"demark"的腳本搜尋結果

[FTA] Curvature of Meniscus Introduction: Curvature of Meniscus is a hybrid indicator comprised of three parts: 1. The first part is a heavily modified Demarker RSI which is represented as the histogram and is the main trend indicator C1 . 2. The second part is the 0 line which is replaced by the Waddah Attar Explosion V2 indicator C2 . 3. The third part is the background color which is borrowed from the Klinger Safety Zones volume indicator Vol . It is smooth and has almost zero lag. The signals can be used as per nnfx C1-C2-Vol algorithm. The DeMarker indicator is relatively unknown to trading beginners but enjoys huge support from the more experienced traders. The indicator measures the strength of a trend and thus can give you a warning when a change in the trend direction might occur. This can not only help you to find new trade entries but also prevent you from entering into losing trades or letting your open trades run and ultimately result in a loss. The line of the indicator is calculated as follows: First the indicator finds the minimal and the maximal value of the specified period. Afterwards those values are used to calculate the moving average of these values which then is displayed in the chart. Just like most oscillators the DeMarker indicator uses oversold and overbought zones at 0.3 and 0.7 to define good entry opportunities. The "Curvature of Meniscus" indicator has the DeMarker indicator/RSI modified in three different ways: 1. Smoothed and almost no-lagged; 2. Colored histogram to determine the trend and the divergences; 3. An early signal line for those who like to take counter-trend trades/reversal entries. Play with the settings and let me know what you think about this new hybrid!

Adaptive Valuation [BackQuant]Adaptive Valuation What this is A composite, zero-centered oscillator that standardizes several classic indicators and blends them into one “valuation” line. It computes RSI, CCI, Demarker, and the Price Zone Oscillator, converts each to a rolling z-score, then forms a weighted average. Optional smoothing, dynamic overbought and oversold bands, and an on-chart table make the inputs and the final score easy to inspect. How it works Components • RSI with its own lookback. • CCI with its own lookback. • DM (Demarker) with its own lookback. • PZO (Price Zone Oscillator) with its own lookback. Standardization via z-score Each component is transformed using a rolling z-score over lookback bars: z = (value − mean) ÷ stdev , where the mean is an EMA and the stdev is rolling. This puts all inputs on a comparable scale measured in standard deviations. Weighted blend The z-scores are combined with user weights w_rsi, w_cci, w_dm, w_pzo to produce a single valuation series. If desired, it is then smoothed with a selected moving average (SMA, EMA, WMA, HMA, RMA, DEMA, TEMA, LINREG, ALMA, T3). ALMA’s sigma input shapes its curve. Dynamic thresholds (optional) Two ways to set overbought and oversold: • Static : fixed levels at ob_thres and os_thres . • Dynamic : ±k·σ bands, where σ is the rolling standard deviation of the valuation over dynLen . Bands can be centered at zero or around the valuation’s rolling mean ( centerZero ). Visualization and UI • Zero line at 0 with gradient fill that darkens as the valuation moves away from 0. • Optional plotting of band lines and background highlights when OB or OS is active. • Optional candle and background coloring driven by the valuation. • Summary table showing each component’s current z-score, the final score, and a compact status. How it can be used • Bias filter : treat crosses above 0 as bullish bias and below 0 as bearish bias. • Mean-reversion context : look for exhaustion when the valuation enters the OB or OS region, then watch for exits from those regions or a return toward 0. • Signal confirmation : use the final score to confirm setups from structure or price action. • Adaptive banding : with dynamic thresholds, OB and OS adjust to prevailing variability rather than relying on fixed lines. • Component tuning : change weights to emphasize trend (raise DM, reduce RSI/CCI) or range behavior (raise RSI/CCI, reduce DM). PZO can help in swing environments. Why z-score blending helps Indicators often live on different scales. Z-scoring places them on a common, unitless axis, so a one-sigma move in RSI has comparable influence to a one-sigma move in CCI. This reduces scale bias and allows transparent weighting. It also facilitates regime-aware thresholds because the dynamic bands scale with recent dispersion. Inputs to know • Component lookbacks : rsilb, ccilb, dmlb, pzolb control each raw signal. • Standardization window : lookback sets the z-score memory. Longer smooths, shorter reacts. • Weights : w_rsi, w_cci, w_dm, w_pzo determine each component’s influence. • Smoothing : maType, smoothP, sig govern optional post-blend smoothing. • Dynamic bands : dyn_thres, dynLen, thres_k, centerZero configure the adaptive OB/OS logic. • UI : toggle the plot, table, candle coloring, and threshold lines. Reading the plot • Above 0 : composite pressure is positive. • Below 0 : composite pressure is negative. • OB region : valuation above the chosen OB line. Risk of mean reversion rises and momentum continuation needs evidence. • OS region : mirror logic on the downside. • Band exits : leaving OB or OS can serve as a normalization cue. Strengths • Normalizes heterogeneous signals into one interpretable series. • Adjustable component weights to match instrument behavior. • Dynamic thresholds adapt to changing volatility and drift. • Transparent diagnostics from the on-chart table. • Flexible smoothing choices, including ALMA and T3. Limitations and cautions • Z-scores assume a reasonably stationary window. Sharp regime shifts can make recent bands unrepresentative. • Highly correlated components can overweight the same effect. Consider adjusting weights to avoid double counting. • More smoothing adds lag. Less smoothing adds noise. • Dynamic bands recalibrate with dynLen ; if set too short, bands may swing excessively. If too long, bands can be slow to adapt. Practical tuning tips • Trending symbols: increase w_dm , use a modest smoother like EMA or T3, and use centerZero dynamic bands. • Choppy symbols: increase w_rsi and w_cci , consider ALMA with a higher sigma , and widen bands with a larger thres_k . • Multiday swing charts: lengthen lookback and dynLen to stabilize the scale. • Lower timeframes: shorten component lookbacks slightly and reduce smoothing to keep signals timely. Alerts • Enter and exit of Overbought and Oversold, based on the active band choice. • Bullish and bearish zero crosses. Use alerts as prompts to review context rather than as stand-alone trade commands. Final Remarks We created this to show people a different way of making indicators & trading. You can process normal indicators in multiple ways to enhance or change the signal, especially with this you can utilise machine learning to optimise the weights, then trade accordingly. All of the different components were selected to give some sort of signal, its made out of simple components yet is effective. As long as the user calibrates it to their Trading/ investing style you can find good results. Do not use anything standalone, ensure you are backtesting and creating a proper system.

