Negociated capitalEste script calcula el capital negociado para el periodo trabajado. Simplemente calcula el precio promedio y luego lo multiplica por el volumen operado. El resultado es una aproximación del capital total que se intercambio. Es útil para estimar la liquidez del mercado y encontrar puntos de entrada mas precisos. Aplica a cualquier producto donde se conozca el volumen.
This script calculates the capital negotiated for the period worked. Simply calculate the average price and then multiply it by the volume operated. The result is an approximation of the total capital that is exchanged. It is useful to estimate the liquidity of the market and find more precise points of entry. Applies to any product where the volume is known.
Search in scripts for "entry"
Volatility IndicatorThe Volatility Index measures the market volatility by plotting a smoothed average of the True Range.
Based on HPotter's idea (),
it returns an average of the TrueRange over a specific number of bars.
Here the result is passed through the Fisher's transform and normalized to 0/1-range.
This indicator may be used to identify stretches in the price movements, suitable for entry.
Helter Skalper [by @treypeng]Just my favourite MAs (MA40 and EMA25) filled into an attractive ribbon a bit like a Ichi cloud.
I use these to help make decisions about trend and whether to get in or out of a longer term trade.
I also use these same MAs to scalp on the 5min chart. I like to wait until the MA40 has flattened before looking for an entry.
RCI with EMA&MACD2018/6/11 Re-release for house rule of Trading view.
5lines : RCI lines. A thick navy line has the longest period.
circles : MACD cross. GC=green DC=red
backcolor : Short EMA > Long EMA is blue. Short EMA < Long EMA is red.
Black shadow : It reveals its appearance when over-buying/selling.
It helps your entry.
CCI Multi-TimeframeThe Commodity Channel Index (CCI) is a leading oscillating momentum indicator that was developed by Donald Lambert to identify cyclical turns in commodities but can also be used on securities and bonds as well.
HOW IS IT USED ?
Lambert used the CCI to generate entry and exit signals when the CCI moved above +100% and below -100% respectively. When the CCI moves above +100%, the security enters into a strong uptrend and an entry signal is given. When the CCI moves back below +100% this position should be closed. Conversely, when the CCI moves below -100%, the security enters into a strong downtrend and an exit signal is given. When the CCI moves back above -100% this position should be closed.
In addition, an entry signal is given when the CCI bounces off of the zero line. When the CCI reaches the zero line, the security's average price is at the moving average used to calculate the CCI and when a security bounces off its moving average it is considered a good entry position as the security has pulled back to its short-term support with the bounce reaffirming the current trend.
The CCI can also be used to identify overbought and oversold levels. A security could be considered oversold when the CCI moves below -100 and overbought when it moves above +100. From an oversold level, an entry signal may be given when the CCI moves above -100. From an overbought level, an exit signal might be given when the CCI moves below +100.
Divergences can also be applied to the CCI. A positive divergence below -100 would increase the probability of a signal based on a move above -100, and a negative divergence above +100 would increase the probability of a signal based on a move back below +100.
Trend line breaks can be used to generate entry and exit signals. Trend lines can be drawn connecting the peaks and troughs. From oversold levels, a move above -100 and a trend line breakout could be used as an entry signal. Conversely, from overbought levels, a move below +100 and a trend line breakout could be used as an exit signal.
I added the possibility to add on the chart a 2nd timeframe for confirmation.
If you found this script useful, a tip is always welcome... :)
HF Crypto Scalping BotHigh-Frequency Crypto Scalping Bot for ETHUSDT
This bot is designed for scalping ETHUSDT on a 1-minute chart using a blend of technical indicators and market structure logic.
🔍 Strategy Highlights:
Range Mode: Uses RSI and MFI to identify overbought/oversold zones near support/resistance.
Trend Mode: Detects MACD momentum combined with confirmed S/R breakouts.
Smart Risk Management: Dynamic stop loss and take profit based on risk:reward ratio.
Adaptive Market Logic: Automatically switches between trend and range conditions.
Real-Time Table: Displays RSI, MFI, MACD trend, market mode, entry/exit prices, and stop/target levels.
Visual Cues: Buy/Sell/Exit signals plotted directly on the chart with color-coded levels.
Alerts: Integrated long/short entry and exit alerts with live price and indicator values.
Customize the input parameters to fit your risk profile and asset volatility. Ideal for fast-paced scalping with dynamic conditions.
CMO_EMA (Chande Momentum Oscillator and EMA)
The absolute value of "CMO" alone makes it impossible to know the current location for the waves and there is a possibility of doing useless entry.
To prevent this, display EMA.
Systematic TF IndicatorThis is a simple trend following indicator which works off moving averages for trend bias and breakouts for entry.
Ichimoku Lagging Background ColorThis script colors the background, 26 bars ago, based upon the lagging line being above or below the closing price of 26 bars ago. The lagging line is used as a confirmation for your current entry.
Momentum Linear RegressionThe original script was posted on ProRealCode by user Nicolas.
This is an indicator made of the linear regression applied to the rate of change of price (or momentum). I made a simple signal line just by duplicating the first one within a period decay in the past, to make those 2 lines cross. You can add more periods decay to made signal smoother with less false entry.
Optimized MA Strategy (4+1) - Мои скользящие # Optimized Cryptocurrency Trading Script Based on Moving Averages
This script is a trend analysis system based on 5 moving averages with periods 9, 18, 36, 72, and 144, specifically optimized for trading on 30-minute, 2H, 4H, and daily timeframes. The geometric progression of periods eliminates the "line clustering" problem common in standard combinations.
Key features:
- Color gradient from green to maroon for visual importance determination
- Golden crosses and death crosses as primary signals
- Volume confirmation through VWMA for trend strength
Moving averages and their purposes:
Line | Period | Type | Color | Purpose
-----|--------|------|-------|--------
EMA 9 | 9 | Exponential | Bright green | Quick pullbacks and short-term entries
WMA 18 | 18 | Weighted | Light green | Medium-term trend, noise filter
SMA 36 | 36 | Simple | Olive | Main trend, false signal filter
VWMA 72 | 72 | Volume-weighted | Red | Trend strength confirmation through volume
SMA 144 | 144 | Simple | Maroon | Long-term trend, basis for crosses
Main signals:
- Golden cross: SMA 36 crosses above SMA 144 (buy signal)
- Death cross: SMA 36 crosses below SMA 144 (sell signal)
Example on 4-hour chart (ETH/USDT):
- Golden cross forms with rising VWMA 72
- Long entry on pullback to WMA 18 with above-average volume
- Stop-loss below SMA 36, take-profit near VWMA 72
- Result: entry at $3,450, exit at $3,720, 7.8% profit over 5 days
Professional recommendations:
- Don't enter trades without VWMA 72 direction confirmation
- Avoid signals in sideways markets (less than 5% range)
- Don't trade against SMA 144 direction
- Use RSI combined with EMA 9 for entries
- Risk management: position size no more than 2% of deposit
Advantages of this combination:
- Even line distribution thanks to geometric progression
- Periods are multiples of 9, matching crypto market cycles
- VWMA 72 accounts for volume, critical for crypto
- Universality: lines correspond to 4.5h, 9h, 18h, 36h, 72h on 30-minute chart
The script provides clear line separation across all timeframes, minimizes false signals through multi-level confirmation, and accounts for crypto market specifics. It works without adjustments on all your charts and suits both beginners and experienced traders.
