Price Action and 3 EMAs Momentum plus Sessions FilterThis indicator plots on the chart the parameters and signals of the Price Action and 3 EMAs Momentum plus Sessions Filter Algorithmic Strategy. The strategy trades based on time-series (absolute) and relative momentum of price close, highs, lows and 3 EMAs.
I am still learning PS and therefore I have only been able to write the indicator up to the Signal generation. I plan to expand the indicator to Entry Signals as well as the full Strategy.
The strategy works best on EURUSD in the 15 minutes TF during London and New York sessions with 1 to 1 TP and SL of 30 pips with lots resulting in 3% risk of the account per trade. I have already written the full strategy in another language and platform and back tested it for ten years and it was profitable for 7 of the 10 years with average profit of 15% p.a which can be easily increased by increasing risk per trade. I have been trading it live in that platform for over two years and it is profitable.
Contributions from experienced PS coders in completing the Indicator as well as writing the Strategy and back testing it on Trading View will be appreciated.
STRATEGY AND INDICATOR PARAMETERS
Three periods of 12, 48 and 96 in the 15 min TF which are equivalent to 3, 12 and 24 hours i.e (15 min * period / 60 min) are the foundational inputs for all the parameters of the PA & 3 EMAs Momentum + SF Algo Strategy and its Indicator.
3 EMAs momentum parameters and conditions
• FastEMA = ema of 12 periods
• MedEMA = ema of 48 periods
• SlowEMA = ema of 96 periods
• All the EMAs analyse price close for up to 96 (15 min periods) equivalent to 24 hours
• There’s Upward EMA momentum if price close > FastEMA and FastEMA > MedEMA and MedEMA > SlowEMA
• There’s Downward EMA momentum if price close < FastEMA and FastEMA < MedEMA and MedEMA < SlowEMA
PA momentum parameters and conditions
• HH = Highest High of 48 periods from 1st closed bar before current bar
• LL = Lowest Low of 48 periods from 1st closed bar from current bar
• Previous HH = Highest High of 84 periods from 12th closed bar before current bar
• Previous LL = Lowest Low of 84 periods from 12th closed bar before current bar
• All the HH & LL and prevHH & prevLL are within the 96 periods from the 1st closed bar before current bar and therefore indicative of momentum during the past 24 hours
• There’s Upward PA momentum if price close > HH and HH > prevHH and LL > prevLL
• There’s Downward PA momentum if price close < LL and LL < prevLL and HH < prevHH
Signal conditions and Status (BuySignal, SellSignal or Neutral)
• The strategy generates Buy or Sell Signals if both 3 EMAs and PA momentum conditions are met for each direction and these occur during the London and New York sessions
• BuySignal if price close > FastEMA and FastEMA > MedEMA and MedEMA > SlowEMA and price close > HH and HH > prevHH and LL > prevLL and timeinrange (LDN&NY) else Neutral
• SellSignal if price close < FastEMA and FastEMA < MedEMA and MedEMA < SlowEMA and price close < LL and LL < prevLL and HH < prevHH and timeinrange (LDN&NY) else Neutral
Entry conditions and Status (EnterBuy, EnterSell or Neutral)(NOT CODED YET)
• ENTRY IS NOT AT THE SIGNAL BAR but at the current bar tick price retracement to FastEMA after the signal
• EnterBuy if current bar tick price <= FastEMA and current bar tick price > prevHH at the time of the Buy Signal
• EnterSell if current bar tick price >= FastEMA and current bar tick price > prevLL at the time of the Sell Signal
Recherche dans les scripts pour "美联储议息会议12月时间"
Smart labelling - Candlestick FunctionOftentimes a single look at the candlestick configuration happens to be enough to understand what is going on. The chandlestick function is an experiment in smart labelling that produces candles for various time frames, not only for the fixed 1m, 3m , 5m, 15m, etc. ones, and helps in decision-making when eye-balling the chart. This function generates up to 12 last candlesticks , which is generally more than enough.
Mind that since this is an experiment, the function does not cover all possible combinations. In some time frames the produced candles overlap. This is a todo item for those who are unterested. For instance, the current version covers the following TFs:
Chart - TF in the script
1m - 1-20,24,30,32
3m - 1-10
5m - 1-4,6,9,12,18,36
15m - 1-4,6,12
Tested chart TFs: 1m, 3m ,5m,15m. Tested securities: BTCUSD , EURUSD
[astropark] Power Tools Overlay//******************************************************************************
// Power Tools Overlay
// Inner Version 1.2.1 13/12/2018
// Developer: iDelphi
// Developer: astropark (Ichimoku Cloud), SMA EMA & Cross tools
//------------------------------------------------------------------------------
// 21/11/2018 Added EMA SMA WMA
// 21/11/2018 Added SMA-EMA EMA-WMA WMA-SMA (Thanks to mariobros1 for the idea of the Simultaneous MA)
// 21/11/2018 Added Bollinger Bands
// 21/11/2018 Added Ichimoku Cloud (Thanks to astropark for all the code of the Ichimoku Cloud)
// 23/11/2018 Show all the indicator as default
// 23/11/2018 Added a cross when single Moving Averages crossing (Thanks to astropark for the idea)
// 24/11/2018 Descriptions Fix
// 24/11/2018 Added Option to enable/disable all Moving Averages
// 10/12/2018 Added EMAs and Crosses
// 13/12/2018 indicator number fixes
//******************************************************************************
[Delphi] Power Tools OscillatorsFEATURES
- RSI
- Stochastic
//******************************************************************************
// Power Tools Oscillators
// Inner Version 1.0 04/12/2018
// Developer: iDelphi
//------------------------------------------------------------------------------
// 04/12/2018 Added RSI
// 04/12/2018 Added Stochastic
//******************************************************************************
Multi SMA EMA WMA HMA BB (4x3 MAs Bollinger Bands) Pro MTF - RRBMulti SMA EMA WMA HMA 4x3 Moving Averages with Bollinger Bands Pro MTF by RagingRocketBull 2018
Version 1.0
This indicator shows multiple MAs of any type SMA EMA WMA HMA etc with BB and MTF support, can show MAs as dynamically moving levels.
There are 4 MA groups + 1 BB group. You can assign any type/timeframe combo to a group, for example:
- EMAs 50,100,200 x H1, H4, D1, W1 (4 TFs x 3 MAs x 1 type)
- EMAs 8,13,21,55,100,200 x M15, H1 (2 TFs x 6 MAs x 1 type)
- D1 EMAs and SMAs 12,26,50,100,200,400 (1 TF x 6 MAs x 2 types)
- H1 WMAs 7,77,231; H4 HMAs 50,100,200; D1 EMAs 144,169,233; W1 SMAs 50,100,200 (4 TFs x 3 MAs x 4 types)
- +1 extra MA type/timeframe for BB
compile time: 25-30 sec
full redraw time after parameter change in UI: 3 sec
There are several versions: Simple, MTF, Pro MTF, Advanced MTF and Ultimate MTF. This is the Pro MTF version. The Differences are listed below. All versions have BB
- Simple: you have 2 groups of MAs that can be assigned any type (5+5)
- MTF: +2 custom Timeframes for each group (2x5 MTF)
- Pro MTF: +4 custom Timeframes for each group (4x3 MTF), MA levels and show max bars back options
- Advanced MTF: +2 extra MAs/group (4x5 MTF), custom Ticker/Symbol, backreferences for type, TF and MA lengths in UI
- Ultimate MTF: +individual settings for each MA, custom Ticker/Symbols
Features:
- 4x3 = 12 MAs of any type including Hull Moving Average (HMA)
- 4x MTF groups with step line smoothing
- BB +1 extra TF/type for BB MAs
- 12 MA levels with adjustable group offsets, indents and shift
- show max bars back
- you can show/hide both groups of MAs/levels and individual MAs
Notes:
1. based on 3EmaBB, uses plot*, barssince and security functions
2. you can't set certain constants from input due to Pinescript limitations - change the code as needed, recompile and use as a private version
3. Levels = trackprice implementation
4. Show Max Bars Back = show_last implementation
5. uses timeframe textbox instead of input resolution to allow for 120 240 and other custom TFs. Also supports TFs in hours: 2H or H2
6. swma has a fixed length = 4, alma and linreg have additional offset and smoothing params
7. Smoothing is applied by default for visual aesthetics on MTF. To use exact ma mtf values (lines with stair stepping) - disable it
MTF Notes:
- uses simple timeframe textbox instead of input resolution dropdown to allow for 120, 240 and other custom TFs, also supports timeframes in H: 2H, H2
- Groups that are not assigned a Custom TF will use Current Timeframe (0).
- MTF will work for any MA type assigned to the group
- MTF works both ways: you can display a higher TF MA/BB on a lower TF or a lower TF MA/BB on a higher TF.
- MTF MA values are normally aligned at the boundary of their native timeframe. This produces stair stepping when a higher TF MA is viewed on a lower TF.
Therefore X Y Point Density/Smoothing is applied by default on MA MTF for visual aesthetics. Set both to 0 to disable and see exact ma mtf values (lines with stair stepping and original mtf alignment).
- Smoothing is disabled for BB MTF bands because fill doesn't work with smoothed MAs after duplicate values are replaced with na.
- MTF MA Value fluctuation is possible on the current bar due to default security lookahead
Smoothing:
- X,Y == 0 - X,Y smoothing disabled (stair stepping on high TFs)
- X == 0, Y > 0 - X,Y smoothing applied to all TFs
- Y == 0, X > 0 - X smoothing applied to all TFs < deltaX_max_tf, Y smoothing disabled
- X > 0, Y > 0 - Y smoothing applied to all TFs, then X smoothing applied to all TFs < deltaX_max_tf
X Smoothing with Y == 0 - shows only every deltaX-th point starting from the first bar.
X Smoothing with Y > 0 - shows only every deltaX-th point starting from the last shown Y point, essentially filling huge gaps remaining after Y Smoothing with points and preserving the curve's general shape
X Smoothing on high TFs with already scarce points produces weird curve shapes, it works best only on high density lower TFs
Y Smoothing reduces points on all TFs, removes adjacent points with prices within deltaY, while preserving the smaller curve details.
A combination of X,Y produces the most accurate smoothing. Higher delta value - larger range, more points removed.
Show Max Bars Back:
- can't set plot show_last from input -> implemented using a timenow based range check
- you can't delete/modify history once plotted, so essentially it just sets a start point for plotting (from num_bars bars back) that works only in realtime mode (not in replay)
Levels:
You can plot current MA value using plot trackprice=true or by checking Show Price Line in Style. Problem is:
- you can only change color (not the dashed line style, width), have both ma + price line (not just the line), and it's full screen wide
- you can't set plot trackprice from input => implemented using plotshape/plotchar with fixed text labels serving as levels
- there's no other way of creating a dynamic level: hline, plot, offset - nothing else works.
- you can't plot a text var - all text strings must be constants, so you can't change the style, width and text labels without recompiling.
- from input you can only adjust offset, indent and shift for each level group, and change color
- the dot below each level line is the exact MA value. If you want just the line swap plotshape with plotchar, recompile and save as your private version, adjust Y shift.
To speed up redraw times: reduce last_bars to ~2000, recompile and use as your own private version
Pinescript is a rudimentary language (should be called Painscript instead) that can basically only plot data. You can't do much else. Please see the code for tips and hints.
Certain things just can't be done or require shady workarounds and weeks of testing trying to resolve weird node.js compiler errors.
Feel free to learn from/reuse/change the code as needed and use as your own private version. See comments in code. Good Luck!
Trend MACD [JopAlgo]Trend MACD — momentum made obvious (4-state histogram)
What it does (one line):
A clean MACD histogram using EMA(fast) − EMA(slow) with a signal line. The columns change color to show trend side and momentum change at a glance.
Green = above 0 and rising → positive trend, momentum building
White (upside) = above 0 but fading → still positive, momentum cooling
White (downside) = below 0 but improving → still negative, momentum recovering
Red = below 0 and falling → negative trend, momentum building down
Zero line = the bull/bear divider. Distance from zero = thrust. Color change = momentum shift.
What you’ll see
Dashed zero line for the trend divider
Column histogram with the 4-state color logic above
No clutter—just momentum and regime, clean
Read it in 3 seconds: Which side of 0? Are bars getting bigger or smaller? Did the color flip?
How to use it (simple playbook)
Direction filter
Look for longs while histogram is ≥ 0.
Look for shorts while histogram is ≤ 0.
Timing
Green sequence (above 0, growing): join pullbacks at real levels.
White above 0: positive but cooling—buy pullbacks only at levels, don’t chase.
White below 0: negative but improving—prepare for reclaim trades at levels.
Red sequence: trend down—sell pops at levels.
Location first (always)
Use Volume Profile v3.2 (VAH/VAL/POC/LVNs) and Anchored VWAP (session/weekly/event).
No level, no trade.
Quality check (optional, strong)
CVDv1 : execute when Alignment OK and no Absorption against your side.
RVOL (if you track it): prefer breakouts with RVOL above cutoff.
Entries, exits, risk (keep it tight)
Continuation long: price retests VAL / AVWAP / MA cluster in an up regime (≥ 0). Histogram stays ≥ 0 and turns green again → enter.
Stop: under structure. Targets: POC/HVNs or next swing.
Break + retest: breakout through a level while histogram flips from white→green above 0 (or white→red below 0 for shorts). Enter on the retest that holds.
Trim / avoid: when bars shrink toward 0 (white) into your target / HVN—momentum is cooling. Don’t chase fresh highs with white bars.
Settings that matter (how to tune)
Fast Length (default 25)
Shorter = quicker turns (more noise). Longer = steadier, slower.
Slow Length (default 200)
Big backbone. For intraday you might use 21/55 or 12/26; for swing the default 25/200 or 20/100 is solid.
Signal Smoothing (default 9)
Higher = smoother, fewer flips. Lower = more reactive.
Source
close is fine; if you use hlc3, expect slightly smoother behavior.
