[FTA] Curvature of MeniscusIntroduction:
Curvature of Meniscus is a hybrid indicator comprised of three parts:
1. The first part is a heavily modified Demarker RSI which is represented as the histogram and is the main trend indicator C1 .
2. The second part is the 0 line which is replaced by the Waddah Attar Explosion V2 indicator C2 .
3. The third part is the background color which is borrowed from the Klinger Safety Zones volume indicator Vol .
It is smooth and has almost zero lag.
The signals can be used as per nnfx C1-C2-Vol algorithm.
The DeMarker indicator is relatively unknown to trading beginners but enjoys huge support from the more experienced traders. The indicator measures the strength of a trend and thus can give you a warning when a change in the trend direction might occur. This can not only help you to find new trade entries but also prevent you from entering into losing trades or letting your open trades run and ultimately result in a loss.
The line of the indicator is calculated as follows: First the indicator finds the minimal and the maximal value of the specified period. Afterwards those values are used to calculate the moving average of these values which then is displayed in the chart. Just like most oscillators the DeMarker indicator uses oversold and overbought zones at 0.3 and 0.7 to define good entry opportunities.
The "Curvature of Meniscus" indicator has the DeMarker indicator/RSI modified in three different ways:
1. Smoothed and almost no-lagged;
2. Colored histogram to determine the trend and the divergences;
3. An early signal line for those who like to take counter-trend trades/reversal entries.
Play with the settings and let me know what you think about this new hybrid!
"demark" için komut dosyalarını ara
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.
Adaptive Valuation [BackQuant]Adaptive Valuation
What this is
A composite, zero-centered oscillator that standardizes several classic indicators and blends them into one “valuation” line. It computes RSI, CCI, Demarker, and the Price Zone Oscillator, converts each to a rolling z-score, then forms a weighted average. Optional smoothing, dynamic overbought and oversold bands, and an on-chart table make the inputs and the final score easy to inspect.
How it works
Components
• RSI with its own lookback.
• CCI with its own lookback.
• DM (Demarker) with its own lookback.
• PZO (Price Zone Oscillator) with its own lookback.
Standardization via z-score
Each component is transformed using a rolling z-score over lookback bars:
z = (value − mean) ÷ stdev , where the mean is an EMA and the stdev is rolling.
This puts all inputs on a comparable scale measured in standard deviations.
Weighted blend
The z-scores are combined with user weights w_rsi, w_cci, w_dm, w_pzo to produce a single valuation series. If desired, it is then smoothed with a selected moving average (SMA, EMA, WMA, HMA, RMA, DEMA, TEMA, LINREG, ALMA, T3). ALMA’s sigma input shapes its curve.
Dynamic thresholds (optional)
Two ways to set overbought and oversold:
• Static : fixed levels at ob_thres and os_thres .
• Dynamic : ±k·σ bands, where σ is the rolling standard deviation of the valuation over dynLen .
Bands can be centered at zero or around the valuation’s rolling mean ( centerZero ).
Visualization and UI
• Zero line at 0 with gradient fill that darkens as the valuation moves away from 0.
• Optional plotting of band lines and background highlights when OB or OS is active.
• Optional candle and background coloring driven by the valuation.
• Summary table showing each component’s current z-score, the final score, and a compact status.
How it can be used
• Bias filter : treat crosses above 0 as bullish bias and below 0 as bearish bias.
• Mean-reversion context : look for exhaustion when the valuation enters the OB or OS region, then watch for exits from those regions or a return toward 0.
• Signal confirmation : use the final score to confirm setups from structure or price action.
• Adaptive banding : with dynamic thresholds, OB and OS adjust to prevailing variability rather than relying on fixed lines.
• Component tuning : change weights to emphasize trend (raise DM, reduce RSI/CCI) or range behavior (raise RSI/CCI, reduce DM). PZO can help in swing environments.
Why z-score blending helps
Indicators often live on different scales. Z-scoring places them on a common, unitless axis, so a one-sigma move in RSI has comparable influence to a one-sigma move in CCI. This reduces scale bias and allows transparent weighting. It also facilitates regime-aware thresholds because the dynamic bands scale with recent dispersion.
Inputs to know
• Component lookbacks : rsilb, ccilb, dmlb, pzolb control each raw signal.
