Strong Biotech Screener | ProjectSyndicateStrong Biotech Screener turns dozens of charts into a single institutional-style dashboard, ranking a curated universe of 40 leading biotech names by performance across six timeframes and scoring each one on professional-grade risk metrics — Beta, Sharpe, Sortino, Omega, Z-Score, and Kelly — so you can find the year's biotech leaders and weigh their risk-adjusted quality at a glance, all on one clean, sortable panel. Every figure is computed live on the daily timeframe from real price history, not hard-coded, so the board reflects the market as it actually is right now.
🧬 Curated 40-Name Biotech Universe — the screener watches a hand-picked list of 40 high-momentum biotech, pharma, and life-sciences stocks in one place. Instead of flipping through forty charts, you see every name's performance and risk profile side by side and immediately spot who is leading and who is rolling over.
🗓️ Six-Timeframe Performance — each stock is tracked across Week, Month, Quarter, 6-Month, 12-Month, and Year-to-Date returns, so you can separate a one-week catalyst pop from a genuine long-run trend and see momentum building or fading across horizons in a single row.
🧮 Institutional Risk Metrics, Done Properly — beyond raw returns, every name is scored on Sharpe (excess return per unit of total volatility), Sortino (return per unit of downside risk only), Omega (probability-weighted gains versus losses above the risk-free threshold), Z-Score (how stretched the recent move is in standard deviations), and the Kelly fraction (a theoretical optimal-sizing read from return and variance). The stats are annualized from daily returns over a rolling window with a configurable risk-free rate, so the risk picture is consistent and comparable across the whole list — which matters especially in biotech, where single-name volatility is extreme.
🎯 Basket-Relative Beta — Beta is measured against an equal-weight basket of the 40 names in the screener, so it tells you how a stock moves relative to this specific biotech cohort rather than a broad index. Beta above 1 swings harder than the group; below 1 is steadier. You control the lookback length used for the beta and correlation calculation.
🌡️ Annualized Weekly Volatility — a dedicated Wk Vol column annualizes the standard deviation of recent weekly returns, giving you a fast read on how violent each name's price action is before you size into it — essential in a sector where binary trial and FDA events can move a stock 50% in a session.
🔀 Dynamic Sorting — sort the entire board by any of the six performance columns with a single setting. Rank by YTD to find the year's biotech leaders, by Week to catch what is moving on fresh catalysts, or by any horizon in between — the table re-ranks instantly.
🎨 Bloomberg-Amber Theme with Color-Coded Strength — a clean amber-on-black dashboard with a multi-level gradient that runs from bright amber on the strongest gains through to deep red on the steepest losses, so strength and weakness jump out the moment you look at the panel.
🧩 Fully Customizable Dashboard — place the table anywhere on the chart (Top / Middle / Bottom paired with Left / Center / Right), choose your text size (Tiny / Small / Normal / Large), set the sort column, the beta length, and the risk-free rate and periods — all from the settings menu, no code editing required.
🔒 Daily-Timeframe Lock — the screener is built for daily data and will prompt you to switch if you load it on a lower timeframe, so the returns, volatility, and ratios are always calculated on the basis they are designed for.
⚡ Lightweight and Efficient — the whole 40-name board is built from a tight, well-organized script that runs smoothly on PulseWire, with a clean merged title heading and an alternating-row layout for easy reading. Delisted or halted tickers degrade gracefully to blank rows rather than breaking the panel.
🎯 Why this is different — most watchlists show you price and maybe a percentage move. This screener puts performance and a full institutional risk stack — Sharpe, Sortino, Omega, Z-Score, Kelly, Beta, and annualized volatility — for forty leading biotech names on one sortable, color-coded panel, so you are ranking opportunities by risk-adjusted quality, not just chasing the biggest green number.
🚀 Where to use it — apply it to any daily chart to monitor the biotech leadership group as a whole. Use it for top-down scanning, rotation ideas, and risk screening before you drill into an individual name's chart for entry timing.
⚠️ Important — this is a research and decision-support dashboard, not a buy/sell system, and it makes no performance guarantees. The 40-name universe is a snapshot of the year's momentum leaders and will drift over time; the board re-ranks live, but membership is fixed until updated. All figures are historical and descriptive, computed from past price data, and say nothing certain about the future. Risk metrics like Sharpe, Sortino, Omega, Z-Score, and Kelly are simplified, assumption-based estimates and should inform your judgment, not replace it. Biotech is an especially high-risk sector driven by binary clinical and regulatory events — always pair the screener with your own analysis and risk management. Indicator
DCA + DRIP Simulator [WynTrader]DCA + DRIP Simulator
Hello my friend
This tool simulates real effect of an investment plan when a disciplined DCA+DRIP strategy compounds growth, compared to simply investing and pocketing the dividends. It applies to all kind of security with or without dividend paid, so you can compare what tickers you want..
This isn't a simplified estimate. It reads real price, dividend, and split data directly from PulseWire — bar by bar, month by month — and reconstructs what a real investor would have experienced. Apply it to any security — dividend-paying or not, stock, ETF, closed-end fund — and see the picture side by side: with DRIP and no DRIP.
█ CONCEPTS
This indicator illustrates clearly that DCA+DRIP can build more wealth over time than a standard monthly investment approach where dividends are simply cashed out with no plan in mind.
— Dollar Cost Averaging (DCA) means investing a consistent amount every single month, no matter what the market does. When prices drop, your money buys more shares automatically. Over time, this discipline lowers your average cost and keeps emotion out of the equation.
— Dividend Re-Investment Plan (DRIP) means every dividend payment goes straight back into buying more shares, instead of sitting idle. Those extra shares generate their own dividends, which buy more shares — a compounding effect that grows the longer you stay invested.
Together, DCA + DRIP compound together over time.
█ HOW IT WORKS
Set your chart to MONTHLY intervals, else the table is set to zero.
Once configured, the indicator reads price, dividend, and split data directly from PulseWire and simulates what a real investor would have experienced: buying every month (DCA), collecting dividends, reinvesting them automatically (DRIP) and adjust data with Splits.
█ FEATURES
Configurable DCA Parameters
Set your initial deposit, monthly contribution, and an optional annual DCA increase (e.g. +5%/yr to reflect salary growth and inflation over time). A withholding tax rate can also be applied for US-listed securities (e.g. 15% for ZQQ).
Flexible Date Range
The simulation runs from your chosen: a) Number of years to date; b) Start date to date; c) Visible chart from Start/End dates of your chart screen.
Side-by-Side Comparison
Every result is shown in two columns simultaneously — With DRIP and No DRIP. Capital, monthly dividend income, total dividends received, net gain, and share count — all at a glance.
Fully Customizable Table Display
Position, text size, and all colors are adjustable directly from the settings panel, so the table fits cleanly on any chart layout.
█ CONCLUSION
Load this indicator on any security — stock, ETF, closed-end fund, with or without dividends. DCA+DRIP Simulator applies to all of them, though DRIP's effect is most visible on dividend-paying securities, where reinvested dividends compound over time.
The difference between With DRIP and No DRIP can be significant over time.
█ WynTrader
I am not a Pine Script programmer — I am an investor who builds tools to support my own analysis, and maybe helps others. A few anomalies may have slipped through and there is likely room for improvement.
Hope you find this little tool useful. Enjoy!
WynTrader Indicator
Stocks: Financial Summary [invincible3]Stocks: Financial Summary
Stocks: Financial Summary is a compact fundamental dashboard designed to visualize a company’s key financial statements directly on the PulseWire chart.
The indicator displays three major financial sections:
Income Statement
Revenue, Gross Profit, EBIT or Operating Income, Pretax Income, and Net Income.
Balance Sheet
Total Assets, Total Liabilities, and Shareholders’ Equity.
Cash Flow
Cash Flow from Operations, Cash Flow from Investing, Cash Flow from Financing, and Free Cash Flow.
The dashboard is drawn on the right side of the chart using a clean multi-panel bar-chart layout. It allows traders and investors to quickly compare historical yearly or quarterly financial data without leaving the price chart.
Key Features
* Supports Yearly and Quarterly financial views.
* Optional TTM display for Income Statement and Cash Flow.
* Visual comparison of historical financial periods.
* Auto unit formatting: Raw, K, M, B, and T.
* Customizable dashboard position, spacing, bar width, colors, and transparency.
* Right-side layout designed to keep the price chart readable.
* Uses actual PulseWire financial data through `request.financial()`.
* Includes manual fiscal year and quarter override options when exchange reporting labels need adjustment.
This tool is designed for fundamental analysis, long-term stock screening, and quick financial statement comparison. It can help users visually inspect whether a company’s revenue, profitability, assets, liabilities, cash flow, and free cash flow are improving or weakening over time.
This indicator is for educational and informational purposes only. It is not financial advice.
Indicator
Indicator
Long-Term Growth Stock ScorerLong-Term Growth Stock Scorer plots a 7-criterion scoring system on any stock, identifying high-quality growth setups and signaling 200 EMA retest entries. Designed for long-term investors building positions in trending stocks with confirmed strength across trend, momentum, and participation.
