before entry.
- Combines structure, order blocks, FVG, and equilibrium in one layout.
- Works on any timeframe or asset.
- Prevents emotional trades by giving objective bias context.
Glossary
PD Mid Midpoint between recent swing high and low (market fair value).
Premium Zone Price above PD Mid; sellers control.
Discount Zone Price below PD Mid; buyers control.
CHoCH Change of Character, first reversal signal.
BOS Break of Structure, trend continuation confirmation.
OB Order Block, last institutional candle before move.
FVG Fair Value Gap, price imbalance often revisited.
FAQ
Q: Is this a signal indicator?
A: No. It is a contextual framework that supports manual decision-making.
Q: Does it repaint?
A: No. All structure logic is confirmed on bar close.
Q: Does it work on all markets?
A: Yes. It is purely price-based and timeframe independent.
Q: When does bias change?
A: Only after a new confirmed swing high or low.
Q: Can it be backtested?
A: You can build strategies on top of this context using your own entry and exit rules.
Disclaimer
This script is provided for educational purposes only.
It is not financial advice.
Trading carries risk. Past performance does not guarantee future results.
Use proper risk management and test on demo accounts before applying to live markets.
darshakssc SMC Infinity Enginedarshakssc SMC Infinity Engine is an advanced Smart Money Concepts–based tool designed to help traders visually understand institutional price behavior such as liquidity sweeps, displacement moves, and structure breaks — all without repainting.
This script does not predict the future or guarantee outcomes.
Instead, it provides a structured price-action framework to help traders study how markets move during key intraday phases.
🔍 Core Concepts Used
This indicator highlights:
Liquidity Sweeps (equal highs/lows taken out)
Displacement Candles (strong institutional momentum bars)
Break of Structure (BOS) confirmations
Kill Zone Sessions (optional smart-money timing filter)
Higher Time Frame Trend Alignment
Dynamic Entry, Stop Loss, and Target Levels
Internal trade outcome tagging (TP1/TP2/TP3/SL)
These components are widely used in institutional price-action models and can help users understand how liquidity and structure interact throughout the trading day.
📊 What the Indicator Displays
LONG / SHORT signals after confirmed BOS
Entry, SL, TP1, TP2, TP3 mapped directly on the chart
Background highlighting for liquidity sweep zones
A clean dashboard showing:
Current symbol
Current price
Number of setups recognized
TP1/TP2/TP3 stats
SL count
Live win-rate calculation
Last outcome recorded
All visuals are provided for study purposes to help users review how price reacts during key structure shifts.
🧠 How to Use It (Educational Purpose)
This tool is designed as a market research & educational study aid.
You can use it to:
Observe how liquidity sweeps often precede directional moves
Study how displacement confirms institutional intent
Analyze BOS-based structure shifts
Compare HTF trend alignment with LTF execution
Review trade outcomes historically for self-improvement
It can assist in building discipline and consistency when learning SMC-style concepts — without any automation or strategy execution.
⚠️ Important Notes
This script does not repaint.
This is not a trading system, signal generator, or financial advice.
All information is for educational and informational purposes only.
Past performance does not guarantee future results.
Users should always perform their own analysis and risk management.
🛡️ Compliance Disclaimer
This script is provided for educational purposes only.
It does not constitute investment advice, does not guarantee results, and should not be used as the sole basis for any trading decision.
SMC Clean: Structure + LiquidityThis indicator provides Smart Money Concepts (SMC) tools designed to help traders analyze market structure, liquidity pools, and institutional trading zones. It combines several popular SMC methods into one powerful, customizable tool, with a clean and controlled chart display.
Features and How it Works:
Swing Highs and Lows: The indicator identifies confirmed swing highs and swing lows using a lookback period (default: 15 bars). These points form the basis for market structure analysis.
Equal Highs/Equal Lows (EQH/EQL): When price action creates repeated swing highs or lows within a defined tolerance, the tool automatically marks these areas as potential liquidity pools. These are levels where multiple stop orders may accumulate, sometimes leading to significant market moves.
Liquidity Lines & Sweeps: Liquidity lines highlight unswept highs and lows, making it easy to see where price may hunt liquidity. When price crosses a swing high/low and closes back, a sweep label is shown (optional).
BOS/CHOCH Detection:
Break of Structure (BOS): Signals a continuation of the current trend if price closes beyond the previous swing point.
Change of Character (CHOCH): Highlights when price reverses and breaks a key swing from the opposite direction, hinting at a potential trend change or shift in market regime.
Only confirmed swing points are considered to avoid repainting.
Premium & Discount Zones Explained:
After a new confirmed swing high and swing low, the area between them forms a “range.”
The premium zone is the upper half (from midpoint to swing high): this is typically considered where price is “expensive” or overvalued for the current swing, and is often watched for potential sell setups.