ATAI Volume Pressure Analyzer V 1.0 — Pure Up/Down ATAI Volume Pressure Analyzer V 1.0 — Pure Up/Down Overview Volume is a foundational tool for understanding the supply–demand balance. Classic charts show only total volume and don’t tell us what portion came from buying (Up) versus selling (Down). The ATAI Volume Pressure Analyzer fills that gap. Built on Pine Script v6, it scans a lower timeframe to estimate Up/Down volume for each host‑timeframe candle, and presents “volume pressure” in a compact HUD table that’s comparable across symbols and timeframes. 1) Architecture & Global Settings Global Period (P, bars) A single global input P defines the computation window. All measures—host‑TF volume moving averages and the half‑window segment sums—use this length. Default: 55. Timeframe Handling The core of the indicator is estimating Up/Down volume using lower‑timeframe data. You can set a custom lower timeframe, or rely on auto‑selection: ◉ Second charts → 1S ◉ Intraday → 1 minute ◉ Daily → 5 minutes ◉ Otherwise → 60 minutes Lower TFs give more precise estimates but shorter history; higher TFs approximate buy/sell splits but provide longer history. As a rule of thumb, scan thin symbols at 5–15m, and liquid symbols at 1m. 2) Up/Down Volume & Derived Series The script uses TradingView’s library function tvta.requestUpAndDownVolume(lowerTf) to obtain three values: ◉ Up volume (buyers) ◉ Down volume (sellers) ◉ Delta (Up − Down) From these we define: ◉ TF_buy = |Up volume| ◉ TF_sell = |Down volume| ◉ TF_tot = TF_buy + TF_sell ◉ TF_delta = TF_buy − TF_sell A positive TF_delta indicates buyer dominance; a negative value indicates selling pressure. To smooth noise, simple moving averages of TF_buy and TF_sell are computed over P and used as baselines. 3) Key Performance Indicators (KPIs) Half‑window segmentation To track momentum shifts, the P‑bar window is split in half: ◉ C→B: the older half ◉ B→A: the newer half (toward the current bar) For each half, the script sums buy, sell, and delta. Comparing the two halves reveals strengthening/weakening pressure. Example: if AtoB_delta < CtoB_delta, recent buying pressure has faded. [ 4) HUD (Table) Display /i] Colors & Appearance Two main color inputs define the theme: a primary color and a negative color (used when Δ is negative). The panel background uses a translucent version of the primary color; borders use the solid primary color. Text defaults to the primary color and flips to the negative color when a block’s Δ is negative. Layout The HUD is a 4×5 table updated on the last bar of each candle: ◉ Row 1 (Meta): indicator name, P length, lower TF, host TF ◉ Row 2 (Host TF): current ↑Buy, ↓Sell, ΔDelta; plus Σ total and SMA(↑/↓) ◉ Row 3 (Segments): C→B and B→A blocks with ↑/↓/Δ ◉ Rows 4–5: reserved for advanced modules (Wings, α/β, OB/OS, Top 5) Advanced Modules 5.1 Wings “Wings” visualize volume‑driven movement over C→B (left wing) and B→A (right wing) with top/bottom lines and a filled band. Slopes are ATR‑per‑bar normalized for cross‑symbol/TF comparability and converted to angles (degrees). Coloring mirrors HUD sign logic with a near‑zero threshold (default ~3°): ◉ Both lines rising → blue (bullish) ◉ Both falling → red (bearish) ◉ Mixed/near‑zero → gray Left wing reflects the origin of the recent move; right wing reflects the current state. 