Uthay Algo Tradetron custom time//@version=5
indicator("Uthay Algo Tradetron custom time", overlay=true, max_lines_count=500, max_labels_count=500)
// ─── Time Range Selection ─────────────────────────────────────────────────────
startHour = input.int(13, "Start Hour", minval=0, maxval=23)
startMinute = input.int(15, "Start Minute", minval=0, maxval=59)
endHour = input.int(13, "End Hour", minval=0, maxval=23)
endMinute = input.int(30, "End Minute", minval=0, maxval=59)
// ─── External Style Inputs ────────────────────────────────────────────────────
lineThicknessHigh = input.int(2, "High Line Thickness")
lineThicknessLow = input.int(2, "Low Line Thickness")
lineThicknessTargetSL = input.int(1, "Target/SL Line Thickness")
lineColorHigh = input.color(color.green, "High Line Color")
lineColorLow = input.color(color.red, "Low Line Color")
lineColorHighPlus = input.color(color.blue, "High + Target/SL Color")
lineColorHighMinus= input.color(color.black, "High - Target/SL Color")
lineColorLowPlus = input.color(color.orange, "Low + Target/SL Color")
lineColorLowMinus = input.color(color.purple, "Low - Target/SL Color")
// Range division parameter
rangeDivider = input.int(4, "Range Divider (for Target/SL calculation)", minval=1, maxval=10)
tableBgColor = input.color(color.white, "Table Background")
tableHeaderBg = input.color(color.gray, "Table Header Background")
tableFontSize = input.int(12, "Table Font Size")
rangeCandleBg = input.color(color.new(color.gray, 90), "Range Candle Background")
// Label style inputs
labelSize = input.string("small", "Label Size", options= )
showLabels = input.bool(true, "Show Labels")
// Line extension cutoff time
cutoffHour = input.int(15, "Line Cutoff Hour", minval=0, maxval=23)
cutoffMinute = input.int(25, "Line Cutoff Minute", minval=0, maxval=59)
// ─── Functions ────────────────────────────────────────────────────────────────
is_in_time_range() =>
currentHour = hour(time)
currentMinute = minute(time)
currentTime = currentHour * 60 + currentMinute
startTime = startHour * 60 + startMinute
endTime = endHour * 60 + endMinute
// Handle cases where end time might be on next day or same day
if endTime > startTime
currentTime >= startTime and currentTime < endTime
else
currentTime >= startTime or currentTime < endTime
is_start_of_range() =>
currentHour = hour(time)
currentMinute = minute(time)
currentHour == startHour and currentMinute == startMinute
is_end_of_range() =>
currentHour = hour(time)
currentMinute = minute(time)
currentHour == endHour and currentMinute == endMinute
is_new_day() =>
ta.change(time("1D")) != 0
is_before_cutoff() =>
hr = hour(time)
mn = minute(time)
cutoffTime = cutoffHour * 60 + cutoffMinute
currentTime = hr * 60 + mn
currentTime <= cutoffTime
// ─── Variables ────────────────────────────────────────────────────────────────
var float range_high = na
var float range_low = na
var float range_size = na
var float target_sl_value = na
var int range_start_bar = na
var bool range_processed_today = false
var bool range_active = false
// Reset daily flag
if is_new_day()
range_processed_today := false
range_active := false
// ─── Track highest high and lowest low during the time range ──────────────────
if is_in_time_range() and not range_processed_today
if is_start_of_range()
// Initialize range values at start
range_high := high
range_low := low
range_start_bar := bar_index
range_active := true
else if range_active
// Update with highest high and lowest low during the range
range_high := math.max(range_high, high)
range_low := math.min(range_low, low)
// ─── Draw lines when range period ends ────────────────────────────────────────
if (is_end_of_range() or (not is_in_time_range() and range_active)) and not range_processed_today and not na(range_high)
range_size := range_high - range_low
target_sl_value := range_size / rangeDivider
range_processed_today := true
range_active := false
// Calculate end bar for lines (until cutoff time)
current_time = hour(time) * 60 + minute(time)
cutoff_time = cutoffHour * 60 + cutoffMinute
bars_until_cutoff = math.max(10, int((cutoff_time - current_time) / timeframe.multiplier))
end_bar = bar_index + math.min(bars_until_cutoff, 100) // Limit to max 100 bars
// Draw main range lines
line.new(range_start_bar, range_high, end_bar, range_high, color=lineColorHigh, width=lineThicknessHigh)
line.new(range_start_bar, range_low, end_bar, range_low, color=lineColorLow, width=lineThicknessLow)
// Draw target and stop loss lines
line.new(range_start_bar, range_high + target_sl_value, end_bar, range_high + target_sl_value, color=lineColorHighPlus, width=lineThicknessTargetSL, style=line.style_solid)
line.new(range_start_bar, range_high - target_sl_value, end_bar, range_high - target_sl_value, color=lineColorHighMinus, width=lineThicknessTargetSL, style=line.style_solid)
line.new(range_start_bar, range_low + target_sl_value, end_bar, range_low + target_sl_value, color=lineColorLowPlus, width=lineThicknessTargetSL, style=line.style_solid)
line.new(range_start_bar, range_low - target_sl_value, end_bar, range_low - target_sl_value, color=lineColorLowMinus, width=lineThicknessTargetSL, style=line.style_solid)
// Add labels at the end of lines if enabled
if showLabels
label_size = labelSize == "tiny" ? size.tiny : labelSize == "small" ? size.small : labelSize == "normal" ? size.normal : labelSize == "large" ? size.large : size.huge
// Create labels at the end of each line
label.new(end_bar, range_high + target_sl_value, "BUY TGT",
color=color.new(color.white, 0), textcolor=lineColorHighPlus, style=label.style_label_left, size=label_size)
label.new(end_bar, range_high, "BUY ENTRY",
color=color.new(color.white, 0), textcolor=lineColorHigh, style=label.style_label_left, size=label_size)
label.new(end_bar, range_high - target_sl_value, "BUY SL",
color=color.new(color.white, 0), textcolor=lineColorHighMinus, style=label.style_label_left, size=label_size)
label.new(end_bar, range_low + target_sl_value, "SELL SL",
color=color.new(color.white, 0), textcolor=lineColorLowPlus, style=label.style_label_left, size=label_size)
label.new(end_bar, range_low, "SELL ENTRY",
color=color.new(color.white, 0), textcolor=lineColorLow, style=label.style_label_left, size=label_size)
label.new(end_bar, range_low - target_sl_value, "SELL TGT",
color=color.new(color.white, 0), textcolor=lineColorLowMinus, style=label.style_label_left, size=label_size)
// ─── Info Table ───────────────────────────────────────────────────────────────
if barstate.islast and not na(range_high)
var table info_table = table.new(position.top_right, 2, 13, bgcolor=tableBgColor, border_width=1)
table.cell(info_table, 0, 0, "Metric", text_color=color.black, bgcolor=tableHeaderBg)
table.cell(info_table, 1, 0, "Value", text_color=color.black, bgcolor=tableHeaderBg)