The discount zone is the lower half (from swing low to midpoint): this is where price is “cheap” or undervalued for the current swing, commonly monitored for potential buy setups.
Colored boxes mark these zones on your chart for instant reference.
Dashboard (Movable Position):
A visually enhanced dark-themed dashboard shows the current market structure (Bullish/Bearish), liquidity bias (Buy-Side, Sell-Side, or Balanced, based on unswept levels), and last swept side (i.e., which liquidity pool was last taken by price).
Dashboard position can be set anywhere on your chart for best visibility.
Customization Options:
Enable/disable any feature individually for a cleaner chart.
Control colors, transparency, and swing sensitivity via user settings.
How to Use:
Add the indicator to your chart and adjust settings to fit your trading style.
Use swing lines and dashboard to determine current market structure and bias.
Watch equal highs/lows and liquidity lines for possible sweep events.
Use the premium/discount zones to locate optimal areas for trade entries—with institutional logic, buy when price reaches the discount (lower) zone, and look for sales in the premium (upper) zone.
Use BOS/CHOCH signals as objective confirmations of trend or regime changes. Always interpret signals in context of broader price action.
Important Notes:
This indicator is educational and analytical—NO signals are guaranteed.
All calculations are non-repainting and use only confirmed price data (no lookahead).
No claims of predicting future price movement or performance are made.
Disclaimer:
This tool is for technical analysis education only. It is not a financial advice nor a guaranteed trading system. Please test all signals and concepts before using in live markets.
ONLY LONG – 4H Breakout → 1H EMA(12/21) [Signals]🔹 ONLY LONG – 4H Breakout → 1H EMA(12/21)
Author: SystemsOverFeelings
Type: Signal-only indicator (non-repainting)
Timeframe: Designed for the 1H chart
Markets: BTCUSDT perpetual& major pairs
📖 Concept
A high-timeframe confirmation model for trend-continuation longs.
It detects:
A 4-Hour breakout candle closing above recent range highs,
With very-high volume confirmation, and
Then waits for a 1-Hour pullback into the EMA(12/21) band or a Break of Structure (BOS) to re-enter.
No repainting — all 4H logic uses request.security(..., lookahead_off) for confirmed data.
🧩 Signal Logic
✅ 4H Trigger: Breakout candle with volume > SMA(20) × user multiplier.
✅ Armed Regime: Green background = system ready for 1H entries.
🟢 LONG Signal: 1H candle consolidates inside or touches the EMA band, or shows BOS confirmation.
❌ EXIT Signal: 4H EMA(12) crosses below EMA(21).
All signals are visually marked and alert-ready.
⚙️ Adjustable Parameters
4H volume multiplier
Range lookback days
Pullback strictness (inside/touch)
1H BOS pivot length & mode
Expiry time for invalidated setups
🔔 Alerts
Built-in alerts for:
4H breakout trigger
1H long entry signal
4H band exit
Use them directly via “Create Alert → Condition → This Script → Choose Signal.”
💡 Notes
Works best on BTC/ETH 1H chart.
Non-repainting, multi-timeframe logic.
Use for directional bias or entry timing — not financial advice.
London Breakout Structure by Ale 2This indicator identifies market structure breakouts (CHOCH/BOS) within a specific London session window, highlighting potential breakout trades with automatic entry, stop loss (SL), and take profit (TP) levels.
It helps traders focus on high-probability breakouts when volatility increases after the Asian session, using price structure, ATR-based volatility filters, and a custom risk/reward setup.
🔹 Example of Strategy Application
Define your session (e.g. 04:00 to 05:00).
Wait for a CHOCH (Change of Character) inside this session.
If a bullish CHOCH occurs → go LONG at candle close.
If a bearish CHOCH occurs → go SHORT at candle close.
SL is set below/above the previous swing using ATR × multiplier.
TP is calculated automatically based on your R:R ratio.
📊 Example:
When price breaks above the last swing high within the session, a “BUY” label appears and the indicator draws Entry, SL, and TP levels automatically.
If the breakout fails and price closes below the opposite structure, a “SELL” signal will replace the bullish setup.
🔹 Details
The logic is based on structural shifts (CHOCH/BOS):
A CHOCH occurs when price breaks and closes beyond the most recent high/low.
The indicator dynamically detects these shifts in structure, validating them only inside your chosen time window (e.g. the London Open).
The ATR filter ensures setups are valid only when the range has enough volatility, avoiding false signals in low-volume hours.
You can also visualize:
The session area (purple background)
Entry, Stop Loss, and Take Profit levels
Direction labels (BUY/SELL)
ATR line for volatility context
🔹 Configuration
Start / End Hour: define your preferred trading window.
