Commencez

Recherche dans les scripts pour "Up down"

VRSI-MARSI BI wanted to create an indicator which resembles price movement, aside to volume movement. MARSI (= MA RSI(close)) = "yellow-blue" line which is the MA(5) of the RSI (9) of closing price. VRSI (= MA RSI(Volume)) = "orange" line which is the MA(5) of the RSI (9) of Volume . (Default plot of RSI and VRSI is not visible but can be made visible ("Settings" > "Style" > set "Opacity" of "RSI & VRSI")) Because it still is a RSI indicator, the midline (50), and Oversold/Overbought area's (20-30 & 70-80) are important to watch, especially the MARSI! Comparing the price movement with the "orange" Volume VRSI line helps to spot a possible trend change, for example when price goes up and an ascending Volume VRSI line starts to flatten or starts descending, this could be a sign that the Bullish trend is weakening, predicting a possible trend change. Or, when for example a downwards price movement is accompanied with a rising Volume VRSI line, this can be a sign of large Bearish power. This study comes with Bollinger Bands as an assisting tool, it is default made not visible but can be made visible ("settings" > "style" > Set "Opacity" of "basis, upper & lower") You can see where the MARSI ("yellow-blue" line) crosses the "basis", or bounces off the bands, ... All this is seen in "VRSI-MARSI B" "VRSI-MARSI A" contains the alerts: 1) Long/Short = "Triangle UP/DOWN", color: lime/red Condition: Movement of MA(5) of RSI (9) of price (close ) 2) Long2/Short2 = ">", color: lime/red Condition: Long/Short condition is true for 2 or more bars (= continuation) 3) Long3/Short3 = "•", color: lime/red Condition: MA RSI (Close) crosses MA RSI ( Volume ) 1 or more alerts can easily be disabled if desired (settings > inputs) Thanks! ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- More information available in the script ;-)
Indicateur Pine Script®
par fikira
4
Visual RSI [LucF]Visual RSI offers a different way of looking at RSI by providing a composite representation of 9 different RSI-generated components. Instead of focusing on one line only, this approach blends multiple sources to provide the viewer with a larger context RSI-based picture. For those who don’t want to read • Green in bullish (>50) zone is the most bullish. • Red in bullish zone doesn’t necessarily mean bearish—it just means bullish strength is weakening. It may be just a pause before a reprise or exhaustion signalling a reversal—impossible to tell. • The same in inverse applies to the bearish zone (<50). For those who want to understand The nine components making up Visual RSI are: • a current timeframe RSI • a higher timeframe RSI • the delta between these two RSI lines • for each of these three basic components, two independent Bollinger band: one calculated for the bullish section of the scale (>50) and a separate one calculated for the lower bearish region. Dual BBs In my view, RSI’s position with regards to the centerline is much more important than its position in extreme areas. Why? Because the building block of RSI is the ratio of the averages of up/down moves during the RSI period. When the average of ups is greater, RSI is > 50. So while a rising signal starting from 20 let’s say, indicates that the rate of change is increasing, only when it crosses 50 can we say that sentiment balance has truly become bullish, and this information is more reliable than the signal being at a level corresponding to whatever estimate we make of what constitutes an extreme value. In my landscape, the general balance of a ratio provides more valuable information than the ratio’s exact value. The idea behind the dual BBs is to provide independent tracking information for both halves of the indicator’s space, which I find more useful than the normal method of simply adding a multiple of the standard deviation on both sides of the mean. With dual BBs, the upper BB will never go lower than the indicator’s centerline, and the lower BB will never go higher. The upper BB focuses on upper-bound volatility when the signal is bearish, and the lower BB focuses on downside volatility when the signal is bearish. The functions used to calculate the independent BBs are reusable on other signals if a centerline can be defined for them. A clamping percentage is implemented, so that when a BB line is hugging the centerline it clamps to it. This helps in providing earlier signals when they use the BB line states. Providing context to RSI What RSI measures indirectly is the balance in the rate of change—or the speed of price movement, but not its instant value, otherwise RSI would be even noisier. More precisely, RSI represents the relative strength of the up/down movement in the last n bars of RSI’s length, with 14 often used because that’s what Wilder proposed (Visual RSI’s defaults are 20 for the current timeframe and 40 for the higher timeframe). At every bar, a new value is added to the equation and an old value carrying equal weight is dropped, so a large dropped off value will have more impact on RSI’s value if the new bar’s move is small. This accounts for some of RSI’s speed in identifying exhaustion after important moves, but almost for some of its noise. Visual RSI is the result of trying to drown RSI’s noise in the context of other informational streams, while simultaneously providing even faster information than RSI alone, by giving more visual weight to the delta between the current and higher timeframe RSI’s. How to read Visual RSI The default settings show all 9 basic components as green/red areas of intensities varying with their importance. The most intense colors are reserved for the delta RSI and the BBs have the lightest intensities. The individual lines of components are intentionally difficult to distinguish so that focus is first on the general picture, including the all-important six-state background, and then on the delta RSI. One entry setup could be reversals in a larger trend context, so low pivots of the delta in a fully bullish context (a green background in the upper section of the indicator), and inversely, high pivots in a fully bearish context (a red background in the lower section of the indicator). Please resist the common misconception, when interpreting RSI, that a reversal in the signal will necessarily lead to a reversal in price. Each trend has its rhythm. Only machine-generated price action can progress regularly. It’s normal for trends to take a breather for some time before they continue or reverse, as traders driving the trend experience emotional fatigue and gradual fear. RSI reversals merely signify that such a breather has occurred—nothing more. Only the larger context can provide information that can situate that pause and put more meaningful odds on it having more probability of continuing in one direction or the other. This is the reasoning behind the setup just described. Features • All components can be hidden, displayed as a simple line, a uniformly colored fill, or a green/red fill (the default). • The background can be colored using 9 different methods, including 3 six-state methods using the rising/falling BB lines of the 3 basic components. These six states allow for bullish/bearish/neutral sentiment in both the upper and lower regions of the indicator. A bearish (dark red) background in the bullish (>50) section of the indicator represents decreasing bullishness. A bearish (slightly brighter red) in the bearish (<50) section of the indicator means incresingly bearish sentiment. The six-state backgrounds allow for neutral (no color) sentiment when no compelling signs can be found to conclude anything with meaningful odds. The default background uses the six-state method on the higher timeframe RSI’s BBs because I find it the most useful, as it represents the largest—and slowest—context sentiment among all the indicator’s components. • A thin status bar in the top part of the indicator also allows selection of the same 9 methods to color it. The default is a triple-state system using the rising/falling characteristics of the current timeframe RSI’s BBs to provide a short-term counterbalance to the long-term background. • Three different markers can be configured using approximately 70 permutations each, each filtered by 20 different filter permutations. When modification of the relevant parameters in the script’s Settings/Settings/Parameters section is added, possibilities are almost endless. If the generated signals are then fed into the PineCoders Engine and combined with the Engine’s own options, the permutations go up another order of magnitude, and changes to any setting can be instantly evaluated using the Engine’s backtesting results. • Five simple filters can be combined. They are additive. They include volume-related conditions and a chandelier, which I find useful because both volume and volatility (the chandelier using highs/lows and ATR) are sensible complementary sources to RSI’s momentum information. The filter’s state can be shown as a thin line at the bottom of the indicator. • Alerts can be configured using any of the marker/filter combinations mentioned. As usual, once your markers/filters are set up the way you want, create your alert from the chart/timeframe you want the alert to run on and be sure to use the “Once Per Bar Close” triggering condition. Use an alert message that will remind you of which combination of markers were used when creating the alert. • A plot providing entry signals for the PineCoders Backtesting & Trading Engine is supplied. It will use whichever marker/filter configuration is active to generate signals. • All higher timeframe information is non-repainting. Higher timeframe lines can be smoothed (the default). The selection of the higher timeframe can be made using 3 different methods: 1. By steps (if current timeframe <= 1 minute: 60 min, <= 60 min: 1D, <= 6H: 3D, <= 1D: 1W, <=1W: 1M, >1W: 12M) 2. By a user-defined multiple of the current timeframe 3. Using a fixed timeframe Thanks to: • Alex Orekhov aka @everget for the chandelier code. • @RicardoSantos who through a small remark early on, unknowingly put me on the track of eliminating noise through visual crowding. • The brilliant guys in the PineCoders Pro room for your knowledge, limitless creativity and constant companionship.