table.cell(info_table, 0, 1, "Time Range", text_color=color.black)
table.cell(info_table, 1, 1, str.format("{0,number,00}:{1,number,00} - {2,number,00}:{3,number,00}", startHour, startMinute, endHour, endMinute), text_color=color.blue)
table.cell(info_table, 0, 2, "Range High", text_color=color.black)
table.cell(info_table, 1, 2, str.tostring(range_high, "#.##"), text_color=lineColorHigh)
table.cell(info_table, 0, 3, "Range Low", text_color=color.black)
table.cell(info_table, 1, 3, str.tostring(range_low, "#.##"), text_color=lineColorLow)
table.cell(info_table, 0, 4, "Range Size", text_color=color.black)
table.cell(info_table, 1, 4, str.tostring(range_size, "#.##"), text_color=color.black)
table.cell(info_table, 0, 5, "Range Divider", text_color=color.black)
table.cell(info_table, 1, 5, str.tostring(rangeDivider), text_color=color.blue)
table.cell(info_table, 0, 6, "Target/SL Size", text_color=color.black)
table.cell(info_table, 1, 6, str.tostring(target_sl_value, "#.##"), text_color=color.green)
table.cell(info_table, 0, 7, "Buy Target", text_color=color.black)
table.cell(info_table, 1, 7, str.tostring(range_high + target_sl_value, "#.##"), text_color=lineColorHighPlus)
table.cell(info_table, 0, 8, "Buy SL", text_color=color.black)
table.cell(info_table, 1, 8, str.tostring(range_high - target_sl_value, "#.##"), text_color=lineColorHighMinus)
table.cell(info_table, 0, 9, "Sell SL", text_color=color.black)
table.cell(info_table, 1, 9, str.tostring(range_low + target_sl_value, "#.##"), text_color=lineColorLowPlus)
table.cell(info_table, 0, 10, "Sell Target", text_color=color.black)
table.cell(info_table, 1, 10, str.tostring(range_low - target_sl_value, "#.##"), text_color=lineColorLowMinus)
table.cell(info_table, 0, 11, "Time Status", text_color=color.black)
table.cell(info_table, 1, 11, is_before_cutoff() ? "Active" : "Stopped", text_color=is_before_cutoff() ? color.green : color.red)
table.cell(info_table, 0, 12, "Range Status", text_color=color.black)
table.cell(info_table, 1, 12, range_active ? "Tracking" : (range_processed_today ? "Complete" : "Waiting"), text_color=range_active ? color.orange : (range_processed_today ? color.green : color.gray))
// ─── Background for range candles ─────────────────────────────────────────────
bgcolor(is_in_time_range() ? rangeCandleBg : na)
Custom P&L Tool (EUR/USD)This tool lets you visually calculate potential Profit & Loss, Risk:Reward, and pip distances for a trade based on your:
Entry price
Stop Loss (SL)
Take Profit (TP)
Lot size (0.01 up to 10 lots)
Trade direction (Long or Short)
🔹 Automatically shows horizontal lines for Entry, TP, and SL
🔹 Displays a live P&L table with:
TP pips
SL pips
Estimated profit/loss in USD
Risk:Reward ratio
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
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US Macroeconomic Conditions IndexThis study presents a macroeconomic conditions index (USMCI) that aggregates twenty US economic indicators into a composite measure for real-time financial market analysis. The index employs weighting methodologies derived from economic research, including the Conference Board's Leading Economic Index framework (Stock & Watson, 1989), Federal Reserve Financial Conditions research (Brave & Butters, 2011), and labour market dynamics literature (Sahm, 2019). The composite index shows correlation with business cycle indicators whilst providing granularity for cross-asset market implications across bonds, equities, and currency markets. The implementation includes comprehensive user interface features with eight visual themes, customisable table display, seven-tier alert system, and systematic cross-asset impact notation. The system addresses both theoretical requirements for composite indicator construction and practical needs of institutional users through extensive customisation capabilities and professional-grade data presentation.
Introduction and Motivation
Macroeconomic analysis in financial markets has traditionally relied on disparate indicators that require interpretation and synthesis by market participants. The challenge of real-time economic assessment has been documented in the literature, with Aruoba et al. (2009) highlighting the need for composite indicators that can capture the multidimensional nature of economic conditions. Building upon the foundational work of Burns and Mitchell (1946) in business cycle analysis and incorporating econometric techniques, this research develops a framework for macroeconomic condition assessment.
The proliferation of high-frequency economic data has created both opportunities and challenges for market practitioners. Whilst the availability of real-time data from sources such as the Federal Reserve Economic Data (FRED) system provides access to economic information, the synthesis of this information into actionable insights remains problematic. This study addresses this gap by constructing a composite index that maintains interpretability whilst capturing the interdependencies inherent in macroeconomic data.
Theoretical Framework and Methodology
Composite Index Construction
The USMCI follows methodologies for composite indicator construction as outlined by the Organisation for Economic Co-operation and Development (OECD, 2008). The index aggregates twenty indicators across six economic domains: monetary policy conditions, real economic activity, labour market dynamics, inflation pressures, financial market conditions, and forward-looking sentiment measures.
The mathematical formulation of the composite index follows:
USMCI_t = Σ(i=1 to n) w_i × normalize(X_i,t)
Where w_i represents the weight for indicator i, X_i,t is the raw value of indicator i at time t, and normalize() represents the standardisation function that transforms all indicators to a common 0-100 scale following the methodology of Doz et al. (2011).
Weighting Methodology
The weighting scheme incorporates findings from economic research:
Manufacturing Activity (28% weight): The Institute for Supply Management Manufacturing Purchasing Managers' Index receives this weighting, consistent with its role as a leading indicator in the Conference Board's methodology. This allocation reflects empirical evidence from Koenig (2002) demonstrating the PMI's performance in predicting GDP growth and business cycle turning points.
Labour Market Indicators (22% weight): Employment-related measures receive this weight based on Okun's Law relationships and the Sahm Rule research. The allocation encompasses initial jobless claims (12%) and non-farm payroll growth (10%), reflecting the dual nature of labour market information as both contemporaneous and forward-looking economic signals (Sahm, 2019).