Suggested presets
Scalp (1–5m): 12 / 26 / 9
Intraday (15m–1H): 21 / 55 / 9
Swing (2H–4H): 25 / 100 or 25 / 200 / 9
Daily backdrop: 20 / 100 or 50 / 200 / 9 (execute on lower TF)
Pattern cheat sheet
Green staircase above 0 → trend leg; buy pullbacks to VP/AVWAP.
White above 0 → positive but tiring; avoid chasing; wait for retest.
Flip through 0 with expansion → regime change; use the first retest at a level.
Red staircase below 0 → trend down; sell pops at VP edges.
Diverging price vs shrinking bars → momentum thinning; tighten risk.
Best combos (kept simple)
Volume Profile v3.2: entries at VAH/VAL/LVNs, targets at POC/HVNs.
Anchored VWAP: reclaim/reject with matching histogram side is high-quality timing.
CVDv1: take MACD-aligned setups with flow (ALIGN OK, no Absorption).
RVOL: confirmation that the push has participation.
Common mistakes this helps you avoid
Longs with red momentum or shorts with green momentum.
Chasing new highs on white (cooling) bars.
Trading mid-range when histogram keeps whipsawing around 0 (do less; wait for level).
Disclaimer:
This indicator is an educational tool, not financial advice. Markets are risky; you can lose money. Always test your settings, trade at defined levels, and use risk management. Data/feeds vary across venues; outcomes may differ. No guarantees or warranties are provided.
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
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Whaley, R.E. (2009) 'Understanding the VIX', *The Journal of Portfolio Management*, 35(3), pp. 98-105.
Yardeni, E. (2003) 'Stock Valuation Models', *Topical Study*, 51, Yardeni Research.
Zweig, M.E. (1973) 'An Investor Expectations Stock Price Predictive Model Using Closed-End Fund Premiums', *The Journal of Finance*, 28(1), pp. 67-78.
Trend Candle CounterComplete Tutorial: Trend Candle Counter Pine ScriptTable of Contents
Installation Guide
Understanding the Indicator
How It Works
Customization Options
Trading Strategies
Setting Up Alerts
Troubleshooting
1. Installation Guide {#installation}Step-by-Step Installation:Step 1: Open TradingView
Go to www.tradingview.com
Log in to your account
Step 2: Access Pine Editor
Click on "Pine Editor" tab at the bottom of the chart
Or press Alt + E (Windows) or Option + E (Mac)
Step 3: Create New Indicator
Click "Open" → "New blank indicator"
Delete any default code
Step 4: Paste the Script
Copy the entire Trend Candle Counter script
Paste it into the editor
Step 5: Save and Apply
Click "Save" (or Ctrl + S)
Give it a name: "Trend Candle Counter"
Click "Add to Chart"
✅ Done! The indicator should now appear on your chart.2. Understanding the Indicator {#understanding}What Does It Do?This indicator numbers each candle based on the current trend: {scrollbar-width:none;-ms-overflow-style:none;-webkit-overflow-scrolling:touch;} ::-webkit-scrollbar{display:none}Trend TypeNumberingVisualUptrend+1, +2, +3, +4...🟢 Green labelsDowntrend-1, -2, -3, -4...🔴 Red labelsTrend ChangeResets to ±1Label color switchesVisual Components:
Candle Labels - Numbers above each candle
Trend Line (EMA) - Green (up) / Red (down)
Background Shading - Light green/red tint
Info Table - Top-right corner showing:
Current trend direction
Current candle number
Current price
3. How It Works {#how-it-works}Trend Detection Logic:IF Close > EMA → UPTREND (positive counting)
IF Close < EMA → DOWNTREND (negative counting)
Counting Mechanism:Example Uptrend:Candle 1: Close > EMA → Label: +1
Candle 2: Close > EMA → Label: +2
Candle 3: Close > EMA → Label: +3
Candle 4: Close < EMA → Label: -1 (trend changed!)
Example Downtrend:Candle 1: Close < EMA → Label: -1
Candle 2: Close < EMA → Label: -2
Candle 3: Close < EMA → Label: -3
Candle 4: Close > EMA → Label: +1 (trend changed!)
Key Insight:The higher the absolute number, the longer the trend has been running!4. Customization Options {#customization}Accessing Settings:
Click the gear icon ⚙️ next to the indicator name
Go to "Inputs" tab
Available Parameters: {scrollbar-width:none;-ms-overflow-style:none;-webkit-overflow-scrolling:touch;} ::-webkit-scrollbar{display:none}ParameterDefaultDescriptionRecommendationTrend Detection Length14EMA period for trend5-10: Scalping14-20: Day trading50-200: Swing tradingShow Candle Numbers✅ YesDisplay labelsDisable for cleaner chartLabel SizeSmallSize of numbersTiny: Multi-timeframeLarge: Focus on one chartUptrend ColorGreenPositive number colorCustomize to preferenceDowntrend ColorRedNegative number colorCustomize to preferenceOptimization by Trading Style:For Scalpers (1m - 5m charts):Trend Detection Length: 5-10
Label Size: Tiny
Show Labels: Optional (can be cluttered)
For Day Traders (15m - 1h charts):Trend Detection Length: 14-20
Label Size: Small
Show Labels: Yes
For Swing Traders (4h - Daily charts):Trend Detection Length: 50-100
Label Size: Normal
Show Labels: Yes
5. Trading Strategies {#strategies}Strategy 1: Trend Reversal TradingEntry Signals:
Buy: When counter changes from negative to +1
Sell: When counter changes from positive to -1
Confirmation:
Wait for +2 or -2 to confirm trend strength
Use additional indicators (RSI, MACD) for validation
Example:Candle: -5, -6, -7, -8, +1, +2 ← BUY HERE
Stop Loss: Below the -8 candle low
Target: When counter reaches +8 to +10
Strategy 2: Trend Continuation TradingEntry Signals:
Buy: Enter on pullbacks during uptrend (e.g., at +3, +5, +7)
Sell: Enter on bounces during downtrend (e.g., at -3, -5, -7)
Risk Management:
Avoid entering at high numbers (+15, -15) - trend may be exhausted
Example:Candle: +1, +2, +3 ← Small pullback, BUY
Continue: +4, +5, +6, +7
Exit: When counter resets to -1
Strategy 3: Trend Exhaustion DetectionWarning Signs:
Counter reaches +10 or higher → Uptrend may be overextended
Counter reaches -10 or lower → Downtrend may be overextended
Action:
Tighten stop losses
Take partial profits
Watch for reversal patterns (doji, engulfing)
Strategy 4: Multi-Timeframe AnalysisSetup:
Add indicator to 3 timeframes (e.g., 15m, 1h, 4h)
Look for alignment
Best Trades:15m: +1 (new uptrend)
1h: +5 (established uptrend)
4h: +3 (strong uptrend)
→ HIGH PROBABILITY BUY
6. Setting Up Alerts {#alerts}Built-in Alert Conditions:The script includes 2 automatic alerts:
"Uptrend Started" - Triggers when counter = +1
"Downtrend Started" - Triggers when counter = -1
How to Set Up Alerts:Step 1: Right-click on chart
Select "Add Alert"
Step 2: Configure Alert
Condition: Select "Trend Candle Counter"
Choose: "Uptrend Started" or "Downtrend Started"
Options:
Once per bar close (recommended)
Webhook URL (for automation)
Step 3: Notification Settings
✅ Popup
✅ Send email
✅ Push notification (mobile app)
✅ Play sound
Step 4: Create Alert
Click "Create"
Custom Alert Ideas:Alert for Specific Candle Numbers:
Notify when counter reaches +5 or -5
Notify when counter exceeds +10 or -10 (exhaustion)
7. Troubleshooting {#troubleshooting}Common Issues & Solutions:Issue 1: Labels are too cluttered
Solution:
Disable "Show Candle Numbers" in settings
Use larger timeframe
Reduce label size to "tiny"
Issue 2: Too many false signals
Solution:
Increase "Trend Detection Length" (e.g., 20, 50)
Wait for +2 or -2 confirmation
Combine with other indicators
Issue 3: Trend line doesn't match price action
Solution:
Adjust EMA length to match your trading style
Consider using different trend detection (SMA, HMA)
Issue 4: Indicator not showing on chart
Solution:
Check if it's in a separate pane - move to main chart
Refresh the page
Re-add the indicator
Issue 5: Counter seems delayed
Solution:
This is normal - indicator confirms on candle close
For faster signals, use lower timeframe
Reduce EMA length (but expect more noise)
8. Advanced Tips 💡Combining with Other Indicators:Best Combinations:
RSI + Trend Candle Counter
Buy at +1 when RSI > 50
Sell at -1 when RSI < 50
MACD + Trend Candle Counter
Confirm +1 with MACD bullish crossover
Confirm -1 with MACD bearish crossover
Volume + Trend Candle Counter
Strong trends (+1) should have increasing volume
Low volume at high numbers (+10) = exhaustion
Reading Market Psychology: {scrollbar-width:none;-ms-overflow-style:none;-webkit-overflow-scrolling:touch;} ::-webkit-scrollbar{display:none}Counter ValueMarket Psychology+1 to +3Early adopters entering+4 to +7Momentum building+8 to +12FOMO phase+13+Extreme greed - caution!-1 to -3Early sellers-4 to -7Panic building-8 to -12Capitulation-13+Extreme fear - reversal likely9. Real Trading Example 📊Scenario: BTC/USD 1H ChartTime | Counter | Action
--------|---------|----------------------------------
10:00 | -8 | Downtrend established
11:00 | -9 | Still falling
12:00 | -10 | Exhaustion zone - watch closely
13:00 | +1 | ✅ BUY SIGNAL - Trend reversal!
14:00 | +2 | Confirmation - trend valid
15:00 | +3 | Hold position
16:00 | +4 | Add to position (optional)
17:00 | +5 | Move stop loss to breakeven
...
22:00 | +11 | Take partial profits
23:00 | +12 | Tighten stop loss
00:00 | -1 | ❌ EXIT - Trend reversed
Quarterly Theory Cycles + Alerts (Weekly/Daily/90-Minute Cycles)Quarterly Theory Cycles (90m • Daily • Weekly)
Purpose
Built for Quarterly Theory. This indicator maps repeating quarters across three rhythms—90-minute, Daily, and Weekly (18:00 NY → 18:00 NY)—so you can track where price is within the current quarter and how it reacts to the previous quarter’s high/low.
Quarter Structure
90-Minute Quarters
Labels:Q1 / Q2 / Q3 / Q4
Sessions: Asia, London, NY, PM (each split into four 90-minute quarters).
Daily Quarters
Labels: [D-Q1 / D-Q2 / D-Q3 / D-Q4
Windows (America/New_York):
D-Q1: 18:00–00:00
D-Q2: 00:00–06:00
D-Q3: 06:00–12:00
D-Q4: 12:00–18:00
Weekly Quarters
Labels: W-Q1 / W-Q2 / W-Q3 / W-Q4
Trading days defined 18:00 NY → 18:00 NY (DST-aware).
W-Q1 = Monday, W-Q2 = Tuesday, W-Q3 = Wednesday, W-Q4 = Thursday
Friday intentionally excluded (no W-Q5) to preserve theory behavior.
Use for higher-timeframe context and weekly narrative (e.g., expansion vs. distribution days).
What It Draws
Live, extending range boxes for the active quarter (H/L updates in real time).
Stored previous quarter’s high/low for each rhythm (90m, Daily, Weekly).
Alerts (Quarterly Theory-friendly )
Fires when price first breaks the previous quarter’s high/low:
90m: “Previous 90min cycle (…) high/low broken”
Daily: “Previous daily cycle (…) high/low broken”
Weekly: “Previous weekly cycle (…) high/low broken”
One alert per side per new quarter—clean signals for liquidity grabs or SSMTs.
Customization
Master Toggles: Show/hide Asia, London, NY, PM, Daily, Weekly blocks fast.
Independent Transparencies: Separate opacity sliders for 90m vs Daily vs Weekly.
Per-Quarter Controls: Toggle range, edit label (defaults already set to Q1 / D-Q1 / W-Q1 formats), and color.
Styling: Optional outlines and labels for minimal or annotated charts.
Time Zones: Use exchange time or a custom UTC offset for session windows. Weekly boundaries always use America/New_York at 18:00.
Notes
Designed for theory workflows: prior-quarter liquidity, session rotation, and narrative alignment, SSMTS.
Friday is excluded from Weekly quarters by design.
Indicator draws ranges and triggers alerts; it does not place trades.
Alerte Croisement EMA9 & SMA12 (Zone remplie)📊 Moving Average 1
Period: 9 → The average is calculated over the last 9 candles (or time periods).
Shift: 0 → No shift; the average is aligned with the current data.
Method: Exponential → Uses an Exponential Moving Average (EMA), which gives more weight to recent data.
Apply to: Close → The average is based on the closing price of each candle.
📊 Moving Average 2
Period: 12 → Calculated over the last 12 periods.
Shift: 0 → No shift.
Method: Simple → Uses a Simple Moving Average (SMA), which gives equal weight to each period.
Apply to: Close → Based on closing prices.
SCTI - D14SCTI - D14 Comprehensive Technical Analysis Suite
English Description
SCTI D14 is an advanced multi-component technical analysis indicator designed for professional traders and analysts. This comprehensive suite combines multiple analytical tools into a single, powerful indicator that provides deep market insights across various timeframes and methodologies.
