BRT Micro Range Change: Signals** BRT Micro Range Change: Signals - Advanced Range-Based Trading Indicator **
** Overview **
The BRT Micro Range Change indicator represents a sophisticated approach to market analysis, utilizing proprietary range-based methodology combined with inverted signal logic. This unique indicator transforms traditional price action into structured range blocks, providing traders with counter-trend signal opportunities based on micro-range movements.
** Key Features & Unique Innovations **
** Proprietary Range Construction Algorithm **
• Custom range block generation using advanced price smoothing techniques
• Dynamic range sizing with percentage-based or fixed value options
• Multi-timeframe range analysis capability
• Intelligent block trend detection and reversal identification
** Inverted Signal Logic (Core Innovation) **
• ** Unique Counter-Strategy Approach **: When underlying range analysis suggests bearish momentum, the indicator generates LONG signals, and vice versa
• Advanced signal filtering prevents repetitive entries of the same type
• Position-aware logic tracks theoretical strategy state for optimal signal timing
** Comprehensive Risk Management **
• Dual calculation modes for Take Profit and Stop Loss (Fixed percentage or ATR-based)
• Real-time TP/SL level visualization
• Configurable ATR multipliers for volatility-adjusted exits
• Built-in position tracking and exit signal generation
** Advanced Technical Components **
• Integrated Simple Moving Average with multiple source options (Close, OHLC4, HL2, etc.)
• Custom ATR calculation optimized for range-based analysis
• Smart alert system with detailed entry/exit information
• Visual signal overlay with customizable display options
** Configuration Options **
• Range Type: Fixed value or percentage-based sizing
• Range Timeframe: Multi-timeframe analysis support
• SMA Integration: 14 configurable sources with custom coloring
• Alert Management: Comprehensive notification system
• Signal Display: Toggle visual markers and labels
** Technical Innovation **
This indicator's ** core uniqueness ** lies in its inverted signal methodology combined with advanced range-based market structure analysis. Unlike conventional indicators that follow trend direction, this system identifies potential reversal opportunities by analyzing when traditional range-based strategies would enter positions, then providing opposite directional signals. The proprietary range construction algorithm ensures high-quality block formation while the anti-repetition logic prevents signal spam.
** Usage Recommendations **
• Ideal for counter-trend trading strategies
• Effective in ranging and consolidating markets
• Best used in conjunction with additional confirmation indicators
• Suitable for multiple asset classes and timeframes
** Usage Recommendations **
• Ideal for counter-trend trading strategies
• Effective in ranging and consolidating markets
• Best used in conjunction with additional confirmation indicators
• Suitable for multiple asset classes and timeframes
** Historical Backtesting Results **
** Comprehensive Historical Data Verification **
• The indicator has been ** thoroughly tested ** on historical data using default settings
• Testing was conducted across a ** wide spectrum of crypto assets ** to ensure result reliability
• ** Optimal performance ** is demonstrated on the ** 5-minute timeframe **
• ** Entry Methodology **: utilization of limit orders at the lower boundary price of blocks for long positions and upper boundary price of blocks for short positions
• ** Trading Discipline **: strict adherence to the principle of "no repeated entries in the same direction"
• ** Order Management **: one order per trade with activation exclusively upon range block color change
** Technical Support **
For technical support inquiries, questions, or feedback, please feel free to leave comments below or send direct messages to the author. We are committed to providing assistance and continuously improving the indicator based on user experience and feedback.
** Important Risk Disclosure **
** DISCLAIMER: ** ** This indicator is provided for informational and educational purposes only. Trading financial instruments involves substantial risk of loss and may not be suitable for all investors. Past performance does not guarantee future results. Users should thoroughly test any trading strategy in a demo environment before risking real capital. The indicator's signals should not be considered as financial advice or recommendations to buy or sell any financial instrument. Users are solely responsible for their trading decisions and should consult with qualified financial advisors before making investment decisions. The authors and publishers assume no liability for any losses incurred through the use of this indicator. **
** Code Authenticity **
This indicator contains ** 100% original code ** developed specifically for advanced range-based analysis. All algorithms, mathematical calculations, and signal generation logic have been created from scratch, ensuring unique functionality not available in standard indicators.
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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.
Tzotchev Trend Measure [EdgeTools]Are you still measuring trend strength with moving averages? Here is a better variant at scientific level:
Tzotchev Trend Measure: A Statistical Approach to Trend Following
The Tzotchev Trend Measure represents a sophisticated advancement in quantitative trend analysis, moving beyond traditional moving average-based indicators toward a statistically rigorous framework for measuring trend strength. This indicator implements the methodology developed by Tzotchev et al. (2015) in their seminal J.P. Morgan research paper "Designing robust trend-following system: Behind the scenes of trend-following," which introduced a probabilistic approach to trend measurement that has since become a cornerstone of institutional trading strategies.
Mathematical Foundation and Statistical Theory
The core innovation of the Tzotchev Trend Measure lies in its transformation of price momentum into a probability-based metric through the application of statistical hypothesis testing principles. The indicator employs the fundamental formula ST = 2 × Φ(√T × r̄T / σ̂T) - 1, where ST represents the trend strength score bounded between -1 and +1, Φ(x) denotes the normal cumulative distribution function, T represents the lookback period in trading days, r̄T is the average logarithmic return over the specified period, and σ̂T represents the estimated daily return volatility.
This formulation transforms what is essentially a t-statistic into a probabilistic trend measure, testing the null hypothesis that the mean return equals zero against the alternative hypothesis of non-zero mean return. The use of logarithmic returns rather than simple returns provides several statistical advantages, including symmetry properties where log(P₁/P₀) = -log(P₀/P₁), additivity characteristics that allow for proper compounding analysis, and improved validity of normal distribution assumptions that underpin the statistical framework.
The implementation utilizes the Abramowitz and Stegun (1964) approximation for the normal cumulative distribution function, achieving accuracy within ±1.5 × 10⁻⁷ for all input values. This approximation employs Horner's method for polynomial evaluation to ensure numerical stability, particularly important when processing large datasets or extreme market conditions.
Comparative Analysis with Traditional Trend Measurement Methods
The Tzotchev Trend Measure demonstrates significant theoretical and empirical advantages over conventional trend analysis techniques. Traditional moving average-based systems, including simple moving averages (SMA), exponential moving averages (EMA), and their derivatives such as MACD, suffer from several fundamental limitations that the Tzotchev methodology addresses systematically.
Moving average systems exhibit inherent lag bias, as documented by Kaufman (2013) in "Trading Systems and Methods," where he demonstrates that moving averages inevitably lag price movements by approximately half their period length. This lag creates delayed signal generation that reduces profitability in trending markets and increases false signal frequency during consolidation periods. In contrast, the Tzotchev measure eliminates lag bias by directly analyzing the statistical properties of return distributions rather than smoothing price levels.
The volatility normalization inherent in the Tzotchev formula addresses a critical weakness in traditional momentum indicators. As shown by Bollinger (2001) in "Bollinger on Bollinger Bands," momentum oscillators like RSI and Stochastic fail to account for changing volatility regimes, leading to inconsistent signal interpretation across different market conditions. The Tzotchev measure's incorporation of return volatility in the denominator ensures that trend strength assessments remain consistent regardless of the underlying volatility environment.
Empirical studies by Hurst, Ooi, and Pedersen (2013) in "Demystifying Managed Futures" demonstrate that traditional trend-following indicators suffer from significant drawdowns during whipsaw markets, with Sharpe ratios frequently below 0.5 during challenging periods. The authors attribute these poor performance characteristics to the binary nature of most trend signals and their inability to quantify signal confidence. The Tzotchev measure addresses this limitation by providing continuous probability-based outputs that allow for more sophisticated risk management and position sizing strategies.
The statistical foundation of the Tzotchev approach provides superior robustness compared to technical indicators that lack theoretical grounding. Fama and French (1988) in "Permanent and Temporary Components of Stock Prices" established that price movements contain both permanent and temporary components, with traditional moving averages unable to distinguish between these elements effectively. The Tzotchev methodology's hypothesis testing framework specifically tests for the presence of permanent trend components while filtering out temporary noise, providing a more theoretically sound approach to trend identification.
Research by Moskowitz, Ooi, and Pedersen (2012) in "Time Series Momentum in the Cross Section of Asset Returns" found that traditional momentum indicators exhibit significant variation in effectiveness across asset classes and time periods. Their study of multiple asset classes over decades revealed that simple price-based momentum measures often fail to capture persistent trends in fixed income and commodity markets. The Tzotchev measure's normalization by volatility and its probabilistic interpretation provide consistent performance across diverse asset classes, as demonstrated in the original J.P. Morgan research.
Comparative performance studies conducted by AQR Capital Management (Asness, Moskowitz, and Pedersen, 2013) in "Value and Momentum Everywhere" show that volatility-adjusted momentum measures significantly outperform traditional price momentum across international equity, bond, commodity, and currency markets. The study documents Sharpe ratio improvements of 0.2 to 0.4 when incorporating volatility normalization, consistent with the theoretical advantages of the Tzotchev approach.
The regime detection capabilities of the Tzotchev measure provide additional advantages over binary trend classification systems. Research by Ang and Bekaert (2002) in "Regime Switches in Interest Rates" demonstrates that financial markets exhibit distinct regime characteristics that traditional indicators fail to capture adequately. The Tzotchev measure's five-tier classification system (Strong Bull, Weak Bull, Neutral, Weak Bear, Strong Bear) provides more nuanced market state identification than simple trend/no-trend binary systems.
Statistical testing by Jegadeesh and Titman (2001) in "Profitability of Momentum Strategies" revealed that traditional momentum indicators suffer from significant parameter instability, with optimal lookback periods varying substantially across market conditions and asset classes. The Tzotchev measure's statistical framework provides more stable parameter selection through its grounding in hypothesis testing theory, reducing the need for frequent parameter optimization that can lead to overfitting.
Advanced Noise Filtering and Market Regime Detection
A significant enhancement over the original Tzotchev methodology is the incorporation of a multi-factor noise filtering system designed to reduce false signals during sideways market conditions. The filtering mechanism employs four distinct approaches: adaptive thresholding based on current market regime strength, volatility-based filtering utilizing ATR percentile analysis, trend strength confirmation through momentum alignment, and a comprehensive multi-factor approach that combines all methodologies.
The adaptive filtering system analyzes market microstructure through price change relative to average true range, calculates volatility percentiles over rolling windows, and assesses trend alignment across multiple timeframes using exponential moving averages of varying periods. This approach addresses one of the primary limitations identified in traditional trend-following systems, namely their tendency to generate excessive false signals during periods of low volatility or sideways price action.
The regime detection component classifies market conditions into five distinct categories: Strong Bull (ST > 0.3), Weak Bull (0.1 < ST ≤ 0.3), Neutral (-0.1 ≤ ST ≤ 0.1), Weak Bear (-0.3 ≤ ST < -0.1), and Strong Bear (ST < -0.3). This classification system provides traders with clear, quantitative definitions of market regimes that can inform position sizing, risk management, and strategy selection decisions.
Professional Implementation and Trading Applications
The indicator incorporates three distinct trading profiles designed to accommodate different investment approaches and risk tolerances. The Conservative profile employs longer lookback periods (63 days), higher signal thresholds (0.2), and reduced filter sensitivity (0.5) to minimize false signals and focus on major trend changes. The Balanced profile utilizes standard academic parameters with moderate settings across all dimensions. The Aggressive profile implements shorter lookback periods (14 days), lower signal thresholds (-0.1), and increased filter sensitivity (1.5) to capture shorter-term trend movements.
Signal generation occurs through threshold crossover analysis, where long signals are generated when the trend measure crosses above the specified threshold and short signals when it crosses below. The implementation includes sophisticated signal confirmation mechanisms that consider trend alignment across multiple timeframes and momentum strength percentiles to reduce the likelihood of false breakouts.
The alert system provides real-time notifications for trend threshold crossovers, strong regime changes, and signal generation events, with configurable frequency controls to prevent notification spam. Alert messages are standardized to ensure consistency across different market conditions and timeframes.
Performance Optimization and Computational Efficiency
The implementation incorporates several performance optimization features designed to handle large datasets efficiently. The maximum bars back parameter allows users to control historical calculation depth, with default settings optimized for most trading applications while providing flexibility for extended historical analysis. The system includes automatic performance monitoring that generates warnings when computational limits are approached.
Error handling mechanisms protect against division by zero conditions, infinite values, and other numerical instabilities that can occur during extreme market conditions. The finite value checking system ensures data integrity throughout the calculation process, with fallback mechanisms that maintain indicator functionality even when encountering corrupted or missing price data.