5.2 α / β at Point B We compute the oriented angle between the two wings at the midpoint B: β is the bottom‑arc angle; α = 360° − β is the top‑arc angle. ◉ Large α (>180°) or small β (<180°) flags meaningful imbalance. ◉ Intuition: large α suggests potential selling pressure; small β implies fragile support. HUD cells highlight these conditions. 5.3 OB/OS Spike OverBought/OverSold (OB/OS) labels appear when directional volume spikes align with a 7‑oscillator vote (RSI, Stoch, %R, CCI, MFI, DeMarker, StochRSI). ◉ OB label (red): unusually high sell volume + enough OB votes ◉ OS label (teal): unusually high buy volume + enough OS votes Minimum votes and sync window are user‑configurable; dotted connectors can link labels to the candle wick. 5.4 Top3 Volume Peaks Within the P window the script ranks the top three BUY peaks (B1–B3) and top three SELL peaks (S1–S3). ◉ B1 and S1 are drawn as horizontal resistance (at B1 High) and support (at S1 Low) zones with adjustable thickness (ticks/percent/ATR). ◉ The HUD dedicates six cells to show ↑/↓/Δ for each rank, and prints the exact High (B1) and Low (S1) inline in their cells. 6) Reading the HUD — A Quick Checklist ◉ Meta: Confirm P and both timeframes (host & lower). ◉ Host TF block: Compare current ↑/↓/Δ against their SMAs. ◉ Segments: Contrast C→B vs B→A deltas to gauge momentum change. ◉ Wings: Right‑wing color/angle = now; left wing = recent origin. ◉ α / β: Look for α > 180° or β < 180° as imbalance cues. ◉ OB/OS: Note labels, color (red/teal), and the vote count. ◉Top3: Keep B1 (resistance) and S1 (support) on your radar. Use these together to sketch scenarios and invalidation levels; never rely on a single signal in isolation. [ 7) Example Highlights (What the table conveys) /i] ◉ Row 1 shows the indicator name, the analysis length P (default 55), and both TFs used for computation and display. ◉ B1 / S1 blocks summarize each side’s peak within the window, with Δ indicating buyer/seller dominance at that peak and inline price (B1 High / S1 Low) for actionable levels. ◉ Angle cells for each wing report the top/bottom line angles vs. the horizontal, reflecting the directional posture. ◉ Ranks B2/B3 and S2/S3 extend context beyond the top peak on each side. ◉ α / β cells quantify the orientation gap at B; changes reflect shifting buyer/seller influence on trend strength. Together these visuals often reveal whether the “wings” resemble a strong, upward‑tilted arm supported by buyer volume—but always corroborate with your broader toolkit 8) Practical Tips & Tuning ◉ Choose P by market structure. For daily charts, 34–89 bars often works well. ◉ Lower TF choice: Thin symbols → 5–15m; liquid symbols → 1m. ◉ Near‑zero angle: In noisy markets, consider 5–7° instead of 3°. ◉ OB/OS votes: Daily charts often work with 3–4 votes; lower TFs may prefer 4–5. ◉ Zone thickness: Tie B1/S1 zone thickness to ATR so it scales with volatility. ◉ Colors: Feel free to theme the primary/negative colors; keep Δ<0 mapped to the negative color for readability. Combine with price action: Use this indicator alongside structure, trendlines, and other tools for stronger decisions. Technical Notes Pine Script v6. ◉ Up/Down split via TradingView/ta library call requestUpAndDownVolume(lowerTf). ◉ HUD‑first design; drawings for Wings/αβ/OBOS/Top3 align with the same sign/threshold logic used in the table. Disclaimer: This indicator is provided solely for educational and analytical purposes. It does not constitute financial advice, nor is it a recommendation to buy or sell any security. Always conduct your own research and use multiple tools before making trading decisions.