Core Components:
1. EMA System (Exponential Moving Averages)
13 customizable EMA lines with periods ranging from 8 to 2584
Fibonacci-based periods (8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584)
Color-coded visualization for easy trend identification
Individual toggle controls for each EMA line
2. TFMA (Multi-Timeframe Moving Averages)
Cross-timeframe analysis with 3 independent EMA calculations
Real-time labels showing trend direction and price relationships
Customizable timeframes for each moving average
Percentage deviation display from current price
3. PMA (Precision Moving Average Cloud)
7-layer moving average system with customizable periods
Fill areas between moving averages for trend visualization
Support and resistance zone identification
Dynamic color-coded trend clouds
4. VWAP (Volume Weighted Average Price)
Multiple anchor points (Session, Week, Month, Quarter, Year, Earnings, Dividends, Splits)
Standard deviation bands for volatility analysis
Automatic session detection and anchoring
Statistical price level identification
5. Advanced Divergence Detector
12 technical indicators for divergence analysis (MACD, RSI, Stochastic, CCI, Williams %R, Bias, Momentum, OBV, VW-MACD, CMF, MFI, External)
Regular and hidden divergences detection
Bullish and bearish signals with visual confirmation
Customizable sensitivity and filtering options
Real-time alerts for divergence formations
6. Volume Profile & Node Analysis
Comprehensive volume distribution analysis
Point of Control (POC) identification
Value Area High/Low (VAH/VAL) calculations
Volume peaks and troughs detection
Support and resistance levels based on volume
7. Smart Money Concepts
Market structure analysis with Break of Structure (BOS) and Change of Character (CHoCH)
Internal and swing structure detection
Equal highs and lows identification
Fair Value Gaps (FVG) detection and visualization
Liquidity zones and institutional flow analysis
8. Trading Sessions
9 major trading sessions (Asia, Sydney, Tokyo, Shanghai, Hong Kong, Europe, London, New York, NYSE)
Real-time session status and countdown timers
Session volume and performance tracking
Customizable session boxes and labels
Statistical session analysis table
Key Features:
Modular Design: Enable/disable any component independently
Real-time Analysis: Live updates with market data
Multi-timeframe Support: Works across all chart timeframes
Customizable Alerts: Set alerts for any detected pattern or signal
Professional Visualization: Clean, organized display with customizable colors
Performance Optimized: Efficient code for smooth chart performance
Use Cases:
Trend Analysis: Identify market direction using multiple EMA systems
Entry/Exit Points: Use divergences and structure breaks for timing
Risk Management: Utilize volume profiles and session analysis for better positioning
Multi-timeframe Analysis: Confirm signals across different timeframes
Institutional Analysis: Track smart money flows and market structure
Perfect For:
Day traders seeking comprehensive market analysis
Swing traders needing multi-timeframe confirmation
Professional analysts requiring detailed market structure insights
Algorithmic traders looking for systematic signal generation
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中文描述
SCTI - D14是一个先进的多组件技术分析指标，专为专业交易者和分析师设计。这个综合套件将多种分析工具整合到一个强大的指标中，在各种时间框架和方法论中提供深度市场洞察。
核心组件：
1. EMA系统（指数移动平均线）
13条可定制EMA线，周期从8到2584
基于斐波那契的周期（8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584）
颜色编码可视化，便于趋势识别
每条EMA线的独立切换控制
2. TFMA（多时间框架移动平均线）
跨时间框架分析，包含3个独立的EMA计算
实时标签显示趋势方向和价格关系
每个移动平均线的可定制时间框架
显示与当前价格的百分比偏差
3. PMA（精密移动平均云）
7层移动平均系统，周期可定制
移动平均线间填充区域用于趋势可视化
支撑阻力区域识别
动态颜色编码趋势云
4. VWAP（成交量加权平均价格）
多个锚点（交易时段、周、月、季、年、财报、分红、拆股）
标准差带用于波动性分析
自动时段检测和锚定
统计价格水平识别
5. 高级背离检测器
12个技术指标用于背离分析（MACD、RSI、随机指标、CCI、威廉姆斯%R、Bias、动量、OBV、VW-MACD、CMF、MFI、外部指标）
常规和隐藏背离检测
看涨看跌信号配视觉确认
可定制敏感度和过滤选项
背离形成的实时警报
6. 成交量分布与节点分析
全面的成交量分布分析
控制点（POC）识别
价值区域高/低点（VAH/VAL）计算
成交量峰值和低谷检测
基于成交量的支撑阻力水平
7. 聪明钱概念
市场结构分析，包括结构突破（BOS）和结构转变（CHoCH）
内部和摆动结构检测
等高等低识别
公允价值缺口（FVG）检测和可视化
流动性区域和机构资金流分析
8. 交易时区
9个主要交易时段（亚洲、悉尼、东京、上海、香港、欧洲、伦敦、纽约、纽交所）
实时时段状态和倒计时器
时段成交量和表现跟踪
可定制时段框和标签
统计时段分析表格
主要特性：
模块化设计：可独立启用/禁用任何组件
实时分析：随市场数据实时更新
多时间框架支持：适用于所有图表时间框架
可定制警报：为任何检测到的模式或信号设置警报
专业可视化：清洁、有序的显示界面，颜色可定制
性能优化：高效代码确保图表流畅运行
使用场景：
趋势分析：使用多重EMA系统识别市场方向
入场/出场点：利用背离和结构突破进行时机选择
风险管理：利用成交量分布和时段分析进行更好定位
多时间框架分析：在不同时间框架间确认信号
机构分析：跟踪聪明钱流向和市场结构
适用于：
寻求全面市场分析的日内交易者
需要多时间框架确认的摆动交易者
需要详细市场结构洞察的专业分析师
寻求系统化信号生成的算法交易者
Foresight Cone (HoltxF1xVWAP) [KedArc Quant]Description:
This is a time-series forecasting indicator that estimates the next bar (F1) and projects a path a few bars ahead. It also draws a confidence cone based on how accurate the recent forecasts have been. You can optionally color the projection only when price agrees with VWAP.
Why it’s different
* One clear model: Everything comes from Holt’s trend-aware forecasting method—no mix of unrelated indicators.
* Transparent visuals: You see the next-bar estimate (F1), the forward projection, and a cone that widens or narrows based on recent forecast error.
* Context, not signals: The VWAP option only changes colors. It doesn’t add trade rules.
* No look-ahead: Accuracy is measured using the forecast made on the previous bar versus the current bar.
Inputs (what they mean)
* Source: Price series to forecast (default: Close).
* Preset: Quick profiles for fast, smooth, or momentum markets (see below).
* Alpha (Level): How fast the model reacts to new prices. Higher = faster, twitchier.
* Beta (Trend): How fast the model updates the slope. Higher = faster pivots, more flips in chop.
* Horizon: How many bars ahead to project. Bigger = wider cone.
* Residual Window: How many bars to judge recent accuracy. Bigger = steadier cone.
* Confidence Z: How wide the cone should be (typical setting ≈ “95% style” width).
* Show Bands / Draw Forward Path: Turn the cone and forward lines on/off.
* Color only when aligned with VWAP: Highlights projections only when price agrees with the trend side of VWAP.
* Colors / Show Panel: Styling plus a small panel with RMSE, MAPE, and trend slope.
Presets (when to pick which)
* Scalp / Fast (1-min): Very responsive; best for quick moves. More twitch in chop.
* Smooth Intraday (1–5 min): Calmer and steadier; a good default most days.
* Momentum / Breakout: Quicker slope tracking during strong pushes; may over-react in ranges.
* Custom: Set your own values if you know exactly what you want.
What is F1 here?
F1 is the model’s next-bar fair value. Crosses of price versus F1 can hint at short-term momentum shifts or mean-reversion, especially when viewed with VWAP or the cone.
How this helps
* Gives a baseline path of where price may drift and a cone that shows normal wiggle room.
* Helps you tell routine noise (inside cone) from information (edges or breaks outside the cone).
* Keeps you aware of short-term bias via the trend slope and F1.
How to use (step by step)
1. Add to chart → choose a Preset (start with Smooth Intraday).
2. Set Horizon around 8–15 bars for intraday.
3. (Optional) Turn on VWAP alignment to color only when price agrees with the trend side of VWAP.
4. Watch where price sits relative to the cone and F1:
* Inside = normal noise.
* At edges = stretched.
* Outside = possible regime change.
5. Check the panel: if RMSE/MAPE spike, expect a wider cone; consider a smoother preset or a higher timeframe.
6. Tweak Alpha/Beta only if needed: faster for momentum, slower for chop.
7. Combine with your own plan for entries, exits, and risk.
Accuracy Panel — what it tells you
Preset & Horizon: Shows which preset you’re using and how many bars ahead the projection goes. Longer horizons mean more uncertainty.
RMSE (error in price units): A “typical miss” measured in the chart’s currency (e.g., ₹).
Lower = tighter fit and a usually narrower cone. Rising = conditions getting noisier; the cone will widen.
MAPE (error in %): The same idea as RMSE but in percent.
Good for comparing different symbols or timeframes. Sudden spikes often hint at a regime change.
Slope T: The model’s short-term trend reading.
Positive = gentle up-bias; negative = gentle down-bias; near zero = mostly flat/drifty.
How to read it at a glance
Calm & directional: RMSE/MAPE steady or falling + Slope T positive (or negative) → trends tend to respect the cone’s mid/upper (or mid/lower) area.
Choppy/uncertain: RMSE/MAPE climbing or jumping → expect more whipsaw; rely more on the cone edges and higher-TF context.
Flat tape: Slope T near zero → mean-revert behavior is common; treat cone edges as stretch zones rather than breakout zones.
Warm-up & tweaks
Warm-up: Right after adding the indicator, the panel may be blank for a short time while it gathers enough bars.
Too twitchy? Switch to Smooth Intraday or increase the Residual Window.
Too slow? Use Scalp/Fast or Momentum/Breakout to react quicker.
Timeframe tips
* 1–3 min: Scalp/Fast or Momentum/Breakout; horizon \~8–12.
* 5–15 min: Smooth Intraday; horizon \~12–15.
* 30–60 min+: Consider a larger residual window for a steadier cone.
FAQ
Q: Is this a strategy or an indicator?
A: It’s an indicator only. It does not place orders, TP/SL, or run backtests.
Q: Does it repaint?
A: The next-bar estimate (F1) and the cone are calculated using only information available at that time. The forward path is a projection drawn on the last bar and will naturally update as new bars arrive. Historical bars aren’t revised with future data.
Q: What is F1?
A: F1 is the indicator’s best guess for the next bar.
Price crossing above/below F1 can hint at short-term momentum shifts or mean-reversion.
Q: What do “Alpha” and “Beta” do?
A: Alpha controls how fast the indicator reacts to new prices
(higher = faster, twitchier). Beta controls how fast the slope updates (higher = quicker pivots, more flips in chop).
Q: Why does the cone width change?
A: It reflects recent forecast accuracy. When the market gets noisy, the cone widens. When the tape is calm, it narrows.
Q: What does the Accuracy Panel tell me?
A:
* Preset & Horizon you’re using.
* RMSE: typical forecast miss in price units.
* MAPE: typical forecast miss in percent.
* Slope T: short-term trend reading (up, down, or flat).
If RMSE/MAPE rise, expect a wider cone and more whipsaw.
Q: The panel shows “…” or looks empty. Why?
A: It needs a short warm-up to gather enough bars. This is normal after you add the indicator or change settings/timeframes.
Q: Which timeframe is best?
A:
* 1–3 min: Scalp/Fast or Momentum/Breakout, horizon \~8–12.
* 5–15 min: Smooth Intraday, horizon \~12–15.
Higher timeframes work too; consider a larger residual window for steadier cones.
Q: Which preset should I start with?
A: Start with Smooth Intraday. If the market is trending hard, try Momentum/Breakout.
For very quick tapes, use Scalp/Fast. Switch back if things get choppy.
Q: What does the VWAP option do?
A: It only changes colors (highlights when price agrees with the trend side of VWAP).
It does not add or remove signals.
Q: Are there alerts?
A: Yes—alerts for price crossing F1 (up/down). Use “Once per bar close” to reduce noise on fast charts.
Q: Can I use this on stocks, futures, crypto, or FX?
A: Yes. It works on any symbol/timeframe. You may want to adjust Horizon and the Residual Window based on volatility.
Q: Can I use it with Heikin Ashi or other non-standard bars?
A: You can, but remember you’re forecasting the synthetic series of those bars. For pure price behavior, use regular candles.
Q: The cone feels too wide/too narrow. What do I change?
A:
* Too wide: lower Alpha/Beta a bit or increase the Residual Window.
* Too narrow (misses moves): raise Alpha/Beta slightly or try Momentum/Breakout.
Q: Why do results change when I switch timeframe or symbol?
A: Different noise levels and trends. The accuracy stats reset per chart, so the cone adapts to each context.
Q: Any limits or gotchas?
A: Extremely large Horizon may hit TradingView’s line-object limits; reduce Horizon or turn
off extra visuals if needed. Big gaps or news spikes will widen errors—expect the cone to react.
Q: Can this predict exact future prices?
A: No. It provides a baseline path and context. Always combine with your own rules and risk management.
Glossary
* TS (Time Series): Data over time (prices).
* Holt’s Method: A forecasting approach that tracks a current level and a trend to predict the next bars.
* F1: The indicator’s best guess for the next bar.
* F(h): The projected value h bars ahead.
* VWAP: Volume-Weighted Average Price—used here for optional color alignment.
* RMSE: Typical forecast miss in price units (how far off, on average).
* MAPE: Typical forecast miss in percent (scale-free, easy to compare).
Notes & limitations
* The panel needs a short warm-up; stats may be blank at first.
* The cone reflects recent conditions; sudden volatility changes will widen it.
* This is a tool for context. It does not place trades and does not promise results.
⚠️ Disclaimer
This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
BioSwarm Imprinter™BioSwarm Imprinter™ — Agent-Based Consensus for Traders
What it is
BioSwarm Imprinter™ is a non-repainting, agent-based sentiment oscillator. It fuses many short-to-medium lookback “opinions” into one 0–100 consensus line that is easy to read at a glance (50 = neutral, >55 bullish bias, <45 bearish bias). The engine borrows from swarm intelligence: many simple voters (agents) adapt their influence over time based on how well they’ve been predicting price, so the crowd gets smarter as conditions change.
Use it to:
• Detect emerging trends sooner without overreacting to noise.
• Filter mean-reversion vs continuation opportunities.
• Gate entries with a confidence score that reflects both strength and persistence of the move.
• Combine with your execution tools (VWAP/ORB/levels) as a state filter rather than a trade signal by itself.
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Why it’s different
• Swarm learning: Each agent improves or decays its “fitness” depending on whether its vote matched the next bar’s direction. High-fitness agents matter more; weak agents fade.
• Multi-horizon by design: The crowd is composed of fixed, simple lookbacks spread from lenMin to lenMax. You get a blended, robust view instead of a single fragile parameter.