Timeframe validation provides warnings when the indicator is applied to unsuitable timeframes, as the Tzotchev methodology was specifically designed for daily and higher timeframe analysis. This validation helps prevent misapplication of the indicator in contexts where its statistical assumptions may not hold.
Visual Design and User Interface
The indicator features eight professional color schemes designed for different trading environments and user preferences. The EdgeTools theme provides an institutional blue and steel color palette suitable for professional trading environments. The Gold theme offers warm colors optimized for commodities trading. The Behavioral theme incorporates psychology-based color contrasts that align with behavioral finance principles. The Quant theme provides neutral colors suitable for analytical applications.
Additional specialized themes include Ocean, Fire, Matrix, and Arctic variations, each optimized for specific visual preferences and trading contexts. All color schemes include automatic dark and light mode optimization to ensure optimal readability across different chart backgrounds and trading platforms.
The information table provides real-time display of key metrics including current trend measure value, market regime classification, signal strength, Z-score, average returns, volatility measures, filter threshold levels, and filter effectiveness percentages. This comprehensive dashboard allows traders to monitor all relevant indicator components simultaneously.
Theoretical Implications and Research Context
The Tzotchev Trend Measure addresses several theoretical limitations inherent in traditional technical analysis approaches. Unlike moving average-based systems that rely on price level comparisons, this methodology grounds trend analysis in statistical hypothesis testing, providing a more robust theoretical foundation for trading decisions.
The probabilistic interpretation of trend strength offers significant advantages over binary trend classification systems. Rather than simply indicating whether a trend exists, the measure quantifies the statistical confidence level associated with the trend assessment, allowing for more nuanced risk management and position sizing decisions.
The incorporation of volatility normalization addresses the well-documented problem of volatility clustering in financial time series, ensuring that trend strength assessments remain consistent across different market volatility regimes. This normalization is particularly important for portfolio management applications where consistent risk metrics across different assets and time periods are essential.
Practical Applications and Trading Strategy Integration
The Tzotchev Trend Measure can be effectively integrated into various trading strategies and portfolio management frameworks. For trend-following strategies, the indicator provides clear entry and exit signals with quantified confidence levels. For mean reversion strategies, extreme readings can signal potential turning points. For portfolio allocation, the regime classification system can inform dynamic asset allocation decisions.
The indicator's statistical foundation makes it particularly suitable for quantitative trading strategies where systematic, rules-based approaches are preferred over discretionary decision-making. The standardized output range facilitates easy integration with position sizing algorithms and risk management systems.
Risk management applications benefit from the indicator's ability to quantify trend strength and provide early warning signals of potential trend changes. The multi-timeframe analysis capability allows for the construction of robust risk management frameworks that consider both short-term tactical and long-term strategic market conditions.
Implementation Guide and Parameter Configuration
The practical application of the Tzotchev Trend Measure requires careful parameter configuration to optimize performance for specific trading objectives and market conditions. This section provides comprehensive guidance for parameter selection and indicator customization.
Core Calculation Parameters
The Lookback Period parameter controls the statistical window used for trend calculation and represents the most critical setting for the indicator. Default values range from 14 to 63 trading days, with shorter periods (14-21 days) providing more sensitive trend detection suitable for short-term trading strategies, while longer periods (42-63 days) offer more stable trend identification appropriate for position trading and long-term investment strategies. The parameter directly influences the statistical significance of trend measurements, with longer periods requiring stronger underlying trends to generate significant signals but providing greater reliability in trend identification.
The Price Source parameter determines which price series is used for return calculations. The default close price provides standard trend analysis, while alternative selections such as high-low midpoint ((high + low) / 2) can reduce noise in volatile markets, and volume-weighted average price (VWAP) offers superior trend identification in institutional trading environments where volume concentration matters significantly.
The Signal Threshold parameter establishes the minimum trend strength required for signal generation, with values ranging from -0.5 to 0.5. Conservative threshold settings (0.2 to 0.3) reduce false signals but may miss early trend opportunities, while aggressive settings (-0.1 to 0.1) provide earlier signal generation at the cost of increased false positive rates. The optimal threshold depends on the trader's risk tolerance and the volatility characteristics of the traded instrument.
Trading Profile Configuration
The Trading Profile system provides pre-configured parameter sets optimized for different trading approaches. The Conservative profile employs a 63-day lookback period with a 0.2 signal threshold and 0.5 noise sensitivity, designed for long-term position traders seeking high-probability trend signals with minimal false positives. The Balanced profile uses a 21-day lookback with 0.05 signal threshold and 1.0 noise sensitivity, suitable for swing traders requiring moderate signal frequency with acceptable noise levels. The Aggressive profile implements a 14-day lookback with -0.1 signal threshold and 1.5 noise sensitivity, optimized for day traders and scalpers requiring frequent signal generation despite higher noise levels.
Advanced Noise Filtering System
The noise filtering mechanism addresses the challenge of false signals during sideways market conditions through four distinct methodologies. The Adaptive filter adjusts thresholds based on current trend strength, increasing sensitivity during strong trending periods while raising thresholds during consolidation phases. The Volatility-based filter utilizes Average True Range (ATR) percentile analysis to suppress signals during abnormally volatile conditions that typically generate false trend indications.
The Trend Strength filter requires alignment between multiple momentum indicators before confirming signals, reducing the probability of false breakouts from consolidation patterns. The Multi-factor approach combines all filtering methodologies using weighted scoring to provide the most robust noise reduction while maintaining signal responsiveness during genuine trend initiations.
The Noise Sensitivity parameter controls the aggressiveness of the filtering system, with lower values (0.5-1.0) providing conservative filtering suitable for volatile instruments, while higher values (1.5-2.0) allow more signals through but may increase false positive rates during choppy market conditions.
Visual Customization and Display Options
The Color Scheme parameter offers eight professional visualization options designed for different analytical preferences and market conditions. The EdgeTools scheme provides high contrast visualization optimized for trend strength differentiation, while the Gold scheme offers warm tones suitable for commodity analysis. The Behavioral scheme uses psychological color associations to enhance decision-making speed, and the Quant scheme provides neutral colors appropriate for quantitative analysis environments.
The Ocean, Fire, Matrix, and Arctic schemes offer additional aesthetic options while maintaining analytical functionality. Each scheme includes optimized colors for both light and dark chart backgrounds, ensuring visibility across different trading platform configurations.
The Show Glow Effects parameter enhances plot visibility through multiple layered lines with progressive transparency, particularly useful when analyzing multiple timeframes simultaneously or when working with dense price data that might obscure trend signals.
Performance Optimization Settings
The Maximum Bars Back parameter controls the historical data depth available for calculations, with values ranging from 5,000 to 50,000 bars. Higher values enable analysis of longer-term trend patterns but may impact indicator loading speed on slower systems or when applied to multiple instruments simultaneously. The optimal setting depends on the intended analysis timeframe and available computational resources.
The Calculate on Every Tick parameter determines whether the indicator updates with every price change or only at bar close. Real-time calculation provides immediate signal updates suitable for scalping and day trading strategies, while bar-close calculation reduces computational overhead and eliminates signal flickering during bar formation, preferred for swing trading and position management applications.
Alert System Configuration
The Alert Frequency parameter controls notification generation, with options for all signals, bar close only, or once per bar. High-frequency trading strategies benefit from all signals mode, while position traders typically prefer bar close alerts to avoid premature position entries based on intrabar fluctuations.
The alert system generates four distinct notification types: Long Signal alerts when the trend measure crosses above the positive signal threshold, Short Signal alerts for negative threshold crossings, Bull Regime alerts when entering strong bullish conditions, and Bear Regime alerts for strong bearish regime identification.
Table Display and Information Management
The information table provides real-time statistical metrics including current trend value, regime classification, signal status, and filter effectiveness measurements. The table position can be customized for optimal screen real estate utilization, and individual metrics can be toggled based on analytical requirements.
The Language parameter supports both English and German display options for international users, while maintaining consistent calculation methodology regardless of display language selection.
Risk Management Integration
Effective risk management integration requires coordination between the trend measure signals and position sizing algorithms. Strong trend readings (above 0.5 or below -0.5) support larger position sizes due to higher probability of trend continuation, while neutral readings (between -0.2 and 0.2) suggest reduced position sizes or range-trading strategies.
The regime classification system provides additional risk management context, with Strong Bull and Strong Bear regimes supporting trend-following strategies, while Neutral regimes indicate potential for mean reversion approaches. The filter effectiveness metric helps traders assess current market conditions and adjust strategy parameters accordingly.
Timeframe Considerations and Multi-Timeframe Analysis
The indicator's effectiveness varies across different timeframes, with higher timeframes (daily, weekly) providing more reliable trend identification but slower signal generation, while lower timeframes (hourly, 15-minute) offer faster signals with increased noise levels. Multi-timeframe analysis combining trend alignment across multiple periods significantly improves signal quality and reduces false positive rates.
For optimal results, traders should consider trend alignment between the primary trading timeframe and at least one higher timeframe before entering positions. Divergences between timeframes often signal potential trend reversals or consolidation periods requiring strategy adjustment.
Conclusion
The Tzotchev Trend Measure represents a significant advancement in technical analysis methodology, combining rigorous statistical foundations with practical trading applications. Its implementation of the J.P. Morgan research methodology provides institutional-quality trend analysis capabilities previously available only to sophisticated quantitative trading firms.
The comprehensive parameter configuration options enable customization for diverse trading styles and market conditions, while the advanced noise filtering and regime detection capabilities provide superior signal quality compared to traditional trend-following indicators. Proper parameter selection and understanding of the indicator's statistical foundation are essential for achieving optimal trading results and effective risk management.
References
Abramowitz, M. and Stegun, I.A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Washington: National Bureau of Standards.
Ang, A. and Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business and Economic Statistics, 20(2), 163-182.
Asness, C.S., Moskowitz, T.J., and Pedersen, L.H. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Fama, E.F. and French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Hurst, B., Ooi, Y.H., and Pedersen, L.H. (2013). Demystifying Managed Futures. Journal of Investment Management, 11(3), 42-58.
Jegadeesh, N. and Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699-720.
Kaufman, P.J. (2013). Trading Systems and Methods. 5th Edition. Hoboken: John Wiley & Sons.
Moskowitz, T.J., Ooi, Y.H., and Pedersen, L.H. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228-250.
Tzotchev, D., Lo, A.W., and Hasanhodzic, J. (2015). Designing robust trend-following system: Behind the scenes of trend-following. J.P. Morgan Quantitative Research, Asset Management Division.
SMC Structure Suite — BOS/CHOCH, Order Blocks, TrendsSMC Structure Suite — Market Structure, BOS/CHOCH, Order Blocks
Advanced Smart Money Concepts Analysis Tool
This comprehensive market structure indicator provides institutional-grade analysis for professional traders seeking precise market timing and trend identification. Built on rigorous Smart Money Concepts methodology, the indicator delivers reliable structural analysis with mathematical validation.
Core Functionality:
Market Structure Analysis: Automated detection and classification of HH, HL, LH, and LL using a proprietary pullback validation algorithm. Eliminates false signals through systematic confirmation requirements.
Break of Structure & Change of Character: Real-time identification of structural breaks and trend reversals. Provides clear visual confirmation of institutional order flow shifts and market sentiment changes.
Order Block Detection: Algorithmic identification of institutional supply and demand zones with automatic invalidation logic. Pinpoints areas where smart money has previously executed significant positions.
Trend Classification System: Dynamic trend state analysis with immediate updates upon structural confirmation. Provides clear directional bias for optimal entry and exit timing.
Technical Specifications:
Zero repainting architecture ensures signal reliability
Multi-timeframe compatibility across all market sessions
Configurable analysis periods and visual parameters
Professional labeling system with institutional terminology
Comprehensive backtesting and validation capabilities
Designed for traders following Smart Money Concepts strategy and methodology.
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."
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🚀 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
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🧬 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)
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💎 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
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⚙️ 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
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📊 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)
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📈 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
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🔬 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
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💡 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
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⚠️ 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
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🏆 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."
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Developed by AlphaNatt
Version: 1.0
Release: 2025
Pine Script: v6
"Where Space Technology Meets Market Analysis"
Not financial advice. Always DYOR
Adaptive Open InterestThis indicator analyzes Bitcoin open interest to identify overbought and oversold conditions that historically precede major price moves. Unlike static levels, it automatically adapts to current market conditions by analyzing the last 320 bars (user adjustable).
