Fisher Volume Transform | AlphaNattFisher Volume Transform | AlphaNatt
A powerful oscillator that applies the Fisher Transform - converting price into a Gaussian normal distribution - while incorporating volume weighting to identify high-probability reversal points with institutional participation.
"The Fisher Transform reveals what statistics professors have known for decades: when you transform market data into a normal distribution, turning points become crystal clear."
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🎲 THE MATHEMATICS
Fisher Transform Formula:
The Fisher Transform converts any bounded dataset into a Gaussian distribution:
y = 0.5 × ln((1 + x) / (1 - x))
Where x is normalized price (-1 to 1 range)
Why This Matters:
Market extremes become statistically identifiable
Turning points are amplified and clarified
Removes the skew from price distributions
Creates nearly instantaneous signals at reversals
Volume Integration:
Unlike standard Fisher Transform, this version weights price by relative volume:
High volume moves get more weight
Low volume moves get filtered out
Identifies institutional participation
Reduces false signals from retail chop
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
💎 KEY ADVANTAGES
Statistical Edge: Transforms price into normal distribution where extremes are mathematically defined
Volume Confirmation: Only signals with volume support
Early Reversal Detection: Fisher Transform amplifies turning points
Clean Signals: Gaussian distribution reduces noise
No Lag: Mathematical transformation, not averaging
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⚙️ SETTINGS OPTIMIZATION
Fisher Period (5-30):
5-9: Very sensitive, many signals
10: Default - balanced sensitivity
15-20: Moderate smoothing
25-30: Major reversals only
Volume Weight (0.1-1.0):
0.1-0.3: Minimal volume influence
0.5-0.7: Balanced price/volume
0.7: Default - strong volume weight
0.8-1.0: Volume dominant
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 TRADING SIGNALS
Primary Signals:
Zero Cross Up: Bullish momentum shift
Zero Cross Down: Bearish momentum shift
Signal Line Cross: Early reversal warning
Extreme Readings (±75): Potential reversal zones
Visual Interpretation:
Cyan zones: Bullish momentum
Magenta zones: Bearish momentum
Gradient intensity: Strength of move
Histogram: Raw momentum power
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🎯 OPTIMAL USAGE
Best Market Conditions:
Range-bound markets (reversals clear)
High volume periods
Major support/resistance levels
Divergence hunting
Trading Strategies:
1. Extreme Reversal:
Enter when oscillator exceeds ±75 and reverses
2. Zero Line Momentum:
Trade crosses of zero line with volume confirmation
3. Signal Line Strategy:
Early entry on signal line crosses
4. Divergence Trading:
Price makes new high/low but Fisher doesn't
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Developed by AlphaNatt | Quantitative Trading Systems
Version: 1.0
Classification: Statistical Transform Oscillator
Not financial advice. Always DYOR.
Pengayun
Decision Matrix [Cnagda] — Adaptive Multi-Signal Trading SystemDecision Matrix is a cutting-edge trading ecosystem that leverages the synergy of price action, volume dynamics, and key momentum indicators to create a powerful, real-time signal engine for traders.
Key Features:
Multi-Source Signal Fusion: Buy/Sell Power score, TWAP patterns, EMA/RSI crossovers, Volume zones — 7 layers of confirmation per candle.
Adaptive Intelligence: Dynamic lookback window, label-size, and threshold are auto-adjusted based on market volatility.
Volume Secrets Decoded: Adaptive ‘Low Volume’ (LV) candle detection reveals reversal and hidden accumulation patterns.
Order Block Auto-Zoning: Detect Demand & Supply zones to highlight boxes on the chart — market structure is instantly clear.
Heikin-Ashi Inspired Trend Mapping: Candle coloring based on trend intensity — clear visual feedback on every phase of a security.
Intelligent Pattern Recognition: 4-candle TWAP & Volume structures help to quickly spot rare reversals and momentum shifts.
Live Dashboard: Live Buy/Sell power on the chart, long/short trigger levels, multiple timeframes support — decision making lightning fast!
Use Cases:
Quick Reversal Detection
Momentum Entry/Exit Confirmation
False Breakout Filtering
Volume-Driven Structural Insights
Trailing Stop Placement based on True Demand/Supply Zones
What difference will it make?
Decision Matrix analyzes every uncertain market move from a multi-angle perspective to show where the market bias is right now, where is the best entry, when is the exit alert — and what is the real volume game behind every trend or even the most subtle reversal move — in just one glance!
Every curated visual & signal will give you pure information clarity and high-confidence trading advantage.
TRAPPER TRENDLINES — RSIBuilds dynamic RSI trendlines by connecting the two most recent confirmed RSI swing points (highs→highs for resistance, lows→lows for support). Includes optional channel shading for the 30–70 zone, an RSI moving average, clean break alerts, and simple bullish/bearish divergence alerts versus price.
How it works
RSI pivots: A point on RSI is a swing high/low only if it is the most extreme value compared with a set number of bars on the left and the right (the Pivot Lookback).
RSI trendlines:
Resistance connects the last two confirmed RSI swing highs.
Support connects the last two confirmed RSI swing lows.
Lines can be Full Extend (update into the future) or Pivot Only.
Channel block: Optional fill of the 30–70 range for fast visual context.
Alerts:
Breaks of RSI support/resistance trendlines.
Basic bullish/bearish RSI divergences versus price pivots.
Inputs
RSI
RSI Length: Default 14 (standard).
Pivot Lookback: Bars to the left/right required to confirm an RSI swing.
Overbought / Oversold: 70 / 30 by default.
Line Extension: Full Extend or Pivot Only.
Visuals
Show RSI Moving Average / Signal Length: Optional smoothing line on RSI.
RSI/Signal colors: Customize plot colors.
Show 30–70 Channel Block: Toggle the middle-zone fill.
Tint pane background when RSI in channel: Optional subtle background when RSI is between OB/OS.
Divergences & Alerts
Enable RSI TL Break Alerts: Alert conditions for RSI line breaks.
Enable Divergence Alerts: Bullish/Bearish divergence alerts versus price.
Pairing with price for confluence/divergence
For accurate confluence and clearer divergences, align this RSI tool with your price trendline tool (for example, TRAPPER TRENDLINES — PRICE):
Set RSI Pivot Lookback equal to the Pivot Left/Right size used on price.
Example: Price uses Pivot Left = 50 and Pivot Right = 50 → set RSI Pivot Lookback = 50.
Keep RSI Length = 14 and OB/OS = 70/30 unless you have a specific edge.
Interpretation:
Confluence: Price reacts at its trendline while RSI reacts at its own line in the same direction.
Divergence: Price makes a higher high while RSI makes a lower high (bearish), or price makes a lower low while RSI makes a higher low (bullish), using matched pivot windows.
Suggested settings
Higher timeframes (4H / 1D / 1W): Pivot Lookback = 50; optional RSI MA length 14; channel block ON.
Intraday (15m / 30m / 1H): Pivot Lookback = 30; optional RSI MA length 14.
Always mirror your price pivot size to this RSI Pivot Lookback for consistent swings.
Reading the signals
RSI trendline touch/hold: Momentum reacting at structure; look for confluence with price levels.
RSI Trendline Break Up / Down: Momentum shift; consider price structure and retests.
Bullish/Bearish Divergence: Confirm only when pivots are matched and the new swing is confirmed.
Notes & limitations
Pivots require future bars to confirm by design; trendlines update as new swings confirm.
Divergence logic compares RSI pivots to price pivots with the same lookback; mismatched windows can produce false positives.
No strategy entries/exits or performance claims are provided. This is an analytical tool.
Alerts (titles/messages)
RSI: Trendline Break Up — “RSI broke falling resistance line.”
RSI: Trendline Break Down — “RSI broke rising support line.”
RSI: Bullish Divergence — “Bullish RSI divergence confirmed.”
RSI: Bearish Divergence — “Bearish RSI divergence confirmed.”
Quick start
Add the indicator to a separate pane.
Set Pivot Lookback to match your price tool’s pivot size (e.g., 50).
Optionally toggle the RSI MA and Channel Block for clarity.
Enable alerts if you want notifications on RSI line breaks and divergences.
Use with TRAPPER TRENDLINES — PRICE or any price-based trendline tool for confluence/divergence analysis.
Compliance
This script is for educational purposes only and does not constitute financial advice. Trading involves risk. Past performance does not guarantee future results. No performance claims are made.
Supertrend [TradingConToto]Supertrend — ADX/DI + EMA Gap + Breakout (with Mobile UI)
What makes it original
Supertrend combines trend strength (ADX/DI), multi-timeframe bias (EMA63 and EMA 200D equivalent), a structural filter based on the distance between EMA2400 and EMA4800 expressed in ATR units, and a momentum confirmation through a previous high breakout.
This is not a random mashup — it’s a sequence of filters designed to reduce trades in ranging markets and prioritize mature trends:
Direction: +DI > -DI (trend led by buyers).
Strength: ADX > mean(ADX) (avoids weak, choppy phases).
Short-term bias: Close > EMA63.
Long-term bias: Close > EMA4800 ≈ EMA200 daily on H1.
Momentum: Close > High (immediate breakout).
Structure: (EMA2400 − EMA4800) > k·ATR (ensures separation in ATR units, filters out flat phases).
Entries & exits
Entry: when all six conditions are met and no open position exists.
Exit: if +DI < -DI or Close < EMA63.
Visuals: EMA63 is painted green while in position and red otherwise, with a supertrend-style band; “BUY” labels appear below the green band and “SELL” labels above the red band.
UI: includes a compact table (mobile-friendly) showing the state of each condition.
Default parameters used in this publication
Initial capital: 10,000
Position size: 10% of equity (≤10% per trade is considered sustainable).
Commission: 0.01% per side (adjust to your broker/market).
Slippage: 1 tick
Pyramiding: 0 (only one position at a time)
Adjust commission/slippage to match your market. For US equities, commissions are often per share; for spot crypto, 0.10–0.20% total is common. I publish with 0.01% per side as a conservative example to avoid overestimating results.