• Two complementary lenses: Each agent evaluates RSI-style balance (via Wilder’s RMA) and momentum (EMA deviation). You decide the weight of each.
• No repaint, no MTF pitfalls: Everything runs on the chart’s timeframe with bar-close confirmation; no request.security() or forward references.
• Actionable UI: A clean consensus line, optional regime background, confidence heat, and triangle markers when thresholds are crossed.
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What you see on the chart
• Consensus line (0–100): Smoothed to your preference; color/area makes bull/bear zones obvious.
• Regime coloring (optional): Light green in bull zone, light red in bear zone; neutral otherwise.
• Confidence heat: A small gauge/number (0–100) that combines distance from neutral and recent persistence.
• Markers (optional): Triangles when consensus crosses up through your bull threshold (e.g., 55) or down through your bear threshold (e.g., 45).
• Info panel (optional): Consensus value, regime, confidence, number of agents, and basic diagnostics.
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How it works (under the hood)
1. Horizon bins: The range is divided into numBins. Each bin has a fixed, simple integer length (crucial for Pine’s safety rules).
2. Per-bin features (computed every bar):
• RSI-style balance using Wilder’s RMA (not ta.rsi()), then mapped to −1…+1.
• Momentum as (close − EMA(L)) / EMA(L) (dimensionless drift).
3. Agent vote: For its assigned bin, an agent forms a weighted score: score = wRSI*RSI_like + wMOM*Momentum. A small dead-band near zero suppresses chop; votes are +1/−1/0.
4. Fitness update (bar close): If the agent’s previous vote agreed with the next bar’s direction, multiply its fitness by learnGain; otherwise by learnPain. Fitness is clamped so it never explodes or dies.
5. Consensus: Weighted average of all votes using fitness as weights → map to 0–100 and smooth with EMA.
Why it doesn’t repaint:
• No future references, no MTF resampling, fitness updates only on confirmed bars.
• All TA primitives (RMA/EMA/deltas) are computed every bar unconditionally.
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Signals & confidence
• Bullish bias: consensus ≥ bullThr (e.g., 55).
• Bearish bias: consensus ≤ bearThr (e.g., 45).
• Confidence (0–100):
• Distance score: how far consensus is from 50.
• Momentum score: how strong the recent change is versus its recent average.
• Combined into a single gate; start filtering entries at ≥60 for higher quality.
Tip: For range sessions, raise thresholds (60/40) and increase smoothing; for momentum sessions, lower smoothing and keep thresholds at 55/45.
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Inputs you’ll actually tune
• Agents & horizons:
• N_agents (e.g., 64–128)
• lenMin / lenMax (e.g., 6–30 intraday, 10–60 swing)
• numBins (e.g., 12–24)
• Weights & smoothing:
• wRSI vs wMOM (e.g., 0.7/0.3 for FX & indices; 0.6/0.4 for crypto)
• deadBand (0.03–0.08)
• consSmooth (3–8)
• Thresholds & hygiene:
• bullThr/bearThr (55/45 default)
• cooldownBars to avoid signal spam
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Playbooks (ready-to-use)
1) Breakout / Trend continuation
• Timeframe: 15m–1h for day/swing.
• Filter: Take longs only when consensus > 55 and confidence ≥ 60.
• Execution: Use your ORB/VWAP/pullback trigger for entry. Trail with swing lows or 1.5×ATR. Exit on a close back under 50 or when a bearish signal prints.
2) Mean reversion (fade)
• When: Sideways days or low-volatility clusters.
• Setup: Increase deadBand and consSmooth.
• Signal: Bearish fades when consensus rolls over below ≈55 but stays above 50; bullish fades when it rolls up above ≈45 but stays below 50.
• Targets: The neutral zone (~50) as the first take-profit.
3) Multi-TF alignment
• Keep BioSwarm on 1H for bias, execute on 5–15m:
• Only take entries in the direction of the 1H consensus.
• Skip counter-bias scalps unless confidence is very low (explicit mean-reversion plan).
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Integrations that work
• DynamoSent Pro+ (macro bias): Only act when macro bias and swarm consensus agree.
• ORB + Session VWAP Pro: Trade London/NY ORB breakouts that retest while consensus >55 (long) or <45 (short).
• Levels/Orderflow: BioSwarm is your “go / no-go”; execution stays with your usual triggers.
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Quick start
1. Drop the indicator on a 1H chart.
2. Start with: N_agents=64, lenMin=6, lenMax=30, numBins=16, deadBand=0.06, consSmooth=5, thresholds 55/45.
3. Trade only when confidence ≥ 60.
4. Add your favorite execution tool (VWAP/levels/OR) for entries & exits.
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Non-repainting & safety notes
• No request.security(); no hidden lookahead.
• Bar-close confirmation for fitness and signals.
• All TA calls are unconditional (no “sometimes called” warnings).
• No series-length inputs to RSI/EMA — we use RMA/EMA formulas that accept fixed simple ints per bin.
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Known limits & tips
• Too many signals? Raise deadBand, increase consSmooth, widen thresholds to 60/40.
• Too few signals? Lower deadBand, reduce consSmooth, narrow thresholds to 53/47.
• Over-fitting risk: Keep learnGain/learnPain modest (e.g., ×1.04 / ×0.96).
• Compute load: Large N_agents × numBins is heavier; scale to your device.
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Example recipes
EURUSD 1H (swing):
lenMin=8, lenMax=34, numBins=16, wRSI=0.7, wMOM=0.3, deadBand=0.06, consSmooth=6, thr=55/45
Buy breakouts when consensus >55 and confidence ≥60; confirm with 5–15m pullback to VWAP or level.
SPY 15m (US session):
lenMin=6, lenMax=24, numBins=12, consSmooth=4, deadBand=0.05
On trend days, stay with longs as long as consensus >55; add on shallow pullbacks.
BTC 1H (24/7):
Increase momentum weight: wRSI=0.6, wMOM=0.4, extend lenMax to ~50. Use dynamic stops (ATR) and partials on strong verticals.
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Final word
BioSwarm is a state engine: it tells you when the market is primed to continue or mean-revert. Pair it with your entries and risk framework to turn that state into trades. If you’d like, I can supply a companion strategy template that consumes the consensus and back-tests the three playbooks (Breakout/Fade/Flip) with standard risk management.
Piano Frequency LevelsPiano Frequency Levels
This indicator applies the mathematical principles of musical harmony to market analysis, creating support and resistance levels based on authentic piano frequency ratios. Drawing from centuries-old musical theory, it maps the precise mathematical relationships between piano keys to price levels.
How It Works: The indicator uses the exact frequency ratios from equal temperament tuning - the same mathematical system that makes pianos sound harmonious. Each level represents an actual piano key frequency, scaled proportionally to your chosen anchor price.
Key Features:
• Piano-Based Ratios: Uses authentic 12-tone equal temperament frequency relationships (1.05946 ratio between semitones)
• Directional Intelligence: Automatically creates ascending levels from lows (resistance) or descending levels from highs (support)
• Musical Note Labels: Optional display of actual piano key names (C4, D#5, F6, etc.) alongside price levels
• Black Key Subdivisions: Toggle authentic sharp/flat keys between natural notes for additional precision
• Octave Color Coding: Each musical octave displays in a different color for easy visual identification
• Anchor Reference: Bright green line clearly marks your C-note reference point
Musical Foundation: Every level corresponds to an actual piano key. The anchor point represents "C" (the musical root), with levels progressing through the natural musical sequence: C, D, E, F, G, A, B, then repeating in higher octaves. This creates proportional spacing that mirrors the harmonic relationships musicians have used for centuries.
Usage:
1. Set your anchor to a significant market high or low
2. Choose your desired number of levels (typically 12-24 for 1-2 octaves)
3. Enable "Add Black Keys" for additional intermediate levels
4. Enable "Show Note Names" to see which piano key each level represents
The Theory: Musical harmony is based on precise mathematical ratios that create pleasing relationships between frequencies. These same mathematical principles may manifest in market movements, as price action often exhibits proportional relationships similar to musical intervals.
Unique Advantages:
• Based on established mathematical principles rather than arbitrary ratios
• Provides both major levels (white keys) and intermediate levels (black keys)
• Automatically adapts direction based on anchor type (high vs low)
• Maintains authentic musical relationships across all timeframes and price ranges
Important Note: This indicator presents a theoretical framework for market analysis. Like all technical analysis tools, it should be used in conjunction with other forms of analysis and proper risk management. The musical ratios provide a unique perspective on potential support and resistance levels, but past performance does not guarantee future results.
Transform your charts into a musical instrument and discover the hidden harmonies in market movements.
Trend FriendTrend Friend — What it is and how to use it
I built Trend Friend to stop redrawing the same trendlines all day. It automatically connects confirmed swing points (fractals) and keeps the most relevant lines in front of you. The goal: give you clean, actionable structure without the guesswork.
What it does (in plain English)
Finds swing highs/lows using a Fractal Period you choose.
Draws auto-trendlines between the two most recent confirmed highs and the two most recent confirmed lows.
Colours by intent:
Lines drawn from highs (potential resistance / bearish) = Red
Lines drawn from lows (potential support / bullish) = Green
Keeps the chart tidy: The newest lines are styled as “recent,” older lines are dimmed as “historical,” and it prunes anything beyond your chosen limit.
Optional crosses & alerts: You can highlight when price closes across the most recent line and set alerts for new lines formed and upper/lower line crosses.
Structure labels: It tags HH, LH, HL, LL at the swing points, so you can quickly read trend/rotation.
How it works (under the hood)
A “fractal” here is a confirmed pivot: the highest high (or lowest low) with n bars on each side. That means pivots only confirm after n bars, so signals are cleaner and less noisy.
When a new pivot prints, the script connects it to the prior pivot of the same type (high→high, low→low). That gives you one “bearish” line from highs and one “bullish” line from lows.
The newest line is marked as recent (brighter), and the previous recent line becomes historical (dimmed). You can keep as many pairs as you want, but I usually keep it tight.
Inputs you’ll actually use
Fractal Period (n): this is the big one. It controls how swingy/strict the pivots are.
Lower n → more swings, more lines (faster, noisier)
Higher n → fewer swings, cleaner lines (slower, swing-trade friendly)
Max pair of lines: how many pairs (up+down) to keep on the chart. 1–3 is a sweet spot.
Extend: extend lines Right (my default) or Both ways if you like the context.
Line widths & colours: recent vs. historical are separate so you can make the active lines pop.
Show crosses: toggle the X markers when price crosses a line. I turn this on when I’m actively hunting breakouts/retests.
Reading the chart
Red lines (from highs): I treat these as potential resistance. A clean break + hold above a red line often flips me from “fade” to “follow.”
Green lines (from lows): Potential support. Same idea in reverse: break + hold below and I stop buying dips until I see structure reclaim.
HH / LH / HL / LL dots: quick read on structure.
HH/HL bias = uptrend continuation potential
LH/LL bias = downtrend continuation potential
Mixed prints = rotation/chop—tighten risk or wait for clarity.
My H1 guidance (fine-tuning Fractal Period)
If you’re mainly on H1 (my use case), tune like this:
Fast / aggressive: n = 6–8 (lots of signals, good for momentum days; more chop risk)
Balanced (recommended): n = 9–12 (keeps lines meaningful but responsive)
Slow / swing focus: n = 13–21 (filters noise; better for trend days and higher-TF confluence)
Rule of thumb: if you’re getting too many touches and whipsaws, increase n. If you’re late to obvious breaks, decrease n.
How I trade it (example workflow)
Pick your n for the session (H1: start at 9–12).
Mark the recent red & green lines. That’s your immediate structure.
Look for interaction:
Rejections from a line = fade potential back into the range.
Break + close across a line = watch the retest for continuation.
Confirm with context: session bias, HTF structure, and your own tools (VWAP, RSI, volume, FVG/OB, etc.).
Plan the trade: enter on retest or reclaim, stop beyond the line/last swing, target the opposite side or next structure.
Alerts (set and forget)
“New trendline formed” — fires when a new high/low pivot confirms and a fresh line is drawn.
“Upper/lower trendline crossed” — fires when price crosses the most recent red/green line.
Use these to track structure shifts without staring at the screen.
Good to know (honest limitations)
Confirmation lag: pivots need n bars on both sides, so signals arrive after the swing confirms. That’s by design—less noise, fewer fake lines.
Lines update as structure evolves: when a new pivot forms, the previous “recent” line becomes “historical,” and older ones can be removed based on your max setting.
Not an auto trendline crystal ball: it won’t predict which line holds or breaks—it just keeps the most relevant structure clean and up to date.
Final notes
Works on any timeframe; I built it with H1 in mind and scale to H4/D1 by increasing n.
Pairs nicely with session tools and VWAP for intraday, or with supply/demand / FVGs for swing planning.
Risk first: lines are structure, not guarantees. Manage position size and stops as usual.
Not financial advice. Trade your plan. Stay nimble.
RSI Crossover AlertRSI Crossover Alert Indicator - User Guide
The RSI Crossover Alert Indicator is a comprehensive technical analysis tool that detects multiple types of RSI crossovers and generates real-time alerts. It combines traditional RSI analysis with signal lines, divergence detection, and multi-level crossing alerts.
1. Multiple Crossover Detection
- RSI/Signal Line Cross: Signals a primary trend change.
- RSI/Second Signal Cross: Confirmation signals for stronger trends.
- Level Crossings: Crosses of Overbought 70, Oversold 30, and Midline 50.
- Divergence Detection: Hidden and regular divergences for reversal signals.
2. Alert Types
- Alert: RSI > Signal
Description: Bullish momentum is building.
Signal: Consider long positions.
- Alert: RSI < Signal
Description: Bearish momentum is building.
Signal: Consider short positions.
- Alert: RSI > 70
Description: Entering the overbought zone.
Signal: Prepare for a potential reversal.
- Alert: RSI < 30
Description: Entering the oversold zone.
Signal: Watch for a bounce opportunity.
- Alert: RSI crosses 50
Description: A shift in momentum.
Signal: Trend confirmation.