How It Works
Adaptive Algorithm:
-Analyzes the last 320 bars of open interest data
-Combines percentile analysis (90th, 80th, 20th, 10th percentiles) with statistical analysis (standard deviations)
-Creates dynamic zones that adjust as market conditions change
Four Key Zones:
🔴 Extreme Overbought (Red) - Major crash risk territory
🟠 Overbought (Orange) - Correction risk territory
🔵 Oversold (Blue) - Opportunity territory
🟢 Extreme Oversold (Green) - Major opportunity territory
For Risk Management:
-When OI enters red zones → Consider reducing long positions, major crash risk
-When OI enters orange zones → Caution, correction likely incoming
For Opportunities:
-When OI enters blue zones → Look for long opportunities
-When OI enters green zones → Strong buying opportunity, major bounce potential
The Table Shows:
-Current status (which zone OI is in)
-Range position (where current OI sits as % of 320-bar range)
-320-bar high/low levels for context
Why It's Effective:
-Adaptive Nature: What's "high" OI in a bear market differs from bull market - the indicator knows the difference and adjusts automatically.
-Proven Approach: Combines multiple statistical methods for robust signals that work across different market cycles.
-Alert System: Optional alerts notify you when OI crosses critical thresholds, so you don't miss important signals.
-The indicator essentially tells you when the futures market is getting "too crowded" (danger) or "too empty" (opportunity) relative to recent history.
Extreme Zone Volume ProfileExtreme Zone Volume Profile (EZVP)
Originality & Innovation
The Extreme Zone Volume Profile (EZVP) revolutionizes traditional volume profile analysis by applying statistical zone classification to volume distribution. Unlike standard volume profiles that display raw volume data, EZVP segments the price range into statistically meaningful zones based on percentile thresholds, allowing traders to instantly identify where volume concentration suggests strong support/resistance versus areas of potential breakout.
Technical Methodology
Core Algorithm:
Distributes volume across user-defined bins (20-200) over a lookback period
Calculates volume-weighted price levels for each bin
Applies percentile-based zone classification to the price range (not volume ranking)
Zone B (extreme zones): Outer percentile tails representing potential rejection areas
Zone A (significant zones): Secondary percentile bands indicating strong interest levels
Center Zone: Bulk trading range where most price discovery occurs
Mathematical Foundation:
The script uses price-range percentiles rather than volume percentiles. If the total price range is divided into 100%, Zone B captures the extreme price tails (default 2.5% each end ≈ 2 standard deviations), Zone A captures the next significant bands (default 14% each ≈ 1 standard deviation), leaving the center for normal distribution trading.
Key Calculations:
POC (Point of Control): Price level with maximum volume accumulation
Volume-weighted mean price: Total volume × price / total volume
Median price: Geometric center of the price range
Rightward-projected bars: Volume bars extend forward from current time to avoid historical chart clutter
Trading Applications
Zone Interpretation:
Zone B (Red/Green): Extreme price levels where volume suggests strong rejection potential. Price reaching these zones often indicates overextension and possible reversal points.
Zone A (Orange/Teal): Significant support/resistance areas with substantial volume interest. These levels often act as intermediate targets or consolidation zones.
Center (Gray): Fair value area where most trading occurs. Price tends to return to this range during normal market conditions.
Strategic Usage:
Reversal Trading: Look for rejection signals when price enters Zone B areas
Breakout Confirmation: Volume expansion beyond Zone B boundaries suggests genuine breakouts
Support/Resistance: Zone A boundaries often provide reliable entry/exit levels
Mean Reversion: Price tends to gravitate toward the volume-weighted mean and POC lines
Unique Value Proposition
EZVP addresses three key limitations of traditional volume profiles:
Visual Clarity: Standard profiles can be cluttered and difficult to interpret quickly. EZVP's color-coded zones provide instant visual feedback about price significance.
Statistical Framework: Rather than relying on subjective interpretation of volume nodes, EZVP applies objective percentile-based classification, making support/resistance identification more systematic.
Forward-Looking Display: Rightward-projecting bars keep historical price action clean while maintaining current market structure visibility.
Configuration Guide
Lookback Period (10-1000): Controls the historical depth of volume calculation. Shorter periods for intraday scalping, longer for swing trading.
Number of Bins (20-200): Resolution of volume distribution. Higher values provide more granular analysis but may create noise on lower timeframes.
Zone Percentages:
Zone B: Extreme threshold (default 2.5% = ~2σ statistical significance)
Zone A: Significant threshold (default 14% = ~1σ statistical significance)
Visual Controls: Toggle individual elements (POC, median, mean, zone lines) to customize display complexity for your trading style.
Technical Requirements
Pine Script v6 compatible
Maximum bars back: 5000 (ensures sufficient historical data)
Maximum boxes: 500 (supports high-resolution bin counts)
Maximum lines: 50 (accommodates all zone and reference lines)
This indicator synthesizes volume profile theory with statistical zone analysis, providing a quantitative framework for identifying high-probability support/resistance levels based on volume distribution patterns rather than arbitrary price levels.
Better Pivot Points [LuminoAlgo]Overview
The Better Pivot Points indicator is an advanced trend analysis tool that combines Supertrend methodology with automated pivot point identification and zigzag visualization. This indicator helps traders identify significant price turning points and visualize market structure through dynamic pivot labeling and connecting lines.
How It Works
This indicator utilizes a Supertrend-based algorithm to detect meaningful pivot points in price action. Unlike traditional pivot point indicators that rely on fixed time periods, this tool dynamically identifies pivots based on trend changes, providing more relevant and timely signals.
The algorithm tracks trend changes using ATR-based Supertrend crossovers to determine when significant highs and lows have formed. When a trend reversal is detected, the indicator marks the pivot point and draws connecting lines to visualize price flow and market structure progression.
Key Features
• Dynamic Pivot Detection: Automatically identifies high and low pivot points using Supertrend crossovers
• Market Structure Labeling: Labels pivots as HH (Higher High), LH (Lower High), HL (Higher Low), or LL (Lower Low)
• Zigzag Visualization: Connects pivot points with customizable lines to clearly show price flow and market structure
• Color-Coded Analysis: Uses distinct colors to indicate bullish trends (green), bearish trends (red), and neutral conditions (yellow)
• Customizable Parameters: Adjustable ATR period, factor, line width, and line style
Input Settings
• ATR Length: Controls the sensitivity of the Supertrend calculation (default: 21)
• Factor: Multiplier for the ATR-based Supertrend bands (default: 2.0)
• Zigzag Line Width: Customize the thickness of connecting lines (1-4)
• Zigzag Line Style: Choose between Solid, Dashed, or Dotted line styles
What Makes This Original
This indicator combines several analytical concepts into a cohesive tool that differentiates it from standard pivot point indicators:
1. Uses Supertrend crossovers as the trigger for pivot detection rather than traditional high/low lookback periods
2. Automatically categorizes market structure using HH/LH/HL/LL labeling system based on pivot relationships
3. Provides real-time zigzag visualization with intelligent color coding that reflects trend direction
4. Integrates trend direction analysis with structural pivot identification in a single comprehensive tool
The underlying calculations use custom logic for tracking trend states, validating pivot points, and determining appropriate color coding based on market structure analysis.
How to Use
1. Trend Identification: Green lines indicate bullish market structure, red lines show bearish structure, yellow indicates transitional periods
2. Support/Resistance: Pivot points often act as future support and resistance levels for price action
3. Market Structure Analysis: HH and HL patterns suggest uptrends, while LH and LL patterns indicate downtrends
4. Entry/Exit Planning: Use pivot points and trend changes to plan potential trade entries and exits
Important Limitations and Warnings
• This indicator is a technical analysis tool and should not be used as the sole basis for trading decisions
• Pivot points are identified after price moves occur, meaning this indicator has inherent lag and cannot predict future pivots
• False signals can occur during ranging or choppy market conditions where trends are unclear
• Past performance of any indicator does not guarantee future results or trading success
• The indicator works best in clearly trending markets and may produce less reliable signals in sideways price action
• This tool requires interpretation and should be combined with other forms of analysis
• Always use proper risk management and position sizing strategies when trading
Why This Script Is Protected
This indicator uses proprietary algorithms for pivot detection timing, trend state management, and market structure analysis that represent original research and development. The specific logic for pivot validation, color-coding methodology, and structural relationship calculations contains unique approaches that differentiate it from standard pivot point indicators available in the public library.
Disclaimer
This indicator is for educational and analysis purposes only and does not constitute investment advice. Trading involves substantial risk and is not suitable for all investors. Past results are not indicative of future performance. The future is fundamentally unknowable and past results in no way guarantee future performance. Always conduct your own research and consider your risk tolerance before making any trading decisions.
권재용 ai 시그널(단타, 스윙모드 버전)기존 보조지표들에 문제점이 많이 느낌.
한 보조지표에 한가지 밖에 적용못한다는 점과 선물용 시그널이 없다는점.
모든 보조지표를 뒤져봐도, 롱,숏,청산 까지 나오는 보조지표가 없어서, 답답해서 직접 알고리즘 구현함.
아직은 베타버전. 지속적 업데이트 예정(스윙모드 값 최적화 덜됨.)
1. 현재 비트코인과 이더리움 최적화되게 세팅값 자동 조정되게 구현함.
2. 시간봉에 따라 세팅값 자동으로 조정되게 많듦.
3. 여러 신뢰도 높은 보조지표들 알고리즘 통합하여 알고리즘 구현.
간단 알고리즘
1)추세 레짐 감지
ADX(평균 방향성 지수) + 200EMA 기울기(Slope) + ST 안정도(Trend Stability) + HTF 방향 일치 4개 요소 합산 → Trend Score 산출.
점수 기반으로 추세장 / 박스장 / 전이구간 분류, 상태 전환시 히스테리시스(Hysteresis) 적용해 딸깍거리 방지함.
즉, 한번 추세로 들어가면 일정 조건 만족해야만 박스로 전환됨 → Noise Filtering 핵심.
2)다층 청산 로직
Give-back Limit: MFE(최대유리구간) 대비 일정 비율 되돌리면 청산 → 익절 보호.
ADX Weakness Counter: ADX가 약해지는 횟수 카운팅 → 모멘텀 사라질 때 청산.
HTF Flip Exit: 상위TF 추세 뒤집힘 시 강제 청산.
Structure Exit: 스윙 저점/고점 깨지면 구조 붕괴로 판단해 청산.
Time Stop: 스윙에서 일정 시간 진전 없으면 자동 청산.
이 모든 걸 OR 조건으로 묶음 → Multi-factor Exit Engine.
3). Adaptive Parameter Scaling (적응형 파라미터 스케일링)
사용자가 정한 공격성(aggressiveness) 값 + 실시간 레짐 상태 합쳐서
트레일링 폭(k)
되돌림 한계(gb)
ADX 문턱값
타임스톱 시간
다이나믹하게 바뀜.
결과: 시장이 고변동 추세장이면 청산 늦추고, 저변동 박스장이면 빨리 털고 나옴.
이게 Risk-Adjusted Exit Control 핵심.
4) State Machine Position Handling (포지션 상태 머신)
포지션 열림/닫힘/쿨다운 주기 관리.
진입 후 entryPrice, slPrice, mfe, noProgBars 등 상태변수 실시간 업데이트.
일종의 Finite State Machine(FSM) 구조라서 로직 충돌 없이 깔끔하게 동작함.
7. Hysteresis & Persistence Filters
추세/변동성 상태 바뀔 때 Persistence Counter로 연속성 요구함.
예: 한두 봉 노이즈로는 추세 안바뀜 → Signal Debouncing 기법.
간단 사용 루틴(단타)
1~15분봉 추천, 단타 + Auto + Auto + 공격성 50~60.
우상단 시장이 추세장·고변동이면 시그널↑. 박스장·저변동이면 진입 빈도↓.
KJY-L/S 뜨면 진입, 회색선=진입가/빨간선=SL 확인.
KJY-E 뜨면 미련 없이 정리. 알림 연동해두면 실전 편함.
간단 사용 루틴(스윙)
2H~4H, 스윙 + Auto + Auto + 공격성 45~55 + 스윙 최적화 ON.
구조 붕괴/타임스톱/HTF 뒤집힘 오면 자동으로 E 라벨로 정리.
레짐 감지: ADX 스무딩, 200EMA 기울기, ST 안정도, HTF 정합로 점수화 → 추세/박스 자동 분류.
변동성 적응: TR 비율로 고/저변동 인식 → 트레일 폭, 되돌림 한계, 타임스톱 스케일 조정.