Recommended backtest dataset
Timeframe: H1
Multi-cycle window (e.g. 2015–today)
Symbols with high liquidity (e.g. NASDAQ-100 large caps, or BTC/ETH spot) to generate 100+ trades. Avoid cherry-picked short windows.
Why each filter matters
+DI > -DI + ADX > mean: reduce counter-trend trades and weak signals.
Close > EMA63 + Close > EMA4800: enforce trend alignment in short and long horizons.
Breakout High : requires immediate momentum, avoids early entries.
EMA gap in ATR units: blocks flat or compressed structures where EMA200D aligns with price.
Limitations
The breakout filter may skip healthy pullbacks; the design prioritizes continuation over perfect entry price.
No fixed trailing stop/TP; exits depend on trend degradation via DI/EMA63.
Results vary with real costs (commissions, slippage, funding). Adjust defaults to your broker.
How to use
Apply it on a clean chart (no other indicators when publishing).
Keep in mind the default parameters above; if you change them, mention it in your notes and use the same values in the Strategy Tester.
Ensure your dataset produces 100+ trades for statistical validity.
MAxRSI Signals [KedArc Quant]Description
Indicator Shows fast/slow moving averages on the chart and prints LONG/SHORT signals when they cross. You can add an RSI filter to confirm signals. Optional higher-timeframe (HTF) smoothing and alert conditions are included.
How signals are made
* LONG: Fast MA crosses above Slow MA and (if enabled) RSI ≥ your “RSI Long Min”.
* SHORT: Fast MA crosses below Slow MA and (if enabled) RSI ≤ your “RSI Short Max”.
* If “Repaint-Safe” is ON, signals confirm at bar close to avoid repainting.
Inputs
* Fast MA / Slow MA Length
* MA Type: SMA / EMA / WMA / HMA
* RSI Filter (length + thresholds)
* Source: Usually close
* Optional HTF for MAs & RSI
* Show MAs / Show Signals toggles
* Repaint-Safe confirmation
* RSI Overlay (visual only)
* Alert Options
On the chart
* Green line = Fast MA, Red line = Slow MA
* Green “LONG” arrow below bar; Red “SHORT” arrow above bar
* Optional bar coloring in trend direction
* Optional RSI overlay line + thresholds
Alerts
* MAxRSI Long / Short fire on crossovers with RSI check
* Bullish/Bearish State fire when the trend flips
Tips
* If no signals appear, check that Fast < Slow and test with RSI filter off
* EMA/HMA often give smoother results
* Use larger MA lengths or HTF for swing setups
Notes & Usage
* Intraday trading: EMA(9–20) as Fast, EMA(50) as Slow is a common setup.
* Swing trading: EMA(20) as Fast, EMA(200) as Slow helps capture broader trend shifts.
* RSI filter works well with default 14 length and 50 level, but you can adjust (e.g., 55/45) for stricter confirmation.
* This script is flexible — test and adapt the settings to your asset and timeframe.
Acknowledgements
This indicator is built on TradingView’s built-in Pine Script v6 functions and features, including:
* `ta.sma`, `ta.ema`, `ta.wma` for moving averages
* Custom Hull MA formula built using `ta.wma`
* `ta.rsi` for Relative Strength Index filtering
* `ta.crossover`, `ta.crossunder` for signal logic
* `request.security` for higher-timeframe (HTF) data
* `plot`, `plotshape`, `barcolor` for visuals
* `alertcondition` for alerts
* General Pine Script v6 features: `input.*` controls, `math.*` helpers, `color.*` utilities
⚠️ Disclaimer
This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
ATAI Volume analysis with price action V 1.00ATAI Volume Analysis with Price Action
1. Introduction
1.1 Overview
ATAI Volume Analysis with Price Action is a composite indicator designed for TradingView. It combines per‑side volume data —that is, how much buying and selling occurs during each bar—with standard price‑structure elements such as swings, trend lines and support/resistance. By blending these elements the script aims to help a trader understand which side is in control, whether a breakout is genuine, when markets are potentially exhausted and where liquidity providers might be active.
The indicator is built around TradingView’s up/down volume feed accessed via the TradingView/ta/10 library. The following excerpt from the script illustrates how this feed is configured:
import TradingView/ta/10 as tvta
// Determine lower timeframe string based on user choice and chart resolution
string lower_tf_breakout = use_custom_tf_input ? custom_tf_input :
timeframe.isseconds ? "1S" :
timeframe.isintraday ? "1" :
timeframe.isdaily ? "5" : "60"
// Request up/down volume (both positive)
= tvta.requestUpAndDownVolume(lower_tf_breakout)
Lower‑timeframe selection. If you do not specify a custom lower timeframe, the script chooses a default based on your chart resolution: 1 second for second charts, 1 minute for intraday charts, 5 minutes for daily charts and 60 minutes for anything longer. Smaller intervals provide a more precise view of buyer and seller flow but cover fewer bars. Larger intervals cover more history at the cost of granularity.
Tick vs. time bars. Many trading platforms offer a tick / intrabar calculation mode that updates an indicator on every trade rather than only on bar close. Turning on one‑tick calculation will give the most accurate split between buy and sell volume on the current bar, but it typically reduces the amount of historical data available. For the highest fidelity in live trading you can enable this mode; for studying longer histories you might prefer to disable it. When volume data is completely unavailable (some instruments and crypto pairs), all modules that rely on it will remain silent and only the price‑structure backbone will operate.
Figure caption, Each panel shows the indicator’s info table for a different volume sampling interval. In the left chart, the parentheses “(5)” beside the buy‑volume figure denote that the script is aggregating volume over five‑minute bars; the center chart uses “(1)” for one‑minute bars; and the right chart uses “(1T)” for a one‑tick interval. These notations tell you which lower timeframe is driving the volume calculations. Shorter intervals such as 1 minute or 1 tick provide finer detail on buyer and seller flow, but they cover fewer bars; longer intervals like five‑minute bars smooth the data and give more history.
Figure caption, The values in parentheses inside the info table come directly from the Breakout — Settings. The first row shows the custom lower-timeframe used for volume calculations (e.g., “(1)”, “(5)”, or “(1T)”)
2. Price‑Structure Backbone
Even without volume, the indicator draws structural features that underpin all other modules. These features are always on and serve as the reference levels for subsequent calculations.
2.1 What it draws
• Pivots: Swing highs and lows are detected using the pivot_left_input and pivot_right_input settings. A pivot high is identified when the high recorded pivot_right_input bars ago exceeds the highs of the preceding pivot_left_input bars and is also higher than (or equal to) the highs of the subsequent pivot_right_input bars; pivot lows follow the inverse logic. The indicator retains only a fixed number of such pivot points per side, as defined by point_count_input, discarding the oldest ones when the limit is exceeded.
• Trend lines: For each side, the indicator connects the earliest stored pivot and the most recent pivot (oldest high to newest high, and oldest low to newest low). When a new pivot is added or an old one drops out of the lookback window, the line’s endpoints—and therefore its slope—are recalculated accordingly.
• Horizontal support/resistance: The highest high and lowest low within the lookback window defined by length_input are plotted as horizontal dashed lines. These serve as short‑term support and resistance levels.
• Ranked labels: If showPivotLabels is enabled the indicator prints labels such as “HH1”, “HH2”, “LL1” and “LL2” near each pivot. The ranking is determined by comparing the price of each stored pivot: HH1 is the highest high, HH2 is the second highest, and so on; LL1 is the lowest low, LL2 is the second lowest. In the case of equal prices the newer pivot gets the better rank. Labels are offset from price using ½ × ATR × label_atr_multiplier, with the ATR length defined by label_atr_len_input. A dotted connector links each label to the candle’s wick.
2.2 Key settings
• length_input: Window length for finding the highest and lowest values and for determining trend line endpoints. A larger value considers more history and will generate longer trend lines and S/R levels.
• pivot_left_input, pivot_right_input: Strictness of swing confirmation. Higher values require more bars on either side to form a pivot; lower values create more pivots but may include minor swings.
• point_count_input: How many pivots are kept in memory on each side. When new pivots exceed this number the oldest ones are discarded.
• label_atr_len_input and label_atr_multiplier: Determine how far pivot labels are offset from the bar using ATR. Increasing the multiplier moves labels further away from price.
• Styling inputs for trend lines, horizontal lines and labels (color, width and line style).
Figure caption, The chart illustrates how the indicator’s price‑structure backbone operates. In this daily example, the script scans for bars where the high (or low) pivot_right_input bars back is higher (or lower) than the preceding pivot_left_input bars and higher or lower than the subsequent pivot_right_input bars; only those bars are marked as pivots.
These pivot points are stored and ranked: the highest high is labelled “HH1”, the second‑highest “HH2”, and so on, while lows are marked “LL1”, “LL2”, etc. Each label is offset from the price by half of an ATR‑based distance to keep the chart clear, and a dotted connector links the label to the actual candle.
The red diagonal line connects the earliest and latest stored high pivots, and the green line does the same for low pivots; when a new pivot is added or an old one drops out of the lookback window, the end‑points and slopes adjust accordingly. Dashed horizontal lines mark the highest high and lowest low within the current lookback window, providing visual support and resistance levels. Together, these elements form the structural backbone that other modules reference, even when volume data is unavailable.
3. Breakout Module
3.1 Concept
This module confirms that a price break beyond a recent high or low is supported by a genuine shift in buying or selling pressure. It requires price to clear the highest high (“HH1”) or lowest low (“LL1”) and, simultaneously, that the winning side shows a significant volume spike, dominance and ranking. Only when all volume and price conditions pass is a breakout labelled.
3.2 Inputs
• lookback_break_input : This controls the number of bars used to compute moving averages and percentiles for volume. A larger value smooths the averages and percentiles but makes the indicator respond more slowly.
• vol_mult_input : The “spike” multiplier; the current buy or sell volume must be at least this multiple of its moving average over the lookback window to qualify as a breakout.
• rank_threshold_input (0–100) : Defines a volume percentile cutoff: the current buyer/seller volume must be in the top (100−threshold)%(100−threshold)% of all volumes within the lookback window. For example, if set to 80, the current volume must be in the top 20 % of the lookback distribution.