THE 7-CRITERION SCORECARD
Each stock is scored 0-7 based on:
Price above daily 200 EMA — primary trend
50 EMA above 200 EMA — golden cross alignment
200 EMA sloping up — confirmed long-term uptrend
Price above weekly 200 EMA — multi-timeframe confirmation
Relative strength vs benchmark (S&P 500, NASDAQ 100, TSX Composite, FTSE, ASX, Russell 2000, or any custom symbol)
RSI in healthy range (40-70) — momentum quality, not exhausted
Volume trend rising — institutional participation expanding
SIGNALS
BUY label fires when a qualified stock (score >= threshold) retests its 200 EMA on volume
WEAK label fires when a held position drops below the qualification threshold
Background highlight when current score is in the qualified zone
Three built-in alerts: buy signal, score weakening, and score strengthening (entering the qualified zone)
FEATURES
Configurable benchmark index — works for any major market or custom symbol
Live dashboard showing each criterion's pass/fail status, current RSI value, and distance from 200 EMA
Six dashboard position options
Adjustable minimum score threshold (default 5/7)
Volume confirmation toggle for retest signals
HOW TO USE
Run on any stock chart on the daily timeframe. Set your benchmark to match the stock's market (S&P 500 for US stocks, TSX Composite for Canadian stocks, etc.). Use the dashboard to scan watchlist stocks for current qualification. When a qualified stock retests its 200 EMA on rising volume, the BUY signal fires.
Best used as a confirmation layer alongside fundamental analysis — strong technical setups need strong businesses underneath.
Open-source. Feedback and forks welcome. Indicator
BTC Valuation Cycle [Alpha Extract]A sophisticated multi-metric Bitcoin valuation framework that synthesizes on-chain analytics including SOPR, MVRV, Price-to-Realized, and Mayer Multiple into a unified 0-100 cycle oscillator with six-tier zone classification for market cycle identification. Utilizing logistic transformation with configurable weighting and z-score normalization, this indicator delivers institutional-grade Bitcoin-specific valuation assessment with pivot-based extreme detection and comprehensive alert system. The system's weighted composite architecture combined with adaptive curve intensity enables precise calibration of cycle sensitivity while maintaining statistical validity across Bitcoin's multi-year market cycles.
🔶 Advanced Multi-Metric Synthesis Engine
Implements sophisticated composite calculation combining four distinct Bitcoin valuation metrics with configurable weighting and normalization framework. The system retrieves SOPR (Spent Output Profit Ratio), MVRV (Market Value to Realized Value), Price-to-Realized ratio, and Mayer Multiple from on-chain sources, applies z-score normalization to each metric over configurable periods, transforms via logistic function for 0-100 scaling, and generates weighted average creating unified cycle score.
// Component Score Calculation
SOPR_Centered = SOPR - 1.0
SOPR_Z = z_score(SOPR_Centered, Normalization_Length)
SOPR_Score = logistic_100(SOPR_Z, Curve_Intensity)
Price_to_Realized_Z = z_score(Price / Realized_Price, Normalization_Length)
PR_Score = logistic_100(Price_to_Realized_Z, Curve_Intensity)
MVRV_Z = z_score(Market_Cap / Realized_Cap, Normalization_Length)
MVRV_Score = logistic_100(MVRV_Z, Curve_Intensity)
Mayer_Z = z_score(Mayer_Multiple, Normalization_Length)
Mayer_Score = logistic_100(Mayer_Z, Curve_Intensity)
// Weighted Composite
Cycle = (SOPR_Score × W_SOPR + PR_Score × W_PR + MVRV_Score × W_MVRV + Mayer_Score × W_Mayer) / (W_SOPR + W_PR + W_MVRV + W_Mayer)
🔶 Understanding Bitcoin Valuation Metrics
SOPR (Spent Output Profit Ratio) measures the degree of profit for coins moved on-chain, calculated as value sold divided by value paid. Values above 1.0 indicate profitable selling (distribution), below 1.0 indicate loss-taking (capitulation). The system centers SOPR around 1.0 for normalization.
MVRV (Market Value to Realized Value) compares current market cap to realized cap (aggregate cost basis). High MVRV signals overvaluation as price exceeds average acquisition cost; low
MVRV suggests undervaluation. The system offers Ratio mode (raw MVRV), Z-Score mode (statistical deviation), or Blend mode (average of both).
Price-to-Realized Ratio directly compares current BTC price to realized price (realized cap divided by circulating supply), providing cleaner valuation signal than MVRV by removing market cap distortions.
Mayer Multiple measures price relative to 200-day moving average. Values above 2.4 historically mark tops; values near or below 1.0 mark bottoms. The system normalizes this classic technical indicator alongside on-chain metrics.
🔶 Logistic Transformation Framework
Features sophisticated logistic function application converting unbounded z-scores into bounded 0-100 range with configurable curve intensity controlling sensitivity. The system applies formula: 100 / (1 + exp(-z × k)) where z is z-score and k is curve intensity (default 0.90), creates S-curve transformation preserving relative relationships while preventing extreme outliers, and enables smooth gradient visualization across entire cycle range.
🔶 Six-Tier Cycle Zone Classification
Implements comprehensive market cycle framework dividing 0-100 range into six distinct zones with configurable thresholds representing Bitcoin's characteristic bubble and bust patterns. The system defines Bottom Extreme (default <10, accumulation zone), Cold Zone (10-25, early recovery), Lower Mid (25-40, neutral to bullish), Upper Mid (40-60, bullish), Hot Zone (60-75, late bull market), and Top Extreme (>75, euphoria/distribution) with dynamic color coding.
🔶 Pivot-Based Extreme Detection System
Provides intelligent local extreme identification using pivot high/low detection with zone threshold filtering and visual capsule markers. The system detects pivot highs above Hot Zone threshold and pivot lows below Cold Zone threshold using configurable left/right bars, creates horizontal capsule visualizations at exact extreme values with color-coded centers (red for tops, cyan for bottoms), and maintains rolling array limited to maximum capsule count for clean chart presentation.
🔶 MVRV Calculation Mode Selection
Offers three distinct MVRV calculation approaches optimizing for different market conditions and analytical preferences. Ratio mode uses raw Market Cap / Realized Cap for direct valuation comparison, Z-Score mode applies statistical normalization emphasizing deviations from historical mean, and Blend mode (default) averages both approaches balancing absolute valuation with statistical context for robust signal generation.
🔶 Configurable Metric Weighting System
Features flexible weight allocation enabling traders to emphasize preferred metrics or disable unreliable components during specific market regimes. The system accepts 0.0-N weight values for each metric (default 1.0 all equal), automatically handles missing data by excluding NA metrics from composite, recalculates weighted average dynamically, and enables custom cycle calibration based on trader's confidence in different on-chain signals.
🔶 Confirmed HTF Data Integration
Implements rigorous anti-repaint methodology using confirmed higher-timeframe values with offset preventing live bar distortion. The system retrieves all on-chain metrics from daily timeframe with 1-bar offset ensuring only completed daily candle data influences cycle score, applies identical offset to Mayer Multiple calculation, and maintains signal stability across real-time updates preventing false extreme alerts.
🔶 Comprehensive Alert Framework
Provides five distinct alert conditions covering critical cycle events and threshold breaches with descriptive messages. The system triggers Top Extreme alert on crossover above top threshold (default 90), Bottom Extreme alert on crossunder below bottom threshold (default 10), Hot Rejection alert when cycle falls from Hot Zone, Cold Reclaim alert when cycle rises from Cold Zone, and Mayer Threshold breach alert for traditional technical confirmation.
🔶 Gradient Zone Visualization Architecture
Creates intuitive color-coded area plot with six distinct color zones reflecting current cycle position through visual spectrum from cyan (extreme bottom) through purple/orange to red (extreme top). The system applies dynamic zone coloring to both area fill and cycle value display, implements configurable area transparency (default opaque), and maintains consistent color scheme across oscillator pane, table values, and capsule markers.
🔶 Real-Time Diagnostics System
Features comprehensive data availability monitoring with missing metric labels and detailed value table showing all component metrics. The system detects NA values in SOPR, Realized Price, MVRV, or Mayer Multiple, displays warning label listing unavailable metrics, and provides table overlay showing current values for Cycle score, all four components, MVRV-Z, Mayer MA, and threshold with color-coded formatting.
🔶 Performance Optimization Framework
Employs efficient calculation methods with null-safe division functions, optimized array management for capsule storage, and conditional plotting minimizing unnecessary rendering. The system includes streamlined weighted average calculation skipping NA metrics, smart capsule cleanup maintaining maximum limit through oldest-first deletion, and minimal recalculation overhead through var declarations and confirmed bar logic.