• Standardization window : lookback sets the z-score memory. Longer smooths, shorter reacts.
• Weights : w_rsi, w_cci, w_dm, w_pzo determine each component’s influence.
• Smoothing : maType, smoothP, sig govern optional post-blend smoothing.
• Dynamic bands : dyn_thres, dynLen, thres_k, centerZero configure the adaptive OB/OS logic.
• UI : toggle the plot, table, candle coloring, and threshold lines.
Reading the plot
• Above 0 : composite pressure is positive.
• Below 0 : composite pressure is negative.
• OB region : valuation above the chosen OB line. Risk of mean reversion rises and momentum continuation needs evidence.
• OS region : mirror logic on the downside.
• Band exits : leaving OB or OS can serve as a normalization cue.
Strengths
• Normalizes heterogeneous signals into one interpretable series.
• Adjustable component weights to match instrument behavior.
• Dynamic thresholds adapt to changing volatility and drift.
• Transparent diagnostics from the on-chart table.
• Flexible smoothing choices, including ALMA and T3.
Limitations and cautions
• Z-scores assume a reasonably stationary window. Sharp regime shifts can make recent bands unrepresentative.
• Highly correlated components can overweight the same effect. Consider adjusting weights to avoid double counting.
• More smoothing adds lag. Less smoothing adds noise.
• Dynamic bands recalibrate with dynLen ; if set too short, bands may swing excessively. If too long, bands can be slow to adapt.
Practical tuning tips
• Trending symbols: increase w_dm , use a modest smoother like EMA or T3, and use centerZero dynamic bands.
• Choppy symbols: increase w_rsi and w_cci , consider ALMA with a higher sigma , and widen bands with a larger thres_k .
• Multiday swing charts: lengthen lookback and dynLen to stabilize the scale.
• Lower timeframes: shorten component lookbacks slightly and reduce smoothing to keep signals timely.
Alerts
• Enter and exit of Overbought and Oversold, based on the active band choice.
• Bullish and bearish zero crosses.
Use alerts as prompts to review context rather than as stand-alone trade commands.
Final Remarks
We created this to show people a different way of making indicators & trading.
You can process normal indicators in multiple ways to enhance or change the signal, especially with this you can utilise machine learning to optimise the weights, then trade accordingly.
All of the different components were selected to give some sort of signal, its made out of simple components yet is effective. As long as the user calibrates it to their Trading/ investing style you can find good results. Do not use anything standalone, ensure you are backtesting and creating a proper system.
ATAI Volume Pressure Analyzer V 1.0 — Pure Up/DownATAI Volume Pressure Analyzer V 1.0 — Pure Up/Down
Overview
Volume is a foundational tool for understanding the supply–demand balance. Classic charts show only total volume and don’t tell us what portion came from buying (Up) versus selling (Down). The ATAI Volume Pressure Analyzer fills that gap. Built on Pine Script v6, it scans a lower timeframe to estimate Up/Down volume for each host‑timeframe candle, and presents “volume pressure” in a compact HUD table that’s comparable across symbols and timeframes.
1) Architecture & Global Settings
Global Period (P, bars)
A single global input P defines the computation window. All measures—host‑TF volume moving averages and the half‑window segment sums—use this length. Default: 55.
Timeframe Handling
The core of the indicator is estimating Up/Down volume using lower‑timeframe data. You can set a custom lower timeframe, or rely on auto‑selection:
◉ Second charts → 1S
◉ Intraday → 1 minute
◉ Daily → 5 minutes
◉ Otherwise → 60 minutes
Lower TFs give more precise estimates but shorter history; higher TFs approximate buy/sell splits but provide longer history. As a rule of thumb, scan thin symbols at 5–15m, and liquid symbols at 1m.
2) Up/Down Volume & Derived Series
The script uses TradingView’s library function tvta.requestUpAndDownVolume(lowerTf) to obtain three values:
◉ Up volume (buyers)
◉ Down volume (sellers)
◉ Delta (Up − Down)
From these we define:
◉ TF_buy = |Up volume|
◉ TF_sell = |Down volume|
◉ TF_tot = TF_buy + TF_sell
◉ TF_delta = TF_buy − TF_sell
A positive TF_delta indicates buyer dominance; a negative value indicates selling pressure. To smooth noise, simple moving averages of TF_buy and TF_sell are computed over P and used as baselines.