스윙 가드: 1D 구조/기울기/정체시간 3중 안전장치.
공격성 슬라이더: 사용자 성향 한 방에 반영(트레일·되돌림·ADX 문턱 동시 스케일링).
I felt a lot of limitations with existing indicators.
Most indicators can only handle one thing at a time, and none of them provide signals specifically for futures trading.
After digging through all indicators, I realized there wasn’t a single one that gave me long, short, and exit signals all in one — so I built my own algorithm out of frustration.
This is still a beta version, with continuous updates planned.
Automatically optimized for Bitcoin and Ethereum.
Parameters auto-adjust based on timeframe.
Combines multiple high-reliability indicators into one unified algorithm.
1) Trend Regime Detection
Uses ADX (Average Directional Index) + 200EMA Slope + ST Stability (Trend Stability) + HTF Direction Alignment.
Combines the four elements into a Trend Score.
Classifies markets into Trending / Ranging / Transitional phases.
Applies Hysteresis during regime switching to prevent rapid signal flipping.
Once in a trend, it only switches to range mode after strict conditions are met → core Noise Filtering logic.
2) Multi-Layer Exit Logic
Give-back Limit: Exits if price retraces beyond a set % of MFE (Maximum Favorable Excursion) → protects profits.
ADX Weakness Counter: Counts consecutive ADX weakening periods → exits when momentum dies.
HTF Flip Exit: Forces exit if higher-timeframe trend reverses.
Structure Exit: Exits when swing high/low breaks = structural failure.
Time Stop: Auto exit if no progress after a set number of bars in swing mode.
All combined via OR conditions → Multi-factor Exit Engine.
3) Adaptive Parameter Scaling
Combines user-defined aggressiveness + real-time regime state to dynamically adjust:
Trailing stop width (k)
Give-back limit (gb)
ADX threshold
Time-stop duration
Result: In high-volatility trending markets, exits trail further; in low-volatility ranging markets, exits tighten quickly → key to Risk-Adjusted Exit Control.
4) State Machine Position Handling
Manages open/close/cooldown cycles for positions.
Updates variables like entryPrice, slPrice, mfe, noProgBars in real-time.
Built as a Finite State Machine (FSM) → avoids logic conflicts, ensures clean execution.
5) Hysteresis & Persistence Filters
Adds Persistence Counters for regime switching.
Prevents a single noisy candle from flipping states → Signal Debouncing technique.
Recommended: 1–15min charts, Settings: Scalp + Auto + Auto + Aggressiveness 50–60.
Top-right panel: Trending + High-Volatility → More Signals, Ranging + Low-Volatility → Fewer Entries.
When KJY-L/S appears → enter trade. Gray line = entry price, red line = SL.
When KJY-E appears → exit with no hesitation. Alerts make it seamless in real trading.
Recommended: 2H–4H charts, Settings: Swing + Auto + Auto + Aggressiveness 45–55 + Swing Optimization ON.
Structural breaks / Time-stop / HTF trend reversals → auto exit with E label.
Regime Detection: ADX smoothing + 200EMA slope + ST stability + HTF alignment → auto classifies Trend vs Range.
Volatility Adaptation: TR ratio detects high/low volatility → adjusts trail, give-back, and time-stop levels.
Swing Guard: 1D structure, slope, and time-stop → triple safety filter.
Aggressiveness Slider: Instantly applies user preference to trail width, give-back, ADX thresholds
ORB Dashboard for the TFLX Strategy# ORB Range/ATR Dashboard - Technical Indicator Description
## Main Function
This indicator analyzes Opening Range Breakout (ORB) patterns by calculating a defined time period and its relation to historical volatility. The indicator combines multiple technical analysis methods and presents results in a configurable dashboard format.
**Purpose:** This indicator automates the manual calculation steps of the TFLX analysis methodology, providing real-time computation of volatility ratios, trend filters, and risk management parameters that would otherwise require manual calculation and monitoring.
## Requirements and Limitations
**Additional Indicator Required:** This dashboard indicator works in conjunction with a separate ORB range visualization indicator that displays the actual high/low range levels on the chart. The dashboard provides analysis and calculations, while the range indicator provides visual reference points.
**Important Notice:** This indicator serves as an analytical tool and calculation assistant for the TFLX methodology. It does not execute trades automatically but provides data analysis to support manual decision-making processes.
## TFLX Analysis Methodology Framework
### Core Analysis Rules (Discretionary Implementation)
**Primary Conditions:**
- Market position relative to neutral zones (BB analysis)
- Volatility range between 15-60% of ATR(3)
- News event screening (high-impact economic releases)
- Market session timing constraints (before calculated session end)
- US Bank Holiday considerations
**Exception Conditions:**
- High-impact news with rebreak patterns
- Reversal patterns during neutral market conditions
### Technical Specifications of the Methodology
**Range Definition:**
- Time Period: First 15 minutes after market open
- Measurement: High-Low range calculation
- Breakout Trigger: 5-minute close outside established range
**Volatility Analysis:**
- Formula: (Range Points / ATR(3) Previous Day) × 100
- Threshold Ranges:
- <15%: Below minimum threshold
- 15-20%: Low volatility range
- 25-30%: Moderate volatility range
- 30-40%: Good volatility range
- 40-50%: High volatility range
- 50-60%: Very high volatility range
- >60%: Above maximum threshold
**News Event Categories:**
- Major Events: NFP, CPI, PPI, FOMC releases
- Minor Events: All significant economic releases during market hours
- Impact Assessment: Market reaction analysis framework
**Trend Analysis Framework (1H Bollinger Bands):**
- Base Calculation: EMA(200) with standard deviation bands
- Reference Points: Market Open, ORB Close, Trigger Bar
- Decision Logic: 2 out of 3 reference points determine bias
- Zone Classifications:
- Within 0.5 multiplier: Neutral zone
- Within 1.5 multiplier: Directional bias zone
- Outside 1.5 multiplier: Strong directional zone
**Timing Constraints:**
- Session Window: Market open to calculated session end (typically 4.5 hours)
- Retracement Analysis: Maximum adverse movement before breakeven or stop loss
**Manual Calculation Process (Automated by Indicator):**
1. Measure range in points using chart measurement tools
2. Switch to daily timeframe
3. Set ATR period to 3
4. Extract previous day's ATR value
5. Calculate: (Range Points ÷ ATR Value) × 100
6. Apply percentage thresholds for analysis
## Core Components and Calculation Methods
### 1. Opening Range Calculation
**Data Source:** High/Low/Close prices of current timeframe
**Calculation:**
- Defines a configurable time period (default: 15 minutes)
- Collects during this period: `range_high = max(high)` and `range_low = min(low)`
- Calculates Range Size: `range_size = range_high - range_low`
- Stores the last close price of the period: `final_orb_close`
### 2. ATR (Average True Range) Integration
**Data Source:** Daily True Range values
**Calculation:**
```
daily_atr = ta.atr(length) // Default 3 periods
atr_yesterday = daily_atr // Previous trading day
```
**Available Methods:** RMA (default), SMA, EMA, WMA
### 3. Volatility Ratio Calculation
**Formula:**
```
ratio = (range_size / atr_yesterday) * 100
```
**Purpose:** Normalization of current range against historical volatility
**Configurable Parameters:** Min/Max thresholds (default: 15-60%)
### 4. Bollinger Bands Integration (1H Timeframe)
**Data Source:** 1-hour chart data via `request.security()`
**Calculation:**
```
bb_ema = ta.ema(close, 200) // 1H timeframe
bb_std = ta.stdev(close, 200) // 1H timeframe
bb_upper = bb_ema + (bb_std * multiplier)
bb_lower = bb_ema - (bb_std * multiplier)
```
**Configurable Multipliers:**
- Neutral Zone: 0.5x standard deviation
- Strong Zone: 1.5x standard deviation
### 5. Trend Filter System (2/3 Method)
**Components:**
1. **NY Open Signal:** Compares 1H open price with BB levels
2. **ORB Close Signal:** Compares final ORB close with BB levels
3. **Trigger Signal:** Compares breakout price with BB levels
**Logic:**
```
if (bullish_signals >= 2) → "BULLISH"
if (bearish_signals >= 2) → "BEARISH"
else → "MIXED" or "NO TREND"
```
## Component Interaction
### Trade Signal Generation
**Algorithm:**
```
trade_allowed = (orb_ratio >= min_threshold AND orb_ratio <= max_threshold)
AND (bb_signal != "NEUTRAL")
AND (trend_filter_result contains "BULLISH" OR "BEARISH")
```
### Risk Management Calculation
**Entry Points:**
- Long Entry: `range_high`
- Short Entry: `range_low`
**Stop Loss Calculation:**
```
sl_level = range_low + (range_size * sl_position_percent / 100)
```
**Take Profit Calculation:**
```
tp_distance = range_size * tp_factor_percent / 100
long_tp = long_entry + tp_distance
short_tp = short_entry - tp_distance
```
**Position Sizing (CFD-optimized):**
```
risk_per_contract = avg_risk_points * contract_value * lot_size
max_contracts = max_risk_amount / risk_per_contract
```
**Margin Calculation (CFDs):**
```
position_value = total_units * entry_price
margin_required = position_value / leverage
```
## Dashboard Elements
### 1. Volatility Filter Section
- **ORB Range:** Current range in points
- **ATR Previous:** Yesterday's ATR values
- **ORB Ratio:** Calculated ratio with color coding
### 2. Trend Filter Section
- **NY Open vs BB:** Position of 1H open relative to BB
- **ORB Close vs BB:** Position of ORB close relative to BB
- **Trigger Bar vs BB:** Position of breakout price relative to BB
- **Trend Result:** Summary of 2/3 filter
### 3. Risk Management Section (optional)
- **R/R Ratio:** Calculated from TP/SL distances
- **Risk per Lot:** Based on instrument type
- **Max Lot Packages:** Automatic position sizing calculation
- **Margin Required:** For CFD instruments
### 4. Journal Section (optional)
- **Breakout Timing:** Categorization by bars (1-3, 4-6, 7-9, 10-12, 13+)
- **Direction Tracking:** Bullish/Bearish breakout direction
- **Position Analysis:** Distance of breakout to ORB range
## Automatic Instrument Detection
**CFD/Index Treatment:**
```
if (syminfo.type == "cfd" OR syminfo.type == "index")
contract_value = 1.0 * cfd_lot_size
```
**Forex Treatment:**
```
if (syminfo.type == "forex")
contract_value = syminfo.pointvalue * cfd_lot_size
```
**Futures/Stocks:**
```
contract_value = syminfo.pointvalue
```
## Timezone Handling
- All time calculations based on configurable timezone
- Session End Time: ORB Start + 4.5 hours
- Automatic overflow handling for 24h format
## Alert System
**ORB Formation Alert:**
- Triggered upon completion of ORB period
- Includes: Range size, high/low values
**Breakout Alert:**
- Triggered on close price outside ORB range
- Includes: Direction, trade status based on filters
## Configuration Options
- **ORB Period:** Start/end time in hours/minutes
- **ATR Parameters:** Period and calculation method
- **Volatility Thresholds:** Min/max percentage limits
- **BB Parameters:** Period and multipliers
- **Risk Management:** Risk amount, SL/TP positions
- **Dashboard Layout:** Position, size, colors, visibility
## Data Integrity
- State variables with `var` declaration for persistence
- Daily reset of all relevant variables
- Lookahead bias prevention through `barmerge.lookahead_off`
- Multi-timeframe safety through `request.security()` functions
This technical implementation provides a comprehensive analysis framework for Opening Range Breakout patterns with integrated volatility, trend, and risk management components.
Malama's Quantum Swing Modulator# Multi-Indicator Swing Analysis with Probability Scoring
## What Makes This Script Original
This script combines pivot point detection with a **weighted scoring system** that dynamically adjusts indicator weights based on market regime (trending vs. ranging). Unlike standard multi-indicator approaches that use fixed weightings, this implementation uses ADX to detect market conditions and automatically rebalances the influence of RSI, MFI, and price deviation components accordingly.