This indicator delivers sophisticated Bitcoin-specific valuation analysis through multi-metric on-chain synthesis unavailable in traditional technical indicators. By combining SOPR (profit/loss behavior), MVRV (cost basis valuation), Price-to-Realized (pure valuation), and Mayer Multiple (technical context) into unified cycle framework with statistical normalization, it provides comprehensive market cycle assessment grounded in blockchain fundamentals. The six-tier zone system maps directly to Bitcoin's characteristic 4-year halving cycles with Bottom Extreme zones historically marking generational buying opportunities and Top Extreme zones marking distribution phases. Perfect for long-term Bitcoin investors seeking data-driven cycle timing, position sizing based on valuation extremes (increase allocation in Cold/Bottom zones, reduce in Hot/Top zones), and objective framework for navigating Bitcoin's volatile multi-year cycles with alerts providing advance warning of major cycle transitions requiring portfolio reassessment. Indicator
US Market Long Horizon Momentum Summary in one paragraph
US Market Long Horizon Momentum is a trend following strategy for US index ETFs and futures built around a single eighteen month time series momentum measure. It helps you stay long during persistent bull regimes and step aside or flip short when long term momentum turns negative.
Scope and intent
• Markets. Large cap US equity indices, liquid US index ETFs, index futures
• Timeframes. 4h/ Daily charts
• Default demo used in the publication. SPY on 4h timeframe chart
• Purpose. Provide a minimal long bias index timing model that can reduce deep drawdowns and capture major cycles without parameter mining
• Limits. This is a strategy. Orders are simulated on standard candles only
Originality and usefulness
• Unique concept or fusion. One unscaled multiple month log return of an external benchmark symbol drives all entries and exits, with optional volatility targeting as a single risk control switch.
• Failure mode addressed. Fully passive buy and hold ignores the sign of long horizon momentum and can sit through multi year drawdowns. This script offers a way to step down risk in prolonged negative momentum without chasing short term noise.
• Testability. All parameters are visible in Inputs and the momentum series is plotted so users can verify every regime change in the Tester and on price history.
• Portable yardstick. The log return over a fixed window is a unit that can be applied to any liquid symbol with daily data.
Method overview in plain language
The method looks at how far the benchmark symbol has moved in log return terms over an eighteen month window in our example. If that long horizon return is positive the strategy allows a long stance on the traded symbol. If it is negative and shorts are enabled the strategy can flip short, otherwise it goes flat. There is an optional realised volatility estimate on the traded symbol that can scale position size toward a target annual volatility, but in the default configuration the model uses unit leverage and only the sign of momentum matters.
Base measures
Return basis. The core yardstick is the natural log of close divided by the close eighteen months ago on the benchmark symbol. Daily log returns of the traded symbol feed the realised volatility estimate when volatility targeting is enabled.
Components
• Component one Momentum eighteen months. Log of benchmark close divided by its close mom_lookback bars ago. Its sign defines the trend regime. No extra smoothing is applied beyond the long window itself.
• Component two Realised volatility optional. Standard deviation of daily log returns on the traded symbol over sixty three days. Annualised by the square root of 252. Used only when volatility targeting is enabled.
• Optional component Volatility targeting. Converts target annual volatility and realised volatility into a leverage factor clipped by a maximum leverage setting.
Fusion rule
The model uses a simple gate. First compute the sign of eighteen month log momentum on the benchmark symbol. Optionally compute leverage from volatility. The sign decides whether the strategy wants to be long, short, or flat. Leverage only rescales position size when enabled and does not change direction.
Signal rule
• Long suggestion. When eighteen month log momentum on the benchmark symbol is greater than zero, the strategy wants to be long.
• Short suggestion. When that log momentum is less than zero and shorts are allowed, the strategy wants to be short. If shorts are disabled it stays flat instead.
• Wait state. When the log momentum is exactly zero or history is not long enough the strategy stays flat.
• In position. In practice the strategy sits IN LONG while the sign stays positive and flips to IN SHORT or flat only when the sign changes.
Inputs with guidance
Setup
• Momentum Lookback (months). Controls the horizon of the log return on the benchmark symbol. Typical range 6 to 24 months. Raising it makes the model slower and more selective. Lowering it makes it more reactive and sensitive to medium term noise.
• Symbol. External symbol used for the momentum calculation, SPY by default. Changing it lets you time other indices or run signals from a benchmark while trading a correlated instrument.
Logic
• Allow Shorts. When true the strategy will open short positions during negative momentum regimes. When false it will stay flat whenever momentum is negative. Practical setting is tied to whether you use a margin account or an ETF that supports shorting.
Internal risk parameters (not exposed as inputs in this version) are:
• Target Vol (annual). Target annual volatility for volatility targeting, default 0.2.
• Vol Lookback (days). Window for realised volatility, default 63 trading days.
• Max Leverage. Cap on leverage when volatility targeting is enabled, default 2.
Usage recipes
Swing continuation
• Signal timeframe. Use the daily chart.
• Benchmark symbol. Leave at SPY for US equity index exposure.
• Momentum lookback. Eighteen months as a default, with twelve months as an alternative preset for a faster swing bias.
Properties visible in this publication
• Initial capital. 100000
• Base currency. USD
• Default order size method. 5% of the total capital in this example
• Pyramiding. 0
• Commission. 0.03 percent
• Slippage. 3 ticks
• Process orders on close. On
• Bar magnifier. Off
• Recalculate after order is filled. Off
• Calc on every tick. Off
• All request.security calls use lookahead = barmerge.lookahead_off
Realism and responsible publication
The strategy is for education and research only. It does not claim any guaranteed edge or future performance. All results in Strategy Tester are hypothetical and depend on the data vendor, costs, and slippage assumptions. Intrabar motion is not modeled inside daily bars so extreme moves and gaps can lead to fills that differ from live trading. The logic is built for standard candles and should not be used on synthetic chart types for execution decisions.
Performance is sensitive to regime structure in the US equity market, which may change over time. The strategy does not protect against single day crash risk inside bars and does not model gap risk explicitly. Past behavior of SPY and the momentum effect does not guarantee future persistence.
Honest limitations and failure modes
• Long sideways regimes with small net change over eighteen months can lead to whipsaw around the zero line.
• Very sharp V shaped reversals after deep declines will often be missed because the model waits for momentum to turn positive again.
• The sample size in a full SPY history is small because regime changes are infrequent, so any test must be interpreted as indicative rather than statistically precise.
• The model is highly dependent on the chosen lookback. Users should test nearby values and validate that behavior is qualitatively stable.
Legal
Education and research only. Not investment advice. You are responsible for your own decisions. Always test on historical data and in simulation with realistic costs before any live use.
Strategy
Multi-Mode Seasonality Map [BackQuant]Multi-Mode Seasonality Map
A fast, visual way to expose repeatable calendar patterns in returns, volatility, volume, and range across multiple granularities (Day of Week, Day of Month, Hour of Day, Week of Month). Built for idea generation, regime context, and execution timing.
What is “seasonality” in markets?
Seasonality refers to statistically repeatable patterns tied to the calendar or clock, rather than to price levels. Examples include specific weekdays tending to be stronger, certain hours showing higher realized volatility, or month-end flow boosting volumes. This tool measures those effects directly on your charted symbol.
Why seasonality matters
It’s orthogonal alpha: timing edges independent of price structure that can complement trend, mean reversion, or flow-based setups.
It frames expectations: when a session typically runs hot or cold, you size and pace risk accordingly.
It improves execution: entering during historically favorable windows, avoiding historically noisy windows.
It clarifies context: separating normal “calendar noise” from true anomaly helps avoid overreacting to routine moves.
How traders use seasonality in practice
Timing entries/exits : If Tuesday morning is historically weak for this asset, a mean-reversion buyer may wait for that drift to complete before entering.
Sizing & stops : If 13:00–15:00 shows elevated volatility, widen stops or reduce size to maintain constant risk.
Session playbooks : Build repeatable routines around the hours/days that consistently drive PnL.
Portfolio rotation : Compare seasonal edges across assets to schedule focus and deploy attention where the calendar favors you.
Why Day-of-Week (DOW) can be especially helpful
Flows cluster by weekday (ETF creations/redemptions, options hedging cadence, futures roll patterns, macro data releases), so DOW often encodes a stable micro-structure signal.
Desk behavior and liquidity provision differ by weekday, impacting realized range and slippage.
DOW is simple to operationalize: easy rules like “fade Monday afternoon chop” or “press Thursday trend extension” can be tested and enforced.
What this indicator does
Multi-mode heatmaps : Switch between Day of Week, Day of Month, Hour of Day, Week of Month .
Metric selection : Analyze Returns , Volatility ((high-low)/open), Volume (vs 20-bar average), or Range (vs 20-bar average).
Confidence intervals : Per cell, compute mean, standard deviation, and a z-based CI at your chosen confidence level.
Sample guards : Enforce a minimum sample size so thin data doesn’t mislead.
Readable map : Color palettes, value labels, sample size, and an optional legend for fast interpretation.
Scoreboard : Optional table highlights best/worst DOW and today’s seasonality with CI and a simple “edge” tag.
How it’s calculated (under the hood)
Per bar, compute the chosen metric (return, vol, volume %, or range %) over your lookback window.
Bucket that metric into the active calendar bin (e.g., Tuesday, the 15th, 10:00 hour, or Week-2 of month).
For each bin, accumulate sum , sum of squares , and count , then at render compute mean , std dev , and confidence interval .
Color scale normalizes to the observed min/max of eligible bins (those meeting the minimum sample size).
How to read the heatmap
Color : Greener/warmer typically implies higher mean value for the chosen metric; cooler implies lower.
Value label : The center number is the bin’s mean (e.g., average % return for Tuesdays).