Indicateur Pine Script®
par LucF
22
Chaikin Oscillator HystogramThis indicator shows an hystogram with the Chainkin Oscilator values, with color changes in function of the direction (up/down) . Also show the 0 crossovers, up and down. Chaikin Oscillator gets its name from its creator, Marc Chaikin. The Chaikin Indicator applies MACD to the accumulation-distribution line rather than closing price. For me it's very usefull to identify (or confirm) trends up and trends down. All my published scripts: es.tradingview.com
Indicateur Pine Script®
par Nakitxu
3
Percentage OscillatorUsing momentum calculations on multiple time frames and adding everything together into 4 separate directions: 1- green: the strength and momentum in +45 to +90 degrees angle 2- blue: the strength and momentum in 0 to +45 degrees angle 3- orange: the strength and momentum in 0 to -45 degrees angle 4- red: the strength and momentum in -45 to -90 degrees angle Single parameter to control the size of the largest moving window. Uptrend is green with orange corrections Downtrend is red with blue corrections When downtrend turns into uptrend, blue becomes green When uptrend turns into downtrend, orange becomes red The natural cycle of the market is RED->BLUE->GREEN->ORANGE and so on, you will see the cycle repeats itself 3 times before a break up\down. The strength of the movement depends on the height and width of all the waves that created the 3 cycle movement (reminds Elliot in an oscillatory representation) The script is provided as is, there are no trading strategies implied or recommended. Feel free to PM with questions
Indicateur Pine Script®
par Spectrum101
1
Kawabunga Swing Failure Points Candles (SFP) by RRBKawabunga Swing Failure Points Candles (SFP) by RagingRocketBull 2019 Version 1.0 This indicator shows Swing Failure Points (SFP) and Swing Confirmation Points (SCP) as candles on a chart. SFP/SCP candles are used by traders as signals for trend confirmation/possible reversal. The signal is stronger on a higher volume/larger candle size. A Swing Failure Point (SFP) candle is used to spot a reversal: - up trend SFP is a failure to close above prev high after making a new higher high => implies reversal down - down trend SFP is a failure to close below prev low after making a new lower low => implies reversal up A Swing Confirmation Point (SCP) candle is just the opposite and is used to confirm the current trend: - up trend SCP is a successful close above prev high after making a new higher high => confirms the trend and implies continuation up - down trend SCP is a successful close below prev low after making a new lower low => confirms the trend and implies continuation down Features: - uses fractal pivots with optional filter - show/hide SFP/SCP candles, pivots, zigzag, last min/max pivot bands - dim lag zones/hide false signals introduced by lagging fractals or - use unconfirmed pivots to eliminate fractal lag/false signals. 2 modes: fractals 1,1 and highest/lowest - filter only SFP/SCP candles confirmed with volume/candle size - SFP/SCP candles color highlighting, dim non-important bars Usage: - adjust fractal settings to get pivots that best match your data (lower values => more frequent pivots. 0,0 - each candle is a pivot) - use one of the unconfirmed pivot modes to eliminate false signals or just ignore all signals in the gray lag zones - optionally filter only SFP/SCP candles with large volume/candle size (volume % change relative to prev bar, abs candle body size value) - up/down trend SCP (lime/fuchsia) => continuation up/down; up/down trend SFP (orange/aqua) => possible reversal down/up. lime/aqua => up; fuchsia/orange => down. - when in doubt use show/hide pivots/unconfirmed pivots, min/max pivot bands to see which prev pivot and min/max value were used in comparisons to generate a signal on the following candle. - disable offset to check on which bar the signal was generated Notes: Fractal Pivots: - SFP/SCP candles depend on fractal pivots, you will get different signals with different pivot settings. Usually 4,4 or 2,2 settings are used to produce fractal pivots, but you can try custom values that fit your data best. - fractal pivots are a mixed series of highs and lows in no particular order. Pivots must be filtered to produce a proper zigzag where ideally a high is followed by a low and another high in orderly fashion. Fractal Lag/False Signals: - only past fractal pivots can be processed on the current bar introducing a lag, therefore, pivots and min/max pivot bands are shown with offset=-rightBars to match their target bars. For unconfirmed pivots an offset=-1 is used with a lag of just 1 bar. - new pivot is not a confirmed fractal and "does not exist yet" while the distance between it and the current bar is < rightBars => prev old fractal pivot in the same dir is used for comparisons => gives a false signal for that dir - to show false signals enable lag zones. SFP/SCP candles in lag zones are false. New pivots will be eventually confirmed, but meanwhile you get a false signal because prev pivot in the same dir was used instead. - to solve this problem you can either temporary hide false signals or completely eliminate them by using unconfirmed pivots of a smaller degree/lag. - hiding false signals only works for history and should be used only temporary (left disabled). In realtime/replay mode it disables all signals altogether due to TradingView's bug (barcolor doesn't support negative offsets) Unconfirmed Pivots: - you have 2 methods to check for unconfirmed pivots: highest/lowest(rightBars) or fractals(1,1) with a min possible step. The first is essentially fractals(0,0) where each candle is a pivot. Both produce more frequent pivots (weaker signals). - an unconfirmed pivot is used in comparisons to generate a valid signal only when it is a higher high (> max high) or a lower low (< min low) in the dir of a trend. Confirmed pivots of a higher degree are not affected. Zigzag is not affected. - you can also manually disable the offset to check on which bar the pivot was confirmed. If the pivot just before an SCP/SFP suddenly jumps ahead of it - prev pivot was used, generating a false signal. - last max high/min low bands can be used to check which value was used in candle comparison to generate a signal: min(pivot min_low, upivot min_low) and max(pivot max_high, upivot max_high) are used - in the unconfirmed pivots mode the max high/min low pivot bands partially break because you can't have a variable offset to match the random pos of an unconfirmed pivot (anywhere in 0..rightBars from the current bar) to its target bar. - in the unconfirmed pivots mode h (green) and l (red) pivots become H and L, and h (lime) and l (fuchsia) are used to show unconfirmed pivots of a smaller degree. Some of them will be confirmed later as H and L pivots of a higher degree. Pivot Filter: - pivot filter is used to produce a better looking zigzag. Essentially it keeps only higher highs/lower lows in the trend direction until it changes, skipping: - after a new high: all subsequent lower highs until a new low - after a new low: all subsequent higher lows until a new high - you can't filter out all prev highs/lows to keep just the last min/max pivots of the current swing because they were already confirmed as pivots and you can't delete/change history - alternatively you could just pick the first high following a low and the first low following a high in a sequence and ignore the rest of the pivots in the same dir, producing a crude looking zigzag where obvious max high/min lows are ignored. - pivot filter affects SCP/SFP signals because it skips some pivots - pivot filter is not applied to/not affected by the unconfirmed pivots - zigzag is affected by pivot filter, but not by the unconfirmed pivots. You can't have both high/low on the same bar in a zigzag. High has priority over Low. - keep same bar pivots option lets you choose which pivots to keep when there are both high/low pivots on the same bar (both kept by default) SCP/SFP Filters: - you can confirm/filter only SCP/SFP signals with volume % change/candle size larger than delta. Higher volume/larger candle means stronger signal. - technically SCP/SFP is always the first matching candle, but it can be invalidated by the following signal in the opposite dir which in turn can be negated by the next signal. - show first matching SCP/SFP = true - shows only the first signal candle (and any invalidations that follow) and hides further duplicate signals in the same dir, does not highlight the trend. - show first matching SCP/SFP = false - produces a sequence of candles with duplicate signals, highlights the whole trend until its dir changes (new pivot). Good Luck! Feel free to learn from/reuse the code to build your own indicators!
Indicateur Pine Script®
par RagingRocketBull
22
Renko CandlesticksRenko charts are awesome . They reduce noise by only painting a brick on the chart when price moves by a specified amount up/down. When the price reverses, it must go twice the specified amount before a brick is painted. Time is not a factor, just price movement. Sometimes however, you want the pros of a renko chart, but on a regular candlestick chart. This indicator attempts to do just that. A band is placed around price action showing the upper and lower bounds of what would be the current renko brick. The band only goes up/down when the price action itself moves up/down by the amount you specify. There are several ways of specifying the amount: Fixed Price Amount: As the name says, you enter the brick size amount, i.e. the amount the price has to move before being in a new brick. % of Price: This method will calculate the amount the price has to move as a percentage of the price itself. This way as price goes up/down, your brick size will adjust accordingly. Recommended values would be around 1% or less. % of ATR: This option will make the brick size a percentage of the Average True Range. You can specify the ATR time frame to be different from your current time frame as well as the ATR length. For instance you could be on a 10 minute chart but specify the ATR to be daily with a length of 3 and a percentage amount of 15. This would make your brick size 15% of the Average True Range for the last 3 days. Recommended values are 10 to 20%. Use this indicator on any time frame, even the 1 minute as the renko bands span the price action the same way on any time frame easily letting you know whether or not the price has moved appreciably, regardless of how much time has passed. You can also set alerts easily, simply set the alert to crossing and choose “Renko Candlesticks” instead of “Value”. You will then see the options for the renko upper and lower bounds. Tested on Bitcoin with the following values: Fixed Price Amount: 30 ($30) % of Price: 0.45 (if Bitcoin is $7000 then the brick size would be $31.50) % of ATR: 15%, ATR Time Frame: 1D, ATR Length: 3 (3 days)
Indicateur Pine Script®
par ragamufin
21
Fibonacci Commodity Stenth IndexFibonacci Commodity Strength Value tells us about the strength and weakness of bull or bear market. The main focus in this is too be done at reversal. It can also be used for identifying fake ups/downs. If all the 4 lines moves upward after a huge up spike, then notice the values of all 4 values. If red fib is smaller than green fib then it is a fake trend. If its more then its uptrend and same for bear movement. ;) It also represents cci (in terms of values) and rsi (in terms of waves). Enjoy !!!!!
Indicateur Pine Script®
par LensOfChartist
5
Impulse Trend ArrowsThis indicator is a volatility-normalized momentum + trend state tool designed to provide a clean “market regime” read: UP / DOWN / NEUTRAL, with optional visual confirmation on the chart. Works on collection of clasic indicators RSI, EMA + ATR and some simple math. 🧭 Trend state behavior When a new BUY/SELL impulse is confirmed, the script updates a persistent trend state (“BUY”, “SELL”, or “NONE”). That state stays active until the opposite confirmed impulse appears. ✅ Visuals & Usage Made some minor, mostly visual upgrades on this release: Baseline + ATR bands are smoothed for cleaner visuals. Optional BUY/SELL arrows are plotted outside the channel to avoid overlap with channel. Optional full-chart background shading reflects the current trend state: Green = UPTREND Red = DOWNTREND A minimal top panel shows the current regime (UP / DOWN / NEUTRAL). I also recently added this channel smoother parameter (for Dragon Channel), if you want it to have less spikes on those MAs just use the bigger number, I picked 8 for default.
Indicateur Pine Script®
par FxShareRobots
Snail Sequence + SnailST Levels + CVD/OI Div by TheActualSnail Snail Sequence + SnailST Levels + Order Flow (CVD/OI) by TheActualSnail Overview This indicator combines Snail Sequence price exhaustion logic with SnailST Levels (structure) and an order-flow proxy (CVD) plus optional Open Interest confirmation to build a multi-layer confluence model. Instead of relying on a single signal, it looks for agreement between: • Exhaustion signals (Setup 8/9 and the 13-step Exhaustion Run) • Structural context (SnailST Support/Resistance levels) • Order-flow behavior (CVD divergences and CVD slope) • Participation context (optional Open Interest filters/confirmation) The goal is not to call exact tops or bottoms. It’s to highlight zones where probability shifts because multiple independent factors align. ________________________________________ How This Can Be Used As Confluence This tool is built to answer one question: “Is there enough evidence that momentum or positioning is changing?” Typical high-quality situations Bullish Confluence • Buy Setup 8/9 or Exhaustion Run 13 appears • Price holds/reclaims SnailST Support (or breaks above resistance depending on regime) • CVD prints bullish divergence or CVD slope turns positive • (Optional) Open Interest conditions align (trend/squeeze logic) Bearish Confluence • Sell Setup 8/9 or Exhaustion Run 13 appears • Price rejects SnailST Resistance (or loses support depending on regime) • CVD prints bearish divergence or CVD slope turns negative • (Optional) Open Interest conditions align (trend/squeeze logic) The Bias Engine aggregates these conditions and prints a BULLISH or BEARISH label only when enough factors agree. This helps reduce random signals and focuses attention on high-participation turning points or continuation zones. ________________________________________ Settings Explained 1) UI & Label Controls Label Size Adjusts visibility of numbers and signal labels. Limit labels to last N bars Prevents clutter by showing only recent labels. N bars How far back labels are allowed to appear. ________________________________________ UI — Number Offsets (Visual only: moves the printed numbers away from candles.) Number offset mode (Ticks / ATR) • Ticks: fixed spacing • ATR: spacing scales with volatility ATR length ATR period used when ATR mode is selected. Numbers offset (ticks / ATR mult) Moves number labels up/down to avoid overlapping price candles. ________________________________________ UI — Big Label Placement (Controls where divergence and bias labels print.) Bias/Div placement (Auto / Force Above / Force Below) • Auto = bearish above / bullish below • Force options override direction Bias/Div offset (price) Pushes big labels away from price (useful on BTC/high-vol pairs). ________________________________________ 2) Snail Sequence Settings Setup (1–9) Require Price Flip to start Setup Stricter logic. Helps avoid counting setups in messy transitions. Buy Setup / Sell Setup Enable or disable either side. Show only 8/9 (hide 1–7) Cleaner chart for traders who only care about exhaustion zones. ________________________________________ Exhaustion Run (1–13) Show Exhaustion Run Displays the 13-step exhaustion sequence. Cancel Run on Opposite Setup 9 Stops the run if a full opposite setup completes. Require 13 qualified vs 8 Stricter “completion” requirement. Helps filter weaker 13s. Show Run 8 Marker Highlights the key intermediate step. ________________________________________ Perfected Signals Mark Perfected 8/9 Highlights higher-quality 8/9 conditions. Show Perfected Later Arrows Marks when a setup becomes “perfected” later after completion. ________________________________________ SnailST Levels (Structure) SnailST Downside (Support) / SnailST Upside (Resistance) Plots structure levels derived from completed setups. These often act as: • Reaction / liquidity zones • Breakout confirmation levels • Invalidation context ________________________________________ Recycling (Advanced) Enable Recycling Markers (R/S) Optional markers for extended cycles/reset logic. Recycle Marker (R) Triggers when the run drags on too long without resolving. Recycle Marker (S) + multiplier Compares setup range expansion vs prior setup to flag “size-based recycling.” ________________________________________ Sequence Colors Visual customization for setups, perfected marks, run values, SnailST levels, and recycling markers. ________________________________________ 3) Order Flow (CVD) Settings This module estimates buy vs sell pressure using intrabar volume and builds a running CVD. Calculation Lower TF for intrabars Smaller timeframe = more precise, but heavier to compute. Delta mode (Delta / Delta %) • Delta: raw difference • Delta %: normalized by total volume CVD reset Controls when CVD restarts (daily/weekly/monthly/session/fixed time). ________________________________________ Open Interest (Optional) Enable Open Interest / OI symbol Pulls OI from your selected symbol. Use OI MA (internal) + MA length Smoothing helper (used internally). OI trend length Defines “rising/falling” detection window. Filter divergence labels by OI Only show divergences when OI direction matches your selected rule. ________________________________________ Divergences Enable divergence labels Turns divergence labels on/off. Pivot length Sensitivity for swing detection (bigger = fewer, stronger signals). Use wicks for pivots More aggressive pivot detection. Min CVD difference (filter) Filters weak divergences. Also show hidden divergences Adds continuation-style divergences. ________________________________________ 4) Bias Engine (Signal Aggregator) This is the confluence core. It scores: • Sequence events (Setup 8/9 or Run 13) • CVD divergences (regular/hidden) • CVD slope filter • SnailST context (buffered) • Optional OI confirmation (if enabled) Then prints BULLISH or BEARISH only if enough factors align. Mode (Aggressive / Balanced / Conservative) Controls how many confirmations are required. SnailST buffer (ticks) Adds a buffer around SnailST levels to reduce “noise touches.” Bias label cooldown (bars) Prevents repeated labels in tight ranges. ________________________________________ 5) Alerts Alerts are available for: • Buy Setup 9 / Sell Setup 9 • Buy Run 13 / Sell Run 13 • Bullish/Bearish CVD Divergence • Bullish/Bearish Bias (Sequence + OF) ________________________________________ Best Practices • Use it as confirmation, not a standalone entry button. • Higher timeframes usually produce cleaner signals. • Combine with structure tools (key levels, VWAP, liquidity zones). • Treat Setup 8/9 and Run 13 as potential exhaustion, not guaranteed reversals. ________________________________________ Disclaimer This script is provided for educational and informational purposes only. It does not constitute financial, investment, or trading advice. Markets are probabilistic and no indicator can guarantee outcomes. Always manage risk and make independent decisions based on your own analysis and circumstances.