• ratio_threshold_input (0–1) : Specifies the minimum share of total volume that the buyer (for a bullish breakout) or seller (for bearish) must hold on the current bar; the code also requires that the cumulative buyer volume over the lookback window exceeds the seller volume (and vice versa for bearish cases).
• use_custom_tf_input / custom_tf_input : When enabled, these inputs override the automatic choice of lower timeframe for up/down volume; otherwise the script selects a sensible default based on the chart’s timeframe.
• Label appearance settings : Separate options control the ATR-based offset length, offset multiplier, label size and colors for bullish and bearish breakout labels, as well as the connector style and width.
3.3 Detection logic
1. Data preparation : Retrieve per‑side volume from the lower timeframe and take absolute values. Build rolling arrays of the last lookback_break_input values to compute simple moving averages (SMAs), cumulative sums and percentile ranks for buy and sell volume.
2. Volume spike: A spike is flagged when the current buy (or, in the bearish case, sell) volume is at least vol_mult_input times its SMA over the lookback window.
3. Dominance test: The buyer’s (or seller’s) share of total volume on the current bar must meet or exceed ratio_threshold_input. In addition, the cumulative sum of buyer volume over the window must exceed the cumulative sum of seller volume for a bullish breakout (and vice versa for bearish). A separate requirement checks the sign of delta: for bullish breakouts delta_breakout must be non‑negative; for bearish breakouts it must be non‑positive.
4. Percentile rank: The current volume must fall within the top (100 – rank_threshold_input) percent of the lookback distribution—ensuring that the spike is unusually large relative to recent history.
5. Price test: For a bullish signal, the closing price must close above the highest pivot (HH1); for a bearish signal, the close must be below the lowest pivot (LL1).
6. Labeling: When all conditions above are satisfied, the indicator prints “Breakout ↑” above the bar (bullish) or “Breakout ↓” below the bar (bearish). Labels are offset using half of an ATR‑based distance and linked to the candle with a dotted connector.
Figure caption, (Breakout ↑ example) , On this daily chart, price pushes above the red trendline and the highest prior pivot (HH1). The indicator recognizes this as a valid breakout because the buyer‑side volume on the lower timeframe spikes above its recent moving average and buyers dominate the volume statistics over the lookback period; when combined with a close above HH1, this satisfies the breakout conditions. The “Breakout ↑” label appears above the candle, and the info table highlights that up‑volume is elevated relative to its 11‑bar average, buyer share exceeds the dominance threshold and money‑flow metrics support the move.
Figure caption, In this daily example, price breaks below the lowest pivot (LL1) and the lower green trendline. The indicator identifies this as a bearish breakout because sell‑side volume is sharply elevated—about twice its 11‑bar average—and sellers dominate both the bar and the lookback window. With the close falling below LL1, the script triggers a Breakout ↓ label and marks the corresponding row in the info table, which shows strong down volume, negative delta and a seller share comfortably above the dominance threshold.
4. Market Phase Module (Volume Only)
4.1 Concept
Not all markets trend; many cycle between periods of accumulation (buying pressure building up), distribution (selling pressure dominating) and neutral behavior. This module classifies the current bar into one of these phases without using ATR , relying solely on buyer and seller volume statistics. It looks at net flows, ratio changes and an OBV‑like cumulative line with dual‑reference (1‑ and 2‑bar) trends. The result is displayed both as on‑chart labels and in a dedicated row of the info table.
4.2 Inputs
• phase_period_len: Number of bars over which to compute sums and ratios for phase detection.
• phase_ratio_thresh : Minimum buyer share (for accumulation) or minimum seller share (for distribution, derived as 1 − phase_ratio_thresh) of the total volume.
• strict_mode: When enabled, both the 1‑bar and 2‑bar changes in each statistic must agree on the direction (strict confirmation); when disabled, only one of the two references needs to agree (looser confirmation).
• Color customisation for info table cells and label styling for accumulation and distribution phases, including ATR length, multiplier, label size, colors and connector styles.
• show_phase_module: Toggles the entire phase detection subsystem.
• show_phase_labels: Controls whether on‑chart labels are drawn when accumulation or distribution is detected.
4.3 Detection logic
The module computes three families of statistics over the volume window defined by phase_period_len:
1. Net sum (buyers minus sellers): net_sum_phase = Σ(buy) − Σ(sell). A positive value indicates a predominance of buyers. The code also computes the differences between the current value and the values 1 and 2 bars ago (d_net_1, d_net_2) to derive up/down trends.
2. Buyer ratio: The instantaneous ratio TF_buy_breakout / TF_tot_breakout and the window ratio Σ(buy) / Σ(total). The current ratio must exceed phase_ratio_thresh for accumulation or fall below 1 − phase_ratio_thresh for distribution. The first and second differences of the window ratio (d_ratio_1, d_ratio_2) determine trend direction.
3. OBV‑like cumulative net flow: An on‑balance volume analogue obv_net_phase increments by TF_buy_breakout − TF_sell_breakout each bar. Its differences over the last 1 and 2 bars (d_obv_1, d_obv_2) provide trend clues.
The algorithm then combines these signals:
• For strict mode , accumulation requires: (a) current ratio ≥ threshold, (b) cumulative ratio ≥ threshold, (c) both ratio differences ≥ 0, (d) net sum differences ≥ 0, and (e) OBV differences ≥ 0. Distribution is the mirror case.
• For loose mode , it relaxes the directional tests: either the 1‑ or the 2‑bar difference needs to agree in each category.
If all conditions for accumulation are satisfied, the phase is labelled “Accumulation” ; if all conditions for distribution are satisfied, it’s labelled “Distribution” ; otherwise the phase is “Neutral” .
4.4 Outputs
• Info table row : Row 8 displays “Market Phase (Vol)” on the left and the detected phase (Accumulation, Distribution or Neutral) on the right. The text colour of both cells matches a user‑selectable palette (typically green for accumulation, red for distribution and grey for neutral).
• On‑chart labels : When show_phase_labels is enabled and a phase persists for at least one bar, the module prints a label above the bar ( “Accum” ) or below the bar ( “Dist” ) with a dashed or dotted connector. The label is offset using ATR based on phase_label_atr_len_input and phase_label_multiplier and is styled according to user preferences.
Figure caption, The chart displays a red “Dist” label above a particular bar, indicating that the accumulation/distribution module identified a distribution phase at that point. The detection is based on seller dominance: during that bar, the net buyer-minus-seller flow and the OBV‑style cumulative flow were trending down, and the buyer ratio had dropped below the preset threshold. These conditions satisfy the distribution criteria in strict mode. The label is placed above the bar using an ATR‑based offset and a dashed connector. By the time of the current bar in the screenshot, the phase indicator shows “Neutral” in the info table—signaling that neither accumulation nor distribution conditions are currently met—yet the historical “Dist” label remains to mark where the prior distribution phase began.
Figure caption, In this example the market phase module has signaled an Accumulation phase. Three bars before the current candle, the algorithm detected a shift toward buyers: up‑volume exceeded its moving average, down‑volume was below average, and the buyer share of total volume climbed above the threshold while the on‑balance net flow and cumulative ratios were trending upwards. The blue “Accum” label anchored below that bar marks the start of the phase; it remains on the chart because successive bars continue to satisfy the accumulation conditions. The info table confirms this: the “Market Phase (Vol)” row still reads Accumulation, and the ratio and sum rows show buyers dominating both on the current bar and across the lookback window.
5. OB/OS Spike Module
5.1 What overbought/oversold means here
In many markets, a rapid extension up or down is often followed by a period of consolidation or reversal. The indicator interprets overbought (OB) conditions as abnormally strong selling risk at or after a price rally and oversold (OS) conditions as unusually strong buying risk after a decline. Importantly, these are not direct trade signals; rather they flag areas where caution or contrarian setups may be appropriate.
5.2 Inputs
• minHits_obos (1–7): Minimum number of oscillators that must agree on an overbought or oversold condition for a label to print.
• syncWin_obos: Length of a small sliding window over which oscillator votes are smoothed by taking the maximum count observed. This helps filter out choppy signals.
• Volume spike criteria: kVolRatio_obos (ratio of current volume to its SMA) and zVolThr_obos (Z‑score threshold) across volLen_obos. Either threshold can trigger a spike.
• Oscillator toggles and periods: Each of RSI, Stochastic (K and D), Williams %R, CCI, MFI, DeMarker and Stochastic RSI can be independently enabled; their periods are adjustable.
• Label appearance: ATR‑based offset, size, colors for OB and OS labels, plus connector style and width.
5.3 Detection logic
1. Directional volume spikes: Volume spikes are computed separately for buyer and seller volumes. A sell volume spike (sellVolSpike) flags a potential OverBought bar, while a buy volume spike (buyVolSpike) flags a potential OverSold bar. A spike occurs when the respective volume exceeds kVolRatio_obos times its simple moving average over the window or when its Z‑score exceeds zVolThr_obos.
2. Oscillator votes: For each enabled oscillator, calculate its overbought and oversold state using standard thresholds (e.g., RSI ≥ 70 for OB and ≤ 30 for OS; Stochastic %K/%D ≥ 80 for OB and ≤ 20 for OS; etc.). Count how many oscillators vote for OB and how many vote for OS.
3. Minimum hits: Apply the smoothing window syncWin_obos to the vote counts using a maximum‑of‑last‑N approach. A candidate bar is only considered if the smoothed OB hit count ≥ minHits_obos (for OverBought) or the smoothed OS hit count ≥ minHits_obos (for OverSold).
4. Tie‑breaking: If both OverBought and OverSold spike conditions are present on the same bar, compare the smoothed hit counts: the side with the higher count is selected; ties default to OverBought.
5. Label printing: When conditions are met, the bar is labelled as “OverBought X/7” above the candle or “OverSold X/7” below it. “X” is the number of oscillators confirming, and the bracket lists the abbreviations of contributing oscillators. Labels are offset from price using half of an ATR‑scaled distance and can optionally include a dotted or dashed connector line.