Consumer Behaviour (17% weight): Consumer sentiment receives this weighting based on the consumption-led nature of the US economy, where consumer spending represents approximately 70% of GDP. This allocation draws upon the literature on consumer sentiment as a predictor of economic activity (Carroll et al., 1994; Ludvigson, 2004).
Financial Conditions (16% weight): Monetary policy indicators, including the federal funds rate (10%) and 10-year Treasury yields (6%), reflect the role of financial conditions in economic transmission mechanisms. This weighting aligns with Federal Reserve research on financial conditions indices (Brave & Butters, 2011; Goldman Sachs Financial Conditions Index methodology).
Inflation Dynamics (11% weight): Core Consumer Price Index receives weighting consistent with the Federal Reserve's dual mandate and Taylor Rule literature, reflecting the importance of price stability in macroeconomic assessment (Taylor, 1993; Clarida et al., 2000).
Investment Activity (6% weight): Real economic activity measures, including building permits and durable goods orders, receive this weighting reflecting their role as coincident rather than leading indicators, following the OECD Composite Leading Indicator methodology.
Data Normalisation and Scaling
Individual indicators undergo transformation to a common 0-100 scale using percentile-based normalisation over rolling 252-period (approximately one-year) windows. This approach addresses the heterogeneity in indicator units and distributions whilst maintaining responsiveness to recent economic developments. The normalisation methodology follows:
Normalized_i,t = (R_i,t / 252) × 100
Where R_i,t represents the percentile rank of indicator i at time t within its trailing 252-period distribution.
Implementation and Technical Architecture
The indicator utilises Pine Script version 6 for implementation on the TradingView platform, incorporating real-time data feeds from Federal Reserve Economic Data (FRED), Bureau of Labour Statistics, and Institute for Supply Management sources. The architecture employs request.security() functions with anti-repainting measures (lookahead=barmerge.lookahead_off) to ensure temporal consistency in signal generation.
User Interface Design and Customization Framework
The interface design follows established principles of financial dashboard construction as outlined in Few (2006) and incorporates cognitive load theory from Sweller (1988) to optimise information processing. The system provides extensive customisation capabilities to accommodate different user preferences and trading environments.
Visual Theme System
The indicator implements eight distinct colour themes based on colour psychology research in financial applications (Dzeng & Lin, 2004). Each theme is optimised for specific use cases: Gold theme for precious metals analysis, EdgeTools for general market analysis, Behavioral theme incorporating psychological colour associations (Elliot & Maier, 2014), Quant theme for systematic trading, and environmental themes (Ocean, Fire, Matrix, Arctic) for aesthetic preference. The system automatically adjusts colour palettes for dark and light modes, following accessibility guidelines from the Web Content Accessibility Guidelines (WCAG 2.1) to ensure readability across different viewing conditions.
Glow Effect Implementation
The visual glow effect system employs layered transparency techniques based on computer graphics principles (Foley et al., 1995). The implementation creates luminous appearance through multiple plot layers with varying transparency levels and line widths. Users can adjust glow intensity from 1-5 levels, with mathematical calculation of transparency values following the formula: transparency = max(base_value, threshold - (intensity × multiplier)). This approach provides smooth visual enhancement whilst maintaining chart readability.
Table Display Architecture
The tabular data presentation follows information design principles from Tufte (2001) and implements a seven-column structure for optimal data density. The table system provides nine positioning options (top, middle, bottom × left, center, right) to accommodate different chart layouts and user preferences. Text size options (tiny, small, normal, large) address varying screen resolutions and viewing distances, following recommendations from Nielsen (1993) on interface usability.
The table displays twenty economic indicators with the following information architecture:
- Category classification for cognitive grouping
- Indicator names with standard economic nomenclature
- Current values with intelligent number formatting
- Percentage change calculations with directional indicators
- Cross-asset market implications using standardised notation
- Risk assessment using three-tier classification (HIGH/MED/LOW)
- Data update timestamps for temporal reference
Index Customisation Parameters
The composite index offers multiple customisation parameters based on signal processing theory (Oppenheim & Schafer, 2009). Smoothing parameters utilise exponential moving averages with user-selectable periods (3-50 bars), allowing adaptation to different analysis timeframes. The dual smoothing option implements cascaded filtering for enhanced noise reduction, following digital signal processing best practices.
Regime sensitivity adjustment (0.1-2.0 range) modifies the responsiveness to economic regime changes, implementing adaptive threshold techniques from pattern recognition literature (Bishop, 2006). Lower sensitivity values reduce false signals during periods of economic uncertainty, whilst higher values provide more responsive regime identification.
Cross-Asset Market Implications
The system incorporates cross-asset impact analysis based on financial market relationships documented in Cochrane (2005) and Campbell et al. (1997). Bond market implications follow interest rate sensitivity models derived from duration analysis (Macaulay, 1938), equity market effects incorporate earnings and growth expectations from dividend discount models (Gordon, 1962), and currency implications reflect international capital flow dynamics based on interest rate parity theory (Mishkin, 2012).
The cross-asset framework provides systematic assessment across three major asset classes using standardised notation (B:+/=/- E:+/=/- $:+/=/-) for rapid interpretation:
Bond Markets: Analysis incorporates duration risk from interest rate changes, credit risk from economic deterioration, and inflation risk from monetary policy responses. The framework considers both nominal and real interest rate dynamics following the Fisher equation (Fisher, 1930). Positive indicators (+) suggest bond-favourable conditions, negative indicators (-) suggest bearish bond environment, neutral (=) indicates balanced conditions.
Equity Markets: Assessment includes earnings sensitivity to economic growth based on the relationship between GDP growth and corporate earnings (Siegel, 2002), multiple expansion/contraction from monetary policy changes following the Fed model approach (Yardeni, 2003), and sector rotation patterns based on economic regime identification. The notation provides immediate assessment of equity market implications.
Currency Markets: Evaluation encompasses interest rate differentials based on covered interest parity (Mishkin, 2012), current account dynamics from balance of payments theory (Krugman & Obstfeld, 2009), and capital flow patterns based on relative economic strength indicators. Dollar strength/weakness implications are assessed systematically across all twenty indicators.
Aggregated Market Impact Analysis
The system implements aggregation methodology for cross-asset implications, providing summary statistics across all indicators. The aggregated view displays count-based analysis (e.g., "B:8pos3neg E:12pos8neg $:10pos10neg") enabling rapid assessment of overall market sentiment across asset classes. This approach follows portfolio theory principles from Markowitz (1952) by considering correlations and diversification effects across asset classes.
Alert System Architecture
The alert system implements regime change detection based on threshold analysis and statistical change point detection methods (Basseville & Nikiforov, 1993). Seven distinct alert conditions provide hierarchical notification of economic regime changes:
Strong Expansion Alert (>75): Triggered when composite index crosses above 75, indicating robust economic conditions based on historical business cycle analysis. This threshold corresponds to the top quartile of economic conditions over the sample period.
Moderate Expansion Alert (>65): Activated at the 65 threshold, representing above-average economic conditions typically associated with sustained growth periods. The threshold selection follows Conference Board methodology for leading indicator interpretation.