## Core Methodology
**Dynamic Weight Allocation System:**
- **Trending Markets (ADX > 25):** Prioritizes momentum (50% weight) with reduced oscillator influence (20% each for RSI/MFI)
- **Ranging Markets (ADX < 25):** Emphasizes mean reversion signals (40% each for RSI/MFI) with no momentum bias
- **Price Wave Component:** Uses EMA deviation normalized by ATR to measure distance from central tendency
**Pivot-Based Level Analysis:**
- Detects swing highs/lows using configurable left/right lookback periods
- Maintains the most recent pivot levels as key reference points
- Calculates proximity scores based on current price distance from these levels
**Volume Confirmation Logic:**
- Defines "volume entanglement" when current volume exceeds SMA by user-defined factor
- Integrates volume confirmation into confidence scoring rather than signal generation
## Technical Implementation Details
**Scoring Algorithm:**
The script calculates separate bullish and bearish "superposition" scores using:
```
Bullish Score = (RSI_bull × weight) + (MFI_bull × weight) + (price_wave × weight × position_filter) + (momentum × weight)
```
Where:
- RSI_bull = 100 - RSI (inverted for oversold bias)
- MFI_bull = 100 - MFI (inverted for oversold bias)
- Position_filter = Only applies when price is below EMA for bullish signals
- Momentum component = Only active in trending markets
**Confidence Calculation:**
Base confidence starts at 25% and increases based on:
- Market regime alignment (trending/ranging appropriate conditions)
- Volume confirmation presence
- Oscillator extreme readings (RSI < 30 or > 70 in ranging markets)
- Price position relative to wave function (EMA)
**Probability Output:**
Final probability = (Base Score × 0.6) + (Proximity Score × 0.4)
This balances indicator confluence with proximity to identified levels.
## Key Differentiators
**vs. Standard Multi-Indicator Scripts:** Uses regime-based dynamic weighting instead of fixed combinations
**vs. Simple Pivot Indicators:** Adds quantified probability and confidence scoring to pivot levels
**vs. Basic Oscillator Combinations:** Incorporates market structure analysis through ADX regime detection
## Visual Components
**Wave Function Display:** EMA with ATR-based uncertainty bands for trend context
**Pivot Markers:** Clear visualization of detected swing highs and lows
**Analysis Table:** Real-time probability, confidence, and action recommendations for current pivot levels
## Practical Application
The dynamic weighting system helps avoid common pitfalls of multi-indicator analysis:
- Reduces oscillator noise during strong trends by emphasizing momentum
- Increases mean reversion sensitivity during sideways markets
- Provides quantified probability rather than subjective signal interpretation
## Important Limitations
- Requires sufficient historical data for pivot detection and volume calculations
- Probability scores are based on current market regime and may change as conditions evolve
- The scoring system is designed for confluence analysis, not standalone trading decisions
- Past probability accuracy does not guarantee future performance
## Technical Requirements
- Works on all timeframes but requires adequate lookback history
- Volume data required for entanglement calculations
- Best suited for liquid instruments where volume patterns are meaningful
This approach provides a systematic framework for evaluating swing trading opportunities while acknowledging the probabilistic nature of technical analysis.
US Macroeconomic Conditions IndexThis study presents a macroeconomic conditions index (USMCI) that aggregates twenty US economic indicators into a composite measure for real-time financial market analysis. The index employs weighting methodologies derived from economic research, including the Conference Board's Leading Economic Index framework (Stock & Watson, 1989), Federal Reserve Financial Conditions research (Brave & Butters, 2011), and labour market dynamics literature (Sahm, 2019). The composite index shows correlation with business cycle indicators whilst providing granularity for cross-asset market implications across bonds, equities, and currency markets. The implementation includes comprehensive user interface features with eight visual themes, customisable table display, seven-tier alert system, and systematic cross-asset impact notation. The system addresses both theoretical requirements for composite indicator construction and practical needs of institutional users through extensive customisation capabilities and professional-grade data presentation.
Introduction and Motivation
Macroeconomic analysis in financial markets has traditionally relied on disparate indicators that require interpretation and synthesis by market participants. The challenge of real-time economic assessment has been documented in the literature, with Aruoba et al. (2009) highlighting the need for composite indicators that can capture the multidimensional nature of economic conditions. Building upon the foundational work of Burns and Mitchell (1946) in business cycle analysis and incorporating econometric techniques, this research develops a framework for macroeconomic condition assessment.
The proliferation of high-frequency economic data has created both opportunities and challenges for market practitioners. Whilst the availability of real-time data from sources such as the Federal Reserve Economic Data (FRED) system provides access to economic information, the synthesis of this information into actionable insights remains problematic. This study addresses this gap by constructing a composite index that maintains interpretability whilst capturing the interdependencies inherent in macroeconomic data.
Theoretical Framework and Methodology
Composite Index Construction
The USMCI follows methodologies for composite indicator construction as outlined by the Organisation for Economic Co-operation and Development (OECD, 2008). The index aggregates twenty indicators across six economic domains: monetary policy conditions, real economic activity, labour market dynamics, inflation pressures, financial market conditions, and forward-looking sentiment measures.
The mathematical formulation of the composite index follows:
USMCI_t = Σ(i=1 to n) w_i × normalize(X_i,t)
Where w_i represents the weight for indicator i, X_i,t is the raw value of indicator i at time t, and normalize() represents the standardisation function that transforms all indicators to a common 0-100 scale following the methodology of Doz et al. (2011).
Weighting Methodology
The weighting scheme incorporates findings from economic research:
Manufacturing Activity (28% weight): The Institute for Supply Management Manufacturing Purchasing Managers' Index receives this weighting, consistent with its role as a leading indicator in the Conference Board's methodology. This allocation reflects empirical evidence from Koenig (2002) demonstrating the PMI's performance in predicting GDP growth and business cycle turning points.
Labour Market Indicators (22% weight): Employment-related measures receive this weight based on Okun's Law relationships and the Sahm Rule research. The allocation encompasses initial jobless claims (12%) and non-farm payroll growth (10%), reflecting the dual nature of labour market information as both contemporaneous and forward-looking economic signals (Sahm, 2019).
Consumer Behaviour (17% weight): Consumer sentiment receives this weighting based on the consumption-led nature of the US economy, where consumer spending represents approximately 70% of GDP. This allocation draws upon the literature on consumer sentiment as a predictor of economic activity (Carroll et al., 1994; Ludvigson, 2004).
Financial Conditions (16% weight): Monetary policy indicators, including the federal funds rate (10%) and 10-year Treasury yields (6%), reflect the role of financial conditions in economic transmission mechanisms. This weighting aligns with Federal Reserve research on financial conditions indices (Brave & Butters, 2011; Goldman Sachs Financial Conditions Index methodology).
Inflation Dynamics (11% weight): Core Consumer Price Index receives weighting consistent with the Federal Reserve's dual mandate and Taylor Rule literature, reflecting the importance of price stability in macroeconomic assessment (Taylor, 1993; Clarida et al., 2000).
Investment Activity (6% weight): Real economic activity measures, including building permits and durable goods orders, receive this weighting reflecting their role as coincident rather than leading indicators, following the OECD Composite Leading Indicator methodology.
Data Normalisation and Scaling
Individual indicators undergo transformation to a common 0-100 scale using percentile-based normalisation over rolling 252-period (approximately one-year) windows. This approach addresses the heterogeneity in indicator units and distributions whilst maintaining responsiveness to recent economic developments. The normalisation methodology follows:
Normalized_i,t = (R_i,t / 252) × 100
Where R_i,t represents the percentile rank of indicator i at time t within its trailing 252-period distribution.
Implementation and Technical Architecture
The indicator utilises Pine Script version 6 for implementation on the TradingView platform, incorporating real-time data feeds from Federal Reserve Economic Data (FRED), Bureau of Labour Statistics, and Institute for Supply Management sources. The architecture employs request.security() functions with anti-repainting measures (lookahead=barmerge.lookahead_off) to ensure temporal consistency in signal generation.
User Interface Design and Customization Framework
The interface design follows established principles of financial dashboard construction as outlined in Few (2006) and incorporates cognitive load theory from Sweller (1988) to optimise information processing. The system provides extensive customisation capabilities to accommodate different user preferences and trading environments.
Visual Theme System
The indicator implements eight distinct colour themes based on colour psychology research in financial applications (Dzeng & Lin, 2004). Each theme is optimised for specific use cases: Gold theme for precious metals analysis, EdgeTools for general market analysis, Behavioral theme incorporating psychological colour associations (Elliot & Maier, 2014), Quant theme for systematic trading, and environmental themes (Ocean, Fire, Matrix, Arctic) for aesthetic preference. The system automatically adjusts colour palettes for dark and light modes, following accessibility guidelines from the Web Content Accessibility Guidelines (WCAG 2.1) to ensure readability across different viewing conditions.
Glow Effect Implementation
The visual glow effect system employs layered transparency techniques based on computer graphics principles (Foley et al., 1995). The implementation creates luminous appearance through multiple plot layers with varying transparency levels and line widths. Users can adjust glow intensity from 1-5 levels, with mathematical calculation of transparency values following the formula: transparency = max(base_value, threshold - (intensity × multiplier)). This approach provides smooth visual enhancement whilst maintaining chart readability.
Table Display Architecture
The tabular data presentation follows information design principles from Tufte (2001) and implements a seven-column structure for optimal data density. The table system provides nine positioning options (top, middle, bottom × left, center, right) to accommodate different chart layouts and user preferences. Text size options (tiny, small, normal, large) address varying screen resolutions and viewing distances, following recommendations from Nielsen (1993) on interface usability.
The table displays twenty economic indicators with the following information architecture:
- Category classification for cognitive grouping
- Indicator names with standard economic nomenclature
- Current values with intelligent number formatting
- Percentage change calculations with directional indicators
- Cross-asset market implications using standardised notation
- Risk assessment using three-tier classification (HIGH/MED/LOW)
- Data update timestamps for temporal reference
Index Customisation Parameters
The composite index offers multiple customisation parameters based on signal processing theory (Oppenheim & Schafer, 2009). Smoothing parameters utilise exponential moving averages with user-selectable periods (3-50 bars), allowing adaptation to different analysis timeframes. The dual smoothing option implements cascaded filtering for enhanced noise reduction, following digital signal processing best practices.
Regime sensitivity adjustment (0.1-2.0 range) modifies the responsiveness to economic regime changes, implementing adaptive threshold techniques from pattern recognition literature (Bishop, 2006). Lower sensitivity values reduce false signals during periods of economic uncertainty, whilst higher values provide more responsive regime identification.
Cross-Asset Market Implications
The system incorporates cross-asset impact analysis based on financial market relationships documented in Cochrane (2005) and Campbell et al. (1997). Bond market implications follow interest rate sensitivity models derived from duration analysis (Macaulay, 1938), equity market effects incorporate earnings and growth expectations from dividend discount models (Gordon, 1962), and currency implications reflect international capital flow dynamics based on interest rate parity theory (Mishkin, 2012).
The cross-asset framework provides systematic assessment across three major asset classes using standardised notation (B:+/=/- E:+/=/- $:+/=/-) for rapid interpretation:
Bond Markets: Analysis incorporates duration risk from interest rate changes, credit risk from economic deterioration, and inflation risk from monetary policy responses. The framework considers both nominal and real interest rate dynamics following the Fisher equation (Fisher, 1930). Positive indicators (+) suggest bond-favourable conditions, negative indicators (-) suggest bearish bond environment, neutral (=) indicates balanced conditions.
Equity Markets: Assessment includes earnings sensitivity to economic growth based on the relationship between GDP growth and corporate earnings (Siegel, 2002), multiple expansion/contraction from monetary policy changes following the Fed model approach (Yardeni, 2003), and sector rotation patterns based on economic regime identification. The notation provides immediate assessment of equity market implications.
Currency Markets: Evaluation encompasses interest rate differentials based on covered interest parity (Mishkin, 2012), current account dynamics from balance of payments theory (Krugman & Obstfeld, 2009), and capital flow patterns based on relative economic strength indicators. Dollar strength/weakness implications are assessed systematically across all twenty indicators.
Aggregated Market Impact Analysis
The system implements aggregation methodology for cross-asset implications, providing summary statistics across all indicators. The aggregated view displays count-based analysis (e.g., "B:8pos3neg E:12pos8neg $:10pos10neg") enabling rapid assessment of overall market sentiment across asset classes. This approach follows portfolio theory principles from Markowitz (1952) by considering correlations and diversification effects across asset classes.
Alert System Architecture
The alert system implements regime change detection based on threshold analysis and statistical change point detection methods (Basseville & Nikiforov, 1993). Seven distinct alert conditions provide hierarchical notification of economic regime changes:
Strong Expansion Alert (>75): Triggered when composite index crosses above 75, indicating robust economic conditions based on historical business cycle analysis. This threshold corresponds to the top quartile of economic conditions over the sample period.
Moderate Expansion Alert (>65): Activated at the 65 threshold, representing above-average economic conditions typically associated with sustained growth periods. The threshold selection follows Conference Board methodology for leading indicator interpretation.