[NLX-L1] Trend Index - NLX Modular Trading Framework - This module is build upon the Trend Index by Mango2Juice (thanks for your permission to use the source!) It includes all the common indicators and creates a positive or negative score, which can be used with my Modular Trading Framework and linked to an entry/exit indicator. SuperTrend VWAP Bands Relative Strength Index ( RSI ) Commodity Channel Index ( CCI ) William Percent Range (WPR) Directional Movement Index (DMI) Elder Force Index ( EFI ) Momentum Demarker Parabolic SAR ... and more - Getting Started - 1. Add this Trend Index to your Chart 2. Add one of my Indicator Modules to your Chart, such as the QQE++ Indicator 3. In the QQE Indicator Settings combine it with the Trend Index (and choose L1 Type) 4. Optional: Add the Noise Filter , and in the Noise Filter Settings you select the QQE Indicator as combination (and choose L2 for Type) 5. Add the Backtest Module to your Chart 6. Select the Noise Filter in the Backtest Settings Indicator modules can be combined in many different ways in my framework - have fun! - Alerts for Automated Trading - The alerts module is coming soon and you will be able to create alerts to automated your trades. See my signature below for more information.

Trend Index Trend Index show trend of the instrument based on multiple indicators - Green --> Uptrend - Red --> Downtrend - Gray --> No trend Indicators used:- Relative Strength Index (RSI) Commodity Channel Index (CCI) William Percent Range (WPR) Directional Movement Index (DMI) Elder Force Index (EFI) Momentum Demarker Parabolic SAR

Reversal Points [CC]This original script was created based on a suggestion from @kerpiciwuasile. My original Reversal Points script was removed because I modeled it after an indicator by Demark, but this script will have no such issues. Reversal points are an exciting concept for me because it is such a useful tool when placing trades. This is my first attempt at a new overall layout for my script and I included a bunch of customization so let me know what you think. My script works by finding lows that are surrounded by bars that have higher lows and highs that are surrounded by lower highs. I use this logic to find short term lows or highs and I use the same concept to find mid-term lows or highs but a mid-term high is a short term high surrounded by lower short term highs and a mid-term low is a short term low surrounded by higher short term lows. And of course this means that long term highs or lows use the same logic to find highs or lows that are surrounded by mid-term highs or lows. I would recommend to buy at the long term low points or sell at the long term high points. Keep in mind of course that short term highs and lows are very common and reversal points will get rarer as you look for longer term reversal points. I would recommend to experiment and see which reversal points work best for you and of course, know that there is no magical formula to use for all stocks. Also there are a few scenarios where you want to enable or disable the inside bar setting. You would want to ignore inside bars if the market is currently very volatile or if you are using this indicator on a crypto chart. This is not an exact science but more of a recommendation, so feel free to experiment with it. Reversal points are crucial for traders as they signal a potential change in the market trend, providing opportunities for entry or exit. In summary, this code snippet is a powerful tool for traders to detect and visualize reversal points on a trading chart, providing valuable insights into potential trend changes and facilitating more informed trading decisions. Let me know if you would like me to publish other scripts or if you want me to do something custom for you!

Fuzzy Fib Counts A new approach to trend/time analysis. This indicator counts up/down price moves using fuzzy logic in two time frames. Counts reaching Fibonacci numbers are often significant to trend momentum: either reversal or stronger continuation. See the script’s beginning comments for details. I’m amazed with DeMark and all his indicators. But after coding a TD_Sequential script, I began to wonder if there wasn’t an easier way to identify high probability entry/exit points without all the intricate logic. It took awhile, but a different paradigm eventually came into focus: Fuzzy Fib Counts (FFC). FFC is like “analog” compared to TD_Sequential’s “digital” approach. And FFC is much simpler: no buy/sell/perfected/deferred signals, just up/down counts. It’s uncanny how often a trend is refocused at Fibonacci numbers, but like TD_Sequential… it’s not perfect! As with all indicators, consult your intuition, before pulling the trigger. The default values have been effective for me in Bitcoin (and I’m surprised how well it works in smaller time frames). However, for less volatile markets, you may need to reduce the fuzz level and/or adjust the filter (averaging) parameters. FFC has been helpful to me. Hope it helps you too! If so, donations appreciated… Thanks! BTC: 36FdUcETrwmGxt5qazq7Bo7kad9kuNawMF LTC: MJYHzwJYjsnB35dzb5L9hCpqZf8RyqViEm ETH: 0x3db7557bea61ca4c096c6ed7bdb2a0d69e455fe1 Looking for trailblazers to help explore new possibilities… Add constructive comments below!