Figure caption, In this chart the overbought/oversold module has flagged an OverSold signal. A sell‑off from the prior highs brought price down to the lower trend‑line, where the bar marked “OverSold 3/7 DeM” appears. This label indicates that on that bar the module detected a buy‑side volume spike and that at least three of the seven enabled oscillators—in this case including the DeMarker—were in oversold territory. The label is printed below the candle with a dotted connector, signaling that the market may be temporarily exhausted on the downside. After this oversold print, price begins to rebound towards the upper red trend‑line and higher pivot levels.
Figure caption, This example shows the overbought/oversold module in action. In the left‑hand panel you can see the OB/OS settings where each oscillator (RSI, Stochastic, Williams %R, CCI, MFI, DeMarker and Stochastic RSI) can be enabled or disabled, and the ATR length and label offset multiplier adjusted. On the chart itself, price has pushed up to the descending red trendline and triggered an “OverBought 3/7” label. That means the sell‑side volume spiked relative to its average and three out of the seven enabled oscillators were in overbought territory. The label is offset above the candle by half of an ATR and connected with a dashed line, signaling that upside momentum may be overextended and a pause or pullback could follow.
6. Buyer/Seller Trap Module
6.1 Concept
A bull trap occurs when price appears to break above resistance, attracting buyers, but fails to sustain the move and quickly reverses, leaving a long upper wick and trapping late entrants. A bear trap is the opposite: price breaks below support, lures in sellers, then snaps back, leaving a long lower wick and trapping shorts. This module detects such traps by looking for price structure sweeps, order‑flow mismatches and dominance reversals. It uses a scoring system to differentiate risk from confirmed traps.
6.2 Inputs
• trap_lookback_len: Window length used to rank extremes and detect sweeps.
• trap_wick_threshold: Minimum proportion of a bar’s range that must be wick (upper for bull traps, lower for bear traps) to qualify as a sweep.
• trap_score_risk: Minimum aggregated score required to flag a trap risk. (The code defines a trap_score_confirm input, but confirmation is actually based on price reversal rather than a separate score threshold.)
• trap_confirm_bars: Maximum number of bars allowed for price to reverse and confirm the trap. If price does not reverse in this window, the risk label will expire or remain unconfirmed.
• Label settings: ATR length and multiplier for offsetting, size, colours for risk and confirmed labels, and connector style and width. Separate settings exist for bull and bear traps.
• Toggle inputs: show_trap_module and show_trap_labels enable the module and control whether labels are drawn on the chart.
6.3 Scoring logic
The module assigns points to several conditions and sums them to determine whether a trap risk is present. For bull traps, the score is built from the following (bear traps mirror the logic with highs and lows swapped):
1. Sweep (2 points): Price trades above the high pivot (HH1) but fails to close above it and leaves a long upper wick at least trap_wick_threshold × range. For bear traps, price dips below the low pivot (LL1), fails to close below and leaves a long lower wick.
2. Close break (1 point): Price closes beyond HH1 or LL1 without leaving a long wick.
3. Candle/delta mismatch (2 points): The candle closes bullish yet the order flow delta is negative or the seller ratio exceeds 50%, indicating hidden supply. Conversely, a bearish close with positive delta or buyer dominance suggests hidden demand.
4. Dominance inversion (2 points): The current bar’s buyer volume has the highest rank in the lookback window while cumulative sums favor sellers, or vice versa.
5. Low‑volume break (1 point): Price crosses the pivot but total volume is below its moving average.
The total score for each side is compared to trap_score_risk. If the score is high enough, a “Bull Trap Risk” or “Bear Trap Risk” label is drawn, offset from the candle by half of an ATR‑scaled distance using a dashed outline. If, within trap_confirm_bars, price reverses beyond the opposite level—drops back below the high pivot for bull traps or rises above the low pivot for bear traps—the label is upgraded to a solid “Bull Trap” or “Bear Trap” . In this version of the code, there is no separate score threshold for confirmation: the variable trap_score_confirm is unused; confirmation depends solely on a successful price reversal within the specified number of bars.
Figure caption, In this example the trap module has flagged a Bear Trap Risk. Price initially breaks below the most recent low pivot (LL1), but the bar closes back above that level and leaves a long lower wick, suggesting a failed push lower. Combined with a mismatch between the candle direction and the order flow (buyers regain control) and a reversal in volume dominance, the aggregate score exceeds the risk threshold, so a dashed “Bear Trap Risk” label prints beneath the bar. The green and red trend lines mark the current low and high pivot trajectories, while the horizontal dashed lines show the highest and lowest values in the lookback window. If, within the next few bars, price closes decisively above the support, the risk label would upgrade to a solid “Bear Trap” label.
Figure caption, In this example the trap module has identified both ends of a price range. Near the highs, price briefly pushes above the descending red trendline and the recent pivot high, but fails to close there and leaves a noticeable upper wick. That combination of a sweep above resistance and order‑flow mismatch generates a Bull Trap Risk label with a dashed outline, warning that the upside break may not hold. At the opposite extreme, price later dips below the green trendline and the labelled low pivot, then quickly snaps back and closes higher. The long lower wick and subsequent price reversal upgrade the previous bear‑trap risk into a confirmed Bear Trap (solid label), indicating that sellers were caught on a false breakdown. Horizontal dashed lines mark the highest high and lowest low of the lookback window, while the red and green diagonals connect the earliest and latest pivot highs and lows to visualize the range.
7. Sharp Move Module
7.1 Concept
Markets sometimes display absorption or climax behavior—periods when one side steadily gains the upper hand before price breaks out with a sharp move. This module evaluates several order‑flow and volume conditions to anticipate such moves. Users can choose how many conditions must be met to flag a risk and how many (plus a price break) are required for confirmation.
7.2 Inputs
• sharp Lookback: Number of bars in the window used to compute moving averages, sums, percentile ranks and reference levels.
• sharpPercentile: Minimum percentile rank for the current side’s volume; the current buy (or sell) volume must be greater than or equal to this percentile of historical volumes over the lookback window.
• sharpVolMult: Multiplier used in the volume climax check. The current side’s volume must exceed this multiple of its average to count as a climax.
• sharpRatioThr: Minimum dominance ratio (current side’s volume relative to the opposite side) used in both the instant and cumulative dominance checks.
• sharpChurnThr: Maximum ratio of a bar’s range to its ATR for absorption/churn detection; lower values indicate more absorption (large volume in a small range).
• sharpScoreRisk: Minimum number of conditions that must be true to print a risk label.
• sharpScoreConfirm: Minimum number of conditions plus a price break required for confirmation.
• sharpCvdThr: Threshold for cumulative delta divergence versus price change (positive for bullish accumulation, negative for bearish distribution).
• Label settings: ATR length (sharpATRlen) and multiplier (sharpLabelMult) for positioning labels, label size, colors and connector styles for bullish and bearish sharp moves.
• Toggles: enableSharp activates the module; show_sharp_labels controls whether labels are drawn.
7.3 Conditions (six per side)
For each side, the indicator computes six boolean conditions and sums them to form a score:
1. Dominance (instant and cumulative):
– Instant dominance: current buy volume ≥ sharpRatioThr × current sell volume.
– Cumulative dominance: sum of buy volumes over the window ≥ sharpRatioThr × sum of sell volumes (and vice versa for bearish checks).
2. Accumulation/Distribution divergence: Over the lookback window, cumulative delta rises by at least sharpCvdThr while price fails to rise (bullish), or cumulative delta falls by at least sharpCvdThr while price fails to fall (bearish).
3. Volume climax: The current side’s volume is ≥ sharpVolMult × its average and the product of volume and bar range is the highest in the lookback window.
4. Absorption/Churn: The current side’s volume divided by the bar’s range equals the highest value in the window and the bar’s range divided by ATR ≤ sharpChurnThr (indicating large volume within a small range).
5. Percentile rank: The current side’s volume percentile rank is ≥ sharp Percentile.
6. Mirror logic for sellers: The above checks are repeated with buyer and seller roles swapped and the price break levels reversed.
Each condition that passes contributes one point to the corresponding side’s score (0 or 1). Risk and confirmation thresholds are then applied to these scores.
7.4 Scoring and labels
• Risk: If scoreBull ≥ sharpScoreRisk, a “Sharp ↑ Risk” label is drawn above the bar. If scoreBear ≥ sharpScoreRisk, a “Sharp ↓ Risk” label is drawn below the bar.
• Confirmation: A risk label is upgraded to “Sharp ↑” when scoreBull ≥ sharpScoreConfirm and the bar closes above the highest recent pivot (HH1); for bearish cases, confirmation requires scoreBear ≥ sharpScoreConfirm and a close below the lowest pivot (LL1).
• Label positioning: Labels are offset from the candle by ATR × sharpLabelMult (full ATR times multiplier), not half, and may include a dashed or dotted connector line if enabled.
Figure caption, In this chart both bullish and bearish sharp‑move setups have been flagged. Earlier in the range, a “Sharp ↓ Risk” label appears beneath a candle: the sell‑side score met the risk threshold, signaling that the combination of strong sell volume, dominance and absorption within a narrow range suggested a potential sharp decline. The price did not close below the lower pivot, so this label remains a “risk” and no confirmation occurred. Later, as the market recovered and volume shifted back to the buy side, a “Sharp ↑ Risk” label prints above a candle near the top of the channel. Here, buy‑side dominance, cumulative delta divergence and a volume climax aligned, but price has not yet closed above the upper pivot (HH1), so the alert is still a risk rather than a confirmed sharp‑up move.
Figure caption, In this chart a Sharp ↑ label is displayed above a candle, indicating that the sharp move module has confirmed a bullish breakout. Prior bars satisfied the risk threshold — showing buy‑side dominance, positive cumulative delta divergence, a volume climax and strong absorption in a narrow range — and this candle closes above the highest recent pivot, upgrading the earlier “Sharp ↑ Risk” alert to a full Sharp ↑ signal. The green label is offset from the candle with a dashed connector, while the red and green trend lines trace the high and low pivot trajectories and the dashed horizontals mark the highest and lowest values of the lookback window.