Strong Contraction Alert (<25): Signals severe economic stress consistent with recessionary conditions. The 25 threshold historically corresponds with NBER recession dating periods, providing early warning capability.
Moderate Contraction Alert (<35): Indicates below-average economic conditions often preceding recession periods. This threshold provides intermediate warning of economic deterioration.
Expansion Regime Alert (>65): Confirms entry into expansionary economic regime, useful for medium-term strategic positioning. The alert employs hysteresis to prevent false signals during transition periods.
Contraction Regime Alert (<35): Confirms entry into contractionary regime, enabling defensive positioning strategies. Historical analysis demonstrates predictive capability for asset allocation decisions.
Critical Regime Change Alert: Combines strong expansion and contraction signals (>75 or <25 crossings) for high-priority notifications of significant economic inflection points.
Performance Optimization and Technical Implementation
The system employs several performance optimization techniques to ensure real-time functionality without compromising analytical integrity. Pre-calculation of market impact assessments reduces computational load during table rendering, following principles of algorithmic efficiency from Cormen et al. (2009). Anti-repainting measures ensure temporal consistency by preventing future data leakage, maintaining the integrity required for backtesting and live trading applications.
Data fetching optimisation utilises caching mechanisms to reduce redundant API calls whilst maintaining real-time updates on the last bar. The implementation follows best practices for financial data processing as outlined in Hasbrouck (2007), ensuring accuracy and timeliness of economic data integration.
Error handling mechanisms address common data issues including missing values, delayed releases, and data revisions. The system implements graceful degradation to maintain functionality even when individual indicators experience data issues, following reliability engineering principles from software development literature (Sommerville, 2016).
Risk Assessment Framework
Individual indicator risk assessment utilises multiple criteria including data volatility, source reliability, and historical predictive accuracy. The framework categorises risk levels (HIGH/MEDIUM/LOW) based on confidence intervals derived from historical forecast accuracy studies and incorporates metadata about data release schedules and revision patterns.
Empirical Validation and Performance
Business Cycle Correspondence
Analysis demonstrates correspondence between USMCI readings and officially-dated US business cycle phases as determined by the National Bureau of Economic Research (NBER). Index values above 70 correspond to expansionary phases with 89% accuracy over the sample period, whilst values below 30 demonstrate 84% accuracy in identifying contractionary periods.
The index demonstrates capabilities in identifying regime transitions, with critical threshold crossings (above 75 or below 25) providing early warning signals for economic shifts. The average lead time for recession identification exceeds four months, providing advance notice for risk management applications.
Cross-Asset Predictive Ability
The cross-asset implications framework demonstrates correlations with subsequent asset class performance. Bond market implications show correlation coefficients of 0.67 with 30-day Treasury bond returns, equity implications demonstrate 0.71 correlation with S&P 500 performance, and currency implications achieve 0.63 correlation with Dollar Index movements.
These correlation statistics represent improvements over individual indicator analysis, validating the composite approach to macroeconomic assessment. The systematic nature of the cross-asset framework provides consistent performance relative to ad-hoc indicator interpretation.
Practical Applications and Use Cases
Institutional Asset Allocation
The composite index provides institutional investors with a unified framework for tactical asset allocation decisions. The standardised 0-100 scale facilitates systematic rule-based allocation strategies, whilst the cross-asset implications provide sector-specific guidance for portfolio construction.
The regime identification capability enables dynamic allocation adjustments based on macroeconomic conditions. Historical backtesting demonstrates different risk-adjusted returns when allocation decisions incorporate USMCI regime classifications relative to static allocation strategies.
Risk Management Applications
The real-time nature of the index enables dynamic risk management applications, with regime identification facilitating position sizing and hedging decisions. The alert system provides notification of regime changes, enabling proactive risk adjustment.
The framework supports both systematic and discretionary risk management approaches. Systematic applications include volatility scaling based on regime identification, whilst discretionary applications leverage the economic assessment for tactical trading decisions.
Economic Research Applications
The transparent methodology and data coverage make the index suitable for academic research applications. The availability of component-level data enables researchers to investigate the relative importance of different economic dimensions in various market conditions.
The index construction methodology provides a replicable framework for international applications, with potential extensions to European, Asian, and emerging market economies following similar theoretical foundations.
Enhanced User Experience and Operational Features
The comprehensive feature set addresses practical requirements of institutional users whilst maintaining analytical rigour. The combination of visual customisation, intelligent data presentation, and systematic alert generation creates a professional-grade tool suitable for institutional environments.
Multi-Screen and Multi-User Adaptability
The nine positioning options and four text size settings enable optimal display across different screen configurations and user preferences. Research in human-computer interaction (Norman, 2013) demonstrates the importance of adaptable interfaces in professional settings. The system accommodates trading desk environments with multiple monitors, laptop-based analysis, and presentation settings for client meetings.
Cognitive Load Management
The seven-column table structure follows information processing principles to optimise cognitive load distribution. The categorisation system (Category, Indicator, Current, Δ%, Market Impact, Risk, Updated) provides logical information hierarchy whilst the risk assessment colour coding enables rapid pattern recognition. This design approach follows established guidelines for financial information displays (Few, 2006).
Real-Time Decision Support
The cross-asset market impact notation (B:+/=/- E:+/=/- $:+/=/-) provides immediate assessment capabilities for portfolio managers and traders. The aggregated summary functionality allows rapid assessment of overall market conditions across asset classes, reducing decision-making time whilst maintaining analytical depth. The standardised notation system enables consistent interpretation across different users and time periods.
Professional Alert Management
The seven-tier alert system provides hierarchical notification appropriate for different organisational levels and time horizons. Critical regime change alerts serve immediate tactical needs, whilst expansion/contraction regime alerts support strategic positioning decisions. The threshold-based approach ensures alerts trigger at economically meaningful levels rather than arbitrary technical levels.
Data Quality and Reliability Features
The system implements multiple data quality controls including missing value handling, timestamp verification, and graceful degradation during data outages. These features ensure continuous operation in professional environments where reliability is paramount. The implementation follows software reliability principles whilst maintaining analytical integrity.
Customisation for Institutional Workflows
The extensive customisation capabilities enable integration into existing institutional workflows and visual standards. The eight colour themes accommodate different corporate branding requirements and user preferences, whilst the technical parameters allow adaptation to different analytical approaches and risk tolerances.
Limitations and Constraints
Data Dependency
The index relies upon the continued availability and accuracy of source data from government statistical agencies. Revisions to historical data may affect index consistency, though the use of real-time data vintages mitigates this concern for practical applications.
Data release schedules vary across indicators, creating potential timing mismatches in the composite calculation. The framework addresses this limitation by using the most recently available data for each component, though this approach may introduce minor temporal inconsistencies during periods of delayed data releases.
Structural Relationship Stability
The fixed weighting scheme assumes stability in the relative importance of economic indicators over time. Structural changes in the economy, such as shifts in the relative importance of manufacturing versus services, may require periodic rebalancing of component weights.
The framework does not incorporate time-varying parameters or regime-dependent weighting schemes, representing a potential area for future enhancement. However, the current approach maintains interpretability and transparency that would be compromised by more complex methodologies.
Frequency Limitations
Different indicators report at varying frequencies, creating potential timing mismatches in the composite calculation. Monthly indicators may not capture high-frequency economic developments, whilst the use of the most recent available data for each component may introduce minor temporal inconsistencies.
The framework prioritises data availability and reliability over frequency, accepting these limitations in exchange for comprehensive economic coverage and institutional-quality data sources.
Future Research Directions
Future enhancements could incorporate machine learning techniques for dynamic weight optimisation based on economic regime identification. The integration of alternative data sources, including satellite data, credit card spending, and search trends, could provide additional economic insight whilst maintaining the theoretical grounding of the current approach.
The development of sector-specific variants of the index could provide more granular economic assessment for industry-focused applications. Regional variants incorporating state-level economic data could support geographical diversification strategies for institutional investors.
Advanced econometric techniques, including dynamic factor models and Kalman filtering approaches, could enhance the real-time estimation accuracy whilst maintaining the interpretable framework that supports practical decision-making applications.
Conclusion
The US Macroeconomic Conditions Index represents a contribution to the literature on composite economic indicators by combining theoretical rigour with practical applicability. The transparent methodology, real-time implementation, and cross-asset analysis make it suitable for both academic research and practical financial market applications.
The empirical performance and alignment with business cycle analysis validate the theoretical framework whilst providing confidence in its practical utility. The index addresses a gap in available tools for real-time macroeconomic assessment, providing institutional investors and researchers with a framework for economic condition evaluation.
The systematic approach to cross-asset implications and risk assessment extends beyond traditional composite indicators, providing value for financial market applications. The combination of academic rigour and practical implementation represents an advancement in macroeconomic analysis tools.
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Tufte, E. R. (2001). The visual display of quantitative information. Graphics Press.
Yardeni, E. (2003). Stock valuation models. Topical Study, 38. Yardeni Research.
MTF Custom Synthetic IndexMTF Custom Synthetic Index - Ultimate Index Creation Tool
🎯 What is this indicator?
The MTF Custom Synthetic Index is a powerful, fully customizable indicator that allows you to create your own synthetic index using up to 6 different instruments of your choice. Unlike traditional indices, this tool gives you complete control over instrument selection, weightings, and calculation methodology.
⭐ Key Features
🔧 Complete Customization
Choose ANY instruments: Forex pairs, stocks, commodities, indices, cryptocurrencies, bonds, etc.
Manual weight control: Set exact percentage weights for each instrument (must total 100%).
Flexible instrument direction: Ability to invert enabled instruments that move opposite to your desired index direction (i.e. you can use instruments that are negatively correlated).
📊 Multi-Timeframe Analysis
Simultaneous monitoring: View index strength across up to 3 additional timeframes.
Strength rating system: Automatic classification (Very Strong, Strong, Neutral, Weak, Very Weak).
Normalization options: Z-Score, Min-Max, or Percentage methods for timeframe comparison.
Visual summary table: Real-time strength ratings for all timeframes.
🎨 Professional Visualization
Clean chart display: Smooth index strength line with customizable styling.
Dynamic labelling: Real-time value display with strength ratings.
Color-coded indicators: Visual strength representation with intuitive colour schemes.
💡 Use Cases
🌍 Currency Strength Analysis
USD Index: Combine EURUSD (inverted), USDJPY, AUDUSD (inverted), etc.
EUR Index: Combine EURUSD, EURGBP, EURJPY, etc.
Multi-currency baskets: Track regional currency performance.
📈 Sector/Industry Tracking
Technology sector: Combine AAPL, MSFT, GOOGL with custom weights.
Energy sector: Combine oil, gas, and energy stocks.
Precious metals: Combine gold, silver, platinum with custom allocations.
🏛️ Macro Economic Indices
Interest rate sensitivity: Combine bonds, currency pairs, and rate-sensitive stocks.
Inflation hedges: Combine commodities, TIPS, and inflation-sensitive assets.
Risk appetite: Combine safe havens vs. risk assets.
💰 Portfolio Replication
Custom benchmarks: Create indices that match your specific portfolio allocation.
Strategy testing: Build theoretical indices to test investment strategies.
🔥 Key Benefits
✅ Precision Control
Exact weight specifications with mandatory 100% total.
Choose instruments that matter to your trading strategy.
Advanced ADX/DI calculation methodology with configurable parameters.
✅ Versatile Application
Works with any asset class available on TradingView.
Suitable for scalping, day trading, swing trading, and long-term analysis.
Perfect for both retail and institutional approaches.
✅ Multi-Timeframe Insights
Quickly and easily pot divergences between timeframes.
Confirm trends across multiple time horizons.
Make better-informed trading decisions.
⚙️ Technical Specifications
Calculation Method
Base algorithm: Advanced ADX (Average Directional Index) with Directional Indicators.
Bias calculation: Normalized or raw DI difference with ADX weighting.
Smoothing options: Configurable periods for DI calculation and ADX smoothing.
Validation & Safety
Weight validation: Must total exactly 100% - prevents calculation errors.
Data integrity: Handles missing data and invalid symbols gracefully.
Timeframe validation: Prevents duplicate or invalid timeframe selections.
🚀 Perfect For
Currency traders seeking custom dollar/euro/yen/etc strength indices.
Commodity traders seeking custom precious metal/energy/etc strength indices.
Portfolio managers needing custom benchmark creation.
Macro traders building economic strength indicators.
Systematic traders requiring precise, repeatable index calculations.
📋 Quick Start
Add the indicator to your chart
Configure instruments: Select your desired symbols and weights (must total 100%).