3) Key Performance Indicators (KPIs)
Half‑window segmentation
To track momentum shifts, the P‑bar window is split in half:
◉ C→B: the older half
◉ B→A: the newer half (toward the current bar)
For each half, the script sums buy, sell, and delta. Comparing the two halves reveals strengthening/weakening pressure. Example: if AtoB_delta < CtoB_delta, recent buying pressure has faded.
[ 4) HUD (Table) Display /i]
Colors & Appearance
Two main color inputs define the theme: a primary color and a negative color (used when Δ is negative). The panel background uses a translucent version of the primary color; borders use the solid primary color. Text defaults to the primary color and flips to the negative color when a block’s Δ is negative.
Layout
The HUD is a 4×5 table updated on the last bar of each candle:
◉ Row 1 (Meta): indicator name, P length, lower TF, host TF
◉ Row 2 (Host TF): current ↑Buy, ↓Sell, ΔDelta; plus Σ total and SMA(↑/↓)
◉ Row 3 (Segments): C→B and B→A blocks with ↑/↓/Δ
◉ Rows 4–5: reserved for advanced modules (Wings, α/β, OB/OS, Top
5) Advanced Modules
5.1 Wings
“Wings” visualize volume‑driven movement over C→B (left wing) and B→A (right wing) with top/bottom lines and a filled band. Slopes are ATR‑per‑bar normalized for cross‑symbol/TF comparability and converted to angles (degrees). Coloring mirrors HUD sign logic with a near‑zero threshold (default ~3°):
◉ Both lines rising → blue (bullish)
◉ Both falling → red (bearish)
◉ Mixed/near‑zero → gray
Left wing reflects the origin of the recent move; right wing reflects the current state.
5.2 α / β at Point B
We compute the oriented angle between the two wings at the midpoint B:
β is the bottom‑arc angle; α = 360° − β is the top‑arc angle.
◉ Large α (>180°) or small β (<180°) flags meaningful imbalance.
◉ Intuition: large α suggests potential selling pressure; small β implies fragile support. HUD cells highlight these conditions.
5.3 OB/OS Spike
OverBought/OverSold (OB/OS) labels appear when directional volume spikes align with a 7‑oscillator vote (RSI, Stoch, %R, CCI, MFI, DeMarker, StochRSI).
◉ OB label (red): unusually high sell volume + enough OB votes
◉ OS label (teal): unusually high buy volume + enough OS votes
Minimum votes and sync window are user‑configurable; dotted connectors can link labels to the candle wick.
5.4 Top3 Volume Peaks
Within the P window the script ranks the top three BUY peaks (B1–B3) and top three SELL peaks (S1–S3).
◉ B1 and S1 are drawn as horizontal resistance (at B1 High) and support (at S1 Low) zones with adjustable thickness (ticks/percent/ATR).
◉ The HUD dedicates six cells to show ↑/↓/Δ for each rank, and prints the exact High (B1) and Low (S1) inline in their cells.
6) Reading the HUD — A Quick Checklist
◉ Meta: Confirm P and both timeframes (host & lower).
◉ Host TF block: Compare current ↑/↓/Δ against their SMAs.
◉ Segments: Contrast C→B vs B→A deltas to gauge momentum change.
◉ Wings: Right‑wing color/angle = now; left wing = recent origin.
◉ α / β: Look for α > 180° or β < 180° as imbalance cues.
◉ OB/OS: Note labels, color (red/teal), and the vote count.
◉Top3: Keep B1 (resistance) and S1 (support) on your radar.
Use these together to sketch scenarios and invalidation levels; never rely on a single signal in isolation.
[ 7) Example Highlights (What the table conveys) /i]
◉ Row 1 shows the indicator name, the analysis length P (default 55), and both TFs used for computation and display.
◉ B1 / S1 blocks summarize each side’s peak within the window, with Δ indicating buyer/seller dominance at that peak and inline price (B1 High / S1 Low) for actionable levels.
◉ Angle cells for each wing report the top/bottom line angles vs. the horizontal, reflecting the directional posture.
◉ Ranks B2/B3 and S2/S3 extend context beyond the top peak on each side.