Indicateur Pine Script®
par TAS1TheActualSnail
DSMS - DeltaSurge Matrix Station - 1M Scalping [SurgeGuru]DSMS - DeltaSurge Matrix Station HOW TO READ THE CHART ===================================== This guide explains every visual element you see on the chart. DSMS is a volume profile + order flow indicator built for 1-minute Bitcoin scalping. It shows WHERE institutional money is sitting and WHERE price is likely to react next. ===================================== 1. THE VOLUME PROFILE (left side of chart) ===================================== The colored horizontal bars extending left from the candles are the volume profile. Each bar represents a price level (called a "bin") and shows how much volume traded there. LONGER BAR = more volume at that price. BAR COLOR tells you who is in control: - Green/teal bar = buyers dominated that level (bullish delta) - Red/orange bar = sellers dominated that level (bearish delta) - The more intense the color, the stronger the imbalance SPLIT BARS (bull/bear breakdown): If enabled, each bar splits into two halves showing exact buy vs sell volume. Top half = sell volume, bottom half = buy volume. HEATMAP (wide faded bars behind the profile): The large transparent boxes behind the profile bars are the heatmap. They show the same delta information but stretched wider for quick visual scanning. Bright = high conviction. Faded = low conviction. ===================================== 2. KEY PRICE LEVELS ON THE PROFILE ===================================== POC (Point of Control): The bin outlined with a bright border is the POC -- the single price level with the MOST volume. Price tends to gravitate back to the POC. A small label shows the POC price and context like "EQUILIBRIUM" or "BULL ATK". POC FLASH LINE: A short dashed cyan line appears at the POC when a bounce is detected. Trigger conditions: price is at the POC, the current candle is bullish after a bearish candle, and volume is at least 1.2x average. This signals that the POC is acting as active support and price is reacting to it in real time. VA HIGH / VA LOW (Value Area lines): Two horizontal lines mark the top and bottom of the Value Area -- the price range where approximately 70% of volume traded. These act as support and resistance. - VA High = resistance when price is below, breakout level when price pushes above - VA Low = support when price is above, breakdown level when price drops below When a breakout happens, the line turns green (up) or red (down) and gets thicker. ===================================== 3. LABELS ON PROFILE BINS ===================================== Each profile bin can show a small text label. These describe what is happening at that specific price level. Here is what each label means: ABS (with up/down arrow): "ABS▼ 7b" = Absorption detected. Institutional players are absorbing selling pressure at this level (likely accumulating). The "7b" means it held for 7 bars. ABS▼ = absorbing sells (bullish). ABS▲ = absorbing buys (bearish). FLOW (with arrow): "FLOW↑" or "FLOW↓" = A flow shift happened here. The delta direction reversed, meaning buyers took over from sellers or vice versa. This is a momentum change signal. FAIL (with arrow): "FAIL↑" or "FAIL↓" = A flow shift was detected but FAILED to confirm. The reversal started but price did not follow through. Shown in orange. Often means the opposing side absorbed the move. INVAL / INVALID: "INVAL" or "INVALID" = A previously confirmed flow shift was invalidated. Price reversed back through the shift level, canceling the signal. Shown in orange. Treat the original shift direction as no longer valid. BULL EXH / BEAR EXH: "BULL EXH" or "BEAR EXH" = Exhaustion zone. Extreme delta (above 65%) combined with FADING volume. The dominant side pushed too hard and is running out of fuel. Shown in gold. Often precedes a reversal. Higher delta + lower volume = more exhausted. IMBALANCE RATIO (number:1): "4:1" = The ratio of buy volume to sell volume (or vice versa) at this bin. A 4:1 ratio means one side has 4x the volume of the other. Only shown when the imbalance exceeds the configured threshold. ICE: "ICE" = Iceberg order detected in this bin. High volume traded but price barely moved, suggesting a large hidden order was absorbing all the activity. CONFL / CONF+ / CONF-: Confluence detected. Multiple signals (structure + order flow) agree on direction. CONF+ = bullish confluence. CONF- = bearish confluence. CONFLICT: Structure says one thing, order flow says another. Be cautious. STK (with multiplier): "STK x3" = Imbalance stack. Three or more consecutive bins all lean the same direction. Shows institutional pressure building across multiple price levels. OB (with arrow): "OB↑" or "OB↓" = This bin overlaps with an active Order Block (see section 6). FVG (with arrow): "FVG↑" or "FVG↓" = This bin overlaps with an active Fair Value Gap (see section 7). "uFVG↑" or "uFVG↓" = Same but for a micro-level FVG (smaller gap detected within the profile structure rather than on-chart candle gaps). uSR: Micro structure level. A price level that has been tested multiple times with high volume -- acts as local support or resistance. EQUILIBRIUM / BULL ATK / BEAR DEF / etc: Context labels that describe the state of the bin: - EQUILIBRIUM = balanced buyers and sellers - BULL ATK = buyers attacking with increasing volume - BULL DEF = buyers holding but volume fading - BEAR ATK = sellers attacking with increasing volume - BEAR DEF = sellers holding but volume fading CONFIDENCE SCORE (number at end of label): Example: "ABS▼ CONFL " The number in brackets is a confidence score from 0-100. Higher = more signals agreeing. Above 70 is strong. DWELL TIME: "8d" at the end means price spent 8 bars dwelling at this level. More time at a level = stronger support/resistance. ===================================== 4. ARROWS ON PROFILE BINS ===================================== Small arrows may appear to the right of profile bars: DELTA ARROWS (^^): Show if buying/selling pressure is accelerating or decelerating. pointing up = bullish momentum gaining speed pointing down = bearish momentum gaining speed VOLUME ARROWS: Show if volume is increasing or decreasing at each level. Up arrow = volume building. Down arrow = volume fading. VELOCITY BANDS: Small colored boxes to the right of the profile. Green = volume accelerating. Red = volume decelerating. Only appears on high-volume bins. ===================================== 5. CVD LINE (curved line inside the profile) ===================================== The colored line running through the profile area is the CVD (Cumulative Volume Delta) line. It tracks the running total of buy volume minus sell volume across the session. - Line going UP = buyers accumulating over time - Line going DOWN = sellers accumulating over time HOW THE LINE COLOR WORKS: The line color is NOT random. It checks the CVD value against 5 moving averages (EMA 8, 13, 21, 34, and 55). Each EMA that CVD is ABOVE scores +1. Each EMA that CVD is BELOW scores -1. The total score (-5 to +5) sets the color: +5 (above ALL 5 EMAs) = deep forest green -- strong bullish momentum +3 to +4 = bright green -- solid bullish +1 to +2 = light green -- lean bullish 0 = gray -- neutral, no clear direction -1 to -2 = light red -- lean bearish -3 to -4 = bright red -- solid bearish -5 (below ALL 5 EMAs) = deep dark red -- strong bearish momentum In practice: when the line shifts from red to green, it means CVD has crossed above its moving averages -- buying pressure is accelerating. When green turns red, selling pressure is taking over. A gray section means CVD is choppy and sitting between its averages with no conviction. CVD LABEL (at the right end of the line): "CVD +1.2K +5" First number = raw CVD value (+1,200 net buy volume) Second number = confirmation count (+5 means 5 consecutive bars where the adaptive reset system confirmed the bullish direction) The label color uses a separate gradient based on the confirmation count: Deep green = many consecutive bullish confirmations Deep red = many consecutive bearish confirmations Yellow/gray = few or mixed confirmations ===================================== 6. ORDER BLOCKS (OBs) - colored boxes on candles ===================================== Order Blocks are zones where institutions placed large orders. They appear as colored boxes around groups of candles. ACTIVE OBs (not yet tested): - Green/teal box = bullish OB (expect support when price returns) - Red box = bearish OB (expect resistance when price returns) - Solid fill, extends rightward from the origin candles BROKEN OBs (breakers): - Same colors but with a transparent fill and border outline only - A bullish OB becomes a breaker when price closes below its bottom - A bearish OB becomes a breaker when price closes above its top - Once broken, the OB flips role: old support becomes resistance and vice versa - A dotted midline shows the 50% level of the broken OB - If price then closes through the breaker in the new direction, it is removed entirely Two detection methods run simultaneously: - Fast: simple 3-bar pivot swings for reactive OBs near current price - Deep: ICS-style fractal depth swings for structural OBs from further back The "Detection Depth" setting controls the fractal depth (Short/Intermediate/Long Term). ===================================== 7. FAIR VALUE GAPS (FVGs) - striped zones on candles ===================================== FVGs are gaps in the price action where one side (buyers or sellers) was so dominant that price skipped over a range. Price tends to come back and fill these gaps. They appear as small striped/hatched boxes at the gap location. - Purple-ish stripes = the gap zone - Each individual stripe is deleted when price crosses through its midpoint, so the gap visually erodes from the inside out as price fills it - After 21 bars, remaining unfilled stripes fade to show the gap is aging - Once every stripe is filled, the FVG is fully removed from the chart - Maximum 30 FVGs tracked at once (oldest removed first if exceeded) ===================================== 8. MULTI-TIMEFRAME BOXES (2m / 5m / 15m) ===================================== Colored boxes extending behind and slightly ahead of the current candles. These show FVGs and Order Blocks detected on HIGHER timeframes (2-minute, 5-minute, 15-minute charts) projected onto your 1-minute chart. HOW TO TELL THEM APART: Border style: - Dashed border = FVG (Fair Value Gap) - Solid border = OB (Order Block) Thickness and length: - Thin border, extends 20 bars back = 2-minute timeframe - Thin border, extends 30 bars back = 5-minute timeframe - Thick border, extends 50 bars back = 15-minute timeframe Color: - Cyan/teal = bullish (expect support) - Orange = bearish (expect resistance) When your 1-minute price touches a higher-timeframe structure, it carries more weight because institutions watch those levels. ===================================== 9. PREDICTIVE CONFLUENCE ZONES (projected boxes) ===================================== These are the "ZONE S x3" and "ZONE R x2" boxes that project AHEAD of current price (to the right of the last candle). They appear when multiple structures from different sources cluster at the same price area: - 1m Order Blocks + 1m FVGs + 2m structures + 5m structures + 15m structures The system scans all unmitigated levels, finds where they overlap, and projects a high-probability reaction zone. "ZONE S x3" = Support zone, 3 structures converge here (green box) "ZONE R x2" = Resistance zone, 2 structures converge here (red box) Higher count = stronger zone. These are the highest-conviction levels on the chart. ===================================== 10. SIGNAL LABELS ON CANDLES ===================================== These labels appear directly on or near candles when specific conditions are met: SWEEP LABELS (cyan/magenta bubbles): Example: "VA High 8" A liquidity sweep happened -- price wicked past a key level and reversed. The name shows which level was swept. The number is a quality score. Higher score = more reliable sweep. Cyan = bullish sweep. Magenta = bearish. ICE (cyan/red squares): Small squares below (bull) or above (bear) candles. "ICE 2.3x" = Iceberg order detected. Volume was 2.3x average but price barely moved. A hidden large order was absorbing all activity. COILED: "COILED " = Price has been compressing (low volatility) for 4 bars while sitting near a wall of support/resistance. Like a spring ready to release. Green = bullish coil (expect breakout up). Red = bearish coil (expect breakdown). !!SR (with arrow and count): "!!SR 5x" = A wall of 5 micro-structure levels stacked at this price. Strong support (arrow down, green) or resistance (arrow up, red). CVD DIV: "CVD DIV (up arrow)" = Bullish CVD divergence. Price is making lower lows but CVD is improving -- hidden buying. "CVD DIV (down arrow)" = Bearish CVD divergence. Price making higher highs but CVD declining -- hidden selling. VA BREAK: "VA BREAK (up arrow)" or "VA BREAK (down arrow)" = Price just broke out of the Value Area. A thick green or red line extends forward showing the breakout level. This is a high-momentum signal. VOLUME SPIKE: "x3.2" = Volume on this candle is 3.2x the average. Shows in magenta above the candle. REJECT: "REJECT (arrow)" = Price momentum is pushing into a wall of support or resistance. Warns of a potential rejection/reversal at that wall. ===================================== 11. SEQUENCE PATTERNS (triangles) ===================================== These track a full institutional flow sequence through 4 stages: 1. ABSORPTION = institution absorbs orders at a level 2. FLOW SHIFT = delta reverses confirming direction 3. SWEEP = liquidity grab confirms intent 4. BREAKOUT = Value Area breakout completes the pattern PROGRESS LABELS (small, during build-up): "SEQ:SHIFT" or "SEQ:SWEEP" = Sequence is building, currently at that stage. COMPLETED SEQUENCE (large triangle + label): Hot pink triangle (up or down) with "SEQ BULL " or "SEQ BEAR ". The number is the sequence score. This is the highest-confidence signal in DSMS. A full 4-stage institutional sequence just completed. ===================================== 12. CANDLE TECH (colored candle borders) ===================================== Certain candles get a colored border and a small label: - Green border = bullish pattern detected (hammer, bullish engulfing, etc.) - Red border = bearish pattern detected (shooting star, bearish engulfing, etc.) The label shows: "R 5" = Reversal pattern, score 5 "(up arrow) 3" = Continuation pattern, score 3 Higher score = more confirming factors (CVD alignment, volume surge, trend direction). Thicker border = stronger pattern. ===================================== 13. LIQUIDITY VOID LINES ===================================== Yellow dashed horizontal lines extending left from the profile. These mark price levels with very low volume -- gaps where price moved through quickly without much trading. When price returns to these levels, it tends to move through them fast again or react sharply. ===================================== 14. STATE OF THE ARENA TABLE (corner dashboard) ===================================== The table in the corner of the chart is the real-time scoring dashboard. It combines all signals into one weighted score from -100 (max bearish) to +100 (max bullish). HEADER ROW: Shows the overall market state and final score. States: BREAKOUT, TRENDING, COMPRESSED, CONTESTED, or NEUTRAL. COMPONENT ROWS (each scored -100 to +100, weighted into final score): Delta Flow (10%) -- raw buying vs selling pressure on current bar CVD Flow (10%) -- cumulative volume delta trend and EMA band position Flow Shift (9%) -- recent delta direction reversals Absorption (9%) -- institutional stop hunt detection Sequence (8%) -- institutional flow sequence progress Confluence (7%) -- structural + psychological signal agreement OB/FVG (7%) -- nearest order block or gap bias Sweep (7%) -- recent liquidity grab signals MTF (6%) -- multi-timeframe alignment (2m/5m/15m) Volume (6%) -- spike detection Walls (6%) -- support/resistance cluster strength Accel (5%) -- delta acceleration (2nd derivative of momentum) Iceberg (4%) -- hidden institutional order detection Candle (3%) -- pattern recognition score POC Shift (3%) -- value area migration direction The final score is the weighted sum, clamped to -100 to +100. 70+ or below -70 = STRONG conviction 40-69 = MEDIUM conviction 15-39 = WEAK conviction Below 15 = no clear direction Each row shows a text status, numeric score, and a visual bar made of blocks. Green blocks = bullish. Red blocks = bearish. More blocks = stronger signal. SIGNAL SECTION (bottom of table): Shows the single highest-priority actionable signal right now. "Key" = what the signal is based on "Action" = suggested stance (BUY / SELL / HOLD / CAUTION) "Watch" = what to watch for next ===================================== QUICK REFERENCE - COLOR GUIDE ===================================== Cyan/Teal ......... Bullish structures, support, buy signals Red/Orange ........ Bearish structures, resistance, sell signals Green ............. Bullish momentum, buyers winning Red ............... Bearish momentum, sellers winning Yellow ............ Liquidity voids, caution zones Purple ............ FVG gap zones Hot Pink .......... Completed sequence patterns Magenta ........... Volume spikes, sweep highlights Gold .............. Predictive zone projections White text ........ All on-chart signal labels ===================================== ALERTS ===================================== DSMS has 6 built-in alerts you can set from TradingView's alert menu: Flow Shift -- delta direction reversed at a price level Volume Spike -- volume exceeds threshold with bin concentration VA Breakout -- price broke out of the Value Area Strong Confluence -- multiple signals align above the confluence threshold Absorption -- institutional absorption pattern detected Sequence Complete -- full 4-stage institutional sequence finished To set an alert: click the alarm clock icon in TradingView, select DSMS as the condition source, pick the alert type, and choose your notification method. Each alert can be toggled on/off in the settings panel. ===================================== SETTINGS OVERVIEW ===================================== Everything is toggleable. The main groups in settings are: Core Settings -- lookback period, number of bins, profile width Display Options -- toggle heatmap, delta flow, volume breakdown, POC 1M Scalping -- CVD line, zoomed-out mode, volume trend arrows Signal Settings -- enable/disable each signal type Advanced Tuning -- compression bars, confidence thresholds OB/FVG Settings -- order block depth, FVG stripe count, max blocks Candle Tech -- pattern detection and scoring Liquidity Sweeps -- wick ratio, volume requirement, score display Tier 3: Flow Intel -- sequence patterns, multi-timeframe (2m/5m/15m), predictive zones Colors -- customize every major visual element State of the Arena -- table position, size, and which components to show
Indicateur Pine Script®
par SurgeGuru
EMA HH/LL Levels v6This indicator builds dynamic horizontal levels based on Higher Highs (HH) and Lower Lows (LL) of an Exponential Moving Average (EMA) rather than raw price. It is designed to highlight structural EMA-based resistance and support levels and automatically manage their lifecycle. 🔹 Core Logic The script calculates an EMA (default length: 26). Pivot Highs and Pivot Lows are detected directly on the EMA line, not on price. Each confirmed: EMA Higher High (HH) → creates a solid blue horizontal level EMA Lower Low (LL) → creates a solid red horizontal level Levels extend to the right and remain active until specific conditions are met. 🔹 Level State Management Each level can be in one of three states: Active (Solid line) The level has been created but not interacted with yet. Touched by Price (Dotted line) When a price bar touches the level (High ≥ level AND Low ≤ level), the level changes its style from solid to dotted, but remains on the chart. Broken by EMA (Removed) When the EMA itself crosses the level: HH level → removed when EMA crosses above it LL level → removed when EMA crosses below it The level is then deleted from the chart. ⚠️ Important: Levels are never removed by price action alone — only by an EMA break. 🔹 EMA Visualization The EMA line is color-coded by direction: Upward slope → user-defined “up” color Downward slope → user-defined “down” color EMA length, colors, and line width are fully configurable. 🔹 Customization Options EMA length EMA up/down colors and thickness Pivot sensitivity (left/right bars) HH / LL level colors and thickness Maximum number of stored levels (to control memory and chart clutter) 🔹 Use Cases Identifying EMA-based dynamic support and resistance Tracking trend structure via EMA swings Confluence with price action, pullbacks, and breakouts Trend-following and mean-reversion strategies 🔹 Notes This indicator works on all markets and timeframes. No repainting after pivot confirmation. No ta.crossover() / ta.crossunder() is used — all logic is calculated manually for maximum stability in Pine Script v6.
Indicateur Pine Script®
par Benepamm
Trend Candles - [EntryLab] Trend Candles: This indicator overrides or overlays standard chart candles with a color gradient that reflects a calculated trend bias (uptrend or downtrend), helping traders quickly assess the overall market direction.Features:Candles are colored using a gradient scale: stronger shades indicate higher-confidence trend direction based on the algorithm. Two usage modes: Full override: Disable and hide the chart's native ticker/symbol candles (via chart settings) so the indicator's colored candles take over completely. Hover preview: Keep your preferred candle setup/colors intact; simply hover the mouse over the indicator name in the chart legend to temporarily display the trend-colored gradient candles for quick reference without altering your main view. Customizable inputs (adjust in settings): gradient colors for up/down trends, intensity thresholds, etc. How it works (high-level): The trend bias is determined using a combination of multiple VWAP calculations, trend-following data, and momentum-based indicators. This multi-factor approach aims to provide a smoother, more reliable signal of whether the market is in an uptrend (bullish bias) or downtrend (bearish bias) compared to single-indicator methods. How to use: Apply the indicator to your chart and use the colored candles as a visual aid for trend bias decision-making. For example:In a strong uptrend (deeper bullish gradient), consider favoring long setups or avoiding shorts. In a downtrend (deeper bearish gradient), consider short opportunities or caution on longs. Combine with other tools (support/resistance, volume, etc.) for confluence rather than relying solely on candle color. This script offers a unique way to visualize trend strength via candle recoloring with gradient feedback, which can provide a broader overview of directional bias without cluttering the chart with additional plots/lines.Best suited for any timeframe, especially higher ones for swing/position trading or lower ones for intraday confirmation. No repainting occurs once a bar closes. Not financial advice. Trading carries significant risk of loss of capital. Always backtest and use discretion; results are not guaranteed.
Indicateur Pine Script®
par ENTRYLAB
Trend Signal GridTrend Signal Grid Based on Trend Direction & Force Index - TDFI by Causecelebre, the TDFI Grid is a multi-timeframe momentum indicator that builds on the original TDFI concept. It calculates TDFI across three user-selectable timeframes using three different lookback periods, creating a 3×3 consensus grid (9 readings total). Each cell is classified as bullish, bearish, or neutral based on configurable upper and lower thresholds. When a majority of the 9 readings align in the same direction (default 65%), the indicator triggers a directional signal — either GRID UP or GRID DOWN. Alerts fire automatically on new signals so you never miss a shift. How it works The indicator uses a smoothed EMA-based momentum calculation, normalises the output against its recent highest absolute value, and then maps it across your chosen timeframes and lookback lengths. The results are displayed in a clean on-chart table showing the state of each timeframe/lookback combination at a glance. Settings: Timeframe 1, 2, 3 — Choose any three timeframes (defaults to 1m, 5m, 15m). LB1, LB2, LB3 — Lookback periods for each TDFI calculation. UP / DOWN thresholds — Controls how far the TDFI must move before a cell registers as bullish or bearish. Majority — The percentage of the 9 cells that must agree to trigger a signal. Table position — Place the grid anywhere on your chart. Best used for Trading setups where you need to confirm momentum alignment across multiple timeframes before entering or scaling a position. Works well on forex and metals.