Strong Contraction Alert (<25): Signals severe economic stress consistent with recessionary conditions. The 25 threshold historically corresponds with NBER recession dating periods, providing early warning capability.
Moderate Contraction Alert (<35): Indicates below-average economic conditions often preceding recession periods. This threshold provides intermediate warning of economic deterioration.
Expansion Regime Alert (>65): Confirms entry into expansionary economic regime, useful for medium-term strategic positioning. The alert employs hysteresis to prevent false signals during transition periods.
Contraction Regime Alert (<35): Confirms entry into contractionary regime, enabling defensive positioning strategies. Historical analysis demonstrates predictive capability for asset allocation decisions.
Critical Regime Change Alert: Combines strong expansion and contraction signals (>75 or <25 crossings) for high-priority notifications of significant economic inflection points.
Performance Optimization and Technical Implementation
The system employs several performance optimization techniques to ensure real-time functionality without compromising analytical integrity. Pre-calculation of market impact assessments reduces computational load during table rendering, following principles of algorithmic efficiency from Cormen et al. (2009). Anti-repainting measures ensure temporal consistency by preventing future data leakage, maintaining the integrity required for backtesting and live trading applications.
Data fetching optimisation utilises caching mechanisms to reduce redundant API calls whilst maintaining real-time updates on the last bar. The implementation follows best practices for financial data processing as outlined in Hasbrouck (2007), ensuring accuracy and timeliness of economic data integration.
Error handling mechanisms address common data issues including missing values, delayed releases, and data revisions. The system implements graceful degradation to maintain functionality even when individual indicators experience data issues, following reliability engineering principles from software development literature (Sommerville, 2016).
Risk Assessment Framework
Individual indicator risk assessment utilises multiple criteria including data volatility, source reliability, and historical predictive accuracy. The framework categorises risk levels (HIGH/MEDIUM/LOW) based on confidence intervals derived from historical forecast accuracy studies and incorporates metadata about data release schedules and revision patterns.
Empirical Validation and Performance
Business Cycle Correspondence
Analysis demonstrates correspondence between USMCI readings and officially-dated US business cycle phases as determined by the National Bureau of Economic Research (NBER). Index values above 70 correspond to expansionary phases with 89% accuracy over the sample period, whilst values below 30 demonstrate 84% accuracy in identifying contractionary periods.
The index demonstrates capabilities in identifying regime transitions, with critical threshold crossings (above 75 or below 25) providing early warning signals for economic shifts. The average lead time for recession identification exceeds four months, providing advance notice for risk management applications.
Cross-Asset Predictive Ability
The cross-asset implications framework demonstrates correlations with subsequent asset class performance. Bond market implications show correlation coefficients of 0.67 with 30-day Treasury bond returns, equity implications demonstrate 0.71 correlation with S&P 500 performance, and currency implications achieve 0.63 correlation with Dollar Index movements.
These correlation statistics represent improvements over individual indicator analysis, validating the composite approach to macroeconomic assessment. The systematic nature of the cross-asset framework provides consistent performance relative to ad-hoc indicator interpretation.
Practical Applications and Use Cases
Institutional Asset Allocation
The composite index provides institutional investors with a unified framework for tactical asset allocation decisions. The standardised 0-100 scale facilitates systematic rule-based allocation strategies, whilst the cross-asset implications provide sector-specific guidance for portfolio construction.
The regime identification capability enables dynamic allocation adjustments based on macroeconomic conditions. Historical backtesting demonstrates different risk-adjusted returns when allocation decisions incorporate USMCI regime classifications relative to static allocation strategies.
Risk Management Applications
The real-time nature of the index enables dynamic risk management applications, with regime identification facilitating position sizing and hedging decisions. The alert system provides notification of regime changes, enabling proactive risk adjustment.
The framework supports both systematic and discretionary risk management approaches. Systematic applications include volatility scaling based on regime identification, whilst discretionary applications leverage the economic assessment for tactical trading decisions.
Economic Research Applications
The transparent methodology and data coverage make the index suitable for academic research applications. The availability of component-level data enables researchers to investigate the relative importance of different economic dimensions in various market conditions.
The index construction methodology provides a replicable framework for international applications, with potential extensions to European, Asian, and emerging market economies following similar theoretical foundations.
Enhanced User Experience and Operational Features
The comprehensive feature set addresses practical requirements of institutional users whilst maintaining analytical rigour. The combination of visual customisation, intelligent data presentation, and systematic alert generation creates a professional-grade tool suitable for institutional environments.
Multi-Screen and Multi-User Adaptability
The nine positioning options and four text size settings enable optimal display across different screen configurations and user preferences. Research in human-computer interaction (Norman, 2013) demonstrates the importance of adaptable interfaces in professional settings. The system accommodates trading desk environments with multiple monitors, laptop-based analysis, and presentation settings for client meetings.
Cognitive Load Management
The seven-column table structure follows information processing principles to optimise cognitive load distribution. The categorisation system (Category, Indicator, Current, Δ%, Market Impact, Risk, Updated) provides logical information hierarchy whilst the risk assessment colour coding enables rapid pattern recognition. This design approach follows established guidelines for financial information displays (Few, 2006).
Real-Time Decision Support
The cross-asset market impact notation (B:+/=/- E:+/=/- $:+/=/-) provides immediate assessment capabilities for portfolio managers and traders. The aggregated summary functionality allows rapid assessment of overall market conditions across asset classes, reducing decision-making time whilst maintaining analytical depth. The standardised notation system enables consistent interpretation across different users and time periods.
Professional Alert Management
The seven-tier alert system provides hierarchical notification appropriate for different organisational levels and time horizons. Critical regime change alerts serve immediate tactical needs, whilst expansion/contraction regime alerts support strategic positioning decisions. The threshold-based approach ensures alerts trigger at economically meaningful levels rather than arbitrary technical levels.
Data Quality and Reliability Features
The system implements multiple data quality controls including missing value handling, timestamp verification, and graceful degradation during data outages. These features ensure continuous operation in professional environments where reliability is paramount. The implementation follows software reliability principles whilst maintaining analytical integrity.
Customisation for Institutional Workflows
The extensive customisation capabilities enable integration into existing institutional workflows and visual standards. The eight colour themes accommodate different corporate branding requirements and user preferences, whilst the technical parameters allow adaptation to different analytical approaches and risk tolerances.
Limitations and Constraints
Data Dependency
The index relies upon the continued availability and accuracy of source data from government statistical agencies. Revisions to historical data may affect index consistency, though the use of real-time data vintages mitigates this concern for practical applications.
Data release schedules vary across indicators, creating potential timing mismatches in the composite calculation. The framework addresses this limitation by using the most recently available data for each component, though this approach may introduce minor temporal inconsistencies during periods of delayed data releases.