3. Visual Components
- Lines: RSI blue, Signal orange, Second Signal purple
- Histogram: Visualizes momentum by showing the difference between RSI and the Signal line.
- Background Zones: Red overbought, Green oversold
- Markers: Up/down triangles to indicate crossovers.
- Info Table: Real-time RSI values and status.
Strategy 1: Classic Crossover
- Entry Long: RSI crosses above the Signal Line AND RSI is below 50.
- Entry Short: RSI crosses below the Signal Line AND RSI is above 50.
- Take Profit: On the opposite signal.
- Stop Loss: At the recent swing high/low.
Strategy 2: Extreme Zone Reversal
- Entry Long: RSI is below 30 and crosses above the Signal Line.
- Entry Short: RSI is above 70 and crosses below the Signal Line.
- Risk Management: Higher win rate but fewer signals. Use a minimum 2:1 risk-reward ratio.
Strategy 3: Divergence Trading
- Setup: Enable divergence alerts and look for price/RSI divergence. Wait for an RSI crossover for confirmation.
- Entry: Enter on the crossover after the divergence appears. Place the stop loss beyond the starting point of the divergence.
Strategy 4: Multi-Timeframe Confirmation
1. Check the higher timeframe e.g. Daily to identify the main trend.
2. Use the current timeframe e.g. 4H/1H for your entry.
3. Only enter in the direction of the main trend.
4. Use the RSI crossover as the entry trigger.
Optimal Settings by Market
- Forex Major Pairs
RSI Length: 14, Signal Length: 9, Overbought/Oversold: 70/30
- Crypto High Volatility
RSI Length: 10-12, Signal Length: 6-8, Overbought/Oversold: 75/25
- Stocks Trending
RSI Length: 14-21, Signal Length: 9-12, Overbought/Oversold: 70/30
- Commodities
RSI Length: 14, Signal Length: 9, Overbought/Oversold: 80/20
Risk Management Rules
1. Position Sizing: Never risk more than 1-2% on a single trade. Reduce size in ranging markets.
2. Stop Loss Placement: Place stops beyond the recent swing high/low for crossovers. Using an ATR-based stop is also effective.
3. Profit Taking: Take partial profits at a 1:1 risk-reward ratio. Switch to a trailing stop after reaching 2:1.
1. Filtering Signals
- Combine with volume indicators.
- Confirm the trend on a higher timeframe.
- Wait for candlestick pattern confirmation.
2. Avoid Common Mistakes
- Don't trade every single crossover.
- Avoid taking signals against a strong trend.
- Do not ignore risk management.
3. Market Conditions
- Trending Market: Focus on midline 50 crosses.
- Ranging Market: Look for reversals from overbought/oversold levels.
- Volatile Market: Widen the overbought/oversold levels.
- If you get too many false signals:
Increase the signal line period, add other confirmation indicators, or use a higher timeframe.
- If you are missing major moves:
Decrease the RSI length, shorten the signal line period, or check your alert settings.
Recommended Combinations
1. RSI + MACD: For dual momentum confirmation.
2. RSI + Bollinger Bands: For volatility-adjusted signals.
3. RSI + Volume: To confirm the strength of a signal.
4. RSI + Moving Averages: To use as a trend filter.
This indicator provides a comprehensive RSI analysis. Success depends on proper configuration, risk management, and combining signals with the overall market context. Start with the default settings, then optimize based on your trading style and market conditions.
Live Market - Performance MonitorLive Market — Performance Monitor
Study material (no code) — step-by-step training guide for learners
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1) What this tool is — short overview
This indicator is a live market performance monitor designed for learning. It scans price, volume and volatility, detects order blocks and trendline events, applies filters (volume & ATR), generates trade signals (BUY/SELL), creates simple TP/SL trade management, and renders a compact dashboard summarizing market state, risk and performance metrics.
Use it to learn how multi-factor signals are constructed, how Greeks-style sensitivity is replaced by volatility/ATR reasoning, and how a live dashboard helps monitor trade quality.
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2) Quick start — how a learner uses it (step-by-step)
1. Add the indicator to a chart (any ticker / timeframe).
2. Open inputs and review the main groups: Order Block, Trendline, Signal Filters, Display.
3. Start with defaults (OB periods ≈ 7, ATR multiplier 0.5, volume threshold 1.2) and observe the dashboard on the last bar.
4. Walk the chart back in time (use the last-bar update behavior) and watch how signals, order blocks, trendlines, and the performance counters change.
5. Run the hands-on labs below to build intuition.
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3) Main configurable inputs (what you can tweak)
• Order Block Relevant Periods (default ~7): number of consecutive candles used to define an order block.
• Min. Percent Move for Valid OB (threshold): minimum percent move required for a valid order block.
• Number of OB Channels: how many past order block lines to keep visible.
• Trendline Period (tl_period): pivot lookback for detecting highs/lows used to draw trendlines.
• Use Wicks for Trendlines: whether pivot uses wicks or body.
• Extension Bars: how far trendlines are projected forward.
• Use Volume Filter + Volume Threshold Multiplier (e.g., 1.2): requires volume to be greater than multiplier × average volume.
• Use ATR Filter + ATR Multiplier: require bar range > ATR × multiplier to filter noise.
• Show Targets / Table settings / Colors for visualization.
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4) Core building blocks — what the script computes (plain language)
Price & trend:
• Spot / LTP: current close price.
• EMA 9 / 21 / 50: fast, medium, slow moving averages to define short/medium trend.
o trend_bullish: EMA9 > EMA21 > EMA50
o trend_bearish: EMA9 < EMA21 < EMA50
o trend_neutral: otherwise
Volatility & noise:
• ATR (14): average true range used for dynamic target and filter sizing.
• dynamic_zone = ATR × atr_multiplier: minimum bar range required for meaningful move.
• Annualized volatility: stdev of price changes × sqrt(252) × 100 — used to classify volatility (HIGH/MEDIUM/LOW).
Momentum & oscillators:
• RSI 14: overbought/oversold indicator (thresholds 70/30).
• MACD: EMA(12)-EMA(26) and a 9-period signal line; histogram used for momentum direction and strength.
• Momentum (ta.mom 10): raw momentum over 10 bars.
Mean reversion / band context:
• Bollinger Bands (20, 2σ): upper, mid, lower.
o price_position measures where price sits inside the band range as 0–100.
Volume metrics:
• avg_volume = SMA(volume, 20) and volume_spike = volume > avg_volume × volume_threshold
o volume_ratio = volume / avg_volume
Support & Resistance:
• support_level = lowest low over 20 bars
• resistance_level = highest high over 20 bars
• current_position = percent of price between support & resistance (0–100)
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5) Order Block detection — concept & logic
What it tries to find: a bar (the base) followed by N candles in the opposite direction (a classical order block setup), with a minimum % move to qualify. The script records the high/low of the base candle, averages them, and plots those levels as OB channels.
How learners should think about it (conceptual):
1. An order block is a signature area where institutions (theory) left liquidity — often seen as a large bar followed by a sequence of directional candles.
2. This indicator uses a configurable number of subsequent candles to confirm that the pattern exists.
3. When found, it stores and displays the base candle’s high/low area so students can see how price later reacts to those zones.
Implementation note for learners: the tool keeps a limited history of OB lines (ob_channels). When new OBs exceed the count, the oldest lines are removed — good practice to avoid clutter.
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6) Trendline detection — idea & interpretation
• The script finds pivot highs and lows using a symmetric lookback (tl_period and half that as right/left).
• It then computes a trendline slope from successive pivots and projects the line forward (extension_bars).
• Break detection: Resistance break = close crosses above the projected resistance line; Support break = close crosses below projected support.
Learning tip: trendlines here are computed from pivot points and time. Watch how changing tl_period (bigger = smoother, fewer pivots) alters the trendlines and break signals.
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7) Signal generation & filters — step-by-step
1. Primary triggers:
o Bullish trigger: order block bullish OR resistance trendline break.
o Bearish trigger: bearish order block OR support trendline break.
2. Filters applied (both must pass unless disabled):
o Volume filter: volume must be > avg_volume × volume_threshold.
o ATR filter: bar range (high-low) must exceed ATR × atr_multiplier.
o Not in an existing trade: new trades only start if trade_active is false.
3. Trend confirmation:
o The primary trigger is only confirmed if trend is bullish/neutral for buys or bearish/neutral for sells (EMA alignment).
4. Result:
o When confirmed, a long or short trade is activated with TP/SL calculated from ATR multiples.
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8) Trade management — what the tool does after a signal
• Entry management: the script marks a trade as trade_active and sets long_trade or short_trade flags.
• TP & SL rules:
o Long: TP = high + 2×ATR ; SL = low − 1×ATR
o Short: TP = low − 2×ATR ; SL = high + 1×ATR
• Monitoring & exit:
o A trade closes when price reaches TP or SL.
o When TP/SL hit, the indicator updates win_count and total_pnl using a very simple calculation (difference between TP/SL and previous close).
o Visual lines/labels are drawn for TP and updated as the trade runs.
Important learner notes:
• The script does not store a true entry price (it uses close in its P&L math), so PnL is an approximation — treat this as a learning proxy, not a position accounting system.
• There’s no sizing, slippage, or fee accounted — students must manually factor these when translating to real trades.
• This indicator is not a backtesting strategy; strategy.* functions would be needed for rigorous backtest results.
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9) Signal strength & helper utilities
• Signal strength is a composite score (0–100) made up of four signals worth 25 points each:
1. RSI extreme (overbought/oversold) → 25
2. Volume spike → 25
3. MACD histogram magnitude increasing → 25
4. Trend existence (bull or bear) → 25
• Progress bars (text glyphs) are used to visually show RSI and signal strength on the table.
Learning point: composite scoring is a way to combine orthogonal signals — study how changing weights changes outcomes.
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10) Dashboard — how to read each section (walkthrough)
The dashboard is split into sections; here's how to interpret them:
1. Market Overview
o LTP / Change%: immediate price & daily % change.
2. RSI & MACD
o RSI value plus progress bar (overbought 70 / oversold 30).
o MACD histogram sign indicates bullish/bearish momentum.
3. Volume Analysis
o Volume ratio (current / average) and whether there’s a spike.
4. Order Block Status
o Buy OB / Sell OB: the average base price of detected order blocks or “No Signal.”
5. Signal Status
o 🔼 BUY or 🔽 SELL if confirmed, or ⚪ WAIT.
o No-trade vs Active indicator summarizing market readiness.
6. Trend Analysis
o Trend direction (from EMAs), market sentiment score (composite), volatility level and band/position metrics.
7. Performance
o Win Rate = wins / signals (percentage)
o Total PnL = cumulative PnL (approximate)
o Bull / Bear Volume = accumulated volumes attributable to signals
8. Support & Resistance
o 20-bar highest/lowest — use as nearby reference points.
9. Risk & R:R
o Risk Level from ATR/price as a percent.
o R:R Ratio computed from TP/SL if a trade is active.
10. Signal Strength & Active Trade Status
• Numeric strength + progress bar and whether a trade is currently active with TP/SL display.
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11) Alerts — what will notify you
The indicator includes pre-built alert triggers for:
• Bullish confirmed signal
• Bearish confirmed signal
• TP hit (long/short)
• SL hit (long/short)
• No-trade zone
• High signal strength (score > 75%)
Training use: enable alerts during a replay session to be notified when the indicator would have signalled.
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12) Labs — hands-on exercises for learners (step-by-step)
Lab A — Order Block recognition
1. Pick a 15–30 minute timeframe on a liquid ticker.
2. Use default OB periods (7). Mark each time the dashboard shows a Buy/Sell OB.
3. Manually inspect the chart at the base candle and the following sequence — draw the OB zone by hand and watch later price reactions to it.
4. Repeat with OB periods 5 and 10; note stability vs noise.
Lab B — Trendline break confirmation
1. Increase trendline period (e.g., 20), watch trendlines form from pivots.
2. When a resistance break is flagged, compare with MACD & volume: was momentum aligned?
3. Note false breaks vs confirmed moves — change extension_bars to see projection effects.
Lab C — Filter sensitivity
1. Toggle Use Volume Filter off, and record the number and quality of signals in a 2-day window.
2. Re-enable volume filter and change threshold from 1.2 → 1.6; note how many low-quality signals are filtered out.
Lab D — Trade management simulation
1. For each signalled trade, record the time, close entry approximation, TP, SL, and eventual hit/miss.
2. Compute actual PnL if you had entered at the open of the next bar to compare with the script’s PnL math.
3. Tabulate win rate and average R:R.
Lab E — Performance review & improvement
1. Build a spreadsheet of signals over 30–90 periods with columns: Date, Signal type, Entry price (real), TP, SL, Exit, PnL, Notes.
2. Analyze which filters or indicators contributed most to winners vs losers and adjust weights.
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13) Common pitfalls, assumptions & implementation notes (things to watch)
• P&L simplification: total_pnl uses close as a proxy entry price. Real entry/exit prices and slippage are not recorded — so PnL is approximate.
• No position sizing or money management: the script doesn’t compute position size from equity or risk percent.
• Signal confirmation logic: composite "signal_strength" is a simple 4×25 point scheme — explore different weights or additional signals.
• Order block detection nuance: the script defines the base candle and checks the subsequent sequence. Be sure to verify whether the intended candle direction (base being bullish vs bearish) aligns with academic/your trading definition — read the code carefully and test.
• Trendline slope over time: slope is computed using timestamps; small differences may make lines sensitive on very short timeframes — using bar_index differences is usually more stable.
• Not a true backtester: to evaluate performance statistically you must transform the logic into a strategy script that places hypothetical orders and records exact entry/exit prices.
________________________________________
14) Suggested improvements for advanced learners
• Record true entry price & timestamp for accurate PnL.
• Add position sizing: risk % per trade using SL distance and account size.
• Convert to strategy. (Pine Strategy)* to run formal backtests with equity curves, drawdowns, and metrics (Sharpe, Sortino).
• Log trades to an external spreadsheet (via alerts + webhook) for offline analysis.
• Add statistics: average win/loss, expectancy, max drawdown.