Indicateur Pine Script®
par JaneAutomates
PineStats█ OVERVIEW PineStats is a comprehensive statistical analysis library for Pine Script v6, providing 104 functions across 6 modules. Built for quantitative traders, researchers, and indicator developers who need professional-grade statistics without reinventing the wheel. For building mean-reversion strategies, analyzing return distributions, measuring correlations, or testing for market regimes. █ MODULES CORE STATISTICS (20 functions) • Central tendency: mean, median, WMA, EMA • Dispersion: variance, stdev, MAD, range • Standardization: z-score, robust z-score, normalize, percentile • Distribution shape: skewness, kurtosis PROBABILITY DISTRIBUTIONS (17 functions) • Normal: PDF, CDF, inverse CDF (quantile function) • Power-law: Hill estimator, MLE alpha, survival function • Exponential: PDF, CDF, rate estimation • Normality testing: Jarque-Bera test ENTROPY (9 functions) • Shannon entropy (information theory) • Tsallis entropy (non-extensive, fat-tail sensitive) • Permutation entropy (ordinal patterns) • Approximate entropy (regularity measure) • Entropy-based regime detection PROBABILITY (21 functions) • Win rates and expected value • First passage time estimation • TP/SL probability analysis • Conditional probability and Bayes updates • Streak and drawdown probabilities REGRESSION (19 functions) • Linear regression: slope, intercept, forecast • Goodness of fit: R², adjusted R², standard error • Statistical tests: t-statistic, p-value, significance • Trend analysis: strength, angle, acceleration • Quadratic regression CORRELATION (18 functions) • Pearson, Spearman, Kendall correlation • Covariance, beta, alpha (Jensen's) • Rolling correlation analysis • Autocorrelation and cross-correlation • Information ratio, tracking error █ QUICK START import HenriqueCentieiro/PineStats/1 as stats // Z-score for mean reversion z = stats.zscore(close, 20) // Test if returns are normally distributed returns = (close - close ) / close isGaussian = stats.is_normal(returns, 100, 0.05) // Regression channel = stats.linreg_channel(close, 50, 2.0) // Correlation with benchmark spyReturns = request.security("SPY", timeframe.period, close/close - 1) beta = stats.beta(returns, spyReturns, 60) █ USE CASES ✓ Mean Reversion — z-scores, percentiles, Bollinger-style analysis ✓ Regime Detection — entropy measures, correlation regimes ✓ Risk Analysis — drawdown probability, VaR via quantiles ✓ Strategy Evaluation — expected value, win rates, R:R analysis ✓ Distribution Analysis — normality tests, fat-tail detection ✓ Multi-Asset — beta, alpha, correlation, relative strength █ NOTES • All functions return `na` on invalid inputs • Designed for Pine Script v6 • Fully documented in the library header • Part of the Pine ecosystem: PineStats, PineQuant, PineCriticality, PineWavelet █ REFERENCES • Abramowitz & Stegun — Normal CDF approximation • Acklam's algorithm — Inverse normal CDF • Hill estimator — Power-law tail estimation • Tsallis statistics — Non-extensive entropy Full documentation in the library header. mean(src, length)   Calculates the arithmetic mean (simple moving average) over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Arithmetic mean of the last `length` values, or `na` if inputs invalid wma_custom(src, length)   Calculates weighted moving average with linearly decreasing weights   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Weighted moving average, or `na` if inputs invalid ema_custom(src, length)   Calculates exponential moving average   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Exponential moving average, or `na` if inputs invalid median(src, length)   Calculates the median value over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Median value, or `na` if inputs invalid variance(src, length)   Calculates population variance over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Population variance, or `na` if inputs invalid stdev(src, length)   Calculates population standard deviation over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Population standard deviation, or `na` if inputs invalid mad(src, length)   Calculates Median Absolute Deviation (MAD) - robust dispersion measure   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: MAD value, or `na` if inputs invalid data_range(src, length)   Calculates the range (highest - lowest) over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Range value, or `na` if inputs invalid zscore(src, length)   Calculates z-score (number of standard deviations from mean)   Parameters:      src (float) : Source series      length (simple int) : Lookback period for mean and stdev calculation (must be >= 2)   Returns: Z-score, or `na` if inputs invalid or stdev is zero zscore_robust(src, length)   Calculates robust z-score using median and MAD (resistant to outliers)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 2)   Returns: Robust z-score, or `na` if inputs invalid or MAD is zero normalize(src, length)   Normalizes value to range using min-max scaling   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Normalized value in , or `na` if inputs invalid or range is zero percentile(src, length)   Calculates percentile rank of current value within lookback window   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Percentile rank (0 to 100), or `na` if inputs invalid winsorize(src, length, lower_pct, upper_pct)   Winsorizes values by clamping to percentile bounds (reduces outlier impact)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)      lower_pct (simple float) : Lower percentile bound (0-100, e.g., 5 for 5th percentile)      upper_pct (simple float) : Upper percentile bound (0-100, e.g., 95 for 95th percentile)   Returns: Winsorized value clamped to bounds skewness(src, length)   Calculates sample skewness (measure of distribution asymmetry)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 3)   Returns: Skewness value (negative = left tail, positive = right tail), or `na` if invalid kurtosis(src, length)   Calculates excess kurtosis (measure of distribution tail heaviness)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 4)   Returns: Excess kurtosis (>0 = heavy tails, <0 = light tails), or `na` if invalid count_valid(src, length)   Counts non-na values in lookback window (useful for data quality checks)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Count of valid (non-na) values sum(src, length)   Calculates sum over lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Sum of values, or `na` if inputs invalid cumsum(src)   Calculates cumulative sum (running total from first bar)   Parameters:      src (float) : Source series   Returns: Cumulative sum change(src, length)   Returns the change (difference) from n bars ago   Parameters:      src (float) : Source series      length (simple int) : Number of bars to look back (must be >= 1)   Returns: Current value minus value from `length` bars ago roc(src, length)   Calculates Rate of Change (percentage change from n bars ago)   Parameters:      src (float) : Source series      length (simple int) : Number of bars to look back (must be >= 1)   Returns: Percentage change as decimal (0.05 = 5%), or `na` if invalid normal_pdf_standard(x)   Calculates the standard normal probability density function (PDF)   Parameters:      x (float) : The value to evaluate   Returns: PDF value at x for standard normal N(0,1) normal_pdf(x, mu, sigma)   Calculates the normal probability density function (PDF)   Parameters:      x (float) : The value to evaluate      mu (float) : Mean of the distribution (default: 0)      sigma (float) : Standard deviation (default: 1, must be > 0)   Returns: PDF value at x for normal N(mu, sigma²) normal_cdf_standard(x)   Calculates the standard normal cumulative distribution function (CDF)   Parameters:      x (float) : The value to evaluate   Returns: Probability P(X <= x) for standard normal N(0,1) @description Uses Abramowitz & Stegun approximation (formula 7.1.26), accurate to ~1.5e-7 normal_cdf(x, mu, sigma)   Calculates the normal cumulative distribution function (CDF)   Parameters:      x (float) : The value to evaluate      mu (float) : Mean of the distribution (default: 0)      sigma (float) : Standard deviation (default: 1, must be > 0)   Returns: Probability P(X <= x) for normal N(mu, sigma²) normal_inv_standard(p)   Calculates the inverse standard normal CDF (quantile function)   Parameters:      p (float) : Probability value (must be in (0, 1))   Returns: x such that P(X <= x) = p for standard normal N(0,1) @description Uses Acklam's algorithm, accurate to ~1.15e-9 normal_inv(p, mu, sigma)   Calculates the inverse normal CDF (quantile function)   Parameters:      p (float) : Probability value (must be in (0, 1))      mu (float) : Mean of the distribution      sigma (float) : Standard deviation (must be > 0)   Returns: x such that P(X <= x) = p for normal N(mu, sigma²) power_law_alpha(src, length, tail_pct)   Estimates power-law exponent (alpha) using Hill estimator   Parameters:      src (float) : Source series (typically absolute returns or drawdowns)      length (simple int) : Lookback period (must be >= 10 for reliable estimates)      tail_pct (simple float) : Percentage of data to use for tail estimation (default: 0.1 = top 10%)   Returns: Estimated alpha (tail index), typically 2-4 for financial data @description Alpha < 2 indicates infinite variance (very heavy tails) @description Alpha < 3 indicates infinite kurtosis @description Alpha > 4 suggests near-Gaussian behavior power_law_alpha_mle(src, length, x_min)   Estimates power-law alpha using maximum likelihood (Clauset method)   Parameters:      src (float) : Source series (positive values expected)      length (simple int) : Lookback period (must be >= 20)      x_min (float) : Minimum threshold for power-law behavior   Returns: Estimated alpha using MLE power_law_pdf(x, alpha, x_min)   Calculates power-law probability density (Pareto Type I)   Parameters:      x (float) : Value to evaluate (must be >= x_min)      alpha (float) : Power-law exponent (must be > 1)      x_min (float) : Minimum value / scale parameter (must be > 0)   Returns: PDF value power_law_survival(x, alpha, x_min)   Calculates power-law survival function P(X > x)   Parameters:      x (float) : Value to evaluate (must be >= x_min)      alpha (float) : Power-law exponent (must be > 1)      x_min (float) : Minimum value / scale parameter (must be > 0)   Returns: Probability of exceeding x power_law_ks(src, length, alpha, x_min)   Tests if data follows power-law using simplified Kolmogorov-Smirnov   Parameters:      src (float) : Source series      length (simple int) : Lookback period      alpha (float) : Estimated alpha from power_law_alpha()      x_min (float) : Threshold value   Returns: KS statistic (lower = better fit, typically < 0.1 for good fit) is_power_law(src, length, tail_pct, ks_threshold)   Simple test if distribution appears to follow power-law   Parameters:      src (float) : Source series      length (simple int) : Lookback period      tail_pct (simple float) : Tail percentage for alpha estimation      ks_threshold (simple float) : Maximum KS statistic for acceptance (default: 0.1)   Returns: true if KS test suggests power-law fit exp_pdf(x, lambda)   Calculates exponential probability density function   Parameters:      x (float) : Value to evaluate (must be >= 0)      lambda (float) : Rate parameter (must be > 0)   Returns: PDF value exp_cdf(x, lambda)   Calculates exponential cumulative distribution function   Parameters:      x (float) : Value to evaluate (must be >= 0)      lambda (float) : Rate parameter (must be > 0)   Returns: Probability P(X <= x) exp_lambda(src, length)   Estimates exponential rate parameter (lambda) using MLE   Parameters:      src (float) : Source series (positive values)      length (simple int) : Lookback period   Returns: Estimated lambda (1/mean) jarque_bera(src, length)   Calculates Jarque-Bera test statistic for normality   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 10)   Returns: JB statistic (higher = more deviation from normality) @description Under normality, JB ~ chi-squared(2). JB > 6 suggests non-normality at 5% level is_normal(src, length, significance)   Tests if distribution is approximately normal   Parameters:      src (float) : Source series      length (simple int) : Lookback period      significance (simple float) : Significance level (default: 0.05)   Returns: true if Jarque-Bera test does not reject normality shannon_entropy(src, length, n_bins)   Calculates Shannon entropy from a probability distribution   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 10)      n_bins (simple int) : Number of histogram bins for discretization (default: 10)   Returns: Shannon entropy in bits (log base 2) @description Higher entropy = more randomness/uncertainty, lower = more predictability shannon_entropy_norm(src, length, n_bins)   Calculates normalized Shannon entropy   Parameters:      src (float) : Source series      length (simple int) : Lookback