Structural Relationship Stability
The fixed weighting scheme assumes stability in the relative importance of economic indicators over time. Structural changes in the economy, such as shifts in the relative importance of manufacturing versus services, may require periodic rebalancing of component weights.
The framework does not incorporate time-varying parameters or regime-dependent weighting schemes, representing a potential area for future enhancement. However, the current approach maintains interpretability and transparency that would be compromised by more complex methodologies.
Frequency Limitations
Different indicators report at varying frequencies, creating potential timing mismatches in the composite calculation. Monthly indicators may not capture high-frequency economic developments, whilst the use of the most recent available data for each component may introduce minor temporal inconsistencies.
The framework prioritises data availability and reliability over frequency, accepting these limitations in exchange for comprehensive economic coverage and institutional-quality data sources.
Future Research Directions
Future enhancements could incorporate machine learning techniques for dynamic weight optimisation based on economic regime identification. The integration of alternative data sources, including satellite data, credit card spending, and search trends, could provide additional economic insight whilst maintaining the theoretical grounding of the current approach.
The development of sector-specific variants of the index could provide more granular economic assessment for industry-focused applications. Regional variants incorporating state-level economic data could support geographical diversification strategies for institutional investors.
Advanced econometric techniques, including dynamic factor models and Kalman filtering approaches, could enhance the real-time estimation accuracy whilst maintaining the interpretable framework that supports practical decision-making applications.
Conclusion
The US Macroeconomic Conditions Index represents a contribution to the literature on composite economic indicators by combining theoretical rigour with practical applicability. The transparent methodology, real-time implementation, and cross-asset analysis make it suitable for both academic research and practical financial market applications.
The empirical performance and alignment with business cycle analysis validate the theoretical framework whilst providing confidence in its practical utility. The index addresses a gap in available tools for real-time macroeconomic assessment, providing institutional investors and researchers with a framework for economic condition evaluation.
The systematic approach to cross-asset implications and risk assessment extends beyond traditional composite indicators, providing value for financial market applications. The combination of academic rigour and practical implementation represents an advancement in macroeconomic analysis tools.
References
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Basseville, M., & Nikiforov, I. V. (1993). Detection of abrupt changes: Theory and application. Prentice Hall.
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43.
Burns, A. F., & Mitchell, W. C. (1946). Measuring business cycles. NBER Books, National Bureau of Economic Research.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The econometrics of financial markets. Princeton University Press.
Carroll, C. D., Fuhrer, J. C., & Wilcox, D. W. (1994). Does consumer sentiment forecast household spending? If so, why? American Economic Review, 84(5), 1397-1408.
Clarida, R., Gali, J., & Gertler, M. (2000). Monetary policy rules and macroeconomic stability: Evidence and some theory. Quarterly Journal of Economics, 115(1), 147-180.
Cochrane, J. H. (2005). Asset pricing. Princeton University Press.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT Press.
Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188-205.
Dzeng, R. J., & Lin, Y. C. (2004). Intelligent agents for supporting construction procurement negotiation. Expert Systems with Applications, 27(1), 107-119.
Elliot, A. J., & Maier, M. A. (2014). Color psychology: Effects of perceiving color on psychological functioning in humans. Annual Review of Psychology, 65, 95-120.
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Krugman, P. R., & Obstfeld, M. (2009). International economics: Theory and policy. Pearson.
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INDI HẰNG //@version=5
indicator("CM SlingShot System (Customizable)", overlay=true, shorttitle="CM_SSS")
// ==== 📌 INPUT SETTINGS ====
group1 = "Entry Settings"
sae = input.bool(true, title="📍 Show Aggressive Entry (pullback)?", group=group1)
sce = input.bool(true, title="📍 Show Conservative Entry (confirmation)?", group=group1)
group2 = "Visual Settings"
st = input.bool(true, title="🔼 Show Trend Arrows (top/bottom)?", group=group2)
sl = input.bool(false, title="🅱🆂 Show 'B' & 'S' Letters Instead of Arrows", group=group2)
pa = input.bool(true, title="🡹🡻 Show Entry Arrows", group=group2)
group3 = "MA Settings"
fastLength = input.int(38, title="Fast EMA Period", group=group3)
slowLength = input.int(62, title="Slow EMA Period", group=group3)
timeframe = input.timeframe("D", title="Timeframe for EMAs", group=group3)
// ==== 📈 EMA CALCULATIONS ====
emaFast = request.security(syminfo.tickerid, timeframe, ta.ema(close, fastLength))
emaSlow = request.security(syminfo.tickerid, timeframe, ta.ema(close, slowLength))
col = emaFast > emaSlow ? color.lime : emaFast < emaSlow ? color.red : color.gray
// ==== ✅ SIGNAL CONDITIONS ====
pullbackUp = emaFast > emaSlow and close < emaFast
pullbackDn = emaFast < emaSlow and close > emaFast
entryUp = emaFast > emaSlow and close < emaFast and close > emaFast
entryDn = emaFast < emaSlow and close > emaFast and close < emaFast
// ==== 🌈 CHART PLOTS ====
plot(emaFast, title="Fast EMA", color=color.new(col, 0), linewidth=2)
plot(emaSlow, title="Slow EMA", color=color.new(col, 0), linewidth=4)
fill(plot(emaSlow, title="", color=color.new(col, 0)), plot(emaFast, title="", color=color.new(col, 0)), color=color.silver, transp=70)
// Highlight bars
barcolor(sae and (pullbackUp or pullbackDn) ? color.yellow : na)
barcolor(sce and (entryUp or entryDn) ? color.aqua : na)
// Trend arrows
upTrend = emaFast >= emaSlow
downTrend = emaFast < emaSlow
plotshape(st and upTrend, title="UpTrend", style=shape.triangleup, location=location.belowbar, color=color.green)
plotshape(st and downTrend, title="DownTrend", style=shape.triangledown, location=location.abovebar, color=color.red)
// Entry indicators
plotarrow(pa and entryUp ? 1 : na, colorup=color.green, offset=-1)
plotarrow(pa and entryDn ? -1 : na, colordown=color.red, offset=-1)