8. Market‑Maker / Spread‑Capture Module
8.1 Concept
Liquidity providers often “capture the spread” by buying and selling in almost equal amounts within a very narrow price range. These bars can signal temporary congestion before a move or reflect algorithmic activity. This module flags bars where both buyer and seller volumes are high, the price range is only a few ticks and the buy/sell split remains close to 50%. It helps traders spot potential liquidity pockets.
8.2 Inputs
• scalpLookback: Window length used to compute volume averages.
• scalpVolMult: Multiplier applied to each side’s average volume; both buy and sell volumes must exceed this multiple.
• scalpTickCount: Maximum allowed number of ticks in a bar’s range (calculated as (high − low) / minTick). A value of 1 or 2 captures ultra‑small bars; increasing it relaxes the range requirement.
• scalpDeltaRatio: Maximum deviation from a perfect 50/50 split. For example, 0.05 means the buyer share must be between 45% and 55%.
• Label settings: ATR length, multiplier, size, colors, connector style and width.
• Toggles : show_scalp_module and show_scalp_labels to enable the module and its labels.
8.3 Signal
When, on the current bar, both TF_buy_breakout and TF_sell_breakout exceed scalpVolMult times their respective averages and (high − low)/minTick ≤ scalpTickCount and the buyer share is within scalpDeltaRatio of 50%, the module prints a “Spread ↔” label above the bar. The label uses the same ATR offset logic as other modules and draws a connector if enabled.
Figure caption, In this chart the spread‑capture module has identified a potential liquidity pocket. Buyer and seller volumes both spiked above their recent averages, yet the candle’s range measured only a couple of ticks and the buy/sell split stayed close to 50 %. This combination met the module’s criteria, so it printed a grey “Spread ↔” label above the bar. The red and green trend lines link the earliest and latest high and low pivots, and the dashed horizontals mark the highest high and lowest low within the current lookback window.
9. Money Flow Module
9.1 Concept
To translate volume into a monetary measure, this module multiplies each side’s volume by the closing price. It tracks buying and selling system money default currency on a per-bar basis and sums them over a chosen period. The difference between buy and sell currencies (Δ$) shows net inflow or outflow.
9.2 Inputs
• mf_period_len_mf: Number of bars used for summing buy and sell dollars.
• Label appearance settings: ATR length, multiplier, size, colors for up/down labels, and connector style and width.
• Toggles: Use enableMoneyFlowLabel_mf and showMFLabels to control whether the module and its labels are displayed.
9.3 Calculations
• Per-bar money: Buy $ = TF_buy_breakout × close; Sell $ = TF_sell_breakout × close. Their difference is Δ$ = Buy $ − Sell $.
• Summations: Over mf_period_len_mf bars, compute Σ Buy $, Σ Sell $ and ΣΔ$ using math.sum().
• Info table entries: Rows 9–13 display these values as texts like “↑ USD 1234 (1M)” or “ΣΔ USD −5678 (14)”, with colors reflecting whether buyers or sellers dominate.
• Money flow status: If Δ$ is positive the bar is marked “Money flow in” ; if negative, “Money flow out” ; if zero, “Neutral”. The cumulative status is similarly derived from ΣΔ.Labels print at the bar that changes the sign of ΣΔ, offset using ATR × label multiplier and styled per user preferences.
Figure caption, The chart illustrates a steady rise toward the highest recent pivot (HH1) with price riding between a rising green trend‑line and a red trend‑line drawn through earlier pivot highs. A green Money flow in label appears above the bar near the top of the channel, signaling that net dollar flow turned positive on this bar: buy‑side dollar volume exceeded sell‑side dollar volume, pushing the cumulative sum ΣΔ$ above zero. In the info table, the “Money flow (bar)” and “Money flow Σ” rows both read In, confirming that the indicator’s money‑flow module has detected an inflow at both bar and aggregate levels, while other modules (pivots, trend lines and support/resistance) remain active to provide structural context.
In this example the Money Flow module signals a net outflow. Price has been trending downward: successive high pivots form a falling red trend‑line and the low pivots form a descending green support line. When the latest bar broke below the previous low pivot (LL1), both the bar‑level and cumulative net dollar flow turned negative—selling volume at the close exceeded buying volume and pushed the cumulative Δ$ below zero. The module reacts by printing a red “Money flow out” label beneath the candle; the info table confirms that the “Money flow (bar)” and “Money flow Σ” rows both show Out, indicating sustained dominance of sellers in this period.
10. Info Table
10.1 Purpose
When enabled, the Info Table appears in the lower right of your chart. It summarises key values computed by the indicator—such as buy and sell volume, delta, total volume, breakout status, market phase, and money flow—so you can see at a glance which side is dominant and which signals are active.
10.2 Symbols
• ↑ / ↓ — Up (↑) denotes buy volume or money; down (↓) denotes sell volume or money.
• MA — Moving average. In the table it shows the average value of a series over the lookback period.
• Σ (Sigma) — Cumulative sum over the chosen lookback period.
• Δ (Delta) — Difference between buy and sell values.
• B / S — Buyer and seller share of total volume, expressed as percentages.
• Ref. Price — Reference price for breakout calculations, based on the latest pivot.
• Status — Indicates whether a breakout condition is currently active (True) or has failed.
10.3 Row definitions
1. Up volume / MA up volume – Displays current buy volume on the lower timeframe and its moving average over the lookback period.
2. Down volume / MA down volume – Shows current sell volume and its moving average; sell values are formatted in red for clarity.
3. Δ / ΣΔ – Lists the difference between buy and sell volume for the current bar and the cumulative delta volume over the lookback period.
4. Σ / MA Σ (Vol/MA) – Total volume (buy + sell) for the bar, with the ratio of this volume to its moving average; the right cell shows the average total volume.
5. B/S ratio – Buy and sell share of the total volume: current bar percentages and the average percentages across the lookback period.
6. Buyer Rank / Seller Rank – Ranks the bar’s buy and sell volumes among the last (n) bars; lower rank numbers indicate higher relative volume.
7. Σ Buy / Σ Sell – Sum of buy and sell volumes over the lookback window, indicating which side has traded more.
8. Breakout UP / DOWN – Shows the breakout thresholds (Ref. Price) and whether the breakout condition is active (True) or has failed.
9. Market Phase (Vol) – Reports the current volume‑only phase: Accumulation, Distribution or Neutral.
10. Money Flow – The final rows display dollar amounts and status:
– ↑ USD / Σ↑ USD – Buy dollars for the current bar and the cumulative sum over the money‑flow period.
– ↓ USD / Σ↓ USD – Sell dollars and their cumulative sum.
– Δ USD / ΣΔ USD – Net dollar difference (buy minus sell) for the bar and cumulatively.
– Money flow (bar) – Indicates whether the bar’s net dollar flow is positive (In), negative (Out) or neutral.
– Money flow Σ – Shows whether the cumulative net dollar flow across the chosen period is positive, negative or neutral.
The chart above shows a sequence of different signals from the indicator. A Bull Trap Risk appears after price briefly pushes above resistance but fails to hold, then a green Accum label identifies an accumulation phase. An upward breakout follows, confirmed by a Money flow in print. Later, a Sharp ↓ Risk warns of a possible sharp downturn; after price dips below support but quickly recovers, a Bear Trap label marks a false breakdown. The highlighted info table in the center summarizes key metrics at that moment, including current and average buy/sell volumes, net delta, total volume versus its moving average, breakout status (up and down), market phase (volume), and bar‑level and cumulative money flow (In/Out).
11. Conclusion & Final Remarks
This indicator was developed as a holistic study of market structure and order flow. It brings together several well‑known concepts from technical analysis—breakouts, accumulation and distribution phases, overbought and oversold extremes, bull and bear traps, sharp directional moves, market‑maker spread bars and money flow—into a single Pine Script tool. Each module is based on widely recognized trading ideas and was implemented after consulting reference materials and example strategies, so you can see in real time how these concepts interact on your chart.
A distinctive feature of this indicator is its reliance on per‑side volume: instead of tallying only total volume, it separately measures buy and sell transactions on a lower time frame. This approach gives a clearer view of who is in control—buyers or sellers—and helps filter breakouts, detect phases of accumulation or distribution, recognize potential traps, anticipate sharp moves and gauge whether liquidity providers are active. The money‑flow module extends this analysis by converting volume into currency values and tracking net inflow or outflow across a chosen window.
Although comprehensive, this indicator is intended solely as a guide. It highlights conditions and statistics that many traders find useful, but it does not generate trading signals or guarantee results. Ultimately, you remain responsible for your positions. Use the information presented here to inform your analysis, combine it with other tools and risk‑management techniques, and always make your own decisions when trading.
Multiple Divergence Scanner (move to candles and merge scales)This indicator detects and visualizes multiple types of RSI-based divergences, including Regular, Hidden, and Dual-source (Multi) Bullish/Bearish signals. Not limited with RSI only. You can add move functions and it will automaticly combine your options.
It offers customizable score filtering, label positioning, and visual styling.
Ideal for traders who seek both technical precision and symbolic clarity in their charts.
You have to drag it to your candles after adding to your chart. Then right click on price->Merge all scales to right/left.
Simplified Market ForecastSimplified Market Forecast Indicator
This indicator pairs nicely with the Contrarian 100 MA and can be located here:
Overview
The "Simplified Market Forecast" (SMF) indicator is a streamlined technical analysis tool designed for traders to identify potential buy and sell opportunities based on a momentum-based oscillator. By analyzing price movements relative to a defined lookback period, SMF generates clear buy and sell signals when the oscillator crosses customizable threshold levels. This indicator is versatile, suitable for various markets (e.g., forex, stocks, cryptocurrencies), and optimized for daily timeframes, though it can be adapted to other timeframes with proper testing. Its intuitive design and visual cues make it accessible for both novice and experienced traders.
How It Works
The SMF indicator calculates a momentum oscillator based on the price’s position within a specified range over a user-defined lookback period. It then smooths this value to reduce noise and plots the result as a line in a separate lower pane. Buy and sell signals are generated when the smoothed oscillator crosses above a user-defined buy level or below a user-defined sell level, respectively. These signals are visualized as triangles either on the main chart or in the lower pane, with a table displaying the current ticker and oscillator value for quick reference.