• Add additional filters: news time blackout, market session filters, multi-timeframe confirmation.
• Improve OB detection: combine wick/body, volume spike at base bar, and liquidity sweep detection.
________________________________________
15) Glossary — quick definitions
• ATR (Average True Range): measure of typical range; used to size targets and stops.
• EMA (Exponential Moving Average): trend smoothing giving more weight to recent prices.
• RSI (Relative Strength Index): momentum oscillator; >70 overbought, <30 oversold.
• MACD: momentum oscillator using difference of two EMAs.
• Bollinger Bands: volatility bands around SMA.
• Order Block: a base candle area with subsequent confirmation candles; a zone of institutional interest (learning model).
• Pivot High/Low: local turning point defined by candles on both sides.
• Signal Strength: combined score from multiple indicators.
• Win Rate: proportion of signals that hit TP vs total signals.
• R:R (Risk:Reward): ratio of potential reward (TP distance) to risk (entry to SL).
________________________________________
16) Limitations & assumptions (be explicit)
• This is an indicator for learning — not a trading robot or broker connection.
• No slippage, fees, commissions or tie-in to real orders are considered.
• The logic is heuristic (rule-of-thumb), not a guarantee of performance.
• Results are sensitive to timeframe, market liquidity, and parameter choices.
________________________________________
17) Practical classroom / study plan (4 sessions)
• Session 1 — Foundations: Understand EMAs, ATR, RSI, MACD, Bollinger Bands. Run the indicator and watch how these numbers change on a single day.
• Session 2 — Zones & Filters: Study order blocks and trendlines. Test volume & ATR filters and note changes in false signals.
• Session 3 — Simulated trading: Manually track 20 signals, compute real PnL and compare to the dashboard.
• Session 4 — Improvement plan: Propose changes (e.g., better PnL accounting, alternative OB rule) and test their impact.
________________________________________
18) Quick reference checklist for each signal
1. Was an order block or trendline break detected? (primary trigger)
2. Did volume meet threshold? (filter)
3. Did ATR filter (bar size) show a real move? (filter)
4. Was trend aligned (EMA 9/21/50)? (confirmation)
5. Signal confirmed → mark entry approximation, TP, SL.
6. Monitor dashboard (Signal Strength, Volatility, No-trade zone, R:R).
7. After exit, log real entry/exit, compute actual PnL, update spreadsheet.
________________________________________
19) Educational caveat & final note
This tool is built for training and analysis: it helps you see how common technical building blocks combine into trade ideas, but it is not a trading recommendation. Use it to develop judgment, to test hypotheses, and to design robust systems with proper backtesting and risk control before risking capital.
________________________________________
20) Disclaimer (must include)
Training & Educational Only — This material and the indicator are provided for educational purposes only. Nothing here is investment advice or a solicitation to buy or sell financial instruments. Past simulated or historical performance does not predict future results. Always perform full backtesting and risk management, and consider seeking advice from a qualified financial professional before trading with real capital.
________________________________________
MomentumScriptThis is Momentum Tracker based on Richard Driehaus' research:
1) 12–1 momentum (return from t-12 months to t-1 month
2) FIP / path efficiency (many small up days > one big gap)
3) Proximity to 52-week high/low
Smart Money Precision Structure [BullByte]Smart Money Precision Structure
Advanced Market Structure Analysis Using Institutional Order Flow Concepts
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OVERVIEW
Smart Money Precision Structure (SMPS) is a comprehensive market analysis indicator that combines six analytical frameworks to identify high-probability market structure patterns. The indicator uses multi-dimensional scoring algorithms to evaluate market conditions through institutional order flow concepts, providing traders with professional-grade market analysis.
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PURPOSE AND ORIGINALITY
Why This Indicator Was Developed
• Addresses the gap between retail and institutional analysis methods
• Consolidates multiple analysis techniques that professionals use separately
• Automates complex market structure evaluation into actionable insights
• Eliminates the need for multiple indicators by providing comprehensive analysis
What Makes SMPS Original
• Six-Layer Confluence System - Unique combination of market regime, structure, volume flow, momentum, price action, and adaptive filtering
• Institutional Pattern Recognition - Identifies smart money accumulation and distribution patterns
• Adaptive Intelligence - Parameters automatically adjust based on detected market conditions
• Real-Time Market Scoring - Proprietary algorithm rates market quality from 0-100%
• Structure Break Detection - Advanced pivot analysis identifies trend reversals early
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HOW IT WORKS - TECHNICAL METHODOLOGY
1. Market Regime Analysis Engine
The indicator evaluates five core market dimensions:
• Volatility Score - Measures current volatility against 50-period historical baseline
• Trend Score - Analyzes alignment between 8, 21, and 50-period EMAs
• Momentum Score - Combines RSI divergence with MACD signal alignment
• Structure Score - Evaluates pivot point formation clarity
• Efficiency Score - Calculates directional movement efficiency ratio
These scores combine to classify markets into five regimes:
• TRENDING - Strong directional movement with aligned indicators
• RANGING - Sideways movement with mixed directional signals
• VOLATILE - Elevated volatility with unpredictable price swings
• QUIET - Low volatility consolidation periods
• TRANSITIONAL - Market shifting between different regimes
2. Market Structure Analysis
Advanced pivot point analysis identifies:
• Higher Highs and Higher Lows for bullish structure
• Lower Highs and Lower Lows for bearish structure
• Structure breaks when established patterns fail
• Dynamic support and resistance from recent pivot points
• Key level proximity detection using ATR-based buffers
3. Volume Flow Decoding
Institutional activity detection through:
• Volume surge identification when volume exceeds 2x average
• Buy versus sell pressure analysis using price-volume correlation
• Flow strength measurement through directional volume consistency
• Divergence detection between volume and price movements
• Institutional threshold alerts when unusual volume patterns emerge
4. Multi-Period Momentum Synthesis
Weighted momentum calculation across four timeframes:
• 1-period momentum weighted at 40%
• 3-period momentum weighted at 30%
• 5-period momentum weighted at 20%
• 8-period momentum weighted at 10%
Result smoothed with 6-period EMA for noise reduction.
5. Price Action Quality Assessment
Each bar evaluated for:
• Range quality relative to 20-period average
• Body-to-range ratio for directional conviction
• Wick analysis for rejection pattern identification
• Pattern recognition including engulfing and hammer formations
• Sequential price movement analysis
6. Adaptive Parameter System
Parameters automatically adjust based on detected regime:
• Trending markets reduce sensitivity and confirmation requirements
• Volatile markets increase filtering and require additional confirmations
• Ranging markets maintain neutral settings
• Transitional markets use moderate adjustments
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COMPLETE SETTINGS GUIDE
Section 1: Core Analysis Settings
Analysis Sensitivity (0.3-2.0)
• Default: 1.0
• Lower values require stronger price movements
• Higher values detect more subtle patterns
• Scalpers use 0.8-1.2, swing traders use 1.5-2.0
Noise Reduction Level (2-7)
• Default: 4
• Controls filtering of false patterns
• Higher values reduce pattern frequency
• Increase in volatile markets
Minimum Move % (0.05-0.50)
• Default: 0.15%
• Sets minimum price movement threshold
• Adjust based on instrument volatility
• Forex: 0.05-0.10%, Stocks: 0.15-0.25%, Crypto: 0.20-0.50%
High Confirmation Mode
• Default: True (Enabled)
• Requires all technical conditions to align
• Reduces frequency but increases reliability
• Disable for more aggressive pattern detection
Section 2: Market Regime Detection
Enable Regime Analysis
• Default: True (Enabled)
• Activates market environment evaluation
• Essential for adaptive features
• Keep enabled for best results
Regime Analysis Period (20-100)
• Default: 50 bars
• Determines regime calculation lookback
• Shorter for responsive, longer for stable
• Scalping: 20-30, Swing: 75-100
Minimum Market Clarity (0.2-0.8)
• Default: 0.4
• Quality threshold for pattern generation
• Higher values require clearer conditions
• Lower for more patterns, higher for quality
Adaptive Parameter Adjustment
• Default: True (Enabled)
• Enables automatic parameter optimization
• Adjusts based on market regime
• Highly recommended to keep enabled
Section 3: Market Structure Analysis
Enable Structure Validation
• Default: True (Enabled)
• Validates patterns against support/resistance
• Confirms trend structure alignment
• Essential for reliability
Structure Analysis Period (15-50)
• Default: 30 bars
• Period for structure pattern analysis
• Affects support/resistance calculation
• Match to your trading timeframe
Minimum Structure Alignment (0.3-0.8)
• Default: 0.5
• Required structure score for valid patterns
• Higher values need stronger structure
• Balance with desired frequency
Section 4: Analysis Configuration
Minimum Strength Level (3-5)
• Default: 4
• Minimum confirmations for pattern display
• 5 = Maximum reliability, 3 = More patterns
• Beginners should use 4-5
Required Technical Confirmations (4-6)
• Default: 5
• Number of aligned technical factors
• Higher = fewer but better patterns
• Works with High Confirmation Mode
Pattern Separation (3-20 bars)
• Default: 8 bars
• Minimum bars between patterns
• Prevents clustering and overtrading
• Increase for cleaner charts
Section 5: Technical Filters
Momentum Validation
• Default: True (Enabled)
• Requires momentum alignment
• Filters counter-trend patterns
• Essential for trend following
Volume Confluence Analysis
• Default: True (Enabled)
• Requires volume confirmation
• Identifies institutional participation
• Critical for reliability
Trend Direction Filter
• Default: True (Enabled)
• Only shows patterns with trend
• Reduces counter-trend signals
• Disable for reversal hunting
Section 6: Volume Flow Analysis
Institutional Activity Threshold (1.2-3.5)
• Default: 2.0
• Multiplier for unusual volume detection
• Lower finds more institutional activity
• Stock: 2.0-2.5, Forex: 1.5-2.0, Crypto: 2.5-3.5
Volume Surge Multiplier (1.8-4.5)
• Default: 2.5
• Defines significant volume increases
• Adjust per instrument characteristics
• Higher for stocks, lower for forex
Volume Flow Period (12-35)
• Default: 18 bars
• Smoothing for volume analysis
• Shorter = responsive, longer = smooth
• Match to timeframe used
Section 7: Analysis Frequency Control
Maximum Analysis Points Per Hour (1-5)
• Default: 3
• Limits pattern frequency
• Prevents overtrading
• Scalpers: 4-5, Swing traders: 1-2
Section 8: Target Level Configuration
Target Calculation Method
• Default: Market Adaptive
• Three modes available:
- Fixed: Uses set point distances
- Dynamic: ATR-based calculations
- Market Adaptive: Structure-based levels
Minimum Target/Risk Ratio (1.0-3.0)
• Default: 1.5
• Minimum acceptable reward vs risk
• Higher filters lower probability setups
• Professional standard: 1.5-2.0
Fixed Mode Settings:
• Fixed Target Distance: 50 points default
• Fixed Invalidation Distance: 30 points default
• Use for consistent instruments
Dynamic Mode Settings:
• Dynamic Target Multiplier: 1.8x ATR default
• Dynamic Invalidation Multiplier: 1.0x ATR default
• Adapts to volatility automatically
Market Adaptive Settings:
• Use Structure Levels: True (default)
• Structure Level Buffer: 0.1% default
• Places levels at actual support/resistance
Section 9: Visual Display Settings
Color Theme Options
• Professional (Teal/Red)
- Bullish: Teal (#26a69a)
- Bearish: Red (#ef5350)
- Neutral: Gray (#78909c)
- Best for: Traditional traders, clean appearance
• Dark (Neon Green/Pink)
- Bullish: Neon Green (#00ff88)
- Bearish: Hot Pink (#ff0044)
- Neutral: Dark Gray (#333333)
- Best for: Dark theme users, high contrast
• Light (Green/Red Classic)
- Bullish: Green (#4caf50)
- Bearish: Red (#f44336)
- Neutral: Light Gray (#9e9e9e)
- Best for: Light backgrounds, traditional colors
• Vibrant (Cyan/Magenta)
- Bullish: Cyan (#00ffff)
- Bearish: Magenta (#ff00ff)
- Neutral: Medium Gray (#888888)
- Best for: High visibility, modern appearance
Dashboard Position
• Options: Top Left, Top Right, Bottom Left, Bottom Right, Middle Left, Middle Right
• Default: Top Right
• Choose based on chart layout preference
Dashboard Size
• Full: Complete information display (desktop)
• Mobile: Compact view for small screens
• Default: Full
Analysis Display Style
• Arrows : Simple directional markers
• Labels : Detailed text information
• Zones : Colored areas showing pattern regions
• Default: Labels (most informative)
Display Options:
• Display Analysis Strength: Shows star rating
• Display Target Levels: Shows target/invalidation lines
• Display Market Regime: Shows regime in pattern labels
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HOW TO USE SMPS - DETAILED GUIDE
Understanding the Dashboard
Top Row - Header
• SMPS Dashboard title
• VALUE column: Current readings
• STATUS column: Condition assessments
Market Regime Row
• Shows: TRENDING, RANGING, VOLATILE, QUIET, or TRANSITIONAL
• Color coding: Green = Favorable, Red = Caution
• Status: FAVORABLE or CAUTION trading conditions
Market Score Row
• Percentage from 0-100%
• Above 60% = Strong conditions
• 40-60% = Moderate conditions
• Below 40% = Weak conditions
Structure Row
• Direction: BULLISH, BEARISH, or NEUTRAL
• Status: INTACT or BREAK
• Orange BREAK indicates structure failure
Volume Flow Row
• Direction: BUYING or SELLING
• Intensity: STRONG or WEAK
• Color indicates dominant pressure
Momentum Row
• Numerical momentum value
• Positive = Upward pressure
• Negative = Downward pressure
Volume Status Row
• INST = Institutional activity detected
• HIGH = Above average volume
• NORM = Normal volume levels
Adaptive Mode Row
• ACTIVE = Parameters adjusting
• STATIC = Fixed parameters