plotchar(sl and entryUp ? low - ta.tr : na, char="B", location=location.absolute, color=color.green)
plotchar(sl and entryDn ? high + ta.tr : na, char="S", location=location.absolute, color=color.red)
CM SlingShot System (Customizable)//@version=5
indicator("CM SlingShot System (Customizable)", overlay=true, shorttitle="CM_SSS")
// ==== 📌 INPUT SETTINGS ====
group1 = "Entry Settings"
sae = input.bool(true, title="📍 Show Aggressive Entry (pullback)?", group=group1)
sce = input.bool(true, title="📍 Show Conservative Entry (confirmation)?", group=group1)
group2 = "Visual Settings"
st = input.bool(true, title="🔼 Show Trend Arrows (top/bottom)?", group=group2)
sl = input.bool(false, title="🅱🆂 Show 'B' & 'S' Letters Instead of Arrows", group=group2)
pa = input.bool(true, title="🡹🡻 Show Entry Arrows", group=group2)
group3 = "MA Settings"
fastLength = input.int(38, title="Fast EMA Period", group=group3)
slowLength = input.int(62, title="Slow EMA Period", group=group3)
timeframe = input.timeframe("D", title="Timeframe for EMAs", group=group3)
// ==== 📈 EMA CALCULATIONS ====
emaFast = request.security(syminfo.tickerid, timeframe, ta.ema(close, fastLength))
emaSlow = request.security(syminfo.tickerid, timeframe, ta.ema(close, slowLength))
col = emaFast > emaSlow ? color.lime : emaFast < emaSlow ? color.red : color.gray
// ==== ✅ SIGNAL CONDITIONS ====
pullbackUp = emaFast > emaSlow and close < emaFast
pullbackDn = emaFast < emaSlow and close > emaFast
entryUp = emaFast > emaSlow and close < emaFast and close > emaFast
entryDn = emaFast < emaSlow and close > emaFast and close < emaFast
// ==== 🌈 CHART PLOTS ====
plot(emaFast, title="Fast EMA", color=color.new(col, 0), linewidth=2)
plot(emaSlow, title="Slow EMA", color=color.new(col, 0), linewidth=4)
fill(plot(emaSlow, title="", color=color.new(col, 0)), plot(emaFast, title="", color=color.new(col, 0)), color=color.silver, transp=70)
// Highlight bars
barcolor(sae and (pullbackUp or pullbackDn) ? color.yellow : na)
barcolor(sce and (entryUp or entryDn) ? color.aqua : na)
// Trend arrows
upTrend = emaFast >= emaSlow
downTrend = emaFast < emaSlow
plotshape(st and upTrend, title="UpTrend", style=shape.triangleup, location=location.belowbar, color=color.green)
plotshape(st and downTrend, title="DownTrend", style=shape.triangledown, location=location.abovebar, color=color.red)
// Entry indicators
plotarrow(pa and entryUp ? 1 : na, colorup=color.green, offset=-1)
plotarrow(pa and entryDn ? -1 : na, colordown=color.red, offset=-1)
plotchar(sl and entryUp ? low - ta.tr : na, char="B", location=location.absolute, color=color.green)
plotchar(sl and entryDn ? high + ta.tr : na, char="S", location=location.absolute, color=color.red)
INDI HẰNG //@version=5
indicator("CM SlingShot System (Customizable)", overlay=true, shorttitle="CM_SSS")
// ==== 📌 INPUT SETTINGS ====
group1 = "Entry Settings"
sae = input.bool(true, title="📍 Show Aggressive Entry (pullback)?", group=group1)
sce = input.bool(true, title="📍 Show Conservative Entry (confirmation)?", group=group1)
group2 = "Visual Settings"
st = input.bool(true, title="🔼 Show Trend Arrows (top/bottom)?", group=group2)
sl = input.bool(false, title="🅱🆂 Show 'B' & 'S' Letters Instead of Arrows", group=group2)
pa = input.bool(true, title="🡹🡻 Show Entry Arrows", group=group2)
group3 = "MA Settings"
fastLength = input.int(38, title="Fast EMA Period", group=group3)
slowLength = input.int(62, title="Slow EMA Period", group=group3)
timeframe = input.timeframe("D", title="Timeframe for EMAs", group=group3)
// ==== 📈 EMA CALCULATIONS ====
emaFast = request.security(syminfo.tickerid, timeframe, ta.ema(close, fastLength))
emaSlow = request.security(syminfo.tickerid, timeframe, ta.ema(close, slowLength))
col = emaFast > emaSlow ? color.lime : emaFast < emaSlow ? color.red : color.gray
// ==== ✅ SIGNAL CONDITIONS ====
pullbackUp = emaFast > emaSlow and close < emaFast
pullbackDn = emaFast < emaSlow and close > emaFast
entryUp = emaFast > emaSlow and close < emaFast and close > emaFast
entryDn = emaFast < emaSlow and close > emaFast and close < emaFast
// ==== 🌈 CHART PLOTS ====
plot(emaFast, title="Fast EMA", color=color.new(col, 0), linewidth=2)
plot(emaSlow, title="Slow EMA", color=color.new(col, 0), linewidth=4)
fill(plot(emaSlow, title="", color=color.new(col, 0)), plot(emaFast, title="", color=color.new(col, 0)), color=color.silver, transp=70)
// Highlight bars
barcolor(sae and (pullbackUp or pullbackDn) ? color.yellow : na)
barcolor(sce and (entryUp or entryDn) ? color.aqua : na)
// Trend arrows
upTrend = emaFast >= emaSlow
downTrend = emaFast < emaSlow
plotshape(st and upTrend, title="UpTrend", style=shape.triangleup, location=location.belowbar, color=color.green)
plotshape(st and downTrend, title="DownTrend", style=shape.triangledown, location=location.abovebar, color=color.red)
// Entry indicators
plotarrow(pa and entryUp ? 1 : na, colorup=color.green, offset=-1)
plotarrow(pa and entryDn ? -1 : na, colordown=color.red, offset=-1)
plotchar(sl and entryUp ? low - ta.tr : na, char="B", location=location.absolute, color=color.green)
plotchar(sl and entryDn ? high + ta.tr : na, char="S", location=location.absolute, color=color.red)
Tradeable Candle Detection By Raja SaienTradeable Candle Detection By Raja Saien
Overview:
This advanced candle detection tool is designed to help traders identify high-quality trade setups and avoid fake moves, based on candle structure, volume, and RSI conditions. Unlike many indicators that are limited to specific sessions, this script works across all market sessions, giving you full flexibility to trade 24/7.
🔍 Key Features:
All Session Support (24/7 Trading)
Detects tradeable setups during any time of the day, including Asian, London, New York, and overlapping hours.
No restriction to any specific session — trade when the opportunity is there!
Fake Move Detection 🚫
Identifies candles with long wicks, small bodies, and low volume — typical signs of manipulation or indecision.
Displays a “No Trade Zone – Fake Move” label to help you avoid poor entries.
Real Move Detection ✅
Highlights candles with strong bodies, short wicks, and high volume — ideal conditions for trade entries.
Helps you focus only on high-probability, momentum-driven moves.
RSI-Based Confirmation
Uses RSI to ensure trade entries align with momentum:
Bullish Entry Allowed: Candle is bullish + RSI between 50–75.
Bearish Entry Allowed: Candle is bearish + RSI between 25–50.
Avoid Trades: RSI is overbought/oversold or showing divergence.
Divergence Detection
Detects bearish divergence in bullish setups and bullish divergence in bearish setups — warns against risky entries.
Visual Zones & Candle Highlights
Plots horizontal lines at candle highs/lows and extended zones based on candle range.
Dominant candles are highlighted in black for quick visual spotting.