◉ α / β cells quantify the orientation gap at B; changes reflect shifting buyer/seller influence on trend strength.
Together these visuals often reveal whether the “wings” resemble a strong, upward‑tilted arm supported by buyer volume—but always corroborate with your broader toolkit
8) Practical Tips & Tuning
◉ Choose P by market structure. For daily charts, 34–89 bars often works well.
◉ Lower TF choice: Thin symbols → 5–15m; liquid symbols → 1m.
◉ Near‑zero angle: In noisy markets, consider 5–7° instead of 3°.
◉ OB/OS votes: Daily charts often work with 3–4 votes; lower TFs may prefer 4–5.
◉ Zone thickness: Tie B1/S1 zone thickness to ATR so it scales with volatility.
◉ Colors: Feel free to theme the primary/negative colors; keep Δ<0 mapped to the negative color for readability.
Combine with price action: Use this indicator alongside structure, trendlines, and other tools for stronger decisions.
Technical Notes
Pine Script v6.
◉ Up/Down split via TradingView/ta library call requestUpAndDownVolume(lowerTf).
◉ HUD‑first design; drawings for Wings/αβ/OBOS/Top3 align with the same sign/threshold logic used in the table.
Disclaimer: This indicator is provided solely for educational and analytical purposes. It does not constitute financial advice, nor is it a recommendation to buy or sell any security. Always conduct your own research and use multiple tools before making trading decisions.
taLibrary "ta"
█ OVERVIEW
This library holds technical analysis functions calculating values for which no Pine built-in exists.
Look first. Then leap.
█ FUNCTIONS
cagr(entryTime, entryPrice, exitTime, exitPrice)
It calculates the "Compound Annual Growth Rate" between two points in time. The CAGR is a notional, annualized growth rate that assumes all profits are reinvested. It only takes into account the prices of the two end points — not drawdowns, so it does not calculate risk. It can be used as a yardstick to compare the performance of two instruments. Because it annualizes values, the function requires a minimum of one day between the two end points (annualizing returns over smaller periods of times doesn't produce very meaningful figures).
Parameters:
entryTime : The starting timestamp.
entryPrice : The starting point's price.
exitTime : The ending timestamp.
exitPrice : The ending point's price.
Returns: CAGR in % (50 is 50%). Returns `na` if there is not >=1D between `entryTime` and `exitTime`, or until the two time points have not been reached by the script.
█ v2, Mar. 8, 2022
Added functions `allTimeHigh()` and `allTimeLow()` to find the highest or lowest value of a source from the first historical bar to the current bar. These functions will not look ahead; they will only return new highs/lows on the bar where they occur.
allTimeHigh(src)
Tracks the highest value of `src` from the first historical bar to the current bar.
Parameters:
src : (series int/float) Series to track. Optional. The default is `high`.
Returns: (float) The highest value tracked.
allTimeLow(src)
Tracks the lowest value of `src` from the first historical bar to the current bar.
Parameters:
src : (series int/float) Series to track. Optional. The default is `low`.
Returns: (float) The lowest value tracked.
█ v3, Sept. 27, 2022
This version includes the following new functions:
aroon(length)
Calculates the values of the Aroon indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: ( [float, float ]) A tuple of the Aroon-Up and Aroon-Down values.
coppock(source, longLength, shortLength, smoothLength)
Calculates the value of the Coppock Curve indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
longLength (simple int) : (simple int) Number of bars for the fast ROC value (length).
shortLength (simple int) : (simple int) Number of bars for the slow ROC value (length).
smoothLength (simple int) : (simple int) Number of bars for the weigted moving average value (length).
Returns: (float) The oscillator value.
dema(source, length)
Calculates the value of the Double Exponential Moving Average (DEMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The double exponentially weighted moving average of the `source`.
dema2(src, length)
An alternate Double Exponential Moving Average (Dema) function to `dema()`, which allows a "series float" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The double exponentially weighted moving average of the `src`.
dm(length)
Calculates the value of the "Demarker" indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
donchian(length)
Calculates the values of a Donchian Channel using `high` and `low` over a given `length`.
Parameters:
length (int) : (series int) Number of bars (length).