Key Components
Momentum Oscillator: The indicator measures the price’s position relative to the highest high and lowest low over a specified period, normalized to a 0–100 scale.
Signal Generation: Buy signals occur when the oscillator crosses above the buy level (default: 15), indicating potential oversold conditions. Sell signals occur when the oscillator crosses below the sell level (default: 85), suggesting potential overbought conditions.
Visual Aids: The indicator includes customizable horizontal lines for buy and sell levels, shaded zones for clarity, and a table showing the ticker and current oscillator value.
Mathematical Concepts
Oscillator Calculation: The indicator uses the following formula to compute the raw oscillator value:
c1I = close - lowest(low, medLen)
c2I = highest(high, medLen) - lowest(low, medLen)
fastK_I = (c1I / c2I) * 100
The result is smoothed using a 5-period Simple Moving Average (SMA) to produce the final oscillator value (inter).
Signal Logic:
A buy signal is triggered when the smoothed oscillator crosses above the buy level (ta.crossover(inter, buyLevel)).
A sell signal is triggered when the smoothed oscillator crosses below the sell level (ta.crossunder(inter, sellLevel)).
Entry and Exit Rules
Buy Signal (Blue Triangle): Triggered when the oscillator crosses above the buy level (default: 15), indicating a potential oversold condition and a buying opportunity. The signal appears as a blue triangle either below the price bar (if plotted on the main chart) or at the bottom of the lower pane.
Sell Signal (White Triangle): Triggered when the oscillator crosses below the sell level (default: 85), indicating a potential overbought condition and a selling opportunity. The signal appears as a white triangle either above the price bar (if plotted on the main chart) or at the top of the lower pane.
Exit Rules: Traders can exit positions when an opposite signal occurs (e.g., exit a buy on a sell signal) or based on additional technical analysis tools (e.g., support/resistance, trendlines). Always apply proper risk management.
Recommended Usage
The SMF indicator is optimized for the daily timeframe but can be adapted to other timeframes (e.g., 1H, 4H) with careful testing. It performs best in markets with clear momentum shifts, such as trending or range-bound conditions. Traders should:
Backtest the indicator on their chosen asset and timeframe to validate signal reliability.
Combine with other indicators (e.g., moving averages, support/resistance) or price action for confirmation.
Adjust the lookback period and buy/sell levels to suit market volatility and trading style.
Customization Options
Intermediate Length: Adjust the lookback period for the oscillator calculation (default: 31 bars).
Buy/Sell Levels: Customize the threshold levels for buy (default: 15) and sell (default: 85) signals.
Colors: Modify the colors of the oscillator line, buy/sell signals, and threshold lines.
Signal Display: Toggle whether signals appear on the main chart or in the lower pane.
Visual Aids: The indicator includes dotted horizontal lines at the buy (green) and sell (red) levels, with shaded zones between 0–buy level (green) and sell level–100 (red) for clarity.
Ticker Table: A table in the top-right corner displays the current ticker and oscillator value (in percentage), with customizable colors.
Why Use This Indicator?
The "Simplified Market Forecast" indicator provides a straightforward, momentum-based approach to identifying potential reversals in overbought or oversold markets. Its clear signals, customizable settings, and visual aids make it easy to integrate into various trading strategies. Whether you’re a swing trader or a day trader, SMF offers a reliable tool to enhance decision-making and improve market timing.
Tips for Users
Test the indicator thoroughly on your chosen asset and timeframe to optimize settings.
Use in conjunction with other technical tools for stronger trade confirmation.
Adjust the buy and sell levels based on market conditions (e.g., lower levels for less volatile markets).
Monitor the ticker table for real-time oscillator values to gauge market momentum.
Happy trading with the Simplified Market Forecast indicator!
Savitzky-Golay Hampel Filter | AlphaNattSavitzky-Golay Hampel Filter | AlphaNatt
A revolutionary indicator combining NASA's satellite data processing algorithms with robust statistical outlier detection to create the most scientifically advanced trend filter available on TradingView.
"This is the same mathematics that processes signals from the Hubble Space Telescope and analyzes data from the Large Hadron Collider - now applied to financial markets."
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🚀 SCIENTIFIC PEDIGREE
Savitzky-Golay Filter Applications:
NASA: Satellite telemetry and space probe data processing
CERN: Particle physics data analysis at the LHC
Pharmaceutical: Chromatography and spectroscopy analysis
Astronomy: Processing signals from radio telescopes
Medical: ECG and EEG signal processing
Hampel Filter Usage:
Aerospace: Cleaning sensor data from aircraft and spacecraft
Manufacturing: Quality control in precision engineering
Seismology: Earthquake detection and analysis
Robotics: Sensor fusion and noise reduction
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🧬 THE MATHEMATICS
1. Savitzky-Golay Filter
The SG filter performs local polynomial regression on data points:
Fits a polynomial of degree n to a sliding window of data
Evaluates the polynomial at the center point
Preserves higher moments (peaks, valleys) unlike moving averages
Maintains derivative information for true momentum analysis
Originally published in Analytical Chemistry (1964)
Mathematical Properties:
Optimal smoothing in the least-squares sense
Preserves statistical moments up to polynomial order
Exact derivative calculation without additional lag
Superior frequency response vs traditional filters
2. Hampel Filter
A robust outlier detector based on Median Absolute Deviation (MAD):
Identifies outliers using robust statistics
Replaces spurious values with polynomial-fitted estimates
Resistant to up to 50% contaminated data
MAD is 1.4826 times more robust than standard deviation
Outlier Detection Formula:
|x - median| > k × 1.4826 × MAD
Where k is the threshold parameter (typically 3 for 99.7% confidence)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
💎 WHY THIS IS SUPERIOR
vs Moving Averages:
Preserves peaks and valleys (critical for catching tops/bottoms)
No lag penalty for smoothness
Maintains derivative information
Polynomial fitting > simple averaging
vs Other Filters:
Outlier immunity (Hampel component)
Scientifically optimal smoothing
Preserves higher-order features
Used in billion-dollar research projects
Unique Advantages:
Feature Preservation: Maintains market structure while smoothing
Spike Immunity: Ignores false breakouts and stop hunts
Derivative Accuracy: True momentum without additional indicators
Scientific Validation: 60+ years of academic research
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⚙️ PARAMETER OPTIMIZATION
1. Polynomial Order (2-5)
2 (Quadratic): Maximum smoothing, gentle curves
3 (Cubic): Balanced smoothing and responsiveness (recommended)
4-5 (Higher): More responsive, preserves more features
2. Window Size (7-51)
Must be odd number
Larger = smoother but more lag
Formula: 2×(desired smoothing period) + 1
Default 21 = analyzes 10 bars each side
3. Hampel Threshold (1.0-5.0)
1.0: Aggressive outlier removal (68% confidence)
2.0: Moderate outlier removal (95% confidence)
3.0: Conservative outlier removal (99.7% confidence) (default)
4.0+: Only extreme outliers removed
4. Final Smoothing (1-7)
Additional WMA smoothing after filtering
1 = No additional smoothing
3-5 = Recommended for most timeframes
7 = Ultra-smooth for position trading
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 TRADING STRATEGIES
Signal Recognition:
Cyan Line: Bullish trend with positive derivative
Pink Line: Bearish trend with negative derivative
Color Change: Trend reversal with polynomial confirmation
1. Trend Following Strategy
Enter when price crosses above cyan filter
Exit when filter turns pink
Use filter as dynamic stop loss
Best in trending markets
2. Mean Reversion Strategy
Enter long when price touches filter from below in uptrend
Enter short when price touches filter from above in downtrend
Exit at opposite band or filter color change
Excellent for range-bound markets
3. Derivative Strategy (Advanced)
The SG filter preserves derivative information
Acceleration = second derivative > 0
Enter on positive first derivative + positive acceleration
Exit on negative second derivative (momentum slowing)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📈 PERFORMANCE CHARACTERISTICS
Strengths:
Outlier Immunity: Ignores stop hunts and flash crashes
Feature Preservation: Catches tops/bottoms better than MAs
Smooth Output: Reduces whipsaws significantly
Scientific Basis: Not curve-fitted or optimized to markets
Considerations:
Slight lag in extreme volatility (all filters have this)
Requires odd window sizes (mathematical requirement)
More complex than simple moving averages
Best with liquid instruments
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🔬 SCIENTIFIC BACKGROUND
Savitzky-Golay Publication:
"Smoothing and Differentiation of Data by Simplified Least Squares Procedures"
- Abraham Savitzky & Marcel Golay
- Analytical Chemistry, Vol. 36, No. 8, 1964
Hampel Filter Origin:
"Robust Statistics: The Approach Based on Influence Functions"
- Frank Hampel et al., 1986
- Princeton University Press
These techniques have been validated in thousands of scientific papers and are standard tools in:
NASA's Jet Propulsion Laboratory
European Space Agency
CERN (Large Hadron Collider)
MIT Lincoln Laboratory
Max Planck Institutes
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
💡 ADVANCED TIPS
News Trading: Lower Hampel threshold before major events to catch spikes
Scalping: Use Order=2 for maximum smoothness, Window=11 for responsiveness
Position Trading: Increase Window to 31+ for long-term trends
Combine with Volume: Strong trends need volume confirmation
Multiple Timeframes: Use daily for trend, hourly for entry
Watch the Derivative: Filter color changes when first derivative changes sign
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⚠️ IMPORTANT NOTICES
Not financial advice - educational purposes only
Past performance does not guarantee future results
Always use proper risk management
Test settings on your specific instrument and timeframe
No indicator is perfect - part of complete trading system
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🏆 CONCLUSION
The Savitzky-Golay Hampel Filter represents the pinnacle of scientific signal processing applied to financial markets. By combining polynomial regression with robust outlier detection, traders gain access to the same mathematical tools that:
Guide spacecraft to other planets
Detect gravitational waves from black holes
Analyze particle collisions at near light-speed
Process signals from deep space
This isn't just another indicator - it's rocket science for trading .