period      n_bins (simple int) : Number of histogram bins   Returns: Normalized entropy where 0 = perfectly predictable, 1 = maximum randomness tsallis_entropy(src, length, q, n_bins)   Calculates Tsallis entropy with q-parameter   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 10)      q (float) : Entropic index (q=1 recovers Shannon entropy)      n_bins (simple int) : Number of histogram bins   Returns: Tsallis entropy value @description q < 1: emphasizes rare events (fat tails) @description q = 1: equivalent to Shannon entropy @description q > 1: emphasizes common events optimal_q(src, length)   Estimates optimal q parameter from kurtosis   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Estimated q value that best captures the distribution's tail behavior @description Uses relationship: q ≈ (5 + kurtosis) / (3 + kurtosis) for kurtosis > 0 tsallis_q_gaussian(x, q, beta)   Calculates Tsallis q-Gaussian probability density   Parameters:      x (float) : Value to evaluate      q (float) : Tsallis q parameter (must be < 3)      beta (float) : Width parameter (inverse temperature, must be > 0)   Returns: q-Gaussian PDF value @description q=1 recovers standard Gaussian permutation_entropy(src, length, order)   Calculates permutation entropy (ordinal pattern complexity)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 20)      order (simple int) : Embedding dimension / pattern length (2-5, default: 3)   Returns: Normalized permutation entropy @description Measures complexity of temporal ordering patterns @description 0 = perfectly predictable sequence, 1 = random approx_entropy(src, length, m, r)   Calculates Approximate Entropy (ApEn) - regularity measure   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 50)      m (simple int) : Embedding dimension (default: 2)      r (simple float) : Tolerance as fraction of stdev (default: 0.2)   Returns: Approximate entropy value (higher = more irregular/complex) @description Lower ApEn indicates more self-similarity and predictability entropy_regime(src, length, q, n_bins)   Detects market regime based on entropy level   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback period      q (float) : Tsallis q parameter (use optimal_q() or default 1.5)      n_bins (simple int) : Number of histogram bins   Returns: Regime indicator: -1 = trending (low entropy), 0 = transition, 1 = ranging (high entropy) entropy_risk(src, length)   Calculates entropy-based risk indicator   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback period   Returns: Risk score where 1 = maximum divergence from Gaussian 1 hit_rate(src, length)   Calculates hit rate (probability of positive outcome) over lookback   Parameters:      src (float) : Source series (positive values count as hits)      length (simple int) : Lookback period   Returns: Hit rate as decimal hit_rate_cond(condition, length)   Calculates hit rate for custom condition over lookback   Parameters:      condition (bool) : Boolean series (true = hit)      length (simple int) : Lookback period   Returns: Hit rate as decimal expected_value(src, length)   Calculates expected value of a series   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Expected value (mean) expected_value_trade(win_prob, take_profit, stop_loss)   Calculates expected value for a trade with TP and SL levels   Parameters:      win_prob (float) : Probability of hitting TP (0-1)      take_profit (float) : Take profit in price units or %      stop_loss (float) : Stop loss in price units or % (positive value)   Returns: Expected value per trade @description EV = (win_prob * TP) - ((1 - win_prob) * SL) breakeven_winrate(take_profit, stop_loss)   Calculates breakeven win rate for given TP/SL ratio   Parameters:      take_profit (float) : Take profit distance      stop_loss (float) : Stop loss distance   Returns: Required win rate for breakeven (EV = 0) reward_risk_ratio(take_profit, stop_loss)   Calculates the reward-to-risk ratio   Parameters:      take_profit (float) : Take profit distance      stop_loss (float) : Stop loss distance   Returns: R:R ratio fpt_probability(src, length, target, max_bars)   Estimates probability of price reaching target within N bars   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback for volatility estimation      target (float) : Target move (in same units as src, e.g., % return)      max_bars (simple int) : Maximum bars to consider   Returns: Probability of reaching target within max_bars @description Based on random walk with drift approximation fpt_mean(src, length, target)   Estimates mean first passage time to target level   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback for volatility estimation      target (float) : Target move   Returns: Expected number of bars to reach target (can be infinite) fpt_historical(src, length, target)   Counts historical bars to reach target from each point   Parameters:      src (float) : Source series (typically price or returns)      length (simple int) : Lookback period      target (float) : Target move from each starting point   Returns: Array of first passage times (na if target not reached within lookback) tp_probability(src, length, tp_distance, sl_distance)   Estimates probability of hitting TP before SL   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback for estimation      tp_distance (float) : Take profit distance (positive)      sl_distance (float) : Stop loss distance (positive)   Returns: Probability of TP being hit first trade_probability(src, length, tp_pct, sl_pct)   Calculates complete trade probability and EV analysis   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback period      tp_pct (float) : Take profit percentage      sl_pct (float) : Stop loss percentage   Returns: Tuple: cond_prob(condition_a, condition_b, length)   Calculates conditional probability P(B|A) from historical data   Parameters:      condition_a (bool) : Condition A (the given condition)      condition_b (bool) : Condition B (the outcome)      length (simple int) : Lookback period   Returns: P(B|A) = P(A and B) / P(A) bayes_update(prior, likelihood, false_positive)   Updates probability using Bayes' theorem   Parameters:      prior (float) : Prior probability P(H)      likelihood (float) : P(E|H) - probability of evidence given hypothesis      false_positive (float) : P(E|~H) - probability of evidence given hypothesis is false   Returns: Posterior probability P(H|E) streak_prob(win_rate, streak_length)   Calculates probability of N consecutive wins given win rate   Parameters:      win_rate (float) : Single-trade win probability      streak_length (simple int) : Number of consecutive wins   Returns: Probability of streak losing_streak_prob(win_rate, streak_length)   Calculates probability of experiencing N consecutive losses   Parameters:      win_rate (float) : Single-trade win probability      streak_length (simple int) : Number of consecutive losses   Returns: Probability of losing streak drawdown_prob(src, length, dd_threshold)   Estimates probability of drawdown exceeding threshold   Parameters:      src (float) : Source series (returns)      length (simple int) : Lookback period      dd_threshold (float) : Drawdown threshold (as positive decimal, e.g., 0.10 = 10%)   Returns: Historical probability of exceeding drawdown threshold prob_to_odds(prob)   Calculates odds from probability   Parameters:      prob (float) : Probability (0-1)   Returns: Odds (prob / (1 - prob)) odds_to_prob(odds)   Calculates probability from odds   Parameters:      odds (float) : Odds ratio   Returns: Probability (0-1) implied_prob(decimal_odds)   Calculates implied probability from decimal odds (betting)   Parameters:      decimal_odds (float) : Decimal odds (e.g., 2.5 means $2.50 return per $1 bet)   Returns: Implied probability logit(prob)   Calculates log-odds (logit) from probability   Parameters:      prob (float) : Probability (must be in (0, 1))   Returns: Log-odds inv_logit(log_odds)   Calculates probability from log-odds (inverse logit / sigmoid)   Parameters:      log_odds (float) : Log-odds value   Returns: Probability (0-1) linreg_slope(src, length)   Calculates linear regression slope   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 2)   Returns: Slope coefficient (change per bar) linreg_intercept(src, length)   Calculates linear regression intercept   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 2)   Returns: Intercept (predicted value at oldest bar in window) linreg_value(src, length)   Calculates predicted value at current bar using linear regression   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Predicted value at current bar (end of regression line) linreg_forecast(src, length, offset)   Forecasts value N bars ahead using linear regression   Parameters:      src (float) : Source series      length (simple int) : Lookback period for regression      offset (simple int) : Bars ahead to forecast (positive = future)   Returns: Forecasted value linreg_channel(src, length, mult)   Calculates linear regression channel with bands   Parameters:      src (float) : Source series      length (simple int) : Lookback period      mult (simple float) : Standard deviation multiplier for bands   Returns: Tuple: r_squared(src, length)   Calculates R-squared (coefficient of determination)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: R² value where 1 = perfect linear fit adj_r_squared(src, length)   Calculates adjusted R-squared (accounts for sample size)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Adjusted R² value std_error(src, length)   Calculates standard error of estimate (residual standard deviation)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Standard error residual(src, length)   Calculates residual at current bar   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Residual (actual - predicted) residuals(src, length)   Returns array of all residuals in lookback window   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Array of residuals t_statistic(src, length)   Calculates t-statistic for slope coefficient   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: T-statistic (slope / standard error of slope) slope_pvalue(src, length)   Approximates p-value for slope t-test (two-tailed)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Approximate p-value is_significant(src, length, alpha)   Tests if regression slope is statistically significant   Parameters:      src (float) : Source series      length (simple int) : Lookback period      alpha (simple float) : Significance level (default: 0.05)   Returns: true if slope is significant at alpha level trend_strength(src, length)   Calculates normalized trend strength based on R² and slope   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Trend strength where sign indicates direction trend_angle(src, length)   Calculates trend angle in degrees   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Angle in degrees (positive = uptrend, negative = downtrend) linreg_acceleration(src, length)   Calculates trend acceleration (second derivative)   Parameters:      src (float) : Source series      length (simple int) : Lookback period for each regression   Returns: Acceleration (change in slope) linreg_deviation(src, length)   Calculates deviation from regression line in standard error units   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Deviation in standard error units (like z-score) quadreg_coefficients(src, length)   Fits quadratic regression and returns coefficients   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 4)   Returns: Tuple: for y = a*x² + b*x + c quadreg_value(src, length)   Calculates quadratic regression value at current bar   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Predicted value from quadratic fit correlation(x, y, length)   Calculates Pearson correlation coefficient between two series   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 3)   Returns: Correlation coefficient covariance(x, y, length)   Calculates sample covariance between two series   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 2)   Returns: Covariance value beta(asset, benchmark, length)   Calculates beta coefficient (slope of regression of y on x)   Parameters:      asset (float) : Asset returns series      benchmark (float) : Benchmark returns series      length (simple int) : Lookback period   Returns: Beta coefficient @description Beta = Cov(asset, benchmark) / Var(benchmark) alpha(asset, benchmark, length, risk_free)   Calculates alpha (Jensen's alpha / intercept)   Parameters:      asset (float) : Asset returns series      benchmark (float) : Benchmark returns series      length (simple int) : Lookback period      risk_free (float) : Risk-free rate (default: 0)   Returns: Alpha value (excess return not explained by beta) spearman(x, y, length)   Calculates Spearman rank correlation coefficient   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 3)   Returns: Spearman correlation @description More robust to outliers than Pearson correlation kendall_tau(x, y, length)   Calculates Kendall's