𝙵𝚛𝚊𝚖𝚎𝚠𝚘𝚛𝚔|[𝙰|𝛺]This indicator was designed and coded by me, providing a clean and efficient adaptation of the teachings from Inner Circle Trading (ICT). The tool is intended to display various data points that help streamline and simplify your trading process. However, it does not generate signals or recommendations for trade execution.
It is designed to automatically display different components according to the timeframe you are analyzing. From the Hourly chart down to the seconds, you will be able to visualize a wide range of time-based data points in one indicator.
On the Hourly timeframe, the indicator begins with the Weekly Profile using the True Day . You will be able to visualize Monday’s price extended throughout the entire week, as well as each individual day of the week separately.
You can also visualize the equilibrium and quadrants of each individual day, if desired.
ICT 3-Day Protocol: This feature extends the highs and lows of the previous two days up to the current candle. These levels can serve as potential draws on liquidity or reference points for identifying opportunities on lower timeframes.
M15 Timeframe
On this timeframe, you will be able to visualize the previously mentioned elements, with the addition of the Asian and London sessions. These are included to help outline the potential intraday profile, as well as the highs and lows of these sessions, since they represent relevant data points.
You will also have the option to display projections of these ranges. These projections are useful for anticipating potential price manipulation and distribution levels, using Midnight Open as the reference point for the Daily PO3 .
You will also be able to visualize different Opens , including:
• 00:00
• 08:30
• 09:30
• 13:30
• Previous day’s Settlement Price
These levels represent relevant data points that can be used to frame implied discount or premium conditions relative to the Time of Day .
M1 and Seconds
On this timeframe, you will be able to see the previously mentioned elements, along with additional features.
Market Session Dividers: These are included to provide a clear and organized visual reference of which session the market is currently in, as well as the separation between one session and another.**
Opening Ranges: This feature allows you to visualize the Opening Range of the AM and PM sessions, along with their respective projections. You can also choose whether to extend these ranges over time or keep them limited to their formation period.
First Presentations: This feature allows you to visualize the initial imbalance of the Regular Trading Hours session, including both the AM and PM sessions.
Additionally, an option is included in the menu to indicate if the current day has high-impact news before the 09:30 open, allowing you to consider including the formation of the First Presented Gap from 09:29, as recommended by ICT.
You can also enable alerts to be notified each time a First Presentation is formed.
Table: This feature displays a table with the various Openings mentioned earlier. It shows the price and indicates whether the market is at a Discount or Premium relative to these levels using an arrow.
The table also displays the size of the Opening Range Gap and, with an arrow, indicates whether it is a Premium or Discount Gap.
It provides different possible protocols based on the gap size and other elements taught by ICT to help anticipate certain market scenarios.
Additionally, it shows the current time and changes the color of the time indicator depending on whether you are within a macro session or not. This keeps your chart clean while still allowing you to know if the market is in a macro session.
All elements of the indicator are customizable . You can personalize virtually every component to suit your preferences.
The Engineer.
Educational
SMC OB+HOBSmart Money OB/HOB Indicator — Quick Guide
Use this as a field manual: what you’re seeing, how it’s decided, and which settings to use for different timeframes and trade styles.
What the tool plots
Bullish Order Block (OB) — teal box
The last small down candle before a bullish displacement/BOS. Height = candle body (default) or wick range (if you choose “Wick”).
Pin (small white dot) at the origin candle’s time to make anchoring obvious.
Bearish Order Block (OB) — red box
The last small up candle before a bearish displacement/BOS.
Hidden Order Block (HOB) — same box but yellow-tinted fill
A valid OB with one or more same-bias FVGs “ahead” (i.e., OB sits “behind” inefficiency). These tend to be stronger.
Mitigation state (fill transparency)
Unmitigated (least transparent): price hasn’t meaningfully traded back into the box. Highest priority.
Partial (more transparent): some penetration; still valid.
Full (most transparent): fully consumed; lower priority (optional to hide).
Top-K border — thin white outline
Only the best-scoring OBs/HOBs per direction are drawn to reduce clutter.
Auto-Fibs (optional)
OTE zone (0.62–0.79) — subtle purple band across the current swing leg.
0.618 / 0.705 / 0.786 — thin white horizontal lines. Confluence here adds score.
Trade idea lines (per Top-K block)
Entry — white line (mid/edge per your setting).
Stop — red line (box edge ± your pad).
TP1/TP2 — lime lines, R-based from entry→stop distance.
Label shows LONG/SHORT, entry, SL, TP1, TP2, time-stop (bars).
Note: Fair Value Gaps (FVGs) are tracked internally to classify HOBs and for pruning, not drawn to avoid noise.
How a block is qualified (in plain English)
BOS + Displacement:
Close breaks the recent swing high/low by at least N ticks and the bar shows impulse (body ≥ X·ATR and ≥ Y% of its total range).
(Settings: “Close beyond ≥ ticks”, “Min impulse body (x ATR)”, “Body/TR min %”)
Seed candle:
Look back ≤ N bars for the last opposite small-body candle (body ≤ Z% of its range). That candle’s body/wick becomes the OB height.
(Settings: “Last opposite candle within N bars”, “OB body ≤ % of TR”, “OB height model”)
Hidden OB:
Count same-bias FVGs “ahead”. If ≥ your threshold → tag the OB as HOB.
(Setting: “Require ≥ N same-bias FVGs ahead”)
Mitigation tracking:
As price trades into the box, we compute penetration %, updating unmitigated / partial / full state each bar.
Ranking (Top-K):
Every OB/HOB gets a score: near price, newer, hidden, near fib, and unmitigated boost. We draw only the Top-K per direction.
Inputs you’ll actually tweak
Timeframe
Compute on: Current (uses your chart TF) or Specific (MTF scan).
Process last N bars: reduce for speed, increase to see more history.
Anchoring
Extend: Right, Limited, or Origin only.
Limited draws boxes to a fixed number of bars so charts stay clean.
Show origin pins: Keep on so you always know the source candle.
Structure / BOS (signal frequency vs. quality)
Require FVG on break bar: ON = stricter, OFF = more signals.
Min impulse body (x ATR): higher = stricter.
Body/TR min %: higher = stricter.
Close beyond ≥ ticks: 0–1 for LTF; 1–3 for HTF.
Order Blocks
OB height model: Body (cleaner) or Wick (wider protection).
Last opposite candle within N bars: 3–8 (higher finds more).
OB body ≤ % of TR: 0.35–0.70 (lower = stricter).
Min OB height (ticks): 1–2 (avoid micro slivers).
Expire on first touch: If ON, removes boxes after first reaction.
Hidden OB
Require ≥ N FVGs ahead: 0–1 for LTF (more HOBs), 1–2 for HTF.
Mitigation Filter (what you show)
Toggle Unmitigated / Partial / Full visibility.
For precision trading, keep Unmitigated on; show others while scanning.
Auto-Fibs
Enable fib confluence: On adds score near 0.618/0.705/0.786.
Draw lines / OTE: Visual only; confluence also boosts ranking.
Tolerance (x ATR): how close price must be to count as “near fib”.
Ranking & Draw
Top-K per direction: how many OBs/HOBs you’ll see each side.
Prefer near / newer / hidden / unmitigated: scoring toggles.
Fib boost: how much fib confluence bumps a level.
Trade Ideas
Entry style: 50% of OB (balanced) or OB edge (faster fills).
Stop pad (ticks/ATR): give a little room beyond the box edge.
TP1/TP2 (R): risk-multiple targets (e.g., 1R, 2R).
Time stop (minutes): exit if it doesn’t go in time.
Execution / Alerts (recommended)
Keep on-close workflow: do not enable calc_on_every_tick.