Returns: ( [float, float, float ]) A tuple containing the channel high, low, and median, respectively.
ema2(src, length)
An alternate ema function to the `ta.ema()` built-in, which allows a "series float" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int/float) Number of bars (length).
Returns: (float) The exponentially weighted moving average of the `src`.
eom(length, div)
Calculates the value of the Ease of Movement indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
div (simple int) : (simple int) Divisor used for normalzing values. Optional. The default is 10000.
Returns: (float) The oscillator value.
frama(source, length)
The Fractal Adaptive Moving Average (FRAMA), developed by John Ehlers, is an adaptive moving average that dynamically adjusts its lookback period based on fractal geometry.
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The fractal adaptive moving average of the `source`.
ft(source, length)
Calculates the value of the Fisher Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
ht(source)
Calculates the value of the Hilbert Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
Returns: (float) The oscillator value.
ichimoku(conLength, baseLength, senkouLength)
Calculates values of the Ichimoku Cloud indicator, including tenkan, kijun, senkouSpan1, senkouSpan2, and chikou. NOTE: offsets forward or backward can be done using the `offset` argument in `plot()`.
Parameters:
conLength (int) : (series int) Length for the Conversion Line (Tenkan). The default is 9 periods, which returns the mid-point of the 9 period Donchian Channel.
baseLength (int) : (series int) Length for the Base Line (Kijun-sen). The default is 26 periods, which returns the mid-point of the 26 period Donchian Channel.
senkouLength (int) : (series int) Length for the Senkou Span 2 (Leading Span B). The default is 52 periods, which returns the mid-point of the 52 period Donchian Channel.
Returns: ( [float, float, float, float, float ]) A tuple of the Tenkan, Kijun, Senkou Span 1, Senkou Span 2, and Chikou Span values. NOTE: by default, the senkouSpan1 and senkouSpan2 should be plotted 26 periods in the future, and the Chikou Span plotted 26 days in the past.
ift(source)
Calculates the value of the Inverse Fisher Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
Returns: (float) The oscillator value.
kvo(fastLen, slowLen, trigLen)
Calculates the values of the Klinger Volume Oscillator.
Parameters:
fastLen (simple int) : (simple int) Length for the fast moving average smoothing parameter calculation.
slowLen (simple int) : (simple int) Length for the slow moving average smoothing parameter calculation.
trigLen (simple int) : (simple int) Length for the trigger moving average smoothing parameter calculation.
Returns: ( [float, float ]) A tuple of the KVO value, and the trigger value.
pzo(length)
Calculates the value of the Price Zone Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
rms(source, length)
Calculates the Root Mean Square of the `source` over the `length`.
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The RMS value.
rwi(length)
Calculates the values of the Random Walk Index.
Parameters:
length (simple int) : (simple int) Lookback and ATR smoothing parameter length.
Returns: ( [float, float ]) A tuple of the `rwiHigh` and `rwiLow` values.
stc(source, fast, slow, cycle, d1, d2)
Calculates the value of the Schaff Trend Cycle indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
fast (simple int) : (simple int) Length for the MACD fast smoothing parameter calculation.
slow (simple int) : (simple int) Length for the MACD slow smoothing parameter calculation.
cycle (simple int) : (simple int) Number of bars for the Stochastic values (length).
d1 (simple int) : (simple int) Length for the initial %D smoothing parameter calculation.
d2 (simple int) : (simple int) Length for the final %D smoothing parameter calculation.
Returns: (float) The oscillator value.
stochFull(periodK, smoothK, periodD)
Calculates the %K and %D values of the Full Stochastic indicator.
Parameters:
periodK (simple int) : (simple int) Number of bars for Stochastic calculation. (length).
smoothK (simple int) : (simple int) Number of bars for smoothing of the %K value (length).
periodD (simple int) : (simple int) Number of bars for smoothing of the %D value (length).
Returns: ( [float, float ]) A tuple of the slow %K and the %D moving average values.
stochRsi(lengthRsi, periodK, smoothK, periodD, source)
Calculates the %K and %D values of the Stochastic RSI indicator.
Parameters:
lengthRsi (simple int) : (simple int) Length for the RSI smoothing parameter calculation.
periodK (simple int) : (simple int) Number of bars for Stochastic calculation. (length).
smoothK (simple int) : (simple int) Number of bars for smoothing of the %K value (length).
periodD (simple int) : (simple int) Number of bars for smoothing of the %D value (length).
source (float) : (series int/float) Series of values to process. Optional. The default is `close`.