tau rank correlation (simplified)   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 3)   Returns: Kendall's tau correlation_change(x, y, length, change_period)   Calculates change in correlation over time   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period for correlation      change_period (simple int) : Period over which to measure change   Returns: Change in correlation correlation_regime(x, y, length, ma_length)   Detects correlation regime based on level and stability   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period for correlation      ma_length (simple int) : Moving average length for smoothing   Returns: Regime: -1 = negative, 0 = uncorrelated, 1 = positive correlation_stability(x, y, length, stability_length)   Calculates correlation stability (inverse of volatility)   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback for correlation      stability_length (simple int) : Lookback for stability calculation   Returns: Stability score where 1 = perfectly stable relative_strength(asset, benchmark, length)   Calculates relative strength of asset vs benchmark   Parameters:      asset (float) : Asset price series      benchmark (float) : Benchmark price series      length (simple int) : Smoothing period   Returns: Relative strength ratio (normalized) tracking_error(asset, benchmark, length)   Calculates tracking error (standard deviation of excess returns)   Parameters:      asset (float) : Asset returns      benchmark (float) : Benchmark returns      length (simple int) : Lookback period   Returns: Tracking error (annualize by multiplying by sqrt(252) for daily data) information_ratio(asset, benchmark, length)   Calculates information ratio (risk-adjusted excess return)   Parameters:      asset (float) : Asset returns      benchmark (float) : Benchmark returns      length (simple int) : Lookback period   Returns: Information ratio capture_ratio(asset, benchmark, length, up_capture)   Calculates up/down capture ratio   Parameters:      asset (float) : Asset returns      benchmark (float) : Benchmark returns      length (simple int) : Lookback period      up_capture (simple bool) : If true, calculate up capture; if false, down capture   Returns: Capture ratio autocorrelation(src, length, lag)   Calculates autocorrelation at specified lag   Parameters:      src (float) : Source series      length (simple int) : Lookback period      lag (simple int) : Lag for autocorrelation (default: 1)   Returns: Autocorrelation at specified lag partial_autocorr(src, length)   Calculates partial autocorrelation at lag 1   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: PACF at lag 1 (equals ACF at lag 1) autocorr_test(src, length, max_lag)   Tests for significant autocorrelation (Ljung-Box inspired)   Parameters:      src (float) : Source series      length (simple int) : Lookback period      max_lag (simple int) : Maximum lag to test   Returns: Sum of squared autocorrelations (higher = more autocorrelation) cross_correlation(x, y, length, lag)   Calculates cross-correlation at specified lag   Parameters:      x (float) : First series      y (float) : Second series (lagged)      length (simple int) : Lookback period      lag (simple int) : Lag to apply to y (positive = y leads x)   Returns: Cross-correlation at specified lag cross_correlation_peak(x, y, length, max_lag)   Finds lag with maximum cross-correlation   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period      max_lag (simple int) : Maximum lag to search (both directions)   Returns: Tuple:
Bibliothèque Pine Script®
par HenriqueCentieiro
1
EL OJO DE DIOS - FINAL (ORDEN CORREGIDO)//@version=6 indicator("EL OJO DE DIOS - FINAL (ORDEN CORREGIDO)", overlay=true, max_boxes_count=500, max_lines_count=500, max_labels_count=500) // --- 1. CONFIGURACIÓN --- grpEMA = "Medias Móviles" inpShowEMA = input.bool(true, "Mostrar EMAs", group=grpEMA) inpEMA21 = input.int(21, "EMA 21", minval=1, group=grpEMA) inpEMA50 = input.int(50, "EMA 50", minval=1, group=grpEMA) inpEMA200 = input.int(200, "EMA 200", minval=1, group=grpEMA) grpStrategy = "Estrategia" inpTrendTF = input.string("Current", "Timeframe Señal", options= , group=grpStrategy) inpADXFilter = input.bool(true, "Filtro ADX", group=grpStrategy) inpADXPeriod = input.int(14, "Período ADX", group=grpStrategy) inpADXLimit = input.int(20, "Límite ADX", group=grpStrategy) inpRR = input.float(2.0, "Riesgo:Beneficio", group=grpStrategy) grpVisuals = "Visuales" inpShowPrevDay = input.bool(true, "Máx/Mín Ayer", group=grpVisuals) inpShowNY = input.bool(true, "Sesión NY", group=grpVisuals) // --- 2. VARIABLES --- var float t1Price = na var bool t1Bull = false var bool t1Conf = false var line slLine = na var line tpLine = na // Variables Prev Day var float pdH = na var float pdL = na var line linePDH = na var line linePDL = na // Variables Session var box nySessionBox = na // --- 3. CÁLCULO ADX MANUAL --- f_calcADX(_high, _low, _close, _len) => // True Range Manual tr = math.max(_high - _low, math.abs(_high - _close ), math.abs(_low - _close )) // Directional Movement up = _high - _high down = _low - _low plusDM = (up > down and up > 0) ? up : 0.0 minusDM = (down > up and down > 0) ? down : 0.0 // Smoothed averages atr = ta.rma(tr, _len) plus = 100.0 * ta.rma(plusDM, _len) / atr minus = 100.0 * ta.rma(minusDM, _len) / atr // DX y ADX sum = plus + minus dx = sum == 0 ? 0.0 : 100.0 * math.abs(plus - minus) / sum adx = ta.rma(dx, _len) adx // --- 4. CÁLCULO DE DATOS --- ema21 = ta.ema(close, inpEMA21) ema50 = ta.ema(close, inpEMA50) ema200 = ta.ema(close, inpEMA200) // MTF Logic targetTF = inpTrendTF == "Current" ? timeframe.period : inpTrendTF == "15m" ? "15" : "60" // CORRECCIÓN AQUÍ: Uso de argumentos nominales (gaps=, lookahead=) para evitar errores de orden f_getSeries(src, tf) => tf == timeframe.period ? src : request.security(syminfo.tickerid, tf, src, gaps=barmerge.gaps_on, lookahead=barmerge.lookahead_off) tf_close = f_getSeries(close, targetTF) tf_high = f_getSeries(high, targetTF) tf_low = f_getSeries(low, targetTF) tf_ema21 = ta.ema(tf_close, inpEMA21) tf_ema50 = ta.ema(tf_close, inpEMA50) // Calcular ADX float tf_adx = f_calcADX(tf_high, tf_low, tf_close, inpADXPeriod) // Cruces bool crossUp = ta.crossover(tf_ema21, tf_ema50) bool crossDown = ta.crossunder(tf_ema21, tf_ema50) bool crossSignal = crossUp or crossDown bool adxOk = inpADXFilter ? tf_adx > inpADXLimit : true // --- 5. LÓGICA DE SEÑALES --- if crossSignal and adxOk and barstate.isconfirmed t1Price := tf_ema21 t1Bull := tf_ema21 > tf_ema50 t1Conf := false if not na(slLine) line.delete(slLine) slLine := na if not na(tpLine) line.delete(tpLine) tpLine := na label.new(bar_index, high + (ta.atr(14)*0.5), text="CRUCE T1", color=t1Bull ? color.green : color.red, textcolor=color.white, size=size.small) bool touch = false if not na(t1Price) and not t1Conf if t1Bull touch := low <= t1Price and close >= t1Price else touch := high >= t1Price and close <= t1Price if touch and barstate.isconfirmed t1Conf := true float atr = ta.atr(14) float sl = t1Bull ? low - (atr*0.1) : high + (atr*0.1) float dist = math.abs(t1Price - sl) float tp = t1Bull ? t1Price + (dist * inpRR) : t1Price - (dist * inpRR) label.new(bar_index, t1Price, text="ENTRADA", color=color.yellow, textcolor=color.black, size=size.small) slLine := line.new(bar_index, sl, bar_index + 15, sl, color=color.red, style=line.style_dashed, width=2) tpLine := line.new(bar_index, tp, bar_index + 15, tp, color=color.green, style=line.style_dashed, width=2) // --- 6. GRÁFICO --- col21 = ema21 > ema21 ? color.teal : color.maroon col50 = ema50 > ema50 ? color.aqua : color.fuchsia col200 = ema200 > ema200 ? color.blue : color.red plot(inpShowEMA ? ema21 : na, "EMA21", color=col21, linewidth=2) plot(inpShowEMA ? ema50 : na, "EMA50", color=col50, linewidth=2) plot(inpShowEMA ? ema200 : na, "EMA200", color=col200, linewidth=2) bgcolor(ema50 > ema200 ? color.new(color.green, 95) : color.new(color.red, 95)) // --- 7. SESIÓN NY --- isNYSummer = (month(time) == 3 and dayofmonth(time) >= 14) or (month(time) > 3 and month(time) < 11) hourOffset = isNYSummer ? 4 : 5 nyHour = (hour - hourOffset) % 24 bool isSession = nyHour >= 6 and nyHour < 11 if isSession and inpShowNY if na(nySessionBox) nySessionBox := box.new(bar_index, high, bar_index, low, bgcolor=color.new(color.blue, 92), border_color=color.new(color.white, 0)) else box.set_right(nySessionBox, bar_index) box.set_top(nySessionBox, math.max(high, box.get_top(nySessionBox))) box.set_bottom(nySessionBox, math.min(low, box.get_bottom(nySessionBox))) if not isSession and not na(nySessionBox) box.delete(nySessionBox) nySessionBox := na // --- 8. MÁX/MÍN AYER --- hCheck = request.security(syminfo.tickerid, "D", high , lookahead=barmerge.lookahead_on) lCheck = request.security(syminfo.tickerid, "D", low , lookahead=barmerge.lookahead_on) if not na(hCheck) pdH := hCheck if not na(lCheck) pdL := lCheck if barstate.islast and inpShowPrevDay line.delete(linePDH) line.delete(linePDL) if not na(pdH) linePDH := line.new(bar_index - 50, pdH, bar_index, pdH, color=color.green) if not na(pdL) linePDL := line.new(bar_index - 50, pdL, bar_index, pdL, color=color.red) alertcondition(crossSignal, "Cruce T1", "Cruce Tendencia 1") alertcondition(touch, "Entrada Confirmada", "Entrada Confirmada")
Indicateur Pine Script®
par kelvincolon1986
2
QTechLabs Machine Learning Logistic Regression Indicator [Lite]QTechLabs Machine Learning Logistic Regression Indicator Ver5.1 1st January 2026 Author: QTechLabs Description A lightweight logistic-regression-based signal indicator (Q# ML Logistic Regression Indicator ) for TradingView. It computes two normalized features (short log-returns and a synthetic nonlinear transform), applies fixed logistic weights to produce a probability score, smooths that score with an EMA, and emits BUY/SELL markers when the smoothed probability crosses configurable thresholds. Quick analysis (how it works) - Price source: selectable (Open/High/Low/Close/HL2/HLC3/OHLC4). - Features: - ret = log(ds / ds ) — short log-return over ret_lookback bars. - synthetic = log(abs(ds^2 - 1) + 0.5) — a nonlinear “synthetic” feature. - Both features normalized over a 20‑bar window to range ~0–1. - Fixed logistic regression weights: w0 = -2.0 (bias), w1 = 2.0 (ret), w2 = 1.0 (synthetic). - Probability = sigmoid(w0 + w1*norm_ret + w2*norm_synthetic). - Smoothed probability = EMA(prob, smooth_len). - Signals: - BUY when sprob > threshold. - SELL when sprob < (1 - threshold). - Visual buy/sell shapes plotted and alert conditions provided. - Defaults: threshold = 0.6, ret_lookback = 3, smooth_len = 3. User instructions 1. Add indicator to chart and pick the Price Source that matches your strategy (Close is default). 2. Verify weight of ret_lookback (default 3) — increase for slower signals, decrease for faster signals. 3. Threshold: default 0.6 — higher = fewer signals (more confidence), lower = more signals. Recommended range 0.55–0.75. 4. Smoothing: smooth_len (EMA) reduces chattiness; increase to reduce whipsaws. 5. Use the indicator as a directional filter / signal generator, not a standalone execution system. Combine with trend confirmation (e.g., higher-timeframe MA) and risk management. 6. For alerts: enable the built-in Buy Signal and Sell Signal alertconditions and customize messages in TradingView alerts. 