ATR Length & Multiplier: adjust for volatility.
Risk/Reward Ratio: set your desired R:R (default 1:2).
Minimum Range Filter: avoids signals with tight SLs.
Alerts: receive notifications when breakout conditions occur.
🔹 Recommendations
Works best on 15m or 5m charts during London session.
Designed for breakout and structure-based traders.
Works on Forex, Crypto, and Indices.
Ideal as a visual and educational tool for understanding BOS/CHOCH behavior.
AG_STRATEGY📈 AG_STRATEGY — Smart Money System + Sessions + PDH/PDL
AG_STRATEGY is an advanced Smart Money Concepts (SMC) toolkit built for traders who follow market structure, liquidity and institutional timing.
It combines real-time market structure, session ranges, liquidity levels, and daily institutional levels — all in one clean, professional interface.
✅ Key Features
🧠 Smart Money Concepts Engine
Automatic detection of:
BOS (Break of Structure)
CHoCH (Change of Character)
Dual structure system: Swing & Internal
Historical / Present display modes
Optional structural candle coloring
🎯 Liquidity & Market Structure
Equal Highs (EQH) and Equal Lows (EQL)
Marks strong/weak highs & lows
Real-time swing confirmation
Clear visual labels + smart positioning
⚡ Fair Value Gaps (FVG)
Automatic bullish & bearish FVGs
Higher-timeframe compatible
Extendable boxes
Auto-filtering to remove noise
🕓 Institutional Sessions
Asia
London
New York
Includes:
High/Low of each session
Automatic range plotting
Session background shading
London & NY Open markers
📌 PDH/PDL + Higher-Timeframe Levels
PDH / PDL (Previous Day High/Low)
Dynamic confirmation ✓ when liquidity is swept
Multi-timeframe level support:
Daily
Weekly
Monthly
Line style options: solid / dashed / dotted
🔔 Built-in Alerts
Internal & swing BOS / CHoCH
Equal Highs / Equal Lows
Bullish / Bearish FVG detected
🎛 Fully Adjustable Interface
Colored or Monochrome visual mode
Custom label sizes
Extend levels automatically
Session timezone settings
Clean, modular toggles for each component
🎯 Designed For Traders Who
Follow institutional order flow
Enter on BOS/CHoCH + FVG + Liquidity sweeps
Trade London & New York sessions
Want structure and liquidity clearly mapped
Prefer clean charts with full control
💡 Why AG_STRATEGY Stands Out
✔ Professional SMC engine
✔ Real-time swing & internal structure
✔ Session-based liquidity tracking
✔ Non-cluttered chart — high clarity
✔ Supports institutional trading workflows
ICT Anchored Market Structures with Validation [LuxAlgo]The ICT Anchored Market Structures with Validation indicator is an advanced iteration of the original Pure-Price-Action-Structures tool, designed for price action traders.
It systematically tracks and validates key price action structures, distinguishing between true structural shifts/breaks and short-term sweeps to enhance trend and reversal analysis. The indicator automatically highlights structural points, confirms breakouts, identifies sweeps, and provides clear visual cues for short-term, intermediate-term, and long-term market structures.
A distinctive feature of this indicator is its exclusive reliance on price patterns. It does not depend on any user-defined input, ensuring that its analysis remains robust, objective, and uninfluenced by user bias, making it an effective tool for understanding market dynamics.
🔶 USAGE
Market structure is a cornerstone of price action analysis. This script automatically detects real-time market structures across short-term, intermediate-term, and long-term levels, simplifying trend analysis for traders. It assists in identifying both trend reversals and continuations with greater clarity.
Market structure shifts and breaks help traders identify changes in trend direction. A shift signals a potential reversal, often occurring when a swing high or low is breached, suggesting a transition in trend. A break, on the other hand, confirms the continuation of an established trend, reinforcing the current direction. Recognizing these shifts and breaks allows traders to anticipate price movement with greater accuracy.
It’s important to note that while a CHoCH may signal a potential trend reversal and a BoS suggests a continuation of the prevailing trend, neither guarantees a complete reversal or continuation. In some cases, CHoCH and BoS levels may act as liquidity zones or areas of consolidation rather than indicating a clear shift or continuation in market direction. The indicator’s validation component helps confirm whether the detected CHoCH and BoS are true breakouts or merely liquidity sweeps.
🔶 DETAILS
🔹 Market Structures
Market structures are derived from price action analysis, focusing on identifying key levels and patterns in the market. Swing point detection, a fundamental concept in ICT trading methodologies and teachings, plays a central role in this approach.
Swing points are automatically identified based exclusively on market movements, without requiring any user-defined input.