Returns: ( [float, float ]) A tuple of the slow %K and the %D moving average values.
supertrend(factor, atrLength, wicks)
Calculates the values of the SuperTrend indicator with the ability to take candle wicks into account, rather than only the closing price.
Parameters:
factor (float) : (series int/float) Multiplier for the ATR value.
atrLength (simple int) : (simple int) Length for the ATR smoothing parameter calculation.
wicks (simple bool) : (simple bool) Condition to determine whether to take candle wicks into account when reversing trend, or to use the close price. Optional. Default is false.
Returns: ( [float, int ]) A tuple of the superTrend value and trend direction.
szo(source, length)
Calculates the value of the Sentiment Zone Oscillator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
t3(source, length, vf)
Calculates the value of the Tilson Moving Average (T3).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
vf (simple float) : (simple float) Volume factor. Affects the responsiveness.
Returns: (float) The Tilson moving average of the `source`.
t3Alt(source, length, vf)
An alternate Tilson Moving Average (T3) function to `t3()`, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
vf (simple float) : (simple float) Volume factor. Affects the responsiveness.
Returns: (float) The Tilson moving average of the `source`.
tema(source, length)
Calculates the value of the Triple Exponential Moving Average (TEMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The triple exponentially weighted moving average of the `source`.
tema2(source, length)
An alternate Triple Exponential Moving Average (TEMA) function to `tema()`, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The triple exponentially weighted moving average of the `source`.
trima(source, length)
Calculates the value of the Triangular Moving Average (TRIMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The triangular moving average of the `source`.
trima2(src, length)
An alternate Triangular Moving Average (TRIMA) function to `trima()`, which allows a "series int" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int) Number of bars (length).
Returns: (float) The triangular moving average of the `src`.
trix(source, length, signalLength, exponential)
Calculates the values of the TRIX indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
signalLength (simple int) : (simple int) Length for smoothing the signal line.
exponential (simple bool) : (simple bool) Condition to determine whether exponential or simple smoothing is used. Optional. The default is `true` (exponential smoothing).
Returns: ( [float, float, float ]) A tuple of the TRIX value, the signal value, and the histogram.
uo(fastLen, midLen, slowLen)
Calculates the value of the Ultimate Oscillator.
Parameters:
fastLen (simple int) : (series int) Number of bars for the fast smoothing average (length).
midLen (simple int) : (series int) Number of bars for the middle smoothing average (length).
slowLen (simple int) : (series int) Number of bars for the slow smoothing average (length).
Returns: (float) The oscillator value.
vhf(source, length)
Calculates the value of the Vertical Horizontal Filter.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
vi(length)
Calculates the values of the Vortex Indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: ( [float, float ]) A tuple of the viPlus and viMinus values.
vzo(length)
Calculates the value of the Volume Zone Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
williamsFractal(period)
Detects Williams Fractals.
Parameters:
period (int) : (series int) Number of bars (length).
Returns: ( [bool, bool ]) A tuple of an up fractal and down fractal. Variables are true when detected.
wpo(length)
Calculates the value of the Wave Period Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
█ v7, Nov. 2, 2023
This version includes the following new and updated functions:
atr2(length)
An alternate ATR function to the `ta.atr()` built-in, which allows a "series float" `length` argument.
Parameters:
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The ATR value.
changePercent(newValue, oldValue)
Calculates the percentage difference between two distinct values.
Parameters:
newValue (float) : (series int/float) The current value.
oldValue (float) : (series int/float) The previous value.
Returns: (float) The percentage change from the `oldValue` to the `newValue`.
donchian(length)
Calculates the values of a Donchian Channel using `high` and `low` over a given `length`.
Parameters:
length (int) : (series int) Number of bars (length).
Returns: ( [float, float, float ]) A tuple containing the channel high, low, and median, respectively.
highestSince(cond, source)
Tracks the highest value of a series since the last occurrence of a condition.
Parameters:
cond (bool) : (series bool) A condition which, when `true`, resets the tracking of the highest `source`.
source (float) : (series int/float) Series of values to process. Optional. The default is `high`.