"When NASA needs to separate signal from noise in billion-dollar missions, they use these exact algorithms. Now you can too."
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Developed by AlphaNatt
Version: 1.0
Release: 2025
Pine Script: v6
"Where Space Technology Meets Market Analysis"
Not financial advice. Always DYOR
Script_Algo - Double Smoothed CCI Strategy📉 The uniqueness of this non-trending oscillator strategy lies in the combination of two smoothed CCI lines: one signals entry into a position from overbought/oversold zones, and the other serves as a trend filter for entries. The smoothing of the fast and slow CCI lines significantly reduces market noise, allowing the filtering of false signals often generated by the standard CCI.
📚 For those unfamiliar with CCI:
The Commodity Channel Index (CCI) is a momentum-based oscillator used to identify overbought and oversold conditions.
It helps traders spot potential trend reversals or confirm trend strength by comparing the current price to its average over a period of time.
1️⃣ General Principle of Operation
⚡ Fast CCI: Generates main signals when exiting oversold and overbought zones.
📈 Slow CCI: Acts as a trend filter. For long positions, the slow CCI must be above zero (confirmation of an uptrend), and for short positions, it must be below zero (confirmation of a downtrend). This prevents the strategy from opening trades against the dominant trend.
🛡️ Dynamic ATR Stop-Loss: Unlike fixed-percentage stop-losses, a stop tied to the Average True Range (ATR) considers market volatility. During calm periods, the stop will be narrower, allowing for more profit capture. In highly volatile periods, the stop becomes wider, protecting against premature closures caused by noise.
📊 Comprehensive Risk Management: The strategy uses not only a take-profit based on signals (exit into the opposite zone) but also a protective ATR stop-loss and a mechanism to close trades upon receiving an opposite signal (e.g., closing a long when a short signal appears).
💡 Usefulness of the Strategy:
👨💻 For traders: Provides clear, mathematically justified entry and exit signals with built-in loss protection.
📉 For analysts: Visualizes the behavior of the double CCI on a separate panel, allowing study of the interaction of the fast and slow lines and their reaction to levels without mandatory trades.
📚 For learning: An excellent example of combining multiple indicators and capital management tools into a single trading system.
2️⃣ Detailed Algorithm Logic
📥 Long Entry Signals:
The fast smoothed CCI was below the oversold level (oversold_level, e.g., -100) and crossed this level upward (fast_exits_oversold).
The slow CCI at this moment is above zero (confirming an uptrend).
If both conditions are met, a long position is opened.
📤 Long Exit: Happens under one of these conditions:
The fast CCI crosses the overbought level (overbought_level) downward (exit_long).
The price reaches a stop-loss level calculated as entry price - (ATR * multiplier).
An opposite short signal appears (enter_short).
📥 Short Entry Signals:
The fast CCI was above the overbought level (overbought_level, e.g., 100) and crossed this level downward (fast_exits_overbought).
The slow CCI at this moment is below zero (confirming a downtrend).
If both conditions are met, a short position is opened.
📤 Short Exit: Happens under one of these conditions:
The fast CCI crosses the oversold level (oversold_level) upward (exit_short).
The price reaches a stop-loss level calculated as entry price + (ATR * multiplier).
An opposite long signal appears (enter_long).
3️⃣ Default Settings Description
⚙️ General Strategy Settings (strategy):
overlay=false: The indicator is displayed in a separate panel below the chart, not overlaid on it.
default_qty_type=strategy.cash, default_qty_value=1000, initial_capital=100000: The strategy manages a virtual capital of 100,000 USD, using 1,000 USD per trade.
commission_value=0.1, slippage=1: Commission (0.1%) and slippage (1 tick) are considered for more realistic testing.
⚡ Fast CCI (Signal Generator):
Length: 8 (short enough for quick price reactions).
Source: hlc3 (average of High, Low, Close).
Smoothing: WMA (Weighted Moving Average) for smoother results than SMA.
Smoothing Length: 5 (removes part of the noise).
📈 Slow CCI (Trend Filter):
Length: 20 (standard mid-term trend period).
Source: close.
Smoothing: WMA.
Smoothing Length: 21 (even stronger smoothing for a clean trend line).
📊 Levels:
Overbought Level: 100 (classic CCI level).
Oversold Level: -100 (classic CCI level).
🛡️ Stop-Loss (ATR):
ATR Length: 6 (short period for quick adaptation).
ATR Multiplier: 10.0 (very wide stop, designed for long-term trade holding and noise filtering).
💰 As seen in backtests, this strategy shows a steadily growing equity curve with minor drawdowns. On the highly liquid crypto pair XRPUSDT, the algorithm demonstrated a fairly high win rate and relatively high profit factor on a 4-hour timeframe over 4 years, though the overall profit is moderate.
⚠️ Important Notes
Always remember: Strategy results may not repeat in the future.
The market constantly changes, so:
✅ Monitor the situation
✅ Backtest regularly
✅ Adjust settings for each asset
Also remember about possible bugs in any algorithmic trading strategy.
Even if a script is well-tested, no one knows what unpredictable events the market may bring tomorrow.
⚠️ Risk Management:
Do not risk more than 1% of your deposit per trade, otherwise you may lose your account balance, since this strategy works without stop losses.
⚠️ Disclaimer
The author of the strategy does not encourage anyone to use this algorithm and bears no responsibility for any possible financial losses resulting from its application!
Any decision to use this strategy is made personally by the owners of TradingView accounts and cryptocurrency exchange accounts.
📝 Final Notes
This is not the final version. I already have ideas on how to improve it further, so follow me to not miss updates.
🐞 Bug Reports
If you notice any bugs or inconsistencies in my algorithm,
please let me know — I will try to fix them as quickly as possible.
💬 Feedback & Suggestions
If you have any ideas on how this or any of my other strategies can be improved, feel free to write to me. I will try to implement your suggestions in the script.
Wishing everyone good luck and stable profits! 🚀💰
EMA/SMA Zones 9, 21, 30, 50, 100, 200 + othersMeant for swing trading on the daily chart, feel free to copy and remove/add sections as you wish (Used chatGPT for a lot of it).
Daily CMO + Volume Intraday Strategy v6 by Subirrmomentum strategy. buy on next hourly candle after signal. target 5%, sl 1%
SMI Ergodic Oscillator ColorSMI Ergodic Oscillator Color. A variation of the True Strength Index (TSI), the SMI Ergodic Oscillator is a momentum indicator used to identify trend direction and potential reversals. It consists of a double-smoothed price momentum line and a signal line, helping traders spot buy and sell signals when the two lines cross. It is particularly useful for filtering out market noise and confirming the strength of a trend.
RSI with KAMA and Custom Buy/Sell SignalsUses Kaufman MA on the RSI to generate signals when crossing user thresholds
Hurst Momentum Oscillator | AlphaNattHurst Momentum Oscillator | AlphaNatt
An adaptive oscillator that combines the Hurst Exponent - which identifies whether markets are trending or mean-reverting - with momentum analysis to create signals that automatically adjust to market regime.
"The Hurst Exponent reveals a hidden truth: markets aren't always trending. This oscillator knows when to ride momentum and when to fade it."
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📐 THE MATHEMATICS
Hurst Exponent (H):
Measures the long-term memory of time series:
H > 0.5: Trending (persistent) behavior
H = 0.5: Random walk
H < 0.5: Mean-reverting behavior
Originally developed for analyzing Nile river flooding patterns, now used in:
Fractal market analysis
Network traffic prediction
Climate modeling
Financial markets
The Innovation:
This oscillator multiplies momentum by the Hurst coefficient:
When trending (H > 0.5): Momentum is amplified
When mean-reverting (H < 0.5): Momentum is reduced
Result: Adaptive signals based on market regime
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
💎 KEY ADVANTAGES
Regime Adaptive: Automatically adjusts to trending vs ranging markets
False Signal Reduction: Reduces momentum signals in mean-reverting markets
Trend Amplification: Stronger signals when trends are persistent
Mathematical Edge: Based on fractal dimension analysis
No Repainting: All calculations on historical data
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 TRADING SIGNALS
Visual Interpretation:
Cyan zones: Bullish momentum in trending market
Magenta zones: Bearish momentum or mean reversion
Background tint: Blue = trending, Pink = mean-reverting
Gradient intensity: Signal strength
Trading Strategies:
1. Trend Following:
Trade momentum signals when background is blue (trending)
2. Mean Reversion:
Fade extreme readings when background is pink
3. Regime Transition:
Watch for background color changes as early warning
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🎯 OPTIMAL USAGE
Best Conditions:
Strong trending markets (crypto bull runs)
Clear ranging markets (forex sessions)
Regime transitions
Multi-timeframe analysis
Market Applications:
Crypto: Excellent for identifying trend persistence
Forex: Detects when pairs are ranging
Stocks: Identifies momentum stocks
Commodities: Catches persistent trends
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Developed by AlphaNatt | Fractal Market Analysis
Version: 1.0
Classification: Adaptive Regime Oscillator
Not financial advice. Always DYOR.
Multi-RSI with Stochastic Oscillator - flack0xA sophisticated momentum analysis tool combining 4 customizable RSI oscillators with an innovative Close/Close Stochastic implementation. Designed for traders seeking comprehensive momentum insights across multiple timeframes in a single, organized indicator.
Key Features:
4 Independent RSI Oscillators with default periods: 2, 3, 9, 27
Innovative Close/Close Stochastic - Compares closing prices to closing price ranges (not high/low)
Complete Customization - Individual control over periods, colors, line widths, and visibility
Reference Levels - Customizable overbought (70), oversold (30), and midline (50) levels
Smart Alert System - Crossover notifications for key momentum shifts
Unique Close/Close Stochastic Methodology:
Unlike traditional Stochastic oscillators that use high/low ranges.
Benefits of Close/Close Approach:
Eliminates Gap Noise - Ignores overnight gaps and intraday wicks
Smoother Signals - Reduces whipsaws common in traditional Stochastic
Position-Relevant - Focuses on actual settlement prices traders care about
Cleaner Momentum Reading - Pure closing price momentum without intraday volatility
Stochastic ColorStochastic Color. A momentum indicator that compares a particular closing price of an asset to a range of its prices over a specific period of time. It helps identify overbought and oversold conditions in the market. The indicator ranges from 0 to 100, with readings above 80 typically considered overbought and readings below 20 considered oversold. It is often used to anticipate potential price reversals.