When creating alerts, choose Once per bar close.
How to use it (mechanical checklist)
Scan: Focus on Top-K boxes. HOBs (yellow-tinted) and unmitigated get first look.
Context (optional): If you like, also check HTF structure or obvious liquidity pools (equal highs/lows).
Confluence: Prefer boxes near 0.618/0.705/0.786 or inside the OTE band.
Trigger: Let the bar close. If entry line is touched next, you have a go-signal with a placed stop and R-targets.
Manage: If TP1 hits, move SL to BE. For HOBs, consider a runner (trail under minor swing/FVG) — they often travel further.
Time stop: If it hasn’t moved within N minutes/bars, cut it; don’t babysit.
Preset recipes (copy these settings)
1) Hyper-Scalp (1–3m) — frequent, fast
Structure / BOS:
FVG on break = OFF | Min impulse = 0.6–0.8 | Body/TR = 0.45–0.55 | Close beyond = 0–1
Order Blocks:
Opposite lookback = 5–6 | OB body ≤ 0.55–0.60 | Min height = 1
HOB: Need FVGs = 0–1
Mitigation view: Show Unmit/Partial, optionally Full while scanning
Ranking: Top-K = 4–6, prefer near/new/hidden/unmit = ON, Fib boost = 0.6–1.0
Trade Ideas: Entry = OB edge, Stop pad = 1–2 ticks, Time stop = 5–8 min
Execution: On bar close alerts
2) Intraday (5–15m) — balanced
Structure / BOS:
FVG on break = OFF | Min impulse = 0.8–1.0 | Body/TR = 0.55–0.60 | Close beyond = 1
Order Blocks:
Opposite lookback = 4–5 | OB body ≤ 0.50–0.55 | Min height = 1–2
HOB: Need FVGs = 1
Ranking: Top-K = 3–4, Fib boost = 1.0–1.5
Trade Ideas: Entry = 50%, Stop pad = 2–3 ticks, Time stop = 10–20 min
3) Swing (1H–4H) — selective, higher quality
Structure / BOS:
FVG on break = ON | Min impulse = ≥1.0 | Body/TR = ≥0.65 | Close beyond = 1–3
Order Blocks:
Opposite lookback = 3–4 | OB body ≤ 0.45–0.50 | Min height = 2–4
HOB: Need FVGs = 1–2
Ranking: Top-K = 2–3, Fib boost = 1.5–2.0
Trade Ideas: Entry = 50%, Stop pad = a few ticks + ATR pad, Time stop = few bars
4) HTF (Daily+) — very selective
Keep swing settings, increase Min impulse and Close beyond a bit, reduce Top-K to 1–2.
Priority rules (what to trade first)
HOB over OB
Unmitigated over partial/full
With fib confluence over without
Near price and recent over far/old
Favor levels that follow a sweep (equal highs/lows taken, then return to your box)
If two boxes tie, take the one with the cleaner origin candle and simpler path to TP (fewer nearby obstacles).
Troubleshooting & tips
“I’m not seeing many signals.”
Loosen Structure/BOS (lower ATR and Body/TR), increase Opposite lookback, allow Partial/Full in view, raise Top-K.
“Too many lines/boxes.”
Lower Top-K, use Limited extension (Anchoring), hide Partial/Full, and keep fib lines if you rely on confluence.
“Stuff looks offset.”
Keep origin pins on. Use xloc.bar_time (already in code) and avoid custom time compressions that desync objects.
Execution discipline:
Use on-close alerts. Respect time stops. Size by fixed risk per trade, not fixed leverage.
Trend Exhaustion Signals AjayFx - CypherTradingNetworkExhaustion Signals at trend exhaustion points. Is a work in progress with tweaks and stuff but will get there.
SuperTrendSAP1212This indicator combines Supertrend, VWAP with bands, and an optional RSI filter to generate Buy/Sell signals.
How it works
Supertrend Flip (ATR-based): Detects when trend direction changes (from bearish to bullish, or bullish to bearish).
VWAP Band Filter: Signals only trigger if the candle close is beyond the VWAP bands:
Buy = Supertrend flips up AND close > VWAP Upper Band
Sell = Supertrend flips down AND close < VWAP Lower Band
Optional RSI Filter:
Buy requires RSI < 20
Sell requires RSI > 80
Can be enabled/disabled in settings.
Features
Choice of VWAP band calculation mode: Standard Deviation or ATR.
Adjustable ATR/StDev length and multiplier for VWAP bands.
Toggle Supertrend, VWAP lines, and Buy/Sell labels.
Alerts included: add alerts on BUY or SELL conditions (use Once Per Bar Close to avoid intrabar signals).
Use
Works best on intraday or higher timeframes where VWAP is relevant.
Use the RSI filter for more selective signals.
Can be combined with your own stop-loss and risk management rules.
⚠️ Disclaimer: This script is for educational and research purposes only. It is not financial advice. Always test thoroughly and trade at your own risk.
9 EMA / 20 EMA Crossover with alert for DUKE9 EMA / 20 EMA Crossover with alert for DUKE
has built in alerts to make your life easier
Tzotchev Trend Measure [EdgeTools]Are you still measuring trend strength with moving averages? Here is a better variant at scientific level:
Tzotchev Trend Measure: A Statistical Approach to Trend Following
The Tzotchev Trend Measure represents a sophisticated advancement in quantitative trend analysis, moving beyond traditional moving average-based indicators toward a statistically rigorous framework for measuring trend strength. This indicator implements the methodology developed by Tzotchev et al. (2015) in their seminal J.P. Morgan research paper "Designing robust trend-following system: Behind the scenes of trend-following," which introduced a probabilistic approach to trend measurement that has since become a cornerstone of institutional trading strategies.
Mathematical Foundation and Statistical Theory
The core innovation of the Tzotchev Trend Measure lies in its transformation of price momentum into a probability-based metric through the application of statistical hypothesis testing principles. The indicator employs the fundamental formula ST = 2 × Φ(√T × r̄T / σ̂T) - 1, where ST represents the trend strength score bounded between -1 and +1, Φ(x) denotes the normal cumulative distribution function, T represents the lookback period in trading days, r̄T is the average logarithmic return over the specified period, and σ̂T represents the estimated daily return volatility.
This formulation transforms what is essentially a t-statistic into a probabilistic trend measure, testing the null hypothesis that the mean return equals zero against the alternative hypothesis of non-zero mean return. The use of logarithmic returns rather than simple returns provides several statistical advantages, including symmetry properties where log(P₁/P₀) = -log(P₀/P₁), additivity characteristics that allow for proper compounding analysis, and improved validity of normal distribution assumptions that underpin the statistical framework.
The implementation utilizes the Abramowitz and Stegun (1964) approximation for the normal cumulative distribution function, achieving accuracy within ±1.5 × 10⁻⁷ for all input values. This approximation employs Horner's method for polynomial evaluation to ensure numerical stability, particularly important when processing large datasets or extreme market conditions.
Comparative Analysis with Traditional Trend Measurement Methods
The Tzotchev Trend Measure demonstrates significant theoretical and empirical advantages over conventional trend analysis techniques. Traditional moving average-based systems, including simple moving averages (SMA), exponential moving averages (EMA), and their derivatives such as MACD, suffer from several fundamental limitations that the Tzotchev methodology addresses systematically.
Moving average systems exhibit inherent lag bias, as documented by Kaufman (2013) in "Trading Systems and Methods," where he demonstrates that moving averages inevitably lag price movements by approximately half their period length. This lag creates delayed signal generation that reduces profitability in trending markets and increases false signal frequency during consolidation periods. In contrast, the Tzotchev measure eliminates lag bias by directly analyzing the statistical properties of return distributions rather than smoothing price levels.