Returns: (float) The highest `source` value since the last time the `cond` was `true`.
lowestSince(cond, source)
Tracks the lowest value of a series since the last occurrence of a condition.
Parameters:
cond (bool) : (series bool) A condition which, when `true`, resets the tracking of the lowest `source`.
source (float) : (series int/float) Series of values to process. Optional. The default is `low`.
Returns: (float) The lowest `source` value since the last time the `cond` was `true`.
relativeVolume(length, anchorTimeframe, isCumulative, adjustRealtime)
Calculates the volume since the last change in the time value from the `anchorTimeframe`, the historical average volume using bars from past periods that have the same relative time offset as the current bar from the start of its period, and the ratio of these volumes. The volume values are cumulative by default, but can be adjusted to non-accumulated with the `isCumulative` parameter.
Parameters:
length (simple int) : (simple int) The number of periods to use for the historical average calculation.
anchorTimeframe (simple string) : (simple string) The anchor timeframe used in the calculation. Optional. Default is "D".
isCumulative (simple bool) : (simple bool) If `true`, the volume values will be accumulated since the start of the last `anchorTimeframe`. If `false`, values will be used without accumulation. Optional. The default is `true`.
adjustRealtime (simple bool) : (simple bool) If `true`, estimates the cumulative value on unclosed bars based on the data since the last `anchor` condition. Optional. The default is `false`.
Returns: ( [float, float, float ]) A tuple of three float values. The first element is the current volume. The second is the average of volumes at equivalent time offsets from past anchors over the specified number of periods. The third is the ratio of the current volume to the historical average volume.
rma2(source, length)
An alternate RMA function to the `ta.rma()` built-in, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The rolling moving average of the `source`.
supertrend2(factor, atrLength, wicks)
An alternate SuperTrend function to `supertrend()`, which allows a "series float" `atrLength` argument.
Parameters:
factor (float) : (series int/float) Multiplier for the ATR value.
atrLength (float) : (series int/float) Length for the ATR smoothing parameter calculation.
wicks (simple bool) : (simple bool) Condition to determine whether to take candle wicks into account when reversing trend, or to use the close price. Optional. Default is `false`.
Returns: ( [float, int ]) A tuple of the superTrend value and trend direction.
vStop(source, atrLength, atrFactor)
Calculates an ATR-based stop value that trails behind the `source`. Can serve as a possible stop-loss guide and trend identifier.
Parameters:
source (float) : (series int/float) Series of values that the stop trails behind.
atrLength (simple int) : (simple int) Length for the ATR smoothing parameter calculation.
atrFactor (float) : (series int/float) The multiplier of the ATR value. Affects the maximum distance between the stop and the `source` value. A value of 1 means the maximum distance is 100% of the ATR value. Optional. The default is 1.
Returns: ( [float, bool ]) A tuple of the volatility stop value and the trend direction as a "bool".
vStop2(source, atrLength, atrFactor)
An alternate Volatility Stop function to `vStop()`, which allows a "series float" `atrLength` argument.
Parameters:
source (float) : (series int/float) Series of values that the stop trails behind.
atrLength (float) : (series int/float) Length for the ATR smoothing parameter calculation.
atrFactor (float) : (series int/float) The multiplier of the ATR value. Affects the maximum distance between the stop and the `source` value. A value of 1 means the maximum distance is 100% of the ATR value. Optional. The default is 1.
Returns: ( [float, bool ]) A tuple of the volatility stop value and the trend direction as a "bool".
Removed Functions:
allTimeHigh(src)
Tracks the highest value of `src` from the first historical bar to the current bar.
allTimeLow(src)
Tracks the lowest value of `src` from the first historical bar to the current bar.
trima2(src, length)
An alternate Triangular Moving Average (TRIMA) function to `trima()`, which allows a
"series int" length argument.
[NLX-L1] Trend Index- NLX Modular Trading Framework -
This module is build upon the Trend Index by Mango2Juice (thanks for your permission to use the source!)
It includes all the common indicators and creates a positive or negative score, which can be used with my Modular Trading Framework and linked to an entry/exit indicator.