Confidence bracket : Optional “ ” shows the CI for the mean, helping you gauge stability.
n = sample size : More samples = more reliability. Treat small-n bins with skepticism.
Suggested workflows
Pick the lens : Start with Analysis Type = Returns , Heatmap View = Day of Week , lookback ≈ 252 trading days . Note the best/worst weekdays and their CI width.
Sanity-check volatility : Switch to Volatility to see which bins carry the most realized range. Use that to plan stop width and trade pacing.
Check liquidity proxy : Flip to Volume , identify thin vs thick windows. Execute risk in thicker windows to reduce slippage.
Drill to intraday : Use Hour of Day to reveal opening bursts, lunchtime lulls, and closing ramps. Combine with your main strategy to schedule entries.
Calendar nuance : Inspect Week of Month and Day of Month for end-of-month, options-cycle, or data-release effects.
Codify rules : Translate stable edges into rules like “no fresh risk during bottom-quartile hours” or “scale entries during top-quartile hours.”
Parameter guidance
Analysis Period (Days) : 252 for a one-year view. Shorten (100–150) to emphasize the current regime; lengthen (500+) for long-memory effects.
Heatmap View : Start with DOW for robustness, then refine with Hour-of-Day for your execution window.
Confidence Level : 95% is standard; use 90% if you want wider coverage with fewer false “insufficient data” bins.
Min Sample Size : 10–20 helps filter noise. For Hour-of-Day on higher timeframes, consider lowering if your dataset is small.
Color Scheme : Choose a palette with good mid-tone contrast (e.g., Red-Green or Viridis) for quick thresholding.
Interpreting common patterns
Return-positive but low-vol bins : Favorable drift windows for passive adds or tight-stop trend continuation.
Return-flat but high-vol bins : Opportunity for mean reversion or breakout scalping, but manage risk accordingly.
High-volume bins : Better expected execution quality; schedule size here if slippage matters.
Wide CI : Edge is unstable or sample is thin; treat as exploratory until more data accumulates.
Best practices
Revalidate after regime shifts (new macro cycle, liquidity regime change, major exchange microstructure updates).
Use multiple lenses: DOW to find the day, then Hour-of-Day to refine the entry window.
Combine with your core setup signals; treat seasonality as a filter or weight, not a standalone trigger.
Test across assets/timeframes—edges are instrument-specific and may not transfer 1:1.
Limitations & notes
History-dependent: short histories or sparse intraday data reduce reliability.
Not causal: a hot Tuesday doesn’t guarantee future Tuesday strength; treat as probabilistic bias.
Aggregation bias: changing session hours or symbol migrations can distort older samples.
CI is z-approximate: good for fast triage, not a substitute for full hypothesis testing.
Quick setup
Use Returns + Day of Week + 252d to get a clean yearly map of weekday edge.
Flip to Hour of Day on intraday charts to schedule precise entries/exits.
Keep Show Values and Confidence Intervals on while you calibrate; hide later for a clean visual.
The Multi-Mode Seasonality Map helps you convert the calendar from an afterthought into a quantitative edge, surfacing when an asset tends to move, expand, or stay quiet—so you can plan, size, and execute with intent. Indicator
Kalman Exponentialy Weighted Moving Average | MisinkoMasterThe Kalman Exponentialy Weighted Moving Average is a technical analysis tool providing users with more responsive and smoother signals, providing crystal-clear signals and giving investors valuable insights on market trends, however it could be used in many cases.
A deeper dive into the indicator:
When going through my creation of strategies, I had stumbled on an indicator called "EWMA", which worked decently, but it was far too simple in my opinion so I decided to combine the EMA & WMA, but with a little more complexity, and it has worked .
I began by learning how both MAs work, I already knew how WMA works, but EMA I did not.
After learning both I found out they were quite simple in principle and that there was a way to combine them in such way that you would get really good signals, however it was way too noisy.
While it could avoid major dumps that were not avoided by most indicators, it would lose that edge because of being too noisy.
After testing out many conditions, combinations & more, the best working one was this one:
WMA > KEWMA = long
WMA < KEWMA = short
I will explain this later, but this gave fast signals, and while it still was noisy it was better then before.
To smooth it out, I started testing price filters => Gaussian Filter and many more were tested out, but they either slowed it down to the point it was no longer of much use, or did not smooth it at all.
After testing the Kalman filter on this thing, I was shocked.
It was just right and made the indicator a lot better, smoothed it and kept most of the responsivness it had.
Now to the big question: "How is it calculated?"
Now first it needs to calculate the Kalman source, which smooths the source which will be used.
After that, we calculate the Weighted Moving Average for " n " period on the Kalman source.
Now that we have our WMA values, we need to calculate " a ".
a is calculated in the following formula:
a = 2/(1+ n )
where n is the user defined length
Now for the last part:
KEWMA = WMAyesterday * (1-a) + WMAtoday * a
This creates a very accurate and reactive indicator, that can prove useful in many uses, beyond those I will and did talk about.
For the trend logic as mentioned before:
Long = WMA > KEWMA
Short = WMA < KEWMA
This worked best, but you might find better ways of using it.
I think that is all I have to say about it, I left it open source so you can all code it in your strategies and play around with it.
Enjoy Gs! Indicator
Economic Profit (Fixed & Labeled) — Rated + PeersFRAC (Fundamental-Rated-Asset-Calculate)
FRAC is a fundamentals-driven tool designed to measure whether a company is creating or destroying shareholder value. Unlike surface ratios, FRAC uses Economic Profit (ROIC – WACC) as its engine, showing whether a business truly outperforms its cost of capital.
🔹 What FRAC Does
Calculates ROIC (Return on Invested Capital) vs. WACC (Weighted Average Cost of Capital).
Shows whether a company is creating or destroying shareholder value.
Uses tiered color coding for clarity:
🔵 Superior (Aqua Blue) → Top tier; best of the best.
🟣 Elite (Purple) → Strong value creation.
🟢 Positive (Green) → Solid, creating shareholder value.
🟡 Marginal (Yellow) → Barely covering cost of capital.
🔴 Negative (Red) → Value destruction.
🔹 Composite Ranking System (1–4)
FRAC also assigns each company a Composite Rank so you can compare multiple names side by side. The rank works like this:
Rank 1 → Superior (🔵 Aqua Blue)
Best possible rating; wide gap between ROIC and WACC.
Rank 2 → Elite (🟣 Purple)
Strongly positive; above-average capital efficiency.
Rank 3 → Positive (🟢 Green)
Creating value but only moderately; not a top compounder.
Rank 4 → Marginal/Negative (🟡/🔴)
Weak or destructive; either barely covering WACC or losing money on capital.
✅ How to Use the Ranks
When comparing a set of peers (e.g., NVDA, AMD, INTC):
FRAC will display each company’s color rating + composite rank (1–4).
You can instantly see who is strongest vs. weakest in the group.
Best decisions = overweight Rank 1 & 2 companies, avoid Rank 4 names.
🔹 Key Inputs Explained
Risk-Free Asset → Typically the 10-Year US Treasury yield (US10Y).
Corporate Tax Rate → Effective tax rate for the company’s country (e.g., USCTR).
Expected Market Return → Historical average ~8–10%, adjustable.
Beta Lookback Period → Controls how far back Beta is calculated (longer = more stable, shorter = more reactive).
👉 These must be set correctly for FRAC to calculate WACC accurately.
🔹 Example Comparison
NVDA: ROIC 25% – WACC 7% = +18% → 🔵 Superior → Rank 1
AMD: ROIC 17% – WACC 8% = +9% → 🟣 Elite → Rank 2
INTC: ROIC 11% – WACC 9% = +2% → 🟢 Positive → Rank 3
FSLY: ROIC 5% – WACC 10% = –5% → 🔴 Negative → Rank 4
🔹 Why It Matters
Buffett said: “The best businesses are those that can consistently generate returns on capital above their cost of capital.”
FRAC turns that into a visual + numeric rating system (1–4), making comparisons across peers simple and actionable.
🔹 Credit
FRAC was created by Hunter Hammond (Elite x FineFir), inspired by corporate finance models of Economic Profit and Economic Value Added (EVA).
⚠️ Disclaimer: FRAC is a research framework, not financial advice. Always pair with full due diligence. Indicator
Arnaud Legoux Trend Aggregator | Lyro RSArnaud Legoux Trend Aggregator
Introduction
Arnaud Legoux Trend Aggregator is a custom-built trend analysis tool that blends classic market oscillators with advanced normalization, advanced math functions and Arnaud Legoux smoothing. Unlike conventional indicators, 𝓐𝓛𝓣𝓐 aggregates market momentum, volatility and trend strength.
Signal Insight
The 𝓐𝓛𝓣𝓐 line visually reflects the aggregated directional bias. A rise above the middle line threshold signals bullish strength, while a drop below the middle line indicates bearish momentum.
Another way to interpret the 𝓐𝓛𝓣𝓐 is through overbought and oversold conditions. When the 𝓐𝓛𝓣𝓐 rises above the +0.7 threshold, it suggests an overbought market and signals a strong uptrend. Conversely, a drop below the -0.7 level indicates an oversold condition and a strong downtrend.