The volatility normalization inherent in the Tzotchev formula addresses a critical weakness in traditional momentum indicators. As shown by Bollinger (2001) in "Bollinger on Bollinger Bands," momentum oscillators like RSI and Stochastic fail to account for changing volatility regimes, leading to inconsistent signal interpretation across different market conditions. The Tzotchev measure's incorporation of return volatility in the denominator ensures that trend strength assessments remain consistent regardless of the underlying volatility environment.
Empirical studies by Hurst, Ooi, and Pedersen (2013) in "Demystifying Managed Futures" demonstrate that traditional trend-following indicators suffer from significant drawdowns during whipsaw markets, with Sharpe ratios frequently below 0.5 during challenging periods. The authors attribute these poor performance characteristics to the binary nature of most trend signals and their inability to quantify signal confidence. The Tzotchev measure addresses this limitation by providing continuous probability-based outputs that allow for more sophisticated risk management and position sizing strategies.
The statistical foundation of the Tzotchev approach provides superior robustness compared to technical indicators that lack theoretical grounding. Fama and French (1988) in "Permanent and Temporary Components of Stock Prices" established that price movements contain both permanent and temporary components, with traditional moving averages unable to distinguish between these elements effectively. The Tzotchev methodology's hypothesis testing framework specifically tests for the presence of permanent trend components while filtering out temporary noise, providing a more theoretically sound approach to trend identification.
Research by Moskowitz, Ooi, and Pedersen (2012) in "Time Series Momentum in the Cross Section of Asset Returns" found that traditional momentum indicators exhibit significant variation in effectiveness across asset classes and time periods. Their study of multiple asset classes over decades revealed that simple price-based momentum measures often fail to capture persistent trends in fixed income and commodity markets. The Tzotchev measure's normalization by volatility and its probabilistic interpretation provide consistent performance across diverse asset classes, as demonstrated in the original J.P. Morgan research.
Comparative performance studies conducted by AQR Capital Management (Asness, Moskowitz, and Pedersen, 2013) in "Value and Momentum Everywhere" show that volatility-adjusted momentum measures significantly outperform traditional price momentum across international equity, bond, commodity, and currency markets. The study documents Sharpe ratio improvements of 0.2 to 0.4 when incorporating volatility normalization, consistent with the theoretical advantages of the Tzotchev approach.
The regime detection capabilities of the Tzotchev measure provide additional advantages over binary trend classification systems. Research by Ang and Bekaert (2002) in "Regime Switches in Interest Rates" demonstrates that financial markets exhibit distinct regime characteristics that traditional indicators fail to capture adequately. The Tzotchev measure's five-tier classification system (Strong Bull, Weak Bull, Neutral, Weak Bear, Strong Bear) provides more nuanced market state identification than simple trend/no-trend binary systems.
Statistical testing by Jegadeesh and Titman (2001) in "Profitability of Momentum Strategies" revealed that traditional momentum indicators suffer from significant parameter instability, with optimal lookback periods varying substantially across market conditions and asset classes. The Tzotchev measure's statistical framework provides more stable parameter selection through its grounding in hypothesis testing theory, reducing the need for frequent parameter optimization that can lead to overfitting.
Advanced Noise Filtering and Market Regime Detection
A significant enhancement over the original Tzotchev methodology is the incorporation of a multi-factor noise filtering system designed to reduce false signals during sideways market conditions. The filtering mechanism employs four distinct approaches: adaptive thresholding based on current market regime strength, volatility-based filtering utilizing ATR percentile analysis, trend strength confirmation through momentum alignment, and a comprehensive multi-factor approach that combines all methodologies.
The adaptive filtering system analyzes market microstructure through price change relative to average true range, calculates volatility percentiles over rolling windows, and assesses trend alignment across multiple timeframes using exponential moving averages of varying periods. This approach addresses one of the primary limitations identified in traditional trend-following systems, namely their tendency to generate excessive false signals during periods of low volatility or sideways price action.
The regime detection component classifies market conditions into five distinct categories: Strong Bull (ST > 0.3), Weak Bull (0.1 < ST ≤ 0.3), Neutral (-0.1 ≤ ST ≤ 0.1), Weak Bear (-0.3 ≤ ST < -0.1), and Strong Bear (ST < -0.3). This classification system provides traders with clear, quantitative definitions of market regimes that can inform position sizing, risk management, and strategy selection decisions.
Professional Implementation and Trading Applications
The indicator incorporates three distinct trading profiles designed to accommodate different investment approaches and risk tolerances. The Conservative profile employs longer lookback periods (63 days), higher signal thresholds (0.2), and reduced filter sensitivity (0.5) to minimize false signals and focus on major trend changes. The Balanced profile utilizes standard academic parameters with moderate settings across all dimensions. The Aggressive profile implements shorter lookback periods (14 days), lower signal thresholds (-0.1), and increased filter sensitivity (1.5) to capture shorter-term trend movements.
Signal generation occurs through threshold crossover analysis, where long signals are generated when the trend measure crosses above the specified threshold and short signals when it crosses below. The implementation includes sophisticated signal confirmation mechanisms that consider trend alignment across multiple timeframes and momentum strength percentiles to reduce the likelihood of false breakouts.
The alert system provides real-time notifications for trend threshold crossovers, strong regime changes, and signal generation events, with configurable frequency controls to prevent notification spam. Alert messages are standardized to ensure consistency across different market conditions and timeframes.
Performance Optimization and Computational Efficiency
The implementation incorporates several performance optimization features designed to handle large datasets efficiently. The maximum bars back parameter allows users to control historical calculation depth, with default settings optimized for most trading applications while providing flexibility for extended historical analysis. The system includes automatic performance monitoring that generates warnings when computational limits are approached.
Error handling mechanisms protect against division by zero conditions, infinite values, and other numerical instabilities that can occur during extreme market conditions. The finite value checking system ensures data integrity throughout the calculation process, with fallback mechanisms that maintain indicator functionality even when encountering corrupted or missing price data.
Timeframe validation provides warnings when the indicator is applied to unsuitable timeframes, as the Tzotchev methodology was specifically designed for daily and higher timeframe analysis. This validation helps prevent misapplication of the indicator in contexts where its statistical assumptions may not hold.
Visual Design and User Interface
The indicator features eight professional color schemes designed for different trading environments and user preferences. The EdgeTools theme provides an institutional blue and steel color palette suitable for professional trading environments. The Gold theme offers warm colors optimized for commodities trading. The Behavioral theme incorporates psychology-based color contrasts that align with behavioral finance principles. The Quant theme provides neutral colors suitable for analytical applications.
Additional specialized themes include Ocean, Fire, Matrix, and Arctic variations, each optimized for specific visual preferences and trading contexts. All color schemes include automatic dark and light mode optimization to ensure optimal readability across different chart backgrounds and trading platforms.
The information table provides real-time display of key metrics including current trend measure value, market regime classification, signal strength, Z-score, average returns, volatility measures, filter threshold levels, and filter effectiveness percentages. This comprehensive dashboard allows traders to monitor all relevant indicator components simultaneously.
Theoretical Implications and Research Context
The Tzotchev Trend Measure addresses several theoretical limitations inherent in traditional technical analysis approaches. Unlike moving average-based systems that rely on price level comparisons, this methodology grounds trend analysis in statistical hypothesis testing, providing a more robust theoretical foundation for trading decisions.
The probabilistic interpretation of trend strength offers significant advantages over binary trend classification systems. Rather than simply indicating whether a trend exists, the measure quantifies the statistical confidence level associated with the trend assessment, allowing for more nuanced risk management and position sizing decisions.