SuperTrend
VWAP Bands
Relative Strength Index ( RSI )
Commodity Channel Index ( CCI )
William Percent Range (WPR)
Directional Movement Index (DMI)
Elder Force Index ( EFI )
Momentum
Demarker
Parabolic SAR
... and more
- Getting Started -
1. Add this Trend Index to your Chart
2. Add one of my Indicator Modules to your Chart, such as the QQE++ Indicator
3. In the QQE Indicator Settings combine it with the Trend Index (and choose L1 Type)
4. Optional: Add the Noise Filter , and in the Noise Filter Settings you select the QQE Indicator as combination (and choose L2 for Type)
5. Add the Backtest Module to your Chart
6. Select the Noise Filter in the Backtest Settings
Indicator modules can be combined in many different ways in my framework - have fun!
- Alerts for Automated Trading -
The alerts module is coming soon and you will be able to create alerts to automated your trades.
See my signature below for more information.
Trend Index Trend Index show trend of the instrument based on multiple indicators
- Green --> Uptrend
- Red --> Downtrend
- Gray --> No trend
Indicators used:-
Relative Strength Index (RSI)
Commodity Channel Index (CCI)
William Percent Range (WPR)
Directional Movement Index (DMI)
Elder Force Index (EFI)
Momentum
Demarker
Parabolic SAR
Reversal Points [CC]This original script was created based on a suggestion from @kerpiciwuasile. My original Reversal Points script was removed because I modeled it after an indicator by Demark, but this script will have no such issues. Reversal points are an exciting concept for me because it is such a useful tool when placing trades. This is my first attempt at a new overall layout for my script and I included a bunch of customization so let me know what you think.
My script works by finding lows that are surrounded by bars that have higher lows and highs that are surrounded by lower highs. I use this logic to find short term lows or highs and I use the same concept to find mid-term lows or highs but a mid-term high is a short term high surrounded by lower short term highs and a mid-term low is a short term low surrounded by higher short term lows. And of course this means that long term highs or lows use the same logic to find highs or lows that are surrounded by mid-term highs or lows. I would recommend to buy at the long term low points or sell at the long term high points.
Keep in mind of course that short term highs and lows are very common and reversal points will get rarer as you look for longer term reversal points. I would recommend to experiment and see which reversal points work best for you and of course, know that there is no magical formula to use for all stocks.
Also there are a few scenarios where you want to enable or disable the inside bar setting. You would want to ignore inside bars if the market is currently very volatile or if you are using this indicator on a crypto chart. This is not an exact science but more of a recommendation, so feel free to experiment with it.
Reversal points are crucial for traders as they signal a potential change in the market trend, providing opportunities for entry or exit.
In summary, this code snippet is a powerful tool for traders to detect and visualize reversal points on a trading chart, providing valuable insights into potential trend changes and facilitating more informed trading decisions.
Let me know if you would like me to publish other scripts or if you want me to do something custom for you!
Fuzzy Fib CountsA new approach to trend/time analysis. This indicator counts up/down price moves using fuzzy logic in two time frames. Counts reaching Fibonacci numbers are often significant to trend momentum: either reversal or stronger continuation. See the script’s beginning comments for details.
I’m amazed with DeMark and all his indicators. But after coding a TD_Sequential script, I began to wonder if there wasn’t an easier way to identify high probability entry/exit points without all the intricate logic. It took awhile, but a different paradigm eventually came into focus: Fuzzy Fib Counts (FFC).
FFC is like “analog” compared to TD_Sequential’s “digital” approach. And FFC is much simpler: no buy/sell/perfected/deferred signals, just up/down counts. It’s uncanny how often a trend is refocused at Fibonacci numbers, but like TD_Sequential… it’s not perfect! As with all indicators, consult your intuition, before pulling the trigger.
The default values have been effective for me in Bitcoin (and I’m surprised how well it works in smaller time frames). However, for less volatile markets, you may need to reduce the fuzz level and/or adjust the filter (averaging) parameters.
FFC has been helpful to me. Hope it helps you too! If so, donations appreciated… Thanks!
BTC: 36FdUcETrwmGxt5qazq7Bo7kad9kuNawMF
LTC: MJYHzwJYjsnB35dzb5L9hCpqZf8RyqViEm
ETH: 0x3db7557bea61ca4c096c6ed7bdb2a0d69e455fe1
Looking for trailblazers to help explore new possibilities… Add constructive comments below!