When the oscillator hovers near the zero line, especially within the neutral ±0.3 band, it suggests that no single directional force is dominating—common during consolidation phases or pre-breakout compression.
Real-World Example
Usually 𝓐𝓛𝓣𝓐 is used by following the bar color for simple signals; however, like most indicators there are unique ways to use an indicator. Let’s dive deep into such ways.
The market begins with a green bar color, raising awareness for a potential long setup—but not a direct entry. In this methodology, bar coloring serves as an alert mechanism rather than a strict entry trigger.
The first long position was initiated when the 𝓐𝓛𝓣𝓐 signal line crossed above the +0.3 threshold, suggesting a shift in directional acceleration. This entry coincided with a rising price movement, validating the trade.
As price advanced, the position was exited into cash—not reversed into a short—because the short criteria for this use case are distinct. The exit was prompted by 𝓐𝓛𝓣𝓐 crossing back below the +0.3 level, signaling the potential weakening of the long trend.
Later, as 𝓐𝓛𝓣𝓐 crossed below 0, attention shifted toward short opportunities. A short entry was confirmed when 𝓐𝓛𝓣𝓐 dipped below -0.3, indicating growing downside momentum. The position was eventually closed when 𝓐𝓛𝓣𝓐 crossed back above the -0.3 boundary—signaling a possible deceleration of the bearish move.
This logic was consistently applied in subsequent setups, emphasizing the role of 𝓐𝓛𝓣𝓐’s thresholds in guiding both entries and exits.
Framework
The Arnaud Legoux Trend Aggregator (ALTA) combines multiple technical indicators into a single smoothed signal. It uses RSI, MACD, Bollinger Bands, Stochastic Momentum Index, and ATR.
Each indicator's output is normalized to a common scale to eliminate bias and ensure consistency. These normalized values are then transformed using a hyperbolic tangent function (Tanh).
The final score is refined with a custom Arnaud Legoux Moving Average (ALMA) function, which offers responsive smoothing that adapts quickly to price changes. This results in a clear signal that reacts efficiently to shifting market conditions.
⚠️ WARNING ⚠️: THIS INDICATOR, OR ANY OTHER WE (LYRO RS) PUBLISH, IS NOT FINANCIAL OR INVESTMENT ADVICE. EVERY INDICATOR SHOULD BE COMBINED WITH PRICE ACTION, FUNDAMENTALS, OTHER TECHNICAL ANALYSIS TOOLS & PROPER RISK. MANAGEMENT. Indicator
Aurora Flow Oscillator [QuantAlgo]The Aurora Flow Oscillator is an advanced momentum-based technical indicator designed to identify market direction, momentum shifts, and potential reversal zones using adaptive filtering techniques. It visualizes price momentum through a dynamic oscillator that quantifies trend strength and direction, helping traders and investors recognize momentum shifts and trading opportunities across various timeframes and asset class.
🟢 Technical Foundation
The Aurora Flow Oscillator employs a sophisticated mathematical approach with adaptive momentum filtering to analyze market conditions, including:
Price-Based Momentum Calculation: Calculates logarithmic price changes to measure the rate and magnitude of market movement
Adaptive Momentum Filtering: Applies an advanced filtering algorithm to smooth momentum calculations while preserving important signals
Acceleration Analysis: Incorporates momentum acceleration to identify shifts in market direction before they become obvious
Signal Normalization: Automatically scales the oscillator output to a range between -100 and 100 for consistent interpretation across different market conditions
The indicator processes price data through multiple filtering stages, applying mathematical principles including exponential smoothing with adaptive coefficients. This creates an oscillator that dynamically adjusts to market volatility while maintaining responsiveness to genuine trend changes.
🟢 Key Features & Signals
1. Momentum Flow and Extreme Zone Identification
The oscillator presents market momentum through an intuitive visual display that clearly indicates both direction and strength:
Above Zero: Indicates positive momentum and potential bullish conditions
Below Zero: Indicates negative momentum and potential bearish conditions
Slope Direction: The angle and direction of the oscillator provide immediate insight into momentum strength
Zero Line Crossings: Signal potential trend changes and new directional momentum
The indicator also identifies potential overbought and oversold market conditions through extreme zone markings:
Upper Zone (>50): Indicates strong bullish momentum that may be approaching exhaustion
Lower Zone (<-50): Indicates strong bearish momentum that may be approaching exhaustion
Extreme Boundaries (±95): Mark potentially unsustainable momentum levels where reversals become increasingly likely
These zones are displayed with gradient intensity that increases as the oscillator moves toward extremes, helping traders and investors:
→ Identify potential reversal zones
→ Determine appropriate entry and exit points
→ Gauge overall market sentiment strength
2. Customizable Trading Style Presets
The Aurora Flow Oscillator offers pre-configured settings for different trading approaches:
Default (80,150): Balanced configuration suitable for most trading and investing situations.
Scalping (5,80): Highly responsive settings for ultra-short-term trades. Generates frequent signals and catches quick price movements. Best for 1-15min charts when making many trades per day.
Day Trading (8,120): Optimized for intraday movements with faster response than default settings while maintaining reasonable signal quality. Ideal for 5-60min or 4h-12h timeframes.
Swing Trading (10,200): Designed for multi-day positions with stronger noise filtering. Focuses on capturing larger price swings while avoiding minor fluctuations. Works best on 1-4h and daily charts.
Position Trading (14,250): For longer-term position traders/investors seeking significant market trends. Reduces false signals by heavily filtering market noise. Ideal for daily or even weekly charts.
Trend Following (16,300): Maximum smoothing that prioritizes established directional movements over short-term fluctuations. Best used on daily and weekly charts, but can also be used for lower timeframe trading.
Countertrend (7,100): Tuned to detect potential reversals and exhaustion points in trends. More sensitive to momentum shifts than other presets. Effective on 15min-4h charts, as well as daily and weekly charts.
Each preset automatically adjusts internal parameters for optimal performance in the selected trading context, providing flexibility across different market approaches without requiring complex manual configuration.
🟢 Practical Usage Tips
1/ Trend Analysis and Interpretation
→ Direction Assessment: Evaluate the oscillator's position relative to zero to determine underlying momentum bias
→ Momentum Strength: Measure the oscillator's distance from zero within the -100 to +100 range to quantify momentum magnitude
→ Trend Consistency: Monitor the oscillator's path for sustained directional movement without frequent zero-line crossings
→ Reversal Detection: Watch for oscillator divergence from price and deceleration of movement when approaching extreme zones
2/ Signal Generation Strategies
Depending on your trading approach, multiple signal strategies can be employed:
Trend Following Signals:
Enter long positions when the oscillator crosses above zero
Enter short positions when the oscillator crosses below zero
Add to positions on pullbacks while maintaining the overall trend direction
Countertrend Signals:
Look for potential reversals when the oscillator reaches extreme zones (±95)
Enter contrary positions when momentum shows signs of exhaustion
Use oscillator divergence with price as additional confirmation
Momentum Shift Signals:
Enter positions when oscillator changes direction after establishing a trend
Exit positions when oscillator direction reverses against your position
Scale position size based on oscillator strength percentage
3/ Timeframe Optimization
The indicator can be effectively applied across different timeframes with these considerations:
Lower Timeframes (1-15min):
Use Scalping or Day Trading presets
Focus on quick momentum shifts and zero-line crossings
Be cautious of noise in extreme market conditions
Medium Timeframes (30min-4h):
Use Default or Swing Trading presets
Look for established trends and potential reversal zones
Combine with support/resistance analysis for entry/exit precision
Higher Timeframes (Daily+):
Use Position Trading or Trend Following presets
Focus on major trend identification and long-term positioning
Use extreme zones for position management rather than immediate reversals
🟢 Pro Tips
Price Momentum Period:
→ Lower values (5-7) increase sensitivity to minor price fluctuations but capture more market noise
→ Higher values (10-16) emphasize sustained momentum shifts at the cost of delayed response
→ Adjust based on your timeframe (lower for shorter timeframes, higher for longer timeframes)
Oscillator Filter Period:
→ Lower values (80-120) produce more frequent directional changes and earlier response to momentum shifts
→ Higher values (200-300) filter out shorter-term fluctuations to highlight dominant market cycles
→ Match to your typical holding period (shorter holding time = lower filter values)
Multi-Timeframe Analysis:
→ Compare oscillator readings across different timeframes for confluence
→ Look for alignment between higher and lower timeframe signals
→ Use higher timeframe for trend direction, lower for earlier entries
Volatility-Adaptive Trading:
→ Use oscillator strength to adjust position sizing (stronger = larger)
→ Consider reducing exposure when oscillator reaches extreme zones
→ Implement tighter stops during periods of oscillator acceleration
Combination Strategies:
→ Pair with volume indicators for confirmation of momentum shifts
→ Use with support/resistance levels for strategic entry and exit points
→ Combine with volatility indicators for comprehensive market context Indicator
Indicator
Adaptive Trend Flow [QuantAlgo]Adaptive Trend Flow 📈🌊
The Adaptive Trend Flow by QuantAlgo is a sophisticated technical indicator that harnesses the power of volatility-adjusted EMAs to navigate market trends with precision. By seamlessly integrating a dynamic dual-EMA system with adaptive volatility bands, this premium tool enables traders and investors to identify and capitalize on sustained market moves while effectively filtering out noise. The indicator's unique approach to trend detection combines classical technical analysis with modern adaptive techniques, providing traders and investors with clear, actionable signals across various market conditions and asset class.