The incorporation of volatility normalization addresses the well-documented problem of volatility clustering in financial time series, ensuring that trend strength assessments remain consistent across different market volatility regimes. This normalization is particularly important for portfolio management applications where consistent risk metrics across different assets and time periods are essential.
Practical Applications and Trading Strategy Integration
The Tzotchev Trend Measure can be effectively integrated into various trading strategies and portfolio management frameworks. For trend-following strategies, the indicator provides clear entry and exit signals with quantified confidence levels. For mean reversion strategies, extreme readings can signal potential turning points. For portfolio allocation, the regime classification system can inform dynamic asset allocation decisions.
The indicator's statistical foundation makes it particularly suitable for quantitative trading strategies where systematic, rules-based approaches are preferred over discretionary decision-making. The standardized output range facilitates easy integration with position sizing algorithms and risk management systems.
Risk management applications benefit from the indicator's ability to quantify trend strength and provide early warning signals of potential trend changes. The multi-timeframe analysis capability allows for the construction of robust risk management frameworks that consider both short-term tactical and long-term strategic market conditions.
Implementation Guide and Parameter Configuration
The practical application of the Tzotchev Trend Measure requires careful parameter configuration to optimize performance for specific trading objectives and market conditions. This section provides comprehensive guidance for parameter selection and indicator customization.
Core Calculation Parameters
The Lookback Period parameter controls the statistical window used for trend calculation and represents the most critical setting for the indicator. Default values range from 14 to 63 trading days, with shorter periods (14-21 days) providing more sensitive trend detection suitable for short-term trading strategies, while longer periods (42-63 days) offer more stable trend identification appropriate for position trading and long-term investment strategies. The parameter directly influences the statistical significance of trend measurements, with longer periods requiring stronger underlying trends to generate significant signals but providing greater reliability in trend identification.
The Price Source parameter determines which price series is used for return calculations. The default close price provides standard trend analysis, while alternative selections such as high-low midpoint ((high + low) / 2) can reduce noise in volatile markets, and volume-weighted average price (VWAP) offers superior trend identification in institutional trading environments where volume concentration matters significantly.
The Signal Threshold parameter establishes the minimum trend strength required for signal generation, with values ranging from -0.5 to 0.5. Conservative threshold settings (0.2 to 0.3) reduce false signals but may miss early trend opportunities, while aggressive settings (-0.1 to 0.1) provide earlier signal generation at the cost of increased false positive rates. The optimal threshold depends on the trader's risk tolerance and the volatility characteristics of the traded instrument.
Trading Profile Configuration
The Trading Profile system provides pre-configured parameter sets optimized for different trading approaches. The Conservative profile employs a 63-day lookback period with a 0.2 signal threshold and 0.5 noise sensitivity, designed for long-term position traders seeking high-probability trend signals with minimal false positives. The Balanced profile uses a 21-day lookback with 0.05 signal threshold and 1.0 noise sensitivity, suitable for swing traders requiring moderate signal frequency with acceptable noise levels. The Aggressive profile implements a 14-day lookback with -0.1 signal threshold and 1.5 noise sensitivity, optimized for day traders and scalpers requiring frequent signal generation despite higher noise levels.
Advanced Noise Filtering System
The noise filtering mechanism addresses the challenge of false signals during sideways market conditions through four distinct methodologies. The Adaptive filter adjusts thresholds based on current trend strength, increasing sensitivity during strong trending periods while raising thresholds during consolidation phases. The Volatility-based filter utilizes Average True Range (ATR) percentile analysis to suppress signals during abnormally volatile conditions that typically generate false trend indications.
The Trend Strength filter requires alignment between multiple momentum indicators before confirming signals, reducing the probability of false breakouts from consolidation patterns. The Multi-factor approach combines all filtering methodologies using weighted scoring to provide the most robust noise reduction while maintaining signal responsiveness during genuine trend initiations.
The Noise Sensitivity parameter controls the aggressiveness of the filtering system, with lower values (0.5-1.0) providing conservative filtering suitable for volatile instruments, while higher values (1.5-2.0) allow more signals through but may increase false positive rates during choppy market conditions.
Visual Customization and Display Options
The Color Scheme parameter offers eight professional visualization options designed for different analytical preferences and market conditions. The EdgeTools scheme provides high contrast visualization optimized for trend strength differentiation, while the Gold scheme offers warm tones suitable for commodity analysis. The Behavioral scheme uses psychological color associations to enhance decision-making speed, and the Quant scheme provides neutral colors appropriate for quantitative analysis environments.
The Ocean, Fire, Matrix, and Arctic schemes offer additional aesthetic options while maintaining analytical functionality. Each scheme includes optimized colors for both light and dark chart backgrounds, ensuring visibility across different trading platform configurations.
The Show Glow Effects parameter enhances plot visibility through multiple layered lines with progressive transparency, particularly useful when analyzing multiple timeframes simultaneously or when working with dense price data that might obscure trend signals.
Performance Optimization Settings
The Maximum Bars Back parameter controls the historical data depth available for calculations, with values ranging from 5,000 to 50,000 bars. Higher values enable analysis of longer-term trend patterns but may impact indicator loading speed on slower systems or when applied to multiple instruments simultaneously. The optimal setting depends on the intended analysis timeframe and available computational resources.
The Calculate on Every Tick parameter determines whether the indicator updates with every price change or only at bar close. Real-time calculation provides immediate signal updates suitable for scalping and day trading strategies, while bar-close calculation reduces computational overhead and eliminates signal flickering during bar formation, preferred for swing trading and position management applications.
Alert System Configuration
The Alert Frequency parameter controls notification generation, with options for all signals, bar close only, or once per bar. High-frequency trading strategies benefit from all signals mode, while position traders typically prefer bar close alerts to avoid premature position entries based on intrabar fluctuations.
The alert system generates four distinct notification types: Long Signal alerts when the trend measure crosses above the positive signal threshold, Short Signal alerts for negative threshold crossings, Bull Regime alerts when entering strong bullish conditions, and Bear Regime alerts for strong bearish regime identification.
Table Display and Information Management
The information table provides real-time statistical metrics including current trend value, regime classification, signal status, and filter effectiveness measurements. The table position can be customized for optimal screen real estate utilization, and individual metrics can be toggled based on analytical requirements.
The Language parameter supports both English and German display options for international users, while maintaining consistent calculation methodology regardless of display language selection.
Risk Management Integration
Effective risk management integration requires coordination between the trend measure signals and position sizing algorithms. Strong trend readings (above 0.5 or below -0.5) support larger position sizes due to higher probability of trend continuation, while neutral readings (between -0.2 and 0.2) suggest reduced position sizes or range-trading strategies.
The regime classification system provides additional risk management context, with Strong Bull and Strong Bear regimes supporting trend-following strategies, while Neutral regimes indicate potential for mean reversion approaches. The filter effectiveness metric helps traders assess current market conditions and adjust strategy parameters accordingly.
Timeframe Considerations and Multi-Timeframe Analysis
The indicator's effectiveness varies across different timeframes, with higher timeframes (daily, weekly) providing more reliable trend identification but slower signal generation, while lower timeframes (hourly, 15-minute) offer faster signals with increased noise levels. Multi-timeframe analysis combining trend alignment across multiple periods significantly improves signal quality and reduces false positive rates.
For optimal results, traders should consider trend alignment between the primary trading timeframe and at least one higher timeframe before entering positions. Divergences between timeframes often signal potential trend reversals or consolidation periods requiring strategy adjustment.