• Shows required confirmations
Analysis Level Row
• Minimum strength level setting
• Pattern separation in bars
Market State Row
• Current analysis: BULLISH, BEARISH, NEUTRAL
• Shows analysis price level when active
T:R Ratio Row
• Current target to risk ratio
• GOOD = Meets minimum requirement
• LOW = Below minimum threshold
Strength Row
• BULL or BEAR dominance
• Numerical strength value 0-100
Price Row
• Current price
• Percentage change
Last Analysis Row
• Previous pattern direction
• Bars since last pattern
Reading Pattern Signals
Bullish Structure Pattern
• Upward triangle or "Bullish Structure" label
• Star rating shows strength (★★★★★ = strongest)
• Green line = potential target level
• Red dashed line = invalidation level
• Appears below price bars
Bearish Structure Pattern
• Downward triangle or "Bearish Structure" label
• Star rating indicates reliability
• Green line = potential target level
• Red dashed line = invalidation level
• Appears above price bars
Pattern Strength Interpretation
• ★★★★★ = 6 confirmations (exceptional)
• ★★★★☆ = 5 confirmations (strong)
• ★★★☆☆ = 4 confirmations (moderate)
• ★★☆☆☆ = 3 confirmations (minimum)
• Below minimum = filtered out
Visual Elements on Chart
Lines and Levels:
• Gray Line = 21 EMA trend reference
• Green Stepline = Dynamic support level
• Red Stepline = Dynamic resistance level
• Green Solid Line = Active target level
• Red Dashed Line = Active invalidation level
Pattern Markers:
• Triangles = Arrow display mode
• Text Labels = Label display mode
• Colored Boxes = Zone display mode
Target Completion Labels:
• "Target" = Price reached target level
• "Invalid" = Pattern invalidated by price
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RECOMMENDED USAGE BY TIMEFRAME
1-Minute Charts (Scalping)
• Sensitivity: 0.8-1.2
• Noise Reduction: 3-4
• Pattern Separation: 3-5 bars
• High Confirmation: Optional
• Best for: Quick intraday moves
5-Minute Charts (Precision Intraday)
• Sensitivity: 1.0 (default)
• Noise Reduction: 4 (default)
• Pattern Separation: 8 bars
• High Confirmation: Enabled
• Best for: Day trading
15-Minute Charts (Short Swing)
• Sensitivity: 1.0-1.5
• Noise Reduction: 4-5
• Pattern Separation: 10-12 bars
• High Confirmation: Enabled
• Best for: Intraday swings
30-Minute to 1-Hour (Position Trading)
• Sensitivity: 1.5-2.0
• Noise Reduction: 5-7
• Pattern Separation: 15-20 bars
• Regime Period: 75-100
• Best for: Multi-day positions
Daily Charts (Swing Trading)
• Sensitivity: 1.8-2.0
• Noise Reduction: 6-7
• Pattern Separation: 20 bars
• All filters enabled
• Best for: Long-term analysis
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MARKET-SPECIFIC SETTINGS
Forex Pairs
• Minimum Move: 0.05-0.10%
• Institutional Threshold: 1.5-2.0
• Volume Surge: 1.8-2.2
• Target Mode: Dynamic or Market Adaptive
Stock Indices (ES, NQ, YM)
• Minimum Move: 0.10-0.15%
• Institutional Threshold: 2.0-2.5
• Volume Surge: 2.5-3.0
• Target Mode: Market Adaptive
Individual Stocks
• Minimum Move: 0.15-0.25%
• Institutional Threshold: 2.0-2.5
• Volume Surge: 2.5-3.5
• Target Mode: Dynamic
Cryptocurrency
• Minimum Move: 0.20-0.50%
• Institutional Threshold: 2.5-3.5
• Volume Surge: 3.0-4.5
• Target Mode: Dynamic
• Increase noise reduction
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PRACTICAL APPLICATION EXAMPLES
Example 1: Strong Trending Market
Dashboard Reading:
• Market Regime: TRENDING
• Market Score: 75%
• Structure: BULLISH, INTACT
• Volume Flow: BUYING, STRONG
• Momentum: +0.45
Interpretation:
• Strong uptrend environment
• Institutional buying present
• Look for bullish patterns as continuation
• Higher probability of success
• Consider using lower sensitivity
Example 2: Range-Bound Conditions
Dashboard Reading:
• Market Regime: RANGING
• Market Score: 35%
• Structure: NEUTRAL
• Volume Flow: SELLING, WEAK
• Momentum: -0.05
Interpretation:
• No clear direction
• Low opportunity environment
• Patterns are less reliable
• Consider waiting for regime change
• Or switch to a range-trading approach
Example 3: Structure Break Alert
Dashboard Reading:
• Previous: BULLISH structure
• Current: Structure BREAK
• Volume: INST flag active
• Momentum: Shifting negative
Interpretation:
• Trend reversal potentially beginning
• Institutional participation detected
• Watch for bearish pattern confirmation
• Adjust bias accordingly
• Increase caution on long positions
Example 4: Volatile Market
Dashboard Reading:
• Market Regime: VOLATILE
• Market Score: 45%
• Adaptive Mode: ACTIVE
• Confirmations: Increased to 6
Interpretation:
• Choppy conditions
• Parameters auto-adjusted
• Fewer but higher quality patterns
• Wider stops may be needed
• Consider reducing position size
Below are a few chart examples of the Smart Money Precision Structure (SMPS) indicator in action.
• Example 1 – Bullish Structure Detection on SOLUSD 5m
• Example 2 – Bearish Structure Detected with Strong Confluence on SOLUSD 5m
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TROUBLESHOOTING GUIDE
No Patterns Appearing
Check these settings:
• High Confirmation Mode may be too restrictive
• Minimum Strength Level may be too high
• Market Clarity threshold may be too high
• Regime filter may be blocking patterns
• Try increasing sensitivity
Too Many Patterns
Adjust these settings:
• Enable High Confirmation Mode
• Increase Minimum Strength Level to 5
• Increase Pattern Separation
• Reduce Sensitivity below 1.0
• Enable all technical filters
Dashboard Shows "CAUTION"
This indicates:
• Market conditions are unfavorable
• Regime is RANGING or QUIET
• Market score is low
• Consider waiting for better conditions
• Or adjust expectations accordingly
Patterns Not Reaching Targets
Consider:
• Market may be choppy
• Volatility may have changed
• Try Dynamic target mode
• Reduce target/risk ratio requirement
• Check if regime is VOLATILE
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ALERTS CONFIGURATION
Alert Message Format
Alerts include:
• Pattern type (Bullish/Bearish)
• Strength rating
• Market regime
• Analysis price level
• Target and invalidation levels
• Strength percentage
• Target/Risk ratio
• Educational disclaimer
Setting Up Alerts
• Click Alert button on TradingView
• Select SMPS indicator
• Choose alert frequency
• Customize message if desired
• Alerts fire on pattern detection
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DATA WINDOW INFORMATION
The Data Window displays:
• Market Regime Score (0-100)
• Market Structure Bias (-1 to +1)
• Bullish Strength (0-100)
• Bearish Strength (0-100)
• Bull Target/Risk Ratio
• Bear Target/Risk Ratio
• Relative Volume
• Momentum Value
• Volume Flow Strength
• Bull Confirmations Count
• Bear Confirmations Count
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BEST PRACTICES AND TIPS
For Beginners
• Start with default settings
• Use High Confirmation Mode
• Focus on TRENDING regime only
• Paper trade first
• Learn one timeframe thoroughly
For Intermediate Users
• Experiment with sensitivity settings
• Try different target modes
• Use multiple timeframes
• Combine with price action analysis
• Track pattern success rate
For Advanced Users
• Customize per instrument
• Create setting templates
• Use regime information for bias
• Combine with other indicators
• Develop systematic rules
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IMPORTANT DISCLAIMERS
• This indicator is for educational and informational purposes only
• Not financial advice or a trading system
• Past performance does not guarantee future results
• Trading involves substantial risk of loss
• Always use appropriate risk management
• Verify patterns with additional analysis
• The author is not a registered investment advisor
• No liability accepted for trading losses
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VERSION NOTES
Version 1.0.0 - Initial Release
• Six-layer confluence system
• Adaptive parameter technology
• Institutional volume detection
• Market regime classification
• Structure break identification
• Real-time dashboard
• Multiple display modes
• Comprehensive settings
## My Final Thoughts
Smart Money Precision Structure represents an advanced approach to market analysis, bringing institutional-grade techniques to retail traders through intelligent automation and multi-dimensional evaluation. By combining six analytical frameworks with adaptive parameter adjustment, SMPS provides comprehensive market intelligence that single indicators cannot achieve.
The indicator serves as an educational tool for understanding how professional traders analyze markets, while providing practical pattern detection for those seeking to improve their technical analysis. Remember that all trading involves risk, and this tool should be used as part of a complete analysis approach, not as a standalone trading system.
- BullByte
Rolling Correlation BTC vs Hedge AssetsRolling Correlation BTC vs Hedge Assets
Overview
This indicator calculates and plots the rolling correlation between Bitcoin (BTC) returns and several key hedge assets:
• XAUUSD (Gold)
• EURUSD (proxy for DXY, U.S. Dollar Index)
• VIX (Volatility Index)
• TLT (20y U.S. Treasury Bonds ETF)
By monitoring these dynamic correlations, traders can identify whether BTC is moving in sync with risk assets or decoupling as a hedge, and adjust their trading strategy accordingly.
How it works
1. Computes returns for BTC and each asset using percentage change.
2. Uses the rolling correlation function (ta.correlation) over a configurable window length (default = 12 bars).
3. Plots each correlation as a separate colored line (Gold = Yellow, EURUSD = Blue, VIX = Red, TLT = Green).
4. Adds threshold levels at +0.3 and -0.3 to help classify correlation regimes.
How to use it
• High positive correlation (> +0.3): BTC is moving together with the asset (risk-on behavior).
• Near zero (-0.3 to +0.3): BTC is showing little to no correlation — neutral/independent moves.
• Negative correlation (< -0.3): BTC is moving in the opposite direction — potential hedge opportunity.
Practical strategies:
• Watch BTC vs VIX: a spike in volatility (VIX ↑) usually coincides with BTC selling pressure.
• Track BTC vs EURUSD: stronger USD often puts downside pressure on BTC.
• Observe BTC vs Gold: during “flight to safety” events, gold rises while BTC weakens.
• Monitor BTC vs TLT: rising yields (falling TLT) often align with BTC weakness.
Inputs
• Window Length (bars): Number of bars used to calculate rolling correlations (default = 12).
• Comparison Timeframe: Default = 5m. Can be changed to align with your intraday or swing trading style.
Notes
• Works best on intraday charts (1m, 5m, 15m) for scalping and short-term setups.
• Use correlations as context, not standalone signals — combine with volume, VWAP, and price action.
• Correlations are dynamic; they can switch regimes quickly during macro events (CPI, NFP, FOMC).
This tool is designed for traders who want to manage risk exposure by monitoring whether BTC is behaving as a risk-on asset or hedge, and to exploit opportunities during decoupling phases.
Average True Ranges with IBD RSAdvanced ATR Analysis with IBD Relative Strength
This comprehensive indicator combines Average True Range (ATR) analysis with IBD (Investor's Business Daily) Relative Strength calculation, providing both volatility measurement and momentum analysis in one powerful tool.
Key Features:
ATR Analysis:
Standard ATR: Customizable period (default 14) with multiple smoothing options
1.5x ATR: Extended range for wider stop-loss and target calculations
Smoothing Options: Choose between RMA, SMA, EMA, or WMA for ATR calculation
Customizable Colors: Distinct colors for easy visual identification
IBD Relative Strength:
Professional RS Formula: Uses the same calculation method as Investor's Business Daily
Multi-Timeframe Analysis: Compares current price to 3, 6, 9, and 12-month performance
Weighted Calculation: 40% weight on 3-month, 20% each on 6, 9, and 12-month performance
Zero-Based Scale: Values above 0 indicate outperformance, below 0 indicate underperformance
Trading Applications:
Volatility-Based Stops: Use ATR and 1.5x ATR for dynamic stop-loss placement
Position Sizing: ATR helps determine appropriate position size based on volatility
Relative Strength Analysis: IBD RS identifies stocks with superior momentum
Market Timing: High RS values often precede strong price moves
Risk Management: Combine volatility (ATR) with momentum (RS) for comprehensive analysis
Technical Details:
ATR Calculation: True Range smoothed over selected period with chosen method
IBD RS Formula: (40% × 3M) + (20% × 6M) + (20% × 9M) + (20% × 12M) - 100
Display: Separate pane indicator with customizable colors for each component
How to Interpret:
High ATR: Increased volatility, wider stops needed
Low ATR: Reduced volatility, tighter stops possible
Positive IBD RS: Stock outperforming market over measured periods
Negative IBD RS: Stock underperforming market over measured periods
Customizable Parameters:
ATR calculation length
Smoothing method for ATR
Individual colors for ATR, 1.5x ATR, and IBD RS lines
Perfect for swing traders and position traders who want to combine volatility analysis with relative strength momentum in their decision-making process. Particularly useful for stock selection and risk management.
Market Outlook Score (MOS)Overview
The "Market Outlook Score (MOS)" is a custom technical indicator designed for TradingView, written in Pine Script version 6. It provides a quantitative assessment of market conditions by aggregating multiple factors, including trend strength across different timeframes, directional movement (via ADX), momentum (via RSI changes), volume dynamics, and volatility stability (via ATR). The MOS is calculated as a weighted score that ranges typically between -1 and +1 (though it can exceed these bounds in extreme conditions), where positive values suggest bullish (long) opportunities, negative values indicate bearish (short) setups, and values near zero imply neutral or indecisive markets.
This indicator is particularly useful for traders seeking a holistic "outlook" score to gauge potential entry points or market bias. It overlays on a separate pane (non-overlay mode) and visualizes the score through horizontal threshold lines and dynamic labels showing the numeric MOS value along with a simple trading decision ("Long", "Short", or "Neutral"). The script avoids using the plot function for compatibility reasons (e.g., potential TradingView bugs) and instead relies on hline for static lines and label.new for per-bar annotations.
Key features:
Multi-Timeframe Analysis: Incorporates slope data from 5-minute, 15-minute, and 30-minute charts to capture short-term trends.