🔹 Utilizing Swing Points
Swing points are not identified in real-time as they form. Short-term swing points may appear with a delay of up to one bar, while the identification of intermediate and long-term swing points is entirely dependent on subsequent market movements. Importantly, this detection process is not influenced by any user-defined input, relying solely on pure price action. As a result, swing points are generally not intended for real-time trading scenarios.
Instead, traders often analyze historical swing points to understand market trends and identify potential entry and exit opportunities. By examining swing highs and lows, traders can:
Recognize Trends: Swing highs and lows provide insight into trend direction. Higher swing highs and higher swing lows signify an uptrend, while lower swing highs and lower swing lows indicate a downtrend.
Identify Support and Resistance Levels: Swing highs often act as resistance levels, referred to as Buyside Liquidity Levels in ICT terminology, while swing lows function as support levels, also known as Sellside Liquidity Levels. Traders can leverage these levels to plan their trade entries and exits.
Spot Reversal Patterns: Swing points can form key reversal patterns, such as double tops or bottoms, head and shoulders, and triangles. Recognizing these patterns can indicate potential trend reversals, enabling traders to adjust their strategies effectively.
Set Stop Loss and Take Profit Levels: In ICT teachings, swing levels represent price points with expected clusters of buy or sell orders. Traders can target these liquidity levels/pools for position accumulation or distribution, using swing points to define stop loss and take profit levels in their trades.
Overall, swing points provide valuable information about market dynamics and can assist traders in making more informed trading decisions.
🔹 Logic of Validation
The validation process in this script determines whether a detected market structure shift or break represents a confirmed breakout or a sweep.
The breakout is confirmed when the close price is significantly outside the deviation range of the last detected structural price. This deviation range is defined by the 17-period Average True Range (ATR), which creates a buffer around the detected market structure shift or break.
A sweep occurs when the price breaches the structural level within the deviation range but does not confirm a breakout. In this case, the label is updated to 'SWEEP.'
A visual box is created to represent the price range where the breakout or sweep occurs. If the validation process continues, the box is updated. This box visually highlights the price range involved in a sweep, helping traders identify liquidity events on the chart.
🔶 SETTINGS
The settings for Short-Term, Intermediate-Term, and Long-Term Structures are organized into groups, allowing users to customize swing points, market structures, and visual styles for each.
🔹 Structures
Swings and Size: Enables or disables the display of swing highs and lows, assigns icons to represent the structures, and adjusts the size of the icons.
Market Structures: Toggles the visibility of market structure lines.
Market Structure Validation: Enable or disable validation to distinguish true breakouts from liquidity sweeps.
Market Structure Labels: Displays or hides labels indicating the type of market structure.
Line Style and Width: Allows customization of the style and width of the lines representing market structures.
Swing and Line Colors: Provides options to adjust the colors of swing icons, market structure lines, and labels for better visualization.
🔶 RELATED SCRIPTS
Pure-Price-Action-Structures.
Market-Structures-(Intrabar).
SMC Structure IndicatorTitle: SMC Structures Indicator
Description:
The SMC Structures indicator is a powerful tool designed to identify and visualize key structural elements in price action, based on the principles of Smart Money Concepts (SMC). This indicator helps traders identify potential areas of support, resistance, and price reversals by highlighting significant market structures.
Key Features:
Structure Identification: The indicator automatically detects and marks important high and low structures in the market.
Break of Structure (BOS) Detection: It identifies and labels instances where previous structures are broken, indicating potential trend changes or continuations.
Change of Character (CHoCH) Detection: The indicator recognizes and marks Changes of Character, which are significant shifts in market behavior.
Customizable Visuals: Users can personalize the appearance of BOS and CHoCH markings, including colors, line styles, and widths.
Current Structure Display: The indicator can optionally show the current active structure, helping traders understand the immediate market context.
Historical Structure Tracking: Users can specify the number of historical structure breaks to display, allowing for a cleaner chart while maintaining relevant information.
Flexible Break Confirmation: The indicator offers the option to confirm structure breaks using either the candle body or wick, accommodating different trading styles.
Technical Details:
The indicator uses advanced algorithms to identify significant price structures based on local highs and lows.
It employs a lookback period of 10 bars for structure detection, ensuring relevance to current market conditions.
The code includes safeguards to handle different market phases and avoid false signals during ranging periods.
Customization Options:
Colors for Bullish and Bearish BOS and CHoCH markings
Line styles and widths for all structure markings
Number of historical breaks to display
Option to show or hide the current active structure
Choice between candle body or wick for structure break confirmation
Use Cases:
Trend Analysis: Identify the start of new trends or potential trend reversals.
Support and Resistance: Pinpoint key levels where price may react.
Trade Entry and Exit: Use structure breaks as potential entry or exit signals.
Market Context: Understand the broader market structure to make informed trading decisions.