Conclusion
The Tzotchev Trend Measure represents a significant advancement in technical analysis methodology, combining rigorous statistical foundations with practical trading applications. Its implementation of the J.P. Morgan research methodology provides institutional-quality trend analysis capabilities previously available only to sophisticated quantitative trading firms.
The comprehensive parameter configuration options enable customization for diverse trading styles and market conditions, while the advanced noise filtering and regime detection capabilities provide superior signal quality compared to traditional trend-following indicators. Proper parameter selection and understanding of the indicator's statistical foundation are essential for achieving optimal trading results and effective risk management.
References
Abramowitz, M. and Stegun, I.A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Washington: National Bureau of Standards.
Ang, A. and Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business and Economic Statistics, 20(2), 163-182.
Asness, C.S., Moskowitz, T.J., and Pedersen, L.H. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Fama, E.F. and French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Hurst, B., Ooi, Y.H., and Pedersen, L.H. (2013). Demystifying Managed Futures. Journal of Investment Management, 11(3), 42-58.
Jegadeesh, N. and Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699-720.
Kaufman, P.J. (2013). Trading Systems and Methods. 5th Edition. Hoboken: John Wiley & Sons.
Moskowitz, T.J., Ooi, Y.H., and Pedersen, L.H. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228-250.
Tzotchev, D., Lo, A.W., and Hasanhodzic, J. (2015). Designing robust trend-following system: Behind the scenes of trend-following. J.P. Morgan Quantitative Research, Asset Management Division.
Scalping Oversold/Overbought (RSI + Stochastic + VWAP + MA50)scalping di time frame 1 minute
simple baiii
the moment cross first candle kita buy saja at
second candle
the moment cross below vwap or MA50 kita sell
saja bai , apa problem.
tak payah nak pening kepala dengan macam
teknik turtle soup la , fvg la macam2
ko scalping jer kan
Simplified Market ForecastSimplified Market Forecast Indicator
This indicator pairs nicely with the Contrarian 100 MA and can be located here:
Overview
The "Simplified Market Forecast" (SMF) indicator is a streamlined technical analysis tool designed for traders to identify potential buy and sell opportunities based on a momentum-based oscillator. By analyzing price movements relative to a defined lookback period, SMF generates clear buy and sell signals when the oscillator crosses customizable threshold levels. This indicator is versatile, suitable for various markets (e.g., forex, stocks, cryptocurrencies), and optimized for daily timeframes, though it can be adapted to other timeframes with proper testing. Its intuitive design and visual cues make it accessible for both novice and experienced traders.
How It Works
The SMF indicator calculates a momentum oscillator based on the price’s position within a specified range over a user-defined lookback period. It then smooths this value to reduce noise and plots the result as a line in a separate lower pane. Buy and sell signals are generated when the smoothed oscillator crosses above a user-defined buy level or below a user-defined sell level, respectively. These signals are visualized as triangles either on the main chart or in the lower pane, with a table displaying the current ticker and oscillator value for quick reference.
Key Components
Momentum Oscillator: The indicator measures the price’s position relative to the highest high and lowest low over a specified period, normalized to a 0–100 scale.
Signal Generation: Buy signals occur when the oscillator crosses above the buy level (default: 15), indicating potential oversold conditions. Sell signals occur when the oscillator crosses below the sell level (default: 85), suggesting potential overbought conditions.
Visual Aids: The indicator includes customizable horizontal lines for buy and sell levels, shaded zones for clarity, and a table showing the ticker and current oscillator value.
Mathematical Concepts
Oscillator Calculation: The indicator uses the following formula to compute the raw oscillator value:
c1I = close - lowest(low, medLen)
c2I = highest(high, medLen) - lowest(low, medLen)
fastK_I = (c1I / c2I) * 100
The result is smoothed using a 5-period Simple Moving Average (SMA) to produce the final oscillator value (inter).
Signal Logic:
A buy signal is triggered when the smoothed oscillator crosses above the buy level (ta.crossover(inter, buyLevel)).
A sell signal is triggered when the smoothed oscillator crosses below the sell level (ta.crossunder(inter, sellLevel)).
Entry and Exit Rules
Buy Signal (Blue Triangle): Triggered when the oscillator crosses above the buy level (default: 15), indicating a potential oversold condition and a buying opportunity. The signal appears as a blue triangle either below the price bar (if plotted on the main chart) or at the bottom of the lower pane.
Sell Signal (White Triangle): Triggered when the oscillator crosses below the sell level (default: 85), indicating a potential overbought condition and a selling opportunity. The signal appears as a white triangle either above the price bar (if plotted on the main chart) or at the top of the lower pane.
Exit Rules: Traders can exit positions when an opposite signal occurs (e.g., exit a buy on a sell signal) or based on additional technical analysis tools (e.g., support/resistance, trendlines). Always apply proper risk management.
Recommended Usage
The SMF indicator is optimized for the daily timeframe but can be adapted to other timeframes (e.g., 1H, 4H) with careful testing. It performs best in markets with clear momentum shifts, such as trending or range-bound conditions. Traders should:
Backtest the indicator on their chosen asset and timeframe to validate signal reliability.
Combine with other indicators (e.g., moving averages, support/resistance) or price action for confirmation.
Adjust the lookback period and buy/sell levels to suit market volatility and trading style.
Customization Options
Intermediate Length: Adjust the lookback period for the oscillator calculation (default: 31 bars).
Buy/Sell Levels: Customize the threshold levels for buy (default: 15) and sell (default: 85) signals.
Colors: Modify the colors of the oscillator line, buy/sell signals, and threshold lines.
Signal Display: Toggle whether signals appear on the main chart or in the lower pane.
Visual Aids: The indicator includes dotted horizontal lines at the buy (green) and sell (red) levels, with shaded zones between 0–buy level (green) and sell level–100 (red) for clarity.
Ticker Table: A table in the top-right corner displays the current ticker and oscillator value (in percentage), with customizable colors.
Why Use This Indicator?
The "Simplified Market Forecast" indicator provides a straightforward, momentum-based approach to identifying potential reversals in overbought or oversold markets. Its clear signals, customizable settings, and visual aids make it easy to integrate into various trading strategies. Whether you’re a swing trader or a day trader, SMF offers a reliable tool to enhance decision-making and improve market timing.
Tips for Users
Test the indicator thoroughly on your chosen asset and timeframe to optimize settings.
Use in conjunction with other technical tools for stronger trade confirmation.
Adjust the buy and sell levels based on market conditions (e.g., lower levels for less volatile markets).
Monitor the ticker table for real-time oscillator values to gauge market momentum.
Happy trading with the Simplified Market Forecast indicator!
Momentum Concepts [A1TradeHub]ℹ️ General Information — TSI + Stochastic Z-Score (Momentum Duo)
Purpose: A two-oscillator stack that blends trend strength (TSI) with extreme-move normalization (Stochastic Z-Score) to time entries with confirmation instead of guessing tops/bottoms.
Components
Stochastic Z-Score (SZ): Converts price stretch into a bounded curve.
Red zone ≈ overbought supply, Green zone ≈ oversold demand.
The hook out of a band often marks turning points.
True Strength Index (TSI): Measures momentum quality and direction.
Signal/line cross = timing, Zero-line = trend filter, slope = acceleration.
Core Read
Alignment = edge: SZ leaves a band and TSI agrees (cross/slope).
Divergences: Higher-low on SZ/TSI vs lower-low in price (bullish). Lower-high on SZ/TSI vs higher-high in price (bearish). Best when near bands.
Mid-range = chop: Avoid trades when SZ is centered and TSI is flat.
Best Practices
Use structure (PDH/PDL, EMAs 13/48/200, trendlines) as context.
Scale profits into opposing SZ band or on TSI flatten/cross-back.