💫 Indicator Architecture
The Adaptive Trend Flow provides a sophisticated framework for assessing market trends through a harmonious blend of EMA dynamics and volatility-based boundary calculations. Unlike traditional moving average systems that use fixed parameters, this indicator incorporates smart volatility measurements to automatically adjust its sensitivity to market conditions. The core algorithm employs a dual EMA system combined with standard deviation-based volatility bands, creating a self-adjusting mechanism that expands and contracts based on market volatility. This adaptive approach allows the indicator to maintain its effectiveness across different market phases - from ranging to trending conditions. The volatility-adjusted bands act as dynamic support and resistance levels, while the gradient visualization system provides instant visual feedback on trend strength and duration.
📊 Technical Composition and Calculation
The Adaptive Trend Flow is composed of several technical components that create a dynamic trending system:
Dual EMA System: Utilizes fast and slow EMAs for primary trend detection
Volatility Integration: Computes and smooths volatility for adaptive band calculation
Dynamic Band Generation: Creates volatility-adjusted boundaries for trend validation
Gradient Visualization: Provides progressive visual feedback on trend strength
📈 Key Indicators and Features
The Adaptive Trend Flow utilizes customizable length parameters for both EMAs and volatility calculations to adapt to different trading styles. The trend detection component evaluates price action relative to the dynamic bands to validate signals and identify potential reversals.
The indicator incorporates multi-layered visualization with:
Color-coded basis and trend lines (bullish/bearish)
Adaptive volatility-based bands
Progressive gradient background for trend duration
Clear trend reversal signals (𝑳/𝑺)
Smooth fills between key levels
Programmable alerts for trend changes
⚡️ Practical Applications and Examples
✅ Add the Indicator: Add the indicator to your PulseWire chart by clicking on the star icon to add it to your favorites ⭐️
👀 Monitor Trends: Watch the basis line and trend band interactions to identify trend direction and strength. The gradient background intensity indicates trend duration and conviction.
🎯 Track Signals: Pay attention to the trend reversal markers that appear on the chart:
→ Long signals (𝑳) appear when price action confirms a bullish trend reversal
→ Short signals (𝑺) indicate validated bearish trend reversals
🔔 Set Alerts: Configure alerts for trend changes in both bullish and bearish directions, ensuring you never miss significant technical developments.
🌟 Summary and Tips
The Adaptive Trend Flow by QuantAlgo is a sophisticated technical tool designed to support trend-following strategies across different market environments and asset class. By combining dual EMA analysis with volatility-adjusted bands, it helps traders and investors identify significant trend changes while filtering out market noise, providing validated signals. The tool's adaptability through customizable EMA lengths, volatility smoothing, and sensitivity settings makes it suitable for various trading timeframes and styles, allowing users to capture trending opportunities while maintaining protection against false signals.
Key parameters to optimize for your trading and/or investing style:
Main Length: Adjust for more or less sensitivity to trend changes (default: 10)
Smoothing Length: Fine-tune volatility calculations for signal stability (default: 14)
Sensitivity: Balance band width for trend validation (default: 2.0)
Visual Settings: Customize appearance with color and display options
The Adaptive Trend Flow is particularly effective for:
Identifying sustained market trends
Detecting trend reversals with confirmation
Measuring trend strength and duration
Filtering out market noise and false signals
Remember to:
Allow the indicator to validate trend changes before taking action
Use the gradient background to gauge trend strength
Combine with volume analysis for additional confirmation
Consider multiple timeframes for a complete market view
Adjust sensitivity based on market volatility conditions
Indicator
EMA Volatility Channel [QuantAlgo]EMA Volatility Channel 🌊📈
The EMA Volatility Channel by QuantAlgo is an advanced technical indicator designed to capture price volatility and trend dynamics through adaptive channels based on exponential moving averages. This sophisticated system combines EMA-based trend analysis with dynamic volatility-adjusted bands to help traders and investors identify trend direction, potential reversals, and market volatility conditions. By evaluating both price momentum and volatility together, this tool enables users to make informed trading decisions while adapting to changing market conditions.
💫 Dynamic Channel Architecture
The EMA Volatility Channel provides a unique framework for assessing market trends through a blend of exponential moving averages and volatility-based channel calculations. Unlike traditional channel indicators that use fixed-width bands, this system incorporates dynamic volatility measurements to adjust channel width automatically, helping users determine whether price movements are significant relative to current market conditions. By combining smooth EMA trends with adaptive volatility bands, it evaluates both directional movement and market volatility, while the smoothing parameters ensure stable yet responsive channel adjustments. This adaptive approach allows users to identify trending conditions while remaining aware of volatility expansions and contractions, enhancing both trend-following and reversal strategies.
📊 Indicator Components & Mechanics
The EMA Volatility Channel is composed of several technical components that create a dynamic channel system:
EMA Midline: Calculates a smoothed exponential moving average that serves as the channel's centerline, providing a clear reference for trend direction.
Volatility Measurement: Computes average price movement to determine dynamic channel width, adapting to changing market conditions automatically.
Smooth Band Calculation: Applies additional smoothing to the channel bands, reducing noise while maintaining responsiveness to significant price movements.
📈 Key Indicators and Features
The EMA Volatility Channel combines various technical tools to deliver a comprehensive analysis of market conditions.
The indicator utilizes exponential moving averages with customizable length and smoothing parameters to adapt to different trading styles. Volatility calculations are applied to determine channel width, providing context-aware boundaries for price movement. The trend detection component evaluates price action relative to the channel bands, helping validate trends and identify potential reversals.
The indicator incorporates multi-layered visualization with color-coded channels and bars to signal both trend direction and market position. These adaptive visual cues, combined with programmable alerts for channel breakouts, help traders and investors track both trend changes and volatility conditions, supporting both trend-following and mean-reversion strategies.
⚡️ Practical Applications and Examples
✅ Add the Indicator: Add the indicator to your PulseWire chart by clicking on the star icon to add it to your favorites ⭐️
👀 Monitor Channel Position: Watch the price position relative to the channel bands to identify trend direction and potential reversals. When price moves outside the channel, consider potential trend changes or extreme conditions.
🔔 Set Alerts: Configure alerts for channel breakouts and trend changes, ensuring you can act on significant technical developments promptly.
🌟 Summary and Tips
The EMA Volatility Channel by QuantAlgo is a versatile technical tool, designed to support both trend following and volatility analysis across different market environments. By combining smooth EMA trends with dynamic volatility-based channels, it helps traders and investors identify significant price movements while measuring market volatility, providing reliable technical signals. The tool's adaptability across timeframes makes it suitable for both trend-following and reversal strategies, allowing users to capture opportunities while maintaining awareness of changing market conditions. Indicator
Indicator
Cross-Asset Correlation Trend IndicatorCross-Asset Correlation Trend Indicator
This indicator uses correlations between the charted asset and ten others to calculate an overall trend prediction. Each ticker is configurable, and by analyzing the trend of each asset, the indicator predicts an average trend for the main asset on the chart. The strength of each asset's trend is weighted by its correlation to the charted asset, resulting in a single average trend signal. This can be a rather robust and effective signal, though it is often slow.
Functionality Overview :
The Cross-Asset Correlation Trend Indicator calculates the average trend of a charted asset based on the correlation and trend of up to ten other assets. Each asset is assigned a trend signal using a simple EMA crossover method (two customizable EMAs). If the shorter EMA crosses above the longer one, the asset trend is marked as positive; if it crosses below, the trend is negative. Each trend is then weighted by the correlation coefficient between that asset’s closing price and the charted asset’s closing price. The final output is an average weighted trend signal, which combines each trend with its respective correlation weight.
Input Parameters :
EMA 1 Length : Sets the period of the shorter EMA used to determine trends.
EMA 2 Length : Sets the period of the longer EMA used to determine trends.
Correlation Length : Defines the lookback period used for calculating the correlation between the charted asset and each of the other selected assets.
Asset Tickers : Each of the ten tickers is configurable, allowing you to set specific assets to analyze correlations with the charted asset.
Show Trend Table : Toggle to show or hide a table with each asset’s weighted trend. The table displays green, red, or white text for each weighted trend, indicating positive, negative, or neutral trends, respectively.
Table Position : Choose the position of the trend table on the chart.
Recommended Use :
As always, it’s essential to backtest the indicator thoroughly on your chosen asset and timeframe to ensure it aligns with your strategy. Feel free to modify the input parameters as needed—while the defaults work well for me, they may need adjustment to better suit your assets, timeframes, and trading style.