RSI SMA ColorRSI 14 with SMA 21 Color. A momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is typically used to identify overbought or oversold conditions in a market. An RSI above 70 may indicate that an asset is overbought, while an RSI below 30 may suggest it is oversold.
Swing Oracle Stock// (\_/)
// ( •.•)
// (")_(")
📌 Swing Oracle Stock – Professional Cycle & Trend Detection Indicator
The Swing Oracle Stock is an advanced market analysis tool designed to highlight price cycles, trend shifts, and key trading zones with precision. It combines trendline dynamics, normalized oscillators, and multi-timeframe confirmation into a single comprehensive indicator.
🔑 Key Features
NDOS (Normalized Dynamic Oscillator System):
Measures price strength relative to recent highs and lows to detect overbought, neutral, and oversold zones.
Dynamic Trendline (EMA8 or SMA231):
Flexible source selection for adapting to different trading styles (scalping vs. swing).
Multi-Timeframe H1 Confirmation:
Adds higher-timeframe validation to improve signal reliability.
Automated Buy & Sell Signals:
Triggered only on significant crossovers above/below defined levels.
Weekly Cycles (7-day M5 projection):
Tracks recurring time-based market cycles to anticipate reversal points.
Intuitive Visualization:
Colored zones (high, low, neutral) for quick market context.
Optional background and candlestick coloring for better clarity.
Multi-Timeframe Cross Table:
Automatically compares SMA50 vs. EMA200 across multiple timeframes (1m → 4h), showing clear status:
⭐️⬆️ UP = bullish trend confirmation
💀⬇️ Drop = bearish trend confirmation
📊 Built-in Statistical Tools
Normalized difference between short and long EMA.
Projected normalized mean levels plotted directly on the main chart.
Dynamic analysis of price distance from SMA50 to capture market “waves.”
🎯 Use Cases
Spot trend reversals with multi-timeframe confirmation.
Identify powerful breakout and breakdown zones.
Time entries and exits based on trend + cycle confluence.
Enhance market timing for swing trades, scalps, or long-term positions.
⚡ Swing Oracle Stock brings together cycle detection, oscillator normalization, and multi-timeframe confirmation into one streamlined indicator for traders who want a professional edge.
Momentum Index [BigBeluga]The Momentum Index is an innovative indicator designed to measure the momentum of price action by analyzing the distribution of positive and negative momentum values over a defined period. By incorporating delta-based calculations and smoothing techniques, it provides traders with a clear and actionable representation of market momentum dynamics.
🔵 Key Features:
Delta-Based Momentum Analysis:
Calculates the momentum of price by comparing its current state to its value from a defined number of bars back.
Inside a loop, it evaluates whether momentum values are above or below zero, producing a delta value that reflects the net momentum direction and intensity.
Double EMA Smoothing:
Smooths the raw delta-based momentum values with a double EMA filter, reducing noise and providing a clearer trend signal.
tmi(len) =>
sum = 0.0
sum1 = 0.0
above = 0.0
below = 0.0
src_ = src - src
for i = 0 to len
sum := sum + (src_ > nz(src_ ) ? 1 : -1)
sum1 := sum1 + (sum > 0 ? 1 : -1)
sum1 := emaEma(sum1, 10)
for i = 1 to len
above := above + (sum1 > 0 ? 1 : 0)
below := below + (sum1 > 0 ? 0 : 1)
Directional Momentum Signals:
Generates momentum shift signals and displays them on both the oscillator and the main chart:
- △ Aqua Triangles: Represent upward momentum shifts.
- ▽ Red Triangles: Represent downward momentum shifts.
Dynamic Gradient Display:
Highlights momentum zones with gradient fills:
- Aqua shades for positive momentum (above zero).
- Red shades for negative momentum (below zero).
Dashboard Display:
A dashboard summarizing the count of momentum values above and below zero for the defined period (Sentiment Length e.g. 100), helping traders assess market sentiment at a glance.
🔵 How It Works:
The indicator takes price momentum as its source and evaluates the number of momentum values above and below zero within a defined period.
The delta calculation aggregates this information, providing a net representation of the prevailing market momentum.
A double EMA filter is applied to the delta values, smoothing the momentum line and enhancing signal clarity.
Momentum shifts are highlighted with visual signals on the oscillator and price chart, while the gradient display provides a visual representation of intensity.
🔵 Use Cases:
Momentum Tracking: Identify whether market momentum is predominantly bullish or bearish.
Signal Confirmation: Use chart-based signals to confirm potential trend reversals or continuation.
Analyze Market Strength: Leverage the dashboard to quickly assess the distribution of momentum over the chosen period.
Overbought/Oversold Conditions: Utilize gradient zones to detect areas of momentum extremes and possible price exhaustion.
Momentum Index offers a refined approach to analyzing momentum dynamics, combining delta-based calculations with smoothing techniques and intuitive visuals, making it an essential tool for traders looking to anticipate market movements effectively.
Artharjan ADXArtharjan ADX (AADX) by Rrahul Desai @Artharjan
📌 Overview
The Artharjan ADX (AADX) is an advanced implementation of the Average Directional Index (ADX) with customizable moving averages, momentum thresholds, and visually intuitive grading of bullish and bearish strength.
Unlike the standard ADX indicator that only shows trend strength, AADX adds graded bullish/bearish conditions, alerts, smoothed DI signals, histogram visualizations, and background color fills to help traders quickly interpret market conditions.
It is designed for traders who want early detection of trend strength, clean visual cues, and automated alert triggers for both bullish and bearish momentum setups.
⚙️ Key Features
🔹 Customizable Calculations
DI Length (default 13) – controls sensitivity of directional indicators.
+/- DI Smoothing – smooths DI signals with user-selected MA.
Multiple Moving Average Types – SMA, EMA, WMA, RMA, VWMA, ALMA, Hull, SWMA, SMMA, TMA.
ADX Smoothing – define how smooth/fast the ADX reacts.
🔹 Flexible Display
Toggle between line plots or histogram view.
Adjustable plot thickness.
Option to plot averages of ADX, +DI, -DI for confirmation.
Configurable background fills:
ADX above/below momentum threshold.
ADX rising/falling color shading.
Trend-grade based color intensity.
🔹 Momentum & Thresholds
Momentum Level (default 25) → defines “strong trend” zone.
Crossover Threshold (default 15) → helps detect early DI crossovers.
Color-coded histogram bars for +DI vs -DI difference:
Above/below zero.
Rising/falling momentum.
🔹 Bullish & Bearish Grading System
The indicator assigns grades from 1 to 5 for both bullish and bearish setups, based on DI and ADX conditions:
Bullish Grades
Grade 1 → Very Weak Bullish
Grade 2 → Weak Bullish
Grade 3 → Moderate Bullish
Grade 4 → Strong Bullish
Grade 5 → Very Strong Bullish
Bearish Grades
Grade 1 → Very Weak Bearish
Grade 2 → Weak Bearish
Grade 3 → Moderate Bearish
Grade 4 → Strong Bearish
Grade 5 → Very Strong Bearish
Labels are automatically plotted above bars to indicate the active grade.
🔹 Alerts
Bullish Alert → when +DI crosses above its average below the threshold OR bullish conditions are met.
Bearish Alert → when -DI crosses above its average below the threshold OR bearish conditions are met.
These alerts make it possible to automate trading signals for scalping, intraday, and swing trading.
📊 Use Cases
Trend Strength Measurement
Spot when markets shift from range-bound to trending.
Confirm the reliability of breakouts with strong ADX readings.
Bullish vs Bearish Control
Compare +DI vs -DI strength to gauge trend direction.
Identify trend reversals early with DI slope changes.
Momentum Confirmation
Use ADX rising + DI grades to validate trade entries.
Filter false breakouts with weak ADX.
Trade Grading System
Enter aggressively on Grade 4–5 signals.
Stay cautious on Grade 1–2 signals.
Automated Alerts & Screening
Combine AADX alerts with strategy rules.
Build scanners to highlight strong ADX setups across multiple stocks.
🎯 Trader’s Advantage
More powerful than standard ADX → Adds slope, grading, alerts, and visualization.
Adaptable to any style → Works for intraday scalping, swing trading, and positional analysis.
Visual clarity → Color fills, histograms, and labels simplify decision-making.
Customizable smoothing → Adjusts to fast or slow markets.
✅ Closing Note
The Artharjan ADX (AADX) transforms the traditional ADX into a complete trend and momentum analyzer. It helps traders detect, confirm, and act on directional strength with clarity and confidence.
With Thanks,
Rrahul Desai
@Artharjan
High For Loop | MisinkoMasterThe High For Loop is a new Trend Following tool designed to give traders smooth and fast signals without being too complex, overfit or repainting.
It works by finding how many bars have a higher high than the current high, how many have a lower high, and scores it based on that. This provides users with easy and accurate signals, allowing for gaining a large edge in the market.
It is pretty simple but you can still play around with it pretty well and improve uppon your strategies.
For any backtests using strategies, I left many comments and tried to make it as easy as possible to backtest.
Enjoy G´s
MA-Median For Loop | MisinkoMasterThe MA-Median For Loop is a new Trend Following tool that gives the user smooth yet responsive trend signals, allowing you to see clear and accurate trends by combining the Moving Average & Median in a For Loop concept.
How does it work?
1. Select user defined inputs
=> Adjust it to your liking, everyone can set it to their liking.
2. Calculate the MA and the Median
=> Simple, but important
3. Calculate the For Loop
=> For every bar back where the median or ma of that bar is higher than the current median or ma subtract 0.5 from the trend score, and for every bar back where the current median/ma is higher than the previous one add 0.5 to the trend score.
This simple yet effective approach enhances speed, decreases noise, and produces accurate signals everyone can utilize to get an edge in the market
Enjoy G´s