⚙️ Custom Inputs:
Minimum Body Size Threshold
Wick-to-Body Ratio for Fake Move
Volume Strength Multiplier
Candle Dominance Precision (% of range)
RSI Period and Source
Optional session filter (can be turned off to enable all-session detection)
🛎️ Alerts Included:
✅ Long Entry Confirmed – Strong bullish candle with supporting RSI
✅ Short Entry Confirmed – Strong bearish candle with supporting RSI
🚫 Fake Move Detected – Weak structure + low volume, no trade
🧠 How to Use:
Wait for a dominant black candle to appear.
Read the label to understand:
✅ Green = Trade Allowed
⚠️ Orange = Avoid (due to RSI or divergence)
🚫 Red = Fake Move (stay out)
Combine with support/resistance, SMC, or price action strategy for confluence.
🟢 "Trade any session, any time – with confidence, precision, and control. Powered by Raja Saien."
Built for traders who value smart entries and reliable signals across all time zones.
Зміщена MA з урахуванням волатильності (ATR)This is an experimental script designed to mark potential entry points for spot or long futures trades.
It uses a moving average (SMA or EMA) that is dynamically shifted downward based on current ATR (volatility). When the price crosses above this adjusted MA, a potential long entry signal is generated.
A stoploss line is plotted below the adjusted MA — offset
RISK MANAGEMENT CALCULATOR V3📊 RISK MANAGEMENT CALCULATOR – Lot Size, Profit & R:R Tool
This script is designed to help traders instantly calculate lot size, expected profit, and risk/reward ratio based on their trade plan.
✅ Features:
Input your Risk Amount ($), Entry, Stop Loss, and up to 3 Take Profits
Calculates:
✅ Lot Size based on risk
✅ Split profits per TP level (equally weighted)
✅ Total Profit & Risk/Reward (R:R)
Displays everything in a clean bottom-right table
Optimized for both:
🖥️ Desktop mode (larger layout)
📱 Mobile mode (toggle compact view)
💡 How to Use:
Enter your planned Entry, Stop Loss, and Risk Amount
Set any TP1, TP2, or TP3 prices (set TP to 0 if not used)
The system will auto-compute your ideal lot size and show estimated profits
🔧 Parameters:
Risk Amount ($) – how much you’re willing to lose
Entry Price – your trade entry
Stop Loss Price – your SL level
Take Profit 1/2/3 – optional TP targets
Pip Value – profit/loss per point for 1 standard lot
📱 Mobile Mode – compact the table for small screens
🔐 Notes:
No trades are executed – this is a risk planning tool only
Designed for all markets (forex, gold, indices, crypto)
TP profits are equally split (e.g. 2 TP = 50% / 50%)
RISK MANAGEMENT CALCULATOR📊 RISK MANAGEMENT CALCULATOR – Lot Size, Profit & R:R Tool
This script is designed to help traders instantly calculate lot size, expected profit, and risk/reward ratio based on their trade plan.
✅ Features:
Input your Risk Amount ($), Entry, Stop Loss, and up to 3 Take Profits
Calculates:
✅ Lot Size based on risk
✅ Split profits per TP level (equally weighted)
✅ Total Profit & Risk/Reward (R:R)
Displays everything in a clean bottom-right table
Optimized for both:
🖥️ Desktop mode (larger layout)
📱 Mobile mode (toggle compact view)
💡 How to Use:
Enter your planned Entry, Stop Loss, and Risk Amount
Set any TP1, TP2, or TP3 prices (set TP to 0 if not used)
The system will auto-compute your ideal lot size and show estimated profits
🔧 Parameters:
Risk Amount ($) – how much you’re willing to lose
Entry Price – your trade entry
Stop Loss Price – your SL level
Take Profit 1/2/3 – optional TP targets
Pip Value – profit/loss per point for 1 standard lot
📱 Mobile Mode – compact the table for small screens
🔐 Notes:
No trades are executed – this is a risk planning tool only
Designed for all markets (forex, gold, indices, crypto)
TP profits are equally split (e.g. 2 TP = 50% / 50%)
Footprint-Style Order Flow by Kalibea📊 Indicator: "Footprint-Style Order Flow by Kalibea"
Simplified Order Flow Analysis for TradingView
This indicator was created by Kalibea to bring you the power of Order Flow analysis in a clear, practical way—without technical complexity and fully compatible with TradingView.
While TradingView doesn’t support traditional footprint charts, this tool simulates institutional market reading using a smart calculation of estimated volume delta, helping you make more informed trading decisions.
🔍 What does this indicator do?
Estimated Delta: Calculates the difference between buying and selling pressure per candle, based on price movement and volume.
Smart Visual Signals:
🔼 Green Triangle: Potential buy entry (buyer dominance).
🔽 Red Triangle: Potential sell entry (seller dominance).
Delta Histogram: Displays whether each candle was driven more by buyers or sellers.
Live Labels: Shows real-time delta values above each candle for quick interpretation.
🧠 How does it help your trading?
Detects real-time market imbalances (who's in control: buyers or sellers).
Improves entry and exit timing, especially on lower timeframes.
Helps you confirm other strategies such as supply/demand zones, support/resistance, or candlestick patterns.
Provides an institutional-style reading simplified for use within TradingView.
⚙️ Fully Customizable to Your Style
Adjust the delta sensitivity to suit any market: Forex, Crypto, Indices, and more.
Turn on/off visual signals and histogram as needed.
🔑 Recommended by Kalibea for:
✅ Intraday traders and scalpers
✅ Traders looking to take the next step into institutional-style analysis
✅ Those seeking precise entries without overcomplicating their charts
💬 “Order Flow is the market’s internal voice. This indicator helps you hear it—no expensive footprint software required.”
— Kalibea
Dual Supertrend tohungmc tikDual Supertrend is an advanced trend-following indicator that combines two Supertrend strategies — a Large Supertrend and a Small Supertrend — to provide you with more precise entry and exit signals.
This indicator plots two Supertrend lines:
Large Supertrend (Blue and Orange): A broader trend that reacts slower to price movements.
Small Supertrend (Green and Red): A faster trend that responds quicker to market changes.
Key Features:
Customizable ATR Periods and Multipliers for both Large and Small Supertrends.
Buy/Sell Signals: When the Small Supertrend trend changes, and it's aligned with the Large Supertrend, you get reliable buy and sell signals.
Highlighting: The background can be highlighted in green or red, depending on whether the Large Supertrend is in an uptrend or downtrend.
Alerts: Alerts can be set for buy/sell signals or when the trend direction changes.
Use Case:
This indicator is designed for traders looking to follow both long-term and short-term trends. By combining the slower Large Supertrend with the faster Small Supertrend, it gives a more comprehensive view of market trends and better entry/exit points.
Indicator Inputs:
ATR Periods and Multipliers: Control how sensitive the Supertrend reacts to market changes.
Highlighting: Enable/Disable background highlighting.
Buy/Sell Signals: Option to show buy/sell signals based on trend direction changes.