As always, I wish you the best of luck and immense fortune as you develop your systems. May this indicator help you make well-informed, profitable decisions! Indicator
Dynamic Score SMA [QuantAlgo]Dynamic Score SMA 📈🌊
The Dynamic Score SMA by QuantAlgo offers a powerful trend-following approach that combines the simplicity of the Simple Moving Average (SMA) with an innovative dynamic trend scoring technique . By continuously evaluating price movement relative to the SMA over a customizable window, this indicator adapts to varying market conditions, providing traders and investors with clearer, more adaptable trend signals. With this dynamic scoring approach, the Dynamic Score SMA helps identify trend shifts, allowing for more strategic decision-making.
🌟 Conceptual Foundation and Innovation
At the core of the Dynamic Score SMA is its dynamic trend score system , which assesses price movements by comparing them to the SMA over a series of historical data points. This technique goes beyond traditional SMA indicators by offering a dynamic, probabilistic evaluation of trend strength, delivering a more responsive and nuanced view of market direction. The integration of this scoring system enables traders and investors to navigate both trending and sideway markets with greater confidence and precision.
⚙️ Technical Composition and Calculation
The Dynamic Score SMA leverages the Simple Moving Average to establish a baseline trend, with customizable SMA length to control the indicator’s sensitivity. The dynamic trend scoring technique then evaluates price behavior relative to the SMA over a specified window, generating a trend score that reflects the current market bias.
When the score crosses the designated uptrend or downtrend thresholds, the indicator signals a potential trend shift. By adjusting the SMA length, window duration, and thresholds, users can refine the indicator’s responsiveness to match their preferred trading or investing strategy, making it suitable for both volatile and steady markets.
📈 Features and Practical Applications
Customizable SMA Length: Set the length of the SMA to control how sensitive the trend is to price changes. Longer lengths produce smoother trends, while shorter lengths increase responsiveness.
Window Length for Dynamic Scoring: Adjust the window length to determine how many data points are considered in the dynamic trend score calculation, allowing for more tailored analysis of recent versus long-term trends.
Uptrend/Downtrend Thresholds: Define thresholds for triggering trend signals. Higher thresholds reduce sensitivity, providing clearer signals in volatile markets, while lower thresholds capture shorter-term movements.
Bar and Background Coloring: Visual cues, including bar coloring and background fills, provide a quick reference for current trend direction, making it easier to monitor market conditions.
Trend Confirmation: The dynamic trend scoring system verifies trend strength, offering more reliable entry and exit points by filtering out potential false signals.
⚡️ How to Use
✅ Add the Indicator: Add the Dynamic Score SMA to your favourites, then apply it to your chart. Customize the SMA length, window size, and thresholds to match your trading or investing preferences.
👀 Monitor Trend Shifts: Observe the trend in relation to the SMA and watch for signals when the score crosses key thresholds. Bar and/or background coloring will help identify the current trend direction and any shifts in momentum.
🔔 Set Alerts: Configure alerts for significant trend crossovers and reversals, enabling you to act on market changes in real-time without needing constant chart observation.
💫 Summary and Usage Tips
The Dynamic Score SMA by QuantAlgo is a sophisticated trend-following indicator that combines the familiarity of the SMA with a dynamic trend scoring system, providing a more adaptable and probabilistic approach to trend analysis. By tailoring the SMA length, scoring window, and thresholds, traders and investors can fine-tune the indicator for both short-term adjustments and long-term trend following. For optimal use, adjust sensitivity based on market volatility, and rely on the visual cues for clear trend confirmation. Whether you’re navigating choppy markets or stable trends, the Dynamic Score SMA offers a refined approach to capturing market direction with enhanced precision. Indicator
Dynamic Score Supertrend [QuantAlgo]Dynamic Score Supertrend 📈🚀
The Dynamic Score Supertrend by QuantAlgo introduces a sophisticated trend-following tool that combines the well-known Supertrend indicator with an innovative dynamic trend scoring technique . By tracking market momentum through a scoring system that evaluates price behavior over a customizable window, this indicator adapts to changing market conditions. The result is a clearer, more adaptive tool that helps traders and investors detect and capitalize on trend shifts with greater precision.
💫 Conceptual Foundation and Innovation
At the core of the Dynamic Score Supertrend is the dynamic trend score system , which measures price movements relative to the Supertrend’s upper and lower bands. This scoring technique adds a layer of trend validation, assessing the strength of price trends over time. Unlike traditional Supertrend indicators that rely solely on ATR calculations, this system incorporates a scoring mechanism that provides more insight into trend direction, allowing traders and investors to navigate both trending and choppy markets with greater confidence.
✨ Technical Composition and Calculation
The Dynamic Score Supertrend utilizes the Average True Range (ATR) to calculate the upper and lower Supertrend bands. The dynamic trend scoring technique then compares the price to these bands over a customizable window, generating a trend score that reflects the current market direction.
When the score exceeds the uptrend or downtrend thresholds, it signals a possible shift in market direction. By adjusting the ATR settings and window length, the indicator becomes more adaptable to different market conditions, from steady trends to periods of higher volatility. This customization allows users to refine the Supertrend’s sensitivity and responsiveness based on their trading or investing style.
📈 Features and Practical Applications
Customizable ATR Settings: Adjust the ATR length and multiplier to control the sensitivity of the Supertrend bands. This allows the indicator to smooth out noise or react more quickly to price shifts, depending on market conditions.
Window Length for Dynamic Scoring: Modify the window length to adjust how many data points the scoring system considers, allowing you to tailor the indicator’s responsiveness to short-term or long-term trends.
Uptrend/Downtrend Thresholds: Set thresholds for identifying trend signals. Increase these thresholds for more reliable signals in choppy markets, or lower them for more aggressive entry points in trending markets.
Bar and Background Coloring: Visual cues such as bar coloring and background fills highlight the direction of the current trend, making it easier to spot potential reversals and trend shifts.
Trend Confirmation: The dynamic trend score system provides a clearer confirmation of trend strength, helping you identify strong, sustained movements while filtering out false signals.
⚡️ How to Use
✅ Add the Indicator: Add the Dynamic Score Supertrend to your favourites, then apply it to your chart. Adjust the ATR length, multiplier, and dynamic score settings to suit your trading or investing strategy.
👀 Monitor Trend Shifts: Track price movements relative to the Supertrend bands and use the dynamic trend score to confirm the strength of a trend. Bar and background colors make it easy to visualize key trend shifts.
🔔 Set Alerts: Configure alerts when the dynamic trend score crosses key thresholds, so you can act on significant trend changes without constantly monitoring the charts.
🌟 Summary and Usage Tips
The Dynamic Score Supertrend by QuantAlgo is a robust trend-following tool that combines the power of the Supertrend with an advanced dynamic scoring system. This approach provides more adaptable and reliable trend signals, helping traders and investors make informed decisions in trending markets. The customizable ATR settings and scoring thresholds make it versatile across various market conditions, allowing you to fine-tune the indicator for both short-term momentum and long-term trend following. To maximize its effectiveness, adjust the settings based on current market volatility and use the visual cues to confirm trend shifts. The Dynamic Score Supertrend offers a refined, probabilistic approach to trading and investing, making it a valuable addition to your toolkit. Indicator
Adaptive Volatility-Controlled LSMA [QuantAlgo]Adaptive Volatility-Controlled LSMA by QuantAlgo 📈💫
Introducing the Adaptive Volatility-Controlled LSMA (Least Squares Moving Average) , a powerful trend-following indicator that combines trend detection with dynamic volatility adjustments. This indicator is designed to help traders and investors identify market trends while accounting for price volatility, making it suitable for a wide range of assets and timeframes. By integrating LSMA for trend analysis and Average True Range (ATR) for volatility control, this tool provides clearer signals during both trending and volatile market conditions.
💡 Core Concept and Innovation
The Adaptive Volatility-Controlled LSMA leverages the precision of the LSMA to track market trends and combines it with the sensitivity of the ATR to account for market volatility. LSMA fits a linear regression line to price data, providing a smoothed trend line that is less reactive to short-term noise. The ATR, on the other hand, dynamically adjusts the volatility bands around the LSMA, allowing the indicator to filter out false signals and respond to significant price moves. This combination provides traders with a reliable tool to identify trend shifts while managing risk in volatile markets.
📊 Technical Breakdown and Calculations
The indicator consists of the following components:
1. Least Squares Moving Average (LSMA): The LSMA calculates a linear regression line over a defined period to smooth out price fluctuations and reveal the underlying trend. It is more reactive to recent data than traditional moving averages, allowing for quicker trend detection.
2. ATR-Based Volatility Bands: The Average True Range (ATR) measures market volatility and creates upper and lower bands around the LSMA. These bands expand and contract based on market conditions, helping traders identify when price movements are significant enough to indicate a new trend.
3. Volatility Extensions: To further account for rapid market changes, the bands are extended using additional volatility measures. This ensures that trend signals are generated when price movements exceed both the standard volatility range and the extended volatility range.
⚙️ Step-by-Step Calculation:
1. LSMA Calculation: The LSMA is computed using a least squares regression method over a user-defined length. This provides a trend line that adapts to recent price movements while smoothing out noise.
2. ATR and Volatility Bands: ATR is calculated over a user-defined length and is multiplied by a factor to create upper and lower bands around the LSMA. These bands help detect when price movements are substantial enough to signal a new trend.