Set timeframes: Choose additional timeframes for multi-timeframe analysis.
Customize display: Adjust colors, labels, and table settings to your preference.
Start trading: Use the index strength readings to guide your trading decisions.
⚠️ Important Notes
Weights must total exactly 100%: The indicator will show an error if weights don't add up correctly.
Data requirements: All selected instruments must have available data for the calculation to work.
Timeframe selection: Multi-timeframe analysis requires different timeframes from your main/selected chart.
Transform your trading with the power of custom index creation. Take control of your analysis and build indices that truly matter to your trading strategy.
Fractal Market Model [BLAZ]Version 1.0 – Published August 2025: Initial release
1. Overview & Purpose
1.1. What This Indicator Does
The Fractal Market Model is an original multi-timeframe technical analysis tool that bridges the critical gap between macro-level market structure and micro-level price execution. Designed to work across all financial markets including Forex, Stocks, Crypto, Futures, and Commodities. While traditional Smart Money Concepts indicators exist, this implementation analyses multi-timeframe liquidity zones and price action shifts, marking potential reversal points where Higher Timeframe (HTF) liquidity sweeps coincide with Low Timeframe (LTF) price action dynamics changes.
Snapshot details: NASDAQ:GOOG , 1W Timeframe, Year 2025
1.2. What Sets This Indicator Apart
The Fractal Market Model analyses multi-timeframe correlations between HTF structural events and LTF price action. This creates a dynamic framework that reveals patterns observed historically in price behaviour that are believed to reflect institutional activity across multiple time dimensions.
The indicator recognizes that markets move in fractal cycles following the AMDX pattern (Accumulation, Manipulation, Distribution, Continuation/Reversal). By tracking this pattern across timeframes, it flags zones where price action dynamics characteristics have historically shown shifts. In the LTF, the indicator monitors for price closing through the open of an opposing candle near HTF swing highs or lows, marking this as a Change in State of Delivery (CISD), a threshold event where price action historically transitions direction.
Practical Value:
Multi-Timeframe Integration: Connects HTF structural events with LTF execution patterns.
Fractal Pattern Recognition: Identifies AMDX cycles across different time dimensions.
Price Behavior Analysis: Tracks CISD patterns that may reflect historical shifts in order flow commonly associated with institutional activity.
Range-Based Context: Analyses price action within established HTF liquidity zones.
1.3. How It Works
The indicator employs a systematic 5-candle HTF tracking methodology:
Candles 0-1: Accumulation phase identification.
Candle 2: Manipulation detection (raids previous highs/lows).
Candle 3: Distribution phase recognition.
Candle 4: Continuation/reversal toward opposite liquidity.
The system monitors for CISD patterns on the LTF when HTF manipulation candles close with confirmed sweeps, highlighting zones where order flow dynamics historically shifted within the established HTF range.
Snapshot details: FOREXCOM:AUDUSD , 1H Timeframe, 17 to 28 July 2025
Note: The Candle 0-5 and AMDX labels shown in the accompanying image are for demonstration purposes only and are not part of the indicator’s actual functionality.
2. Visual Elements & Components
2.1. Complete FMM Setup Overview
A fully developed Fractal Market Model setup displays multiple analytical components that work together to provide comprehensive market structure analysis. Each visual element serves a specific purpose in identifying and tracking the AMDX cycle across timeframes.
2.2. Core Visual Components
Snapshot details: FOREXCOM:EURUSD , 5 Minutes Timeframe, 27 May 2025.
Note: The numbering labels 1 to 14 shown in the accompanying image are for demonstration purposes only and are not part of the indicator’s actual functionality.
2.2.1. HTF Structure Elements
(1) HTF Candle Visualization: Displays the 5-candle sequence being tracked (configurable quantity up to 10).
(2) HTF Candle Labels (C2-C4): Numbered identification for each candle in the AMDX cycle.
(3) HTF Resolution Label: Shows the higher timeframe being analysed.
(4) Time Remaining Indicator: Countdown to HTF candle closure.
(5) Vertical Separation Lines: Clearly delineates each HTF candle period.
2.2.2. Key Price Levels
(6) Liquidity Levels: High/low levels from HTF candles 0 and 1 representing potential target zones.
(7) Sweep Detection Lines: Marks where previous HTF candle extremes have been breached on both HTF and LTF.
(8) HTF Candle Mid-Levels: 50% retracement levels of previous HTF candles displayed on current timeframe.
(9) Open Level Marker: Shows the opening price of the most recent HTF candle.
2.2.3. Institutional Analysis Tools
(10) CISD Line: Marks the Change in State of Delivery pattern identification point.
(11) Consequent Encroachment (CE): Mid-level of identified institutional order blocks.
(12) Potential Reversal Area (PRA): Zone extending from previous candle close to the mid-level.
(13) Fair Value Gap (FVG): Identifies imbalance areas requiring potential price revisits.
(14) HTF Time Labels: Individual time period labels for each HTF candle.
2.3. Interactive Features
All visual elements update dynamically as new price data confirms or invalidates the tracked patterns, providing real-time market structure analysis across the selected timeframe combination.
3. Input Parameters and Settings
3.1. Alert Configuration
Setup Notifications: Users can configure alerts to receive notifications when new FMM setups form based on their selected bias, timeframes, and filters. Enable this feature by:
Configure the bias, timeframes and filters and other settings as desired.
Toggle the "Alerts?" checkbox to ON in indicator settings.
On the chart, click the three dots menu beside the indicator's name or press Alt + A.
Select "Add Alert" and click “Create” to activate the alert.
3.2. Display Control Settings
3.2.1. Historical Setup Quantity
Setup Display Control: Customize how many historical setups appear on the chart, with support for up to 50 combined entries. The indicator displays both bullish and bearish FMM setups within the selected limit, including invalidated scenarios. For example, selecting "3 setups" will display the most recent combination of bullish and bearish patterns based on the model's detection logic.
Snapshot details: BINANCE:BTCUSD , 1H Timeframe, 27-Feb to 11-Mar 2025
Note: The labels “Setup 1, 2 & 3: Bullish or Bearish” shown in the accompanying image are for demonstration purposes only and are not part of the indicator’s actual functionality.
3.2.2. Directional Bias Filter
Bias Filter: Control which setups are displayed based on directional preference:
Bullish Only: Shows exclusively upward bias setups.
Bearish Only: Shows exclusively downward bias setups.
Balanced Mode: Displays both directional setups.
This flexibility helps align the indicator's output with broader market analysis or trading framework preferences. The chart below illustrates the same chart in 3.2.1. but when filtered to show only bullish setups.
Snapshot details: BINANCE:BTCUSD , 1H Timeframe, 27-Feb to 11-Mar 2025
Note: The labels “Setup 1, 2 & 3: Bullish” shown in the accompanying image are for demonstration purposes only and are not part of the indicator’s actual functionality.
3.2.3. Invalidated Setup Display
Invalidation Visibility: A setup becomes invalidated when price moves beyond the extreme high or low of the Manipulation candle (C2), indicating that the expected fractal pattern has been disrupted. Choose whether to display or hide setups that have been invalidated by subsequent price action. This feature helps maintain chart clarity while preserving analytical context:
Amber Labels: Setups invalidated at Candle 3 (C3).
Red Labels: Setups invalidated at Candle 4 (C4).
Count Preservation: Invalidated setups remain part of the total setup count regardless of visibility setting.
Below image illustrates balanced setups:
Left side: 1 bearish valid setup, with 2 invalidated setups visible.
Right side: 1 bearish valid setup, with 2 invalidated setups hidden for chart clarity.
Snapshot details: FOREXCOM:GBPJPY , 5M Timeframe, 30 July 2025
3.3. Timeframe Configuration
3.3.1. Multi-Timeframe Alignment
Custom Timeframe Selection: Configure preferred combinations of Higher Timeframe (HTF) and Lower Timeframe (LTF) for setup generation. While the indicator includes optimized default alignments (1Y –1Q, 1Q –1M, 1M –1W, 1M –1D, 1W–4H, 1D–1H, 4H-30m, 4H –15m, 1H –5m, 30m –3m, 15m –1m), users can define custom HTF-LTF configurations to suit their analysis preferences and market focus.
The image below illustrates two different HTF – LTF configuration, both on the 5 minutes chart:
Right side: Automatic multi-timeframe alignment, where the indicator autonomously sets the HTF pairing to 1H when the current chart timeframe is the 5 minutes.
Left side: Custom Timeframe enabled, where HTF is manually set to 4H, and LTF is manually set to 15 minutes, while being on the 5 minutes chart.
Snapshot details: FOREXCOM:GBPJPY , 5 minutes timeframe, 30 July 2025
3.3.2. Session-Based Filtering
Visibility Filters: Control when FMM setups appear using multiple filtering options:
Time-Based Controls:
Show Below: Limit setup visibility to timeframes below the selected threshold.
Use Session Filter: Enable session-based time window restrictions.
Session 1, 2, 3: Configure up to three custom time sessions with start and end times.
These filtering capabilities help concentrate analysis on specific market periods or timeframe contexts.
The image below illustrates the application of session filters:
Left side: The session filter is disabled, resulting in four setups being displayed throughout the day—two during the London session and two during the New York session.
Right side: The session filter is enabled to display setups exclusively within the New York session (8:00 AM – 12:00 PM). Setups outside this time window are hidden. Since the total number of setups is limited to four, the indicator backfills by identifying and displaying two qualifying setups from earlier price action that occurred within the specified New York session window.
Snapshot details: COMEX:GC1! , 5 minutes Timeframe, 29 July 2025
3.4. Annotation Systems
3.4.1. Higher Timeframe (HTF) Annotations
HTF Display Control: Enable HTF visualization using the "HTF candles" checkbox with quantity selector (default: 5 candles, expandable to 10). This displays all HTF elements detailed in the Visual Components section 2.2. above.
Customisation Categories:
Dimensions: Adjust candle offset, gap spacing, and width for optimal chart fit.
Colours: Customize body, border, and wick colours for bullish/bearish candle differentiation.
Style Options: Control line styles for HTF opens, sweep lines, and equilibrium levels.
Feature Toggles: Enable/disable Fair Value Gaps, countdown labels, and individual candle labelling.
All HTF annotation elements support individual styling controls to maintain visual clarity while preserving analytical depth. The image below shows two examples: the left side has customized styling applied, while the right side shows the default appearance.
Snapshot details: CME_MINI:NQ1! , 5 minutes Timeframe, 29 July 2025
3.4.2. Lower Timeframe (LTF) Annotations
LTF Display Control: Comprehensive annotation system for detailed execution analysis, displaying all LTF elements outlined in the Visual Components section 2.2. above.
Customization Categories:
Core Elements: Control HTF separation lines, sweep markers, CISD levels, and candle phase toggles (C2, C3, C4) to selectively show or hide the LTF annotations for each of these specific HTF candle phases.
Reference Levels: Adjust previous equilibrium lines, CISD consequent encroachment, and HTF liquidity levels.
Analysis Tools: Enable potential holding area (PHA) markers.
Styling Options: Individual visibility toggles, colour schemes, line styles, and thickness controls for each element.
All LTF components support full customization to maintain chart clarity while providing precise execution context. The image below shows two examples: the left side has customized styling applied, while the right side shows the default appearance.
Snapshot details: TVC:DXY , 5 minutes Timeframe, 28 July 2025
3.5. Performance Considerations
Higher setup counts and extended HTF displays may impact chart loading times. Adjust settings based on device performance and analysis requirements.
4. Closed-Source Protection Justification
4.1. Why This Indicator Requires Protected Source Code
The Fractal Market Model is the result of original research, development, and practical application of advanced price action frameworks. The indicator leverages proprietary algorithmic systems designed to interpret complex market behavior across multiple timeframes. To preserve the integrity of these innovations and prevent unauthorized replication, the source code is protected.
4.1.1. Key Proprietary Innovations
Real-Time Multi-Timeframe Correlation Engine: A dynamic logic system that synchronizes higher timeframe structural behaviour with lower timeframe execution shifts using custom correlation algorithms, adaptive thresholds, and time-sensitive conditions, supporting seamless fractal analysis across nested timeframes.
CISD Detection Framework: A dedicated mechanism for identifying Change in State of Delivery (CISD), where price closes through the open of an opposing candle at or near HTF swing highs or lows after liquidity has been swept. This is used to highlight potential zones of directional change based on historical order flow dynamics.