Place stops beyond the last swing or key EMA; skip high-volatility news.
Timeframes
Works on intraday (e.g., 5–15m) and swings (1h/4h). Use higher TF for bias, lower TF for entries.
This combo is designed to keep you on the right side of momentum, act at band hooks with TSI confirmation, and stand down when conditions are indecisive.
I. 🔴🟢 TSI Oscillator — Quick Guide
What you’re seeing
Lines: Fast TSI + slow Signal (both EMA-smoothed momentum).
Zones: 🟢 Green = oversold, 🔴 Red = overbought, 0-line = trend regime.
Long: 🟢 hook up → fast crosses above slow → ideally reclaim 0.
Short: 🔴 roll down → fast crosses below slow → ideally lose 0.
Exits: Trim into the opposite zone or on a cross back.
Divergence: TSI ↑ vs price ↓ = bullish; TSI ↓ vs price ↑ = bearish.
Avoid: Both lines chopping around 0.
II. Stochastic Z-Score — Quick Guide
Zones: 🔴 Red = overbought/supply, 🟢 Green = oversold/demand.
Curve: Watch the hook out of a zone for the turn.
Signals
🟢 Green Arrow (from Green zone): Momentum turns up → call/long bias. Enter on first pullback; stop under last swing/13-EMA.
🔻 Red/Bearish Arrow (from Red zone): Momentum rolls down → put/short bias. Enter on first lower-high; stop above last swing/13-EMA.
⚪ Ball = Momentum Shift: Early heads-up (slope change). Use as confirmation/add-on, not a standalone entry.
BTC 1D — Trend START/END Signals (clean, no repaint)
This strategy is designed primarily for BTC on the daily (1D) timeframe in TradingView.
BUY (start of uptrend)
Fast EMA is above Slow EMA.
Price breaks above the previous Donchian high.
Optional filters (if enabled): volume surge and strong momentum/RSI.
Only one BUY per uptrend—no additional buys until a SELL occurs.
SELL (end of uptrend)
Price falls below the previous Donchian low, or
Price drops below the Slow EMA, or
Momentum flips bearish (DI− > DI+ or RSI ≤ threshold).
One SELL marks the end of the uptrend.
Multiple Asset note_table Sections### Features
- **Expanded to 10 independent Sections**: Each Section has a title, content, and associated asset
- **Asset-based filtering**: Section only displays when the Section's asset name is empty or matches the current chart asset
- **Empty asset setting retained**: If Section asset name is left blank, that Section will display across all assets
- **Automatic display of current asset**: Current asset name is automatically shown in the header and footer
### Usage Instructions
1. Each Section can be assigned a specific asset name, such as "BTCUSDT", "ETHUSDT", etc.
2. A Section will only display when the current chart asset matches the asset specified for that Section
3. If you want a Section to display across all assets, simply leave the asset name blank for that Section
4. Each Section has independent title and content that can be customized as needed
5. When switching to different trading instruments, the indicator automatically displays notes relevant to the current instrument
Calculator - AOC📊 Calculator - AOC Indicator 🚀
The Calculator - AOC indicator is a powerful and user-friendly tool designed for TradingView to help traders plan and visualize trades with precision. It calculates key trade metrics, displays entry, take-profit (TP), stop-loss (SL), and liquidation levels, and provides a clear overview of risk management and potential profits. Perfect for both novice and experienced traders! 💡
✨ Features
📈 Trade Planning: Input your Entry Price, Take Profit (TP), Stop Loss (SL), and Trade Direction (Long/Short) to visualize your trade setup on the chart.
💰 Risk Management: Set your Initial Capital and Risk per Trade (%) to calculate the optimal Position Size and Risk Amount for each trade.
⚖️ Leverage Support: Define your Leverage to compute the Required Margin and Liquidation Price, ensuring you stay aware of potential risks.
📊 Risk/Reward Ratio: Automatically calculates the Risk-to-Reward Ratio to evaluate trade profitability.
🎨 Visuals: Displays Entry, TP, SL, and Liquidation levels as lines and boxes on the chart, with customizable Line Width, Line Style, and Label Size.
✅ Trade Validation: Checks if your trade setup is valid (e.g., correct TP/SL placement) and highlights issues like potential liquidation risks with color-coded statuses (Correct ✅, Incorrect ❌, or Liquidation ⚠️).
📋 Summary Table: A clean, top-right table summarizes key metrics: Capital, Risk %, Risk Amount, Position Size, Potential Profit, Risk/Reward, Margin, Liquidation Price, Trade Status, and % to TP/SL.
🖌️ Customization: Adjust Line Extension (Bars) for how far lines extend, and choose from Solid, Dashed, or Dotted line styles for a personalized chart experience.
🛠️ How to Use
Add to Chart: Apply the indicator to your TradingView chart.
Configure Inputs:
Accountability: Set your Initial Capital and Risk per Trade (%).
Target: Enter Entry Price, TP, and SL prices.
Leverage: Specify your leverage (e.g., 10x).
Direction: Choose Long or Short.
Display Settings: Customize Line Width, Line Style, Label Size, and Line Extension.
Analyze: The indicator plots Entry, TP, SL, and Liquidation levels on the chart and displays a table with all trade metrics.
Validate: Check the Trade Status in the table to ensure your setup is valid or if adjustments are needed.
🎯 Why Use It?
Plan Smarter: Visualize your trade setup and understand your risk/reward profile instantly.
Stay Disciplined: Precise position sizing and risk calculations help you stick to your trading plan.
Avoid Mistakes: Clear validation warnings prevent costly errors like incorrect TP/SL placement or liquidation risks.
User-Friendly: Intuitive visuals and a summary table make trade analysis quick and easy.
📝 Notes
Ensure Entry, TP, and SL prices align with your trade direction to avoid "Incorrect" or "Liquidation" statuses.
The indicator updates dynamically on the latest bar, ensuring real-time visuals.
Best used with proper risk management to maximize trading success! 💪
Happy trading! 🚀📈
Golden Duck Runner With TargetsGolden Duck Runner With Targets
Overview
The Golden Duck Runner is a comprehensive trend-following indicator designed for intraday and swing trading. It combines dual EMA analysis with pullback detection to identify high-probability entry points in trending markets.