3. Trend Detection: The price’s relationship to the LSMA and the volatility bands is used to determine trend direction. If the price crosses above the upper volatility band, a bullish trend is detected. Conversely, a cross below the lower band indicates a bearish trend.
✅ Customizable Inputs and Features:
The Adaptive Volatility-Controlled LSMA offers a variety of customizable options to suit different trading or investing styles:
📈 Trend Settings:
1. LSMA Length: Adjust the length of the LSMA to control its sensitivity to price changes. A shorter length reacts quickly to new data, while a longer length smooths the trend line.
2. Price Source: Choose the type of price (e.g., close, high, low) that the LSMA uses to calculate trends, allowing for different interpretations of price data.
🌊 Volatility Controls:
ATR Length and Multiplier: Adjust the length and sensitivity of the ATR to control how volatility is measured. A higher ATR multiplier widens the bands, making the trend detection less sensitive, while a lower multiplier tightens the bands, increasing sensitivity.
🎨 Visualization and Alerts:
1. Bar Coloring: Customize bar colors to visually distinguish between uptrends and downtrends.
2. Volatility Bands: Enable or disable the display of volatility bands on the chart. The bands provide visual cues about trend strength and volatility thresholds.
3. Alerts: Set alerts for when the price crosses the upper or lower volatility bands, signaling potential trend changes.
📈 Practical Applications
The Adaptive Volatility-Controlled LSMA is ideal for traders and investors looking to follow trends while accounting for market volatility. Its key use cases include:
Identifying Trend Reversals: The indicator detects when price movements break through volatility bands, signaling potential trend reversals.
Filtering Market Noise: By applying ATR-based volatility filtering, the indicator helps reduce false signals caused by short-term price fluctuations.
Managing Risk: The volatility bands adjust dynamically to account for market conditions, helping traders manage risk and improve the accuracy of their trend-following strategies.
⭐️ Summary
The Adaptive Volatility-Controlled LSMA by QuantAlgo offers a robust and flexible approach to trend detection and volatility management. Its combination of LSMA and ATR creates clearer, more reliable signals, making it a valuable tool for navigating trending and volatile markets. Whether you're detecting trend shifts or filtering market noise, this indicator provides the tools you need to enhance your trading and investing strategy.
Note: The Adaptive Volatility-Controlled LSMA is a tool to enhance market analysis. It should be used in conjunction with other analytical tools and should not be relied upon as the sole basis for trading or investment decisions. No signals or indicators constitute financial advice, and past performance is not indicative of future results. Indicator
Adaptive EMA with ATR and Standard Deviation [QuantAlgo]Adaptive EMA with ATR and Standard Deviation by QuantAlgo 📈✨
Introducing the Adaptive EMA with ATR and Standard Deviation , a comprehensive trend-following indicator designed to combine the smoothness of an Exponential Moving Average (EMA) with the volatility adjustments of Average True Range (ATR) and Standard Deviation. This synergy allows traders and investors to better identify market trends while accounting for volatility, delivering clearer signals in both trending and volatile market conditions. This indicator is suitable for traders and investors seeking to balance trend detection and volatility management, offering a robust and adaptable approach across various asset classes and timeframes.
💫 Core Concept and Innovation
The Adaptive EMA with ATR and Standard Deviation brings together the trend-smoothing properties of the EMA and the volatility sensitivity of ATR and Standard Deviation. By using the EMA to track price movements over time, the indicator smooths out minor fluctuations while still providing valuable insights into overall market direction. However, market volatility can sometimes distort simple moving averages, so the ATR and Standard Deviation components dynamically adjust the trend signals, offering more nuanced insights into trend strength and reversals. This combination equips traders with a powerful tool to navigate unpredictable markets while minimizing false signals.
📊 Technical Breakdown and Calculations
The Adaptive EMA with ATR and Standard Deviation relies on three key technical components:
1. Exponential Moving Average (EMA): The EMA forms the base of the trend detection. Unlike a Simple Moving Average (SMA), the EMA gives more weight to recent price changes, allowing it to react more quickly to new data. Users can adjust the length of the EMA to make it more or less responsive to price movements.
2. Standard Deviation Bands: These bands are calculated from the standard deviation of the EMA and represent dynamic volatility thresholds. The upper and lower bands expand or contract based on recent price volatility, providing more accurate signals in both calm and volatile markets.
3. ATR-Based Volatility Filter: The Average True Range (ATR) is used to measure market volatility over a user-defined period. It helps refine the trend signals by filtering out false positives caused by minor price swings. The ATR filter ensures that the indicator only signals significant market movements.
⚙️ Step-by-Step Calculation:
1. EMA Calculation: First, the indicator calculates the EMA over a specified period based on the chosen price source (e.g., close, high, low).
2. Standard Deviation Bands: Then, it computes the standard deviation of the EMA and applies a multiplier to create upper and lower bands around the EMA. These bands adjust dynamically with the level of market volatility.
3. ATR Filtering: In addition to the standard deviation bands, the ATR is applied as a secondary filter to help refine the trend signals. This step helps eliminate signals generated by short-term price spikes or corrections, ensuring that the signals are more reliable.
4. Trend Detection: When the price crosses above the upper band, a bullish trend is identified, while a move below the lower band signals a bearish trend. The system accounts for both the standard deviation and ATR bands to generate these signals.
✅ Customizable Inputs and Features
The Adaptive EMA with ATR and Standard Deviation provides a range of customizable options to fit various trading/investing styles:
📈 Trend Settings:
1. Price Source: Choose the price type (e.g., close, high, low) to base the EMA calculation on, influencing how the trend is tracked.
2. EMA Length: Adjust the length to control how quickly the EMA reacts to price changes. A shorter length provides a more responsive EMA, while a longer period smooths out short-term fluctuations.
🌊 Volatility Controls:
1. Standard Deviation Multiplier: This parameter controls the sensitivity of the trend detection by adjusting the distance between the upper and lower bands from the EMA.
2. TR Length and Multiplier: Fine-tune the ATR settings to control how volatility is filtered, adjusting the indicator’s responsiveness during high or low volatility phases.
🎨 Visualization and Alerts:
1. Bar Coloring: Select different colors for uptrends and downtrends, providing a clear visual cue when trends change.
2. Alerts: Set up alerts to notify you when the price crosses the upper or lower bands, signaling a potential long or short trend shift. Alerts can help you stay informed without constant chart monitoring.
📈 Practical Applications
The Adaptive EMA with ATR and Standard Deviation is ideal for traders and investors looking to balance trend-following strategies with volatility management. Key uses include:
Detecting Trend Reversals: The dynamic bands help identify when the market shifts direction, providing clear signals when a trend reversal is likely.
Filtering Market Noise: By applying both Standard Deviation and ATR filtering, the indicator helps reduce false signals during periods of heightened volatility.
Volatility-Based Risk Management: The adaptability of the bands ensures that traders can manage risk more effectively by responding to shifts in volatility while keeping focus on long-term trends.
⭐️ Comprehensive Summary
The Adaptive EMA with ATR and Standard Deviation is a highly customizable indicator that provides traders with clearer signals for trend detection and volatility management. By dynamically adjusting its calculations based on market conditions, it offers a powerful tool for navigating both trending and volatile markets. Whether you're looking to detect early trend reversals or avoid false signals during periods of high volatility, this indicator gives you the flexibility and accuracy to improve your trading and investing strategies.
Note: The Adaptive EMA with ATR and Standard Deviation is designed to enhance your market analysis but should not be relied upon as the sole basis for trading or investing decisions. Always combine it with other analytical tools and practices. No statements or signals from this indicator constitute financial advice. Past performance is not indicative of future results. Indicator
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Stef's Enterprise Value CalculatorI have learned the hard way why Enterprise Value is far more superior than Market Cap. That's why I made this indicator, but more importantly, why I added several features that other similar indicators just don't have. The key thing is to not just show you Enterprise Value of a company (it's true worth) but also the capability to see that line colored in a specific way, with key stats as a neat table, and the ability to chart the key facts that go into Enterprise Value, which are debt and cash.
I'll say it again: Market Cap is not nearly as good as Enterprise Value. Don't get tricked by what Market Cap does NOT show you and instead focus on Enterprise Value. I hope my indicator, and the features you see below, help investors and traders all over the world better understand this.
Here are the key features:
Enterprise Value Indicator Features:
1. Real-Time Enterprise Value (EV) Display: Track the EV of a company directly on your chart, providing a comprehensive measure of its true market value.
2. Custom Color Trends: Customize the color of your EV line based on specific trends you’re monitoring, allowing for personalized and insightful visual analysis.
3. Debt & Cash Visualization: Plot both debt and cash & equivalents on the same chart, offering a clear and concise view of a company’s financial health.
4. Key Metrics Table: View a table displaying essential metrics including:
- Average EV
- Highest EV
- Lowest EV
- MC-EV (Market Cap minus Enterprise Value)
MC-EV Charting: Easily chart MC-EV to understand how much debt a company has relative to its market cap, providing insight into financial leverage and growth potential.
Why MC-EV Matters: This metric is crucial for evaluating a company’s financial risk and operational efficiency, giving you an edge in making informed investment decisions.
Thanks for reading and I hope you find some value in this! More updates to come. Indicator