Trend and Strength Integration: Uses ADX to weight trend bias, ensuring stronger signals in trending markets.
Momentum and Volume: Includes RSI momentum impulses and volume deviations for added confirmation.
Volatility Adjustment: Factors in ATR changes to assess market stability.
Customizable Inputs: Allows users to tweak periods for lookback, ADX, and ATR.
Decision Labels: Automatically classifies the MOS into actionable categories with visual labels.
This indicator is best suited for intraday or swing trading on volatile assets like stocks, forex, or cryptocurrencies. It does not generate buy/sell signals directly but can be combined with other tools (e.g., moving averages or oscillators) for comprehensive strategies.
Inputs
The script provides three user-configurable inputs via TradingView's input panel:
Lookback Period (lookback):
Type: Integer
Default: 20
Range: Minimum 10, Maximum 50
Purpose: Defines the number of bars used in slope calculations for trend analysis. A shorter lookback makes the indicator more sensitive to recent price action, while a longer one smooths out noise for longer-term trends.
ADX Period (adxPeriod):
Type: Integer
Default: 14
Range: Minimum 5, Maximum 30
Purpose: Sets the smoothing period for the Average Directional Index (ADX) and its components (DI+ and DI-). Standard value is 14, but shorter periods increase responsiveness, and longer ones reduce false signals.
ATR Period (atrPeriod):
Type: Integer
Default: 14
Range: Minimum 5, Maximum 30
Purpose: Determines the period for the Average True Range (ATR) calculation, which measures volatility. Adjust this to match your trading timeframe—shorter for scalping, longer for positional trading.
These inputs allow customization without editing the code, making the indicator adaptable to different market conditions or user preferences.
Core Calculations
The MOS is computed through a series of steps, blending trend, momentum, volume, and volatility metrics. Here's a breakdown:
Multi-Timeframe Slopes:
The script fetches data from higher timeframes (5m, 15m, 30m) using request.security.
Slope calculation: For each timeframe, it computes the linear regression slope of price over the lookback period using the formula:
textslope = correlation(close, bar_index, lookback) * stdev(close, lookback) / stdev(bar_index, lookback)
This measures the rate of price change, where positive slopes indicate uptrends and negative slopes indicate downtrends.
Variables: slope5m, slope15m, slope30m.
ATR (Average True Range):
Calculated using ta.atr(atrPeriod).
Represents average volatility over the specified period. Used later to derive volatility stability.
ADX (Average Directional Index):
A detailed, manual implementation (not using built-in ta.adx for customization):
Computes upward movement (upMove = high - high ) and downward movement (downMove = low - low).
Derives +DM (Plus Directional Movement) and -DM (Minus Directional Movement) by filtering non-relevant moves.
Smooths true range (trur = ta.rma(ta.tr(true), adxPeriod)).
Calculates +DI and -DI: plusDI = 100 * ta.rma(plusDM, adxPeriod) / trur, similarly for minusDI.
DX: dx = 100 * abs(plusDI - minusDI) / max(plusDI + minusDI, 0.0001).
ADX: adx = ta.rma(dx, adxPeriod).
ADX values above 25 typically indicate strong trends; here, it's normalized (divided by 50) to influence the trend bias.
Volume Delta (5m Timeframe):
Fetches 5m volume: volume_5m = request.security(syminfo.tickerid, "5", volume, lookahead=barmerge.lookahead_on).
Computes a 12-period SMA of volume: avgVolume = ta.sma(volume_5m, 12).
Delta: (volume_5m - avgVolume) / avgVolume (or 0 if avgVolume is zero).
This measures relative volume spikes, where positive deltas suggest increased interest (bullish) and negative suggest waning activity (bearish).
MOS Components and Final Calculation:
Trend Bias: Average of the three slopes, normalized by close price and scaled by 100, then weighted by ADX influence: (slope5m + slope15m + slope30m) / 3 / close * 100 * (adx / 50).
Emphasizes trends in strong ADX conditions.
Momentum Impulse: Change in 5m RSI(14) over 1 bar, divided by 50: ta.change(request.security(syminfo.tickerid, "5", ta.rsi(close, 14), lookahead=barmerge.lookahead_on), 1) / 50.
Captures short-term momentum shifts.
Volatility Clarity: 1 - ta.change(atr, 1) / max(atr, 0.0001).
Measures ATR stability; values near 1 indicate low volatility changes (clearer trends), while lower values suggest erratic markets.
MOS Formula: Weighted average:
textmos = (0.35 * trendBias + 0.25 * momentumImpulse + 0.2 * volumeDelta + 0.2 * volatilityClarity)
Weights prioritize trend (35%) and momentum (25%), with volume and volatility at 20% each. These can be adjusted in code for experimentation.
Trading Decision:
A variable mosDecision starts as "Neutral".
If mos > 0.15, set to "Long".
If mos < -0.15, set to "Short".
Thresholds (0.15 and -0.15) are hardcoded but can be modified.
Visualization and Outputs
Threshold Lines (using hline):
Long Threshold: Horizontal dashed green line at +0.15.
Short Threshold: Horizontal dashed red line at -0.15.
Neutral Line: Horizontal dashed gray line at 0.
These provide visual reference points for MOS interpretation.
Dynamic Labels (using label.new):
Placed at each bar's index and MOS value.
Text: Formatted MOS value (e.g., "0.2345") followed by a newline and the decision (e.g., "Long").
Style: Downward-pointing label with gray background and white text for readability.
This replaces a traditional plot line, showing exact values and decisions per bar without cluttering the chart.
The indicator appears in a separate pane below the main price chart, making it easy to monitor alongside price action.
Usage Instructions
Adding to TradingView:
Copy the script into TradingView's Pine Script editor.
Save and add to your chart via the "Indicators" menu.
Select a symbol and timeframe (e.g., 1-minute for intraday).
Interpretation:
Long Signal: MOS > 0.15 – Consider bullish positions if supported by other indicators.
Short Signal: MOS < -0.15 – Potential bearish setups.
Neutral: Between -0.15 and 0.15 – Avoid trades or wait for confirmation.
Watch for MOS crossings of thresholds for momentum shifts.
Combine with price patterns, support/resistance, or volume for better accuracy.
Limitations and Considerations:
Lookahead Bias: Uses barmerge.lookahead_on for multi-timeframe data, which may introduce minor forward-looking bias in backtesting (use with caution).
No Alerts Built-In: Add custom alerts via TradingView's alert system based on MOS conditions.
Performance: Tested for compatibility; may require adjustments for illiquid assets or extreme volatility.
Backtesting: Use TradingView's strategy tester to evaluate historical performance, but remember past results don't guarantee future outcomes.
Customization: Edit weights in the MOS formula or thresholds to fit your strategy.
This indicator distills complex market data into a single score, aiding decision-making while encouraging users to verify signals with additional analysis. If you need modifications, such as restoring plot functionality or adding features, provide details for further refinement.
VWAP For Loop [BackQuant]VWAP For Loop
What this tool does—in one sentence
A volume-weighted trend gauge that anchors VWAP to a calendar period (day/week/month/quarter/year) and then scores the persistence of that VWAP trend with a simple for-loop “breadth” count; the result is a clean, threshold-driven oscillator plus an optional VWAP overlay and alerts.
Plain-English overview
Instead of judging raw price alone, this indicator focuses on anchored VWAP —the market’s average price paid during your chosen institutional period. It then asks a simple question across a configurable set of lookback steps: “Is the current anchored VWAP higher than it was i bars ago—or lower?” Each “yes” adds +1, each “no” adds −1. Summing those answers creates a score that reflects how consistently the volume-weighted trend has been rising or falling. Extreme positive scores imply persistent, broad strength; deeply negative scores imply persistent weakness. Crossing predefined thresholds produces objective long/short events and color-coded context.
Under the hood
• Anchoring — VWAP using hlc3 × volume resets exactly when the selected period rolls:
Day → session change, Week → new week, Month → new month, Quarter/Year → calendar quarter/year.
• For-loop scoring — For lag steps i = , compare today’s VWAP to VWAP .
– If VWAP > VWAP , add +1.
– Else, add −1.
The final score ∈ , where N = (end − start + 1). With defaults (1→45), N = 45.
• Signal logic (stateful)
– Long when score > upper (e.g., > 40 with N = 45 → VWAP higher than ~89% of checked lags).
– Short on crossunder of lower (e.g., dropping below −10).
– A compact state variable ( out ) holds the current regime: +1 (long), −1 (short), otherwise unchanged. This “stickiness” avoids constant flipping between bars without sufficient evidence.
Why VWAP + a breadth score?
• VWAP aggregates both price and volume—where participants actually traded.
• The breadth-style count rewards consistency of the anchored trend, not one-off spikes.
• Thresholds give you binary structure when you need it (alerts, automation), without complex math.
What you’ll see on the chart
• Sub-pane oscillator — The for-loop score line, colored by regime (long/short/neutral).
• Main-pane VWAP (optional) — Even though the indicator runs off-chart, the anchored VWAP can be overlaid on price (toggle visibility and whether it inherits trend colors).
• Threshold guides — Horizontal lines for the long/short bands (toggle).
• Cosmetics — Optional candle painting and background shading by regime; adjustable line width and colors.
Input map (quick reference)
• VWAP Anchor Period — Day, Week, Month, Quarter, Year.
• Calculation Start/End — The for-loop lag window . With 1→45, you evaluate 45 comparisons.
• Long/Short Thresholds — Default upper=40, lower=−10 (asymmetric by design; see below).
• UI/Style — Show thresholds, paint candles, background color, line width, VWAP visibility and coloring, custom long/short colors.
Interpreting the score
• Near +N — Current anchored VWAP is above most historical VWAP checkpoints in the window → entrenched strength.
• Near −N — Current anchored VWAP is below most checkpoints → entrenched weakness.
• Between — Mixed, choppy, or transitioning regimes; use thresholds to avoid reacting to noise.
Why the asymmetric default thresholds?
• Long = score > upper (40) — Demands unusually broad upside persistence before declaring “long regime.”
• Short = crossunder lower (−10) — Triggers only on downward momentum events (a fresh breach), not merely being below −10. This combination tends to:
– Capture sustained uptrends only when they’re very strong.
– Flag downside turns as they occur, rather than waiting for an extreme negative breadth.
Tuning guide
Choose an anchor that matches your horizon
– Intraday scalps : Day anchor on intraday charts.
– Swing/position : Month or Quarter anchor on 1h/4h/D charts to capture institutional cycles.
Pick the for-loop window
– Larger N (bigger end) = stronger evidence requirement, smoother oscillator.
– Smaller N = faster, more reactive score.
Set achievable thresholds
– Ensure upper ≤ N and lower ≥ −N ; if N=30, an upper of 40 can never trigger.
– Symmetric setups (e.g., +20/−20) are fine if you want balanced behavior.
Match visuals to intent
– Enabling VWAP coloring lets you see regime directly on price.
– Background shading is useful for discretionary reading; turn it off for cleaner automation displays.
Playbook examples
• Trend confirmation with disciplined entries — On Month anchor, N=45, upper=38–42: when the long regime engages, use pullbacks toward anchored VWAP on the main pane for entries, with stops just beyond VWAP or a recent swing.
• Downside transition detection — Keep lower around −8…−12 and watch for crossunders; combine with price losing anchored VWAP to validate risk-off.
• Intraday bias filter — Day anchor on a 5–15m chart, N=20–30, upper ~ 16–20, lower ~ −6…−10. Only take longs while score is positive and above a midline you define (e.g., 0), and shorts only after a genuine crossunder.
Behavior around resets (important)
Anchored VWAP is hard-reset each period. Immediately after a reset, the series can be young and comparisons to pre-reset values may span two periods. If you prefer within-period evaluation only, choose end small enough not to bridge typical period length on your timeframe, or accept that the breadth test intentionally spans regimes.
Alerts included
• VWAP FL Long — Fires when the long condition is true (score > upper and not in short).
• VWAP FL Short — Fires on crossunder of the lower threshold (event-driven).
Messages include {{ticker}} and {{interval}} placeholders for routing.
Strengths
• Simple, transparent math — Easy to reason about and validate.
• Volume-aware by construction — Decisions reference VWAP, not just price.
• Robust to single-bar noise — Needs many lags to agree before flipping state (by design, via thresholds and the stateful output).
Limitations & cautions
• Threshold feasibility — If N < upper or |lower| > N, signals will never trigger; always cross-check N.
• Path dependence — The state variable persists until a new event; if you want frequent re-evaluation, lower thresholds or reduce N.
• Regime changes — Calendar resets can produce early ambiguity; expect a few bars for the breadth to mature.
• VWAP sensitivity to volume spikes — Large prints can tilt VWAP abruptly; that behavior is intentional in VWAP-based logic.
Suggested starting profiles
• Intraday trend bias : Anchor=Day, N=25 (1→25), upper=18–20, lower=−8, paint candles ON.
• Swing bias : Anchor=Month, N=45 (1→45), upper=38–42, lower=−10, VWAP coloring ON, background OFF.
• Balanced reactivity : Anchor=Week, N=30 (1→30), upper=20–22, lower=−10…−12, symmetric if desired.
Implementation notes
• The indicator runs in a separate pane (oscillator), but VWAP itself is drawn on price using forced overlay so you can see interactions (touches, reclaim/loss).
• HLC3 is used for VWAP price; that’s a common choice to dampen wick noise while still reflecting intrabar range.
• For-loop cap is kept modest (≤50) for performance and clarity.
How to use this responsibly
Treat the oscillator as a bias and persistence meter . Combine it with your entry framework (structure breaks, liquidity zones, higher-timeframe context) and risk controls. The design emphasizes clarity over complexity—its edge is in how strictly it demands agreement before declaring a regime, not in predicting specific turns.
Summary
VWAP For Loop distills the question “How broadly is the anchored, volume-weighted trend advancing or retreating?” into a single, thresholded score you can read at a glance, alert on, and color through your chart. With careful anchoring and thresholds sized to your window length, it becomes a pragmatic bias filter for both systematic and discretionary workflows.