Key Features
Core Signal Logic
Dual EMA System: Uses a fast EMA (default 18) and trend filter EMA (default 111)
Pullback Detection: Identifies when price pulls back to the fast EMA while staying above/below the trend filter
Trend Confirmation: Only generates signals in the direction of the overall trend
Visual Elements
Dynamic EMA Colors: Golden fast EMA, with trend filter changing from teal (uptrend) to orange (downtrend)
Entry Signals: Clear golden arrows marking buy/sell opportunities
Target Levels: Displays three take profit levels and stop loss with visual confirmation
Professional Dashboard: Real-time position and trend information
Risk Management
Fixed Tick-Based Targets: Consistent risk/reward ratios across all instruments
Multiple Take Profits: Three progressive profit-taking levels (30, 50, 75 ticks)
Stop Loss Protection: 36-tick stop loss with visual tracking
Position Duration Limit: Automatic closure after 20 bars if targets not reached
Alert System
Comprehensive alert notifications for:
Long and short entry signals
Individual take profit level hits (TP1, TP2, TP3)
Stop loss activation
Combined alerts for any entry or profit-taking event
How It Works
Entry Conditions
Long Signal:
Market in uptrend (Fast EMA > Trend Filter EMA)
Price pulls back below fast EMA but stays above trend filter EMA
Price closes back above fast EMA with momentum
Short Signal:
Market in downtrend (Fast EMA < Trend Filter EMA)
Price pulls back above fast EMA but stays below trend filter EMA
Price closes back below fast EMA with momentum
Exit Strategy
TP1: 30 ticks from entry (partial profit)
TP2: 50 ticks from entry (partial profit)
TP3: 75 ticks from entry (final target)
Stop Loss: 36 ticks against entry
Time Exit: 20 bars maximum hold time
Customization Options
Adjustable EMA periods for different timeframes
Configurable stop loss and take profit levels
Toggle visibility of EMAs, signals, and visual elements
Professional color scheme optimized for all chart backgrounds
Best Use Cases
Futures Trading: ES, NQ, YM, RTY with tick-based precision
Forex Pairs: Major and minor currency pairs
Crypto Markets: Bitcoin, Ethereum, and altcoins
Stock Indices: SPY, QQQ, and sector ETFs
Recommended Timeframes
Scalping: 1m, 3m, 5m charts
Intraday: 15m, 30m, 1H charts
Swing Trading: 4H, 1D charts
Educational Value
This indicator teaches traders:
Trend identification and confirmation
Pullback trading strategies
Proper risk management techniques
Multi-target profit-taking approaches
Important Notes
Not Financial Advice: This indicator is for educational and analysis purposes only
Backtesting Recommended: Test on historical data before live trading
Risk Management: Always use proper position sizing and risk controls
Market Conditions: Performance may vary in different market environments
Technical Specifications
Version: Pine Script v5
Overlay: True (plots on price chart)
Alerts: Full alert integration for automated trading systems
Performance: Optimized for real-time data processing
Compatibility: Works on all TradingView subscription levels
Disclaimer: Trading involves substantial risk of loss and is not suitable for all investors. Past performance does not guarantee future results. Always trade with proper risk management and never risk more than you can afford to lose.
Market Opening Time### TradingView Pine Script "Market Opening Time" Explanation
This Pine Script (`@version=5`) is an indicator that visually highlights market trading sessions (Sydney, London, New York, etc.) by changing the chart's background color. It adjusts for U.S. and Australian Daylight Saving Time (DST).
---
#### **1. Overview**
- **Purpose**: Changes the chart's background color based on UTC time zones to highlight market sessions.
- **Features**:
- Automatically adjusts for U.S. DST (2nd Sunday of March to 1st Sunday of November) and Australian DST (1st Sunday of October to 1st Sunday of April).
- Assigns colors to four time zones (00:00, 06:30, 14:00, 21:00).
- **Use Case**: Helps forex/stock traders identify active market sessions.
---
#### **2. Key Logic**
- **DST Detection**:
- `f_isUSDst`: Checks U.S. DST status.
- `f_isAustraliaDst`: Checks Australian DST status.
- **Time Adjustment** (`f_getAdjustedTime`):
- U.S. DST off: Shifts `time3` (14:00) forward by 1 hour.
- Australian DST off: Shifts `time4` (21:00) forward by 1 hour.
- **Time Conversion** (`f_timeToMinutes`): Converts time (e.g., "14:00") to minutes (e.g., 840).
- **Current Time** (`f_currentTimeInMinutes`): Gets UTC time in minutes.
- **Background Color** (`f_getBackgroundColor`):
- Applies colors based on time ranges:
- 00:00–06:30: Orange (Asia)
- 06:30–14:00: Purple (London)
- 14:00–21:00: Blue (New York, DST-adjusted)
- 21:00–00:00: Red (Sydney, DST-adjusted)
- Outside ranges: Gray
---
#### **3. Settings**
- **Time Zones**:
- `time1` = 00:00 (Orange)
- `time2` = 06:30 (Purple)
- `time3` = 14:00 (Blue, DST-adjusted)
- `time4` = 21:00 (Red, DST-adjusted)
- **Colors**: Transparency set to 90 for visibility.
---
#### **4. Example**
- **September 5, 2025, 10:25 PM JST (13:25 UTC)**:
- U.S. DST active, Australian DST inactive.
- 13:25 UTC falls between `time2` (06:30) and `time3` (14:00) → Background is **Purple** (London session).
- **Effect**: Background color changes dynamically to reflect active sessions.
---
#### **5. Customization**
- Modify `time1`–`time4` or colors for different sessions.
- Add time zones for other markets (e.g., Tokyo).
---
#### **6. Notes**
- Uses UTC; ensure chart is set to UTC.
- DST rules are U.S./Australia-specific; verify for other regions.
A simple, visual tool for tracking market sessions.
----
### TradingView Pine Script「Market Opening Time」解説
このPine Script（`@version=5`）は、市場の取引時間帯（シドニー、ロンドン、ニューヨークなど）を背景色で視覚化するインジケーターです。米国とオーストラリアの夏時間（DST）を考慮し、時間帯を調整します。
---
#### **1. 概要**
- **目的**: UTC基準の時間帯に基づき、チャートの背景色を変更して市場セッションを強調。
- **機能**:
- 米国DST（3月第2日曜～11月第1日曜）とオーストラリアDST（10月第1日曜～4月第1日曜）を自動調整。
- 4つの時間帯（00:00、06:30、14:00、21:00）に色を割り当て。
- **用途**: FXや株式トレーダーが市場のアクティブ時間を把握。
---
#### **2. 主要ロジック**
- **DST判定**:
- `f_isUSDst`: 米国DSTを判定。
- `f_isAustraliaDst`: オーストラリアDSTを判定。
- **時間調整** (`f_getAdjustedTime`):
- 米国DST非適用時: `time3`（14:00）を1時間遅延。
- オーストラリアDST非適用時: `time4`（21:00）を1時間遅延。
- **時間変換** (`f_timeToMinutes`): 時間（例: "14:00"）を分単位（840）に変換。
- **現在時刻** (`f_currentTimeInMinutes`): UTCの現在時刻を分単位で取得。
- **背景色** (`f_getBackgroundColor`):
- 時間帯に応じた色を適用:
- 00:00～06:30: オレンジ（アジア）
- 06:30～14:00: 紫（ロンドン）
- 14:00～21:00: 青（ニューヨーク、DST調整）
- 21:00～00:00: 赤（シドニー、DST調整）
- 時間外: グレー
---
#### **3. 設定**
- **時間帯**:
- `time1` = 00:00（オレンジ）
- `time2` = 06:30（紫）
- `time3` = 14:00（青、DST調整）
- `time4` = 21:00（赤、DST調整）
- **色**: 透明度90で視認性確保。
---
#### **4. 使用例**
- **2025年9月5日22:25 JST（13:25 UTC）**:
- 米国DST適用、豪DST非適用。
- 13:25は`time2`（06:30）～`time3`（14:00）の間 → 背景色は**紫**（ロンドン）。
- **効果**: 時間帯に応じて背景色が変化し、市場セッションを直感的に把握。
---
#### **5. カスタマイズ**
- 時間帯（`time1`～`time4`）や色を変更可能。
- 他の市場（例: 東京）に対応する時間帯を追加可能。
---
#### **6. 注意点**
- UTC基準のため、チャート設定をUTCに。
- DSTルールは米国・オーストラリア準拠。他地域では要確認。
シンプルで視覚的な市場時間インジケーターです。
US Elections Democrate-Republicain (1920-2025)This script shows the different U.S. presidents and indicates whether each was Democratic or Republican. It allows users to analyze the market based on the president in office.
Major Wars with a signifiant economic impactThis indicator highlights major wars that have had a significant economic impact worldwide. It allows users to easily see their effects on the charts.
PPP – Info Table (Anchor + Corr/Alpha/Beta) v3PPP – Info Table (Anchor + Corr/Alpha/Beta)
- By P3 Analytics, run by Puranam Pradeep Picasso Sharma
🔎 Overview
This indicator creates a clean, dynamic information table on your chart that lets you quickly analyze how your chosen asset is performing relative to BTC, ETH, or any other benchmarks.