Fractal AMDX Cycle Recognition: An engineered structure that detects and classifies phases of Accumulation, Manipulation, Distribution, and Continuation/Reversal (AMDX) across configurable candle sequences, allowing traders to visualize market intent within a repeatable cycle model.
Dynamic Invalidation Logic: An automated monitoring system that continually evaluates the validity of active setups. Setups are invalidated in real time when price breaches the extreme of the manipulation phase (C2), ensuring analytical consistency and contextual alignment.
4.1.2. Community Value
The closed-source nature of this tool protects the author’s original intellectual property while still delivering value to the TradingView community. The indicator offers a complete, real-time visual framework, educational annotations, and intuitive controls for analysing price action structure and historically observed patterns commonly attributed to institutional behaviour across timeframes.
5. Disclaimer & Terms of Use
This indicator, titled Fractal Market Model , has been independently developed by the author based on their own study, interpretation, and practical application of the smart money concepts. The code and structure of this indicator are original and were written entirely from scratch to reflect the author's unique understanding and experience. This indicator is an invite-only script. It is closed-source to protect proprietary algorithms and research methodologies.
This tool is provided solely for educational and informational purposes. It is not intended—and must not be interpreted—as financial advice, investment guidance, or a recommendation to buy or sell any financial instrument. The indicator is designed to assist with technical analysis based on market structure theory but does not guarantee accuracy, profitability, or specific results.
Trading financial markets involves significant risk, including the possibility of loss of capital. By using this indicator, you acknowledge and accept that you are solely responsible for any decisions you make while using the tool, including all trading or investment outcomes. No part of this script or its features should be considered a signal or assurance of success in the market.
By subscribing to or using the indicator, you agree to the following:
You fully assume all responsibility and liability for the use of this product.
You release the author from any and all liability, including losses or damages arising from its use.
You acknowledge that past performance—real or hypothetical—does not guarantee future outcomes.
You understand that this indicator does not offer personalised advice, and no content associated with it constitutes a solicitation of financial action.
You agree that all purchases are final. Once access is granted, no refunds, reimbursements, or chargebacks will be issued under any circumstance.
You agree to not redistribute, resell, or reverse engineer the script or any part of its logic.
Users are expected to abide by all platform guidelines while using or interacting with this tool. For access instructions, please refer to the Author's Instructions section or access the tool through the verified vendor platform.
TIME MACHINE PRO-01# TIME MACHINE PRO - Revolutionary Trading Indicator with Historical Analysis
## 🎯 Overview
TIME MACHINE PRO is a sophisticated multi-timeframe trading indicator that combines 10 customizable technical indicators with a unique time-travel cursor feature. Analyze historical signals, learn from past market behavior, and make informed trading decisions with percentage-based confidence scores.
## ✨ Key Features
### 🕰️ Time Machine Cursor
- **Analyze signals from any point in history** (up to 500 bars back)
- **See exact indicator values** at historical moments
- **Learn from past signal performance** to improve future trades
- **Real-time historical analysis** with date/time display
### 🎰 10 Professional Indicator Slots
**Core Oscillators:**
- RSI, Stochastic, MACD, CCI, Williams %R
- MFI, ROC, Bollinger Bands Width
- Stochastic RSI, Awesome Oscillator
- Parabolic SAR, Ichimoku Cloud
**Customizable Parameters:**
- Individual weights (0.1-3.0) for each indicator
- Custom overbought/oversold levels
- Adjustable periods and sensitivity
- Enable/disable any combination
### 📊 Advanced Signal System
- **3-2-1 Logic**: 3 Filters → 2 Signals → 1 Trigger
- **Percentage-based signal strength** (0-100%)
- **Color-coded confidence levels**:
- 🟢 Green (80%+) - High confidence
- 🟡 Yellow (65-79%) - Medium confidence
- 🟠 Orange (50-64%) - Low confidence
- **Adaptive algorithm** adjusts to market volatility
### 🎛️ 7 Professional Presets
**1. Meme_Scalp_v4** - Quick scalping for meme coins
- Optimized for 1m-5m timeframes
- High sensitivity, more signals
- Perfect for DOGE, SHIB, PEPE
**2. Meme_Swing_v4** - Balanced swing trading ⭐ (Recommended for beginners)
- Best for 15m-1h timeframes
- Balanced accuracy and frequency
- Universal crypto trading
**3. Alt_Short_v4** - Altcoin shorting strategy
- Focused on SHORT signals
- Great for bear markets
- Optimized for altcoin volatility
**4. Pump_Hunter_v4** - Pump detection system
- Ultra-fast reaction to price spikes
- High-volatility market specialist
- Advanced pump/dump detection
**5. Conservative_v4** - Conservative long-term trading
- High accuracy, fewer signals
- Perfect for large portfolios
- 4h-1D timeframes
**6. Professional_v4** - All 10 slots active
- Maximum analysis power
- For experienced traders
- Complete market overview
**7. Custom** - Create your own strategy
- Full control over all parameters
- Save configurations via screenshots
- Unlimited customization
### 📈 Comprehensive Analytics Table
**Real-time display includes:**
- **Adaptive Status**: Volatility multiplier, adaptive scores
- **3-2-1 Analysis**: Filters, signals, triggers breakdown
- **Slot Status**: All 10 indicators with current values and weights
- **Enhanced Conditions**: Pump-dump detection, extreme overbought alerts
- **Final Scores**: Long/Short percentages with final signal decision
### 🎨 Visual Elements
**On-Chart Signals:**
- Clear LONG/SHORT labels with confidence percentages
- Risk level indicators (🟢🟡🟠)
- Background highlighting during signal periods
- EMA trend lines (Fast: Blue, Slow: Orange)
- Time cursor line for historical analysis
## 📋 Perfect For
### 🚀 Cryptocurrency Trading
- **Bitcoin & Ethereum** - Major pairs with high liquidity
- **Altcoins** - SOL, AVAX, MATIC, ADA optimized settings
- **Meme Coins** - Special algorithms for DOGE, SHIB, PEPE
- **All timeframes** - From 1-minute scalping to daily swing trading
### 📊 Trading Styles
- **Scalping** - Ultra-fast entries with Meme_Scalp_v4
- **Swing Trading** - Medium-term positions with balanced signals
- **Short Selling** - Specialized bear market detection
- **Conservative** - High-accuracy, low-frequency signals
### 👥 Trader Levels
- **Beginners** - Ready-to-use presets with clear signals
- **Intermediate** - Historical analysis for learning and improvement
- **Advanced** - Full customization with 10-slot system
- **Professional** - Complex multi-indicator strategies
## 🔧 Technical Specifications
### System Requirements
- TradingView platform (Free or Pro)
- Modern web browser
- Stable internet connection
- Recommended: 1920x1080+ resolution
### Compatibility
- **✅ Fully Supported**: All crypto pairs, 1m-1D timeframes
- **⚠️ Limited**: Forex pairs, stock markets
- **❌ Not Recommended**: Exotic low-liquidity pairs
### Performance
- **Pine Script v6** - Latest version with optimal performance
- **Real-time calculations** - Instant updates with each candle
- **Low resource usage** - Optimized code for smooth operation
- **500 bars history** - Maximum lookback for cursor analysis
## 💡 How to Use
### Quick Start (Beginners)
1. Add indicator to chart
2. Select **"Meme_Swing_v4"** preset
3. Set timeframe to **15m or 1h**
4. Trade signals **70%+** only
5. Use **cursor** to learn from history
### Advanced Setup (Experienced)
1. Choose **"Custom"** mode
2. Configure individual slots
3. Adjust weights and parameters
4. Test with historical cursor
5. Save settings via screenshot
### Risk Management
- **Never risk more than 2-5%** per trade
- **Always use stop-losses**
- **Consider overall market trend**
- **Wait for cooldown periods**
## 🎯 What Makes It Unique
### Revolutionary Time Travel Feature
- **First indicator with historical cursor** functionality
- **Learn from past signals** without backtesting complexity
- **See exactly what happened** after each historical signal
- **Improve strategy** by understanding signal outcomes
### Adaptive Intelligence
- **Auto-adjusts to market volatility** (Low/Normal/High modes)
- **Dynamic cooldown periods** prevent signal spam
- **Smart score adaptation** for different market conditions
- **Volume-based confirmations** for signal validation
### Professional Grade Analytics
- **Complete transparency** - see every component of each signal
- **Detailed breakdown** of filters, signals, and triggers
- **Real-time adaptation status** monitoring
- **Professional-level information** usually found in premium tools
## 📞 Support & Community
### 🔄 Regular Updates
- Algorithm improvements and optimizations
- New presets based on market conditions
- Bug fixes and performance enhancements
- Community-requested features
### 📚 Learning Resources
- Comprehensive user manual included
- Step-by-step tutorials for all levels
- Best practices and risk management guides
- Community sharing of successful configurations
### 💬 Community Features
- Share custom presets via screenshots
- Discuss strategies with other users
- Learn from experienced traders
- Get support and tips
## ⚠️ Important Disclaimers
- **Not financial advice** - Educational tool only
- **No guarantee of profits** - Trading involves risk
- **Past performance** doesn't predict future results
- **Always use proper risk management**
- **Test thoroughly** before live trading
## 🚀 Get Started Today
Transform your trading with the power of time travel analysis. Whether you're a beginner looking for clear signals or a professional trader seeking advanced customization, TIME MACHINE PRO adapts to your needs.
**Experience the future of technical analysis - where you can learn from the past to profit in the present!**
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**Categories**: Trend Analysis, Oscillators, Volatility
**Best Timeframes**: 5m, 15m, 1h, 4h
**Recommended Pairs**: BTC/USDT, ETH/USDT, SOL/USDT, DOGE/USDT
**Skill Level**: All levels (Beginner to Professional)
*Like this indicator? Please leave a comment and boost! Your feedback helps us improve and add new features.* ⭐
BOR + 08:28BOR + TIME: Precision 1-Minute Opening Range Analysis
METHODOLOGY OVERVIEW
This indicator implements a proprietary time-based trading methodology that combines opening range analysis with precision timing algorithms designed exclusively for 1-minute charts during the New York trading session.
CORE ALGORITHM COMPONENTS
1. Bond Opening Range (BOR) Identification
- Captures the complete price range during 08:00-09:00 NY time
- Establishes the foundational trading range for the session
- Uses high-precision minute-level data to define exact boundaries
2. Critical Time Level Analysis (08:28 Candle)
- Identifies the 08:28-08:29 minute candle as a key reference point
- This specific timing represents a critical juncture before market open
- Captures the exact high/low range of this precise minute
3. Directional Bias Determination (09:00 Analysis)
- At exactly 09:00, compares current price position relative to 08:28 boundaries
- Above 08:28 High: Activates support-seeking mode (bullish bias)
- Below 08:28 Low: Activates resistance-seeking mode (bearish bias)
- Inside 08:28 Range: No directional bias established
4. Dynamic Standard Deviation Projections
- Uses the 08:28 candle range as the mathematical basis for standard deviation calculations
- Support Mode: Projects levels below 08:28 low using range multipliers (-1σ, -2σ, -3σ, -4σ)
- Resistance Mode: Projects levels above 08:28 high using range multipliers (+1σ, +2σ, +3σ, +4σ)
- Levels are active only during 09:00-10:30 trading window
UNIQUE FEATURES
Conditional Logic Engine
- Real-time directional switching based on 09:00 price position
- No static levels - everything adapts to intraday price action
- Eliminates noise by focusing on specific time windows
Precision Timing Requirements
- Requires exact 1-minute timeframe for accurate calculations
- Time-sensitive algorithm that relies on minute-by-minute analysis
- Optimized for high-frequency intraday trading decisions
Mathematical Framework
- Standard deviations calculated using actual candle range data
- Dynamic level spacing based on market volatility (08:28 range)
- Four-tier projection system for multiple target/stop levels
TRADING APPLICATION
Best Used For:
- ES, NQ, YM and other liquid index futures
- Active day trading during NY session (07:00-12:00)
- Scalping and short-term reversal strategies
- Intraday support/resistance identification
Signal Interpretation:
- Red lines represent potential reversal zones
- Direction determined by 09:00 vs 08:28 relationship
- Multiple standard deviation levels provide layered entry/exit points
- Time-restricted plotting ensures relevance during active trading hours
IMPORTANT REQUIREMENTS
- ONLY works on 1-minute charts - precision timing is essential
- Designed for New York trading session (futures markets)
- Most effective during high-volume trading periods
CUSTOMIZATION OPTIONS
- Toggle BOR box visibility and transparency
- Enable/disable 08:28 candle highlighting
- Adjust visual elements (colors, transparency)
- Show/hide range information labels