With a single glance, you can see:
% change from today’s open (for the anchor asset, BTC, and ETH)
Previous day % change (self + benchmarks)
Correlation, Beta, and Alpha statistics for the selected window (1W, 1M, 1Y)
Anchor values at any bar you choose (via Bars Back or Anchor Time)
Perfect for traders who want to measure coin strength vs benchmarks and make better rotation, risk, or hedging decisions.
📊 Key Metrics
Correlation (Corr): How closely the asset moves with the benchmark.
+1 = moves together, 0 = no relation, -1 = moves opposite.
Beta (β): Sensitivity of returns vs the benchmark.
β = 1 → moves 1:1 with BTC.
β > 1 → more volatile (amplifies BTC moves).
β < 1 → less volatile (defensive).
Alpha (α): Excess return beyond what Beta predicts.
Positive α = outperforming benchmark-adjusted expectation.
Negative α = underperforming.
⚙️ Features
Flexible Anchor Mode:
Bars Back → quickly step through bars.
Time → pin analysis to a specific historical candle.
Customizable Benchmarks: Default BTC & ETH (futures), but replaceable with any ticker.
Adjustable Stats Window:
1 Week, 1 Month, 1 Year (auto-scales if using chart timeframe).
Compact Mode for a smaller table layout.
Dark/Light Theme, font size, corner placement, transparency, and decimal control.
Runs efficiently with minimal chart clutter.
🧑💻 About P3 Analytics
This indicator is developed under P3 Analytics, a research & trading technology initiative led by Puranam Pradeep Picasso Sharma.
P3 Analytics builds tools that merge machine learning, statistics, and trading strategy into accessible products for traders across crypto, equities, forex, and commodities.
✅ How to Use
Add indicator to your chart.
In settings:
Pick your benchmarks (default = BTCUSDT.P, ETHUSDT.P).
Choose your anchor (Bars Back or Time).
Set window length for correlation/alpha/beta.
Read the table:
Left side = your asset.
Right side = benchmarks.
Colors: Green = positive % change, Red = negative.
🚀 Why Use This?
Quickly compare your asset vs BTC/ETH without juggling multiple charts.
Spot whether a coin is truly leading or just following BTC.
Identify outperformance (alpha) coins for rotation or trend plays.
Manage risk by knowing which assets are high beta (high leverage-like moves).
✦ Indicator by P3 Analytics
✦ Created & published by Puranam Pradeep Picasso Sharma
FVG valid MTF (Fair Value Gaps across Multiple Timeframes)This indicator automatically detects and displays Fair Value Gaps (FVGs) across multiple timeframes (1D, 4H, 1H, 30M, 15M, 5M).
✨ Features:
Detects valid FVGs only when they appear after three consecutive candles in the same direction (bullish or bearish).
Each gap is color-coded by direction (bullish / bearish) and changes color once mitigated.
Automatic timeframe label inside each FVG box.
Fully customizable:
Minimum & maximum FVG size (in ticks),
Extension length of boxes into the future (bars),
Maximum number of FVGs displayed per timeframe.
After mitigation, FVGs are visually updated, making it easy to see whether the market has respected the imbalance zone.
📊 Practical Use:
Identify areas of imbalance where strong price reactions often occur.
Monitor FVGs across multiple timeframes – from daily charts down to intraday.
Useful for defining support/resistance zones, entry levels, or trade exits.
⚙️ Settings:
Adjustable FVG colors for bullish, bearish, and mitigated states.
Independent limit on how many FVGs are displayed for each timeframe.
Optimized for clarity and chart performance.
Dow Theory Indicator## 🎯 Key Features of the Indicator
### 📈 Complete Implementation of Dow Theory
- Three-tier trend structure: primary trend (50 periods), secondary trend (20 periods), and minor trend (10 periods).
- Swing point analysis: automatically detects critical swing highs and lows.
- Trend confirmation mechanism: strict confirmation logic based on consecutive higher highs/higher lows or lower highs/lower lows.
- Volume confirmation: ensures price moves are supported by trading volume.
### 🕐 Flexible Timeframe Parameters
All key parameters are adjustable, making it especially suitable for U.S. equities:
Trend analysis parameters:
- Primary trend period: 20–200 (default 50; recommended 50–100 for U.S. stocks).
- Secondary trend period: 10–100 (default 20; recommended 15–30 for U.S. stocks).
- Minor trend period: 5–50 (default 10; recommended 5–15 for U.S. stocks).
Dow Theory parameters:
- Swing high/low lookback: 5–50 (default 10).
- Trend confirmation bar count: 1–10 (default 3).
- Volume confirmation period: 10–100 (default 20).
### 🇺🇸 U.S. Market Optimizations
- Session awareness: distinguishes Regular Trading Hours (9:30–16:00 EST) from pre-market and after-hours.
- Pre/post-market weighting: adjustable weighting factor for signals during extended hours.
- Earnings season filter: automatically adjusts sensitivity during earnings periods.
- U.S.-optimized default parameters.
## 🎨 Visualization
1. Trend lines: three differently colored trend lines.
2. Background fill: green (uptrend) / red (downtrend) / gray (neutral).
3. Signal markers: arrows, labels, and warning icons.
4. Swing point markers: small triangles at key turning points.
5. Info panel: real-time display of eight key metrics.
## 🚨 Alert System
- Trend turning to up/down.
- Strong bullish/bearish signals (dual confirmation).
- Volume divergence warning.
- New swing high/low formed.
## 📋 How to Use
1. Open the Pine Editor in TradingView.
2. Copy the contents of dow_theory_indicator.pine.
3. Paste and click “Add to chart.”
4. Adjust parameters based on trading style:
- Long-term investing: increase all period parameters.
- Swing trading: use the default parameters.
- Short-term trading: decrease all period parameters.
## 💡 Parameter Tips for U.S. Stocks
- Large-cap blue chips (AAPL, MSFT): primary 60–80, secondary 25–30.
- Mid-cap growth stocks: primary 40–60, secondary 18–25.
- Small-cap high-volatility stocks: primary 30–50, secondary 15–20.
CAP - KC/AC 2.20462 Converter// ───────────────────────────────────────────────────────────────────────────────
// Purpose: Conversion Indicator for ICE “C” (KC) and “C Metric” (AC) Contracts
//
// Background:
// - The Intercontinental Exchange (ICE) is phasing out the legacy Coffee “C” contract (symbol: KC),
// which has been quoted in U.S. cents per pound, and replacing it with the new Coffee “C Metric” contract (symbol: AC),
// quoted in U.S. dollars per metric ton :contentReference {index=0}.
// - The final KC futures expire in March 2028; AC contracts begin trading in September 2025 and use modern specifications
// including pricing per metric ton and flexible bulk delivery formats :contentReference {index=1}.
//
// Why this script matters:
// - Traders are accustomed to the KC pricing format (¢/lb); the AC contract’s USD/MT may create confusion.
// - This indicator visually converts the current chart price—whether from KC or AC contracts—directly into its equivalent unit,
// helping traders quickly assess parity and compare trends across both contract types.
// - It simplifies head-to-head comparison during this transition period, improving clarity on chart price behavior.
//
// Usage instructions:
// - If the symbol starts with "KC", the script divides the price by 2.20462 to convert from ¢/lb to approximate ¢/kg.
// - If the symbol starts with "AC", the script multiplies the price by 2.20462 to reverse the conversion.
// - The results (converted values) are displayed in a table for immediate visual clarity.
// ───────────────────────────────────────────────────────────────────────────────