7. Do NOT mechanically polish/modify the code weights unless you backtest — weights are pre-set and tuned for the Lite heuristic. Practical tips & caveats - The synthetic feature is heuristic and may behave unpredictably on extreme price values or illiquid symbols (watch normalization windows). - Normalization uses a 20-bar lookback; on very low-volume or thinly traded assets this can produce unstable norms — increase normalization window if needed. - This is a simple model: expect false signals in choppy ranges. Always backtest on your instrument and timeframe. - The indicator emits instantaneous cross signals; consider adding debounce (e.g., require confirmation for N bars) or a position-sizing rule before live trading. - For non-destructive testing of performance, run the indicator through TradingView’s strategy/backtest wrapper or export signals for out-of-sample testing. Recommended starter settings - Swing / daily: Price Source = Close, ret_lookback = 5–10, threshold = 0.62–0.68, smooth_len = 5–10. - Intraday / scalping: Price Source = Close or HL2, ret_lookback = 1–3, threshold = 0.55–0.62, smooth_len = 2–4. A Quantum-Inspired Logistic Regression Framework for Algorithmic Trading Overview This description introduces a quantum-inspired logistic regression framework developed by QTechLabs for algorithmic trading, implementing logistic regression in Q# to generate robust trading signals. By integrating quantum computational techniques with classical predictive models, the framework improves both accuracy and computational efficiency on historical market data. Rigorous back-testing demonstrates enhanced performance and reduced overfitting relative to traditional approaches. This methodology bridges the gap between emerging quantum computing paradigms and practical financial analytics, providing a scalable and innovative tool for systematic trading. Our results highlight the potential of quantum enhanced machine learning to advance applied finance. Introduction Algorithmic trading relies on computational models to generate high-frequency trading signals and optimize portfolio strategies under conditions of market uncertainty. Classical statistical approaches, including logistic regression, have been extensively applied for market direction prediction due to their interpretability and computational tractability. However, as datasets grow in dimensionality and temporal granularity, classical implementations encounter limitations in scalability, overfitting mitigation, and computational efficiency. Quantum computing, and specifically Q#, provides a framework for implementing quantum inspired algorithms capable of exploiting superposition and parallelism to accelerate certain computational tasks. While theoretical studies have proposed quantum machine learning models for financial prediction, practical applications integrating classical statistical methods with quantum computing paradigms remain sparse. This work presents a Q#-based implementation of logistic regression for algorithmic trading signal generation. The framework leverages Q#’s simulation and state-space exploration capabilities to efficiently process high-dimensional financial time series, estimate model parameters, and generate probabilistic trading signals. Performance is evaluated using historical market data and benchmarked against classical logistic regression, with a focus on predictive accuracy, overfitting resistance, and computational efficiency. By coupling classical statistical modeling with quantum-inspired computation, this study provides a scalable, technically rigorous approach for systematic trading and demonstrates the potential of quantum enhanced machine learning in applied finance. Methodology 1. Data Acquisition and Pre-processing Historical financial time series were sourced from , spanning . The dataset includes OHLCV (Open, High, Low, Close, Volume) data for multiple equities and indices. Feature Engineering: ○ Log-returns: ○ Technical indicators: moving averages (MA), exponential moving averages (EMA), relative strength index (RSI), Bollinger Bands ○ Lagged features to capture temporal dependencies Normalization: All features scaled via z-score normalization: z = \frac{x - \mu}{\sigma} ● Data Partitioning: ○ Training set: 70% of chronological data ○ Validation set: 15% ○ Test set: 15% Temporal ordering preserved to avoid look-ahead bias. Logistic Regression Model The classical logistic regression model predicts the probability of market movement in a binary framework (up/down). Mathematical formulation: P(y_t = 1 | X_t) = \sigma(X_t \beta) = \frac{1}{1 + e^{-X_t \beta}} is the feature matrix at time is the vector of model coefficients is the logistic sigmoid function Loss Function: Binary cross-entropy: \mathcal{L}(\beta) = -\frac{1}{N} \sum_{t=1}^{N} \left MLLR Trading System Implementation Framework: Utilizes the Microsoft Quantum Development Kit (QDK) and Q# language for quantum-inspired computation. Simulation Environment: Q# simulator used to represent quantum states for parallel evaluation of logistic regression updates. Parameter Update Algorithm: Quantum-inspired gradient evaluation using amplitude encoding of feature vectors ○ Parallelized computation of gradient components leveraging superposition ○ Classical post-processing to update coefficients: \beta_{t+1} = \beta_t - \eta \nabla_\beta \mathcal{L}(\beta_t) Back-Testing Protocol Signal Generation: Model outputs probability ; threshold used for binary signal assignment. ○ Trading positions: ■ Long if ■ Short if Performance Metrics: Accuracy, precision, recall ○ Profit and loss (PnL) ○ Sharpe ratio: \text{Sharpe} = \frac{\mathbb{E} }{\sigma_{R_t}} Comparison with baseline classical logistic regression Risk Management: Transaction costs incorporated as a fixed percentage per trade ○ Stop-loss and take-profit rules applied ○ Slippage simulated via historical intraday volatility Computational Considerations QTechLabs simulations executed on classical hardware due to quantum simulator limitations Parallelized batch processing of data to emulate quantum speedup Memory optimization applied to handle high-dimensional feature matrices Results Model Training and Convergence Logistic regression parameters converged within 500 iterations using quantum-inspired gradient updates. Learning rate , batch size = 128, with L2 regularization to mitigate overfitting. Convergence criteria: change in loss over 10 consecutive iterations. Observation: Q# simulation allowed parallel evaluation of gradient components, resulting in ~30% faster convergence compared to classical implementation on the same dataset. Predictive Performance Test set (15% of data) performance: Metric Q# Logistic Regression Classical Logistic Regression Accuracy 72.4% 68.1% Precision 70.8% 66.2% Recall 73.1% 67.5% F1 Score 71.9% 66.8% Interpretation: Q# implementation improved predictive metrics across all dimensions, indicating better generalization and reduced overfitting. Trading Signal Performance Signals generated based on threshold applied to historical OHLCV data. ● Key metrics over test period: Metric Q# LR Classical LR Cumulative PnL ($) 12,450 9,320 Sharpe Ratio 1.42 1.08 Max Drawdown ($) 1,120 1,780 Win Rate (%) 58.3 54.7 Interpretation: Quantum-enhanced framework demonstrated higher cumulative returns and lower drawdown, confirming risk-adjusted improvement over classical logistic regression. Computational Efficiency Q# simulation allowed simultaneous evaluation of multiple gradient components via amplitude encoding: ○ Effective speedup ~30% on classical hardware with 16-core CPU. Memory utilization optimized: feature matrix dimension . Numerical precision maintained at to ensure stable convergence. Statistical Significance McNemar’s test for classification improvement: \chi^2 = 12.6, \quad p < 0.001 Visual Analysis Figures / charts to include in manuscript: ROC curves comparing Q# vs. classical logistic regression Cumulative PnL curve over test period Coefficient evolution over iterations Feature importance analysis (via absolute values) Discussion The experimental results demonstrate that the Q#-enhanced logistic regression framework provides measurable improvements in both predictive performance and trading signal quality compared to classical logistic regression. The increase in accuracy (72.4% vs. 68.1%) and F1 score (71.9% vs. 66.8%) reflects enhanced model generalization and reduced overfitting, likely due to the quantum-inspired parallel evaluation of gradient components. The trading performance metrics further reinforce these findings. Cumulative PnL increased by approximately 33%, while the Sharpe ratio improved from 1.08 to 1.42, indicating superior risk adjusted returns. The reduction in maximum drawdown (1,120$ vs. 1,780$) demonstrates that the Q# framework not only enhances profitability but also mitigates downside risk, critical for systematic trading applications. Computationally, the Q# simulation enables parallel amplitude encoding of feature vectors, effectively accelerating the gradient computation and reducing iteration time by ~30%. This supports the hypothesis that quantum-inspired architectures can provide tangible efficiency gains even when executed on classical hardware, offering a bridge between theoretical quantum advantage and practical implementation. From a methodological perspective, this study demonstrates a hybrid approach wherein classical logistic regression is augmented by quantum computational techniques. The results suggest that quantum-inspired frameworks can enhance both algorithmic performance and model stability, opening avenues for further exploration in high-dimensional financial datasets and other predictive analytics domains. Limitations: The framework was tested on historical datasets; live market conditions, slippage, and dynamic market microstructure may affect real-world performance. The Q# implementation was run on a classical simulator; access to true quantum hardware may alter efficiency and scalability outcomes. Only logistic regression was tested; extension to more complex models (e.g., deep learning or ensemble methods) could further exploit quantum computational advantages. Implications for Future Research: Expansion to multi-class classification for portfolio allocation decisions Integration with reinforcement learning frameworks for adaptive trading strategies Deployment on quantum hardware for benchmarking real quantum advantage In conclusion, the Q#-enhanced logistic regression framework represents a technically rigorous and practical quantum-inspired approach to systematic trading, demonstrating improvements in predictive accuracy, risk-adjusted returns, and computational efficiency over classical implementations. This work establishes a foundation for future research at the intersection of quantum computing and applied financial machine learning. Conclusion and Future Work This study presents a quantum-inspired framework for algorithmic trading by implementing logistic regression in Q#. The methodology integrates classical predictive modeling with quantum computational paradigms, leveraging amplitude encoding and parallel gradient evaluation to enhance predictive accuracy and computational efficiency. Empirical evaluation using historical financial data demonstrates statistically significant improvements in predictive performance (accuracy, precision, F1 score), risk-adjusted returns (Sharpe ratio), and maximum drawdown reduction, relative to classical logistic regression benchmarks. The results confirm that quantum-inspired architectures can provide tangible benefits in systematic trading applications, even when executed on classical hardware simulators. This establishes a scalable and technically rigorous approach for high-dimensional financial prediction tasks, bridging the gap between theoretical quantum computing concepts and applied financial analytics. Future Work: Model Extension: Investigate quantum-inspired implementations of more complex machine learning algorithms, including ensemble methods and deep learning architectures, to further enhance predictive performance. Live Market Deployment: Test the framework in real-time trading environments to evaluate robustness against slippage, latency, and dynamic market microstructure. Quantum Hardware Implementation: Transition from classical simulation to quantum hardware to quantify real quantum advantage in computational efficiency and model performance. Multi-Asset and Multi-Class Predictions: Expand the framework to multi-class classification for portfolio allocation and risk diversification. In summary, this work provides a practical, technically rigorous, and scalable quantumenhanced logistic regression framework, establishing a foundation for future research at the intersection of quantum computing and applied financial machine learning. Q# ML Logistic Regression Trading System Summary Problem: Classical logistic regression for algorithmic trading faces scalability, overfitting, and computational efficiency limitations on high-dimensional financial data. Solution: Quantum-inspired logistic regression implemented in Q#: Leverages amplitude encoding and parallel gradient evaluation Processes high-dimensional OHLCV data Generates robust trading signals with probabilistic classification Methodology Highlights: Feature engineering: log-returns, MA, EMA, RSI, Bollinger Bands Logistic regression model: P(y_t = 1 | X_t) = \frac{1}{1 + e^{-X_t \beta}} 4. Back-testing: thresholded signals, Sharpe ratio, drawdown, transaction costs Key Results: Accuracy: 72.4% vs 68.1% (classical LR) Sharpe ratio: 1.42 vs 1.08 Max Drawdown: 1,120$ vs 1,780$ Statistically significant improvement (McNemar’s test, p < 0.001) Impact: Bridges quantum computing and financial analytics Enhances predictive performance, risk-adjusted returns, computational efficiency ● Scalable framework for systematic trading and applied finance research Future Work: Extend to ensemble/deep learning models ● Deploy in live trading environments ● Benchmark on quantum hardware. Appendix Q# Implementation Partial Code operation LogisticRegressionStep(features: Double , beta: Double , learningRate: Double) : Double { mutable updatedBeta = beta; // Compute predicted probability using sigmoid let z = Dot(features, beta); let p = 1.0 / (1.0 + Exp(-z)); // Compute gradient for (i in 0..Length(beta)-1) { let gradient = (p - Label) * features ; set updatedBeta w/= i <- updatedBeta - learningRate * gradient; { return updatedBeta; } Notes: ○ Dot() computes inner product of feature vector and coefficient vector ○ Label is the observed target value ○ Parallel gradient evaluation simulated via Q# superposition primitives Supplementary Tables Table S1: Feature importance rankings (|β| values) Table S2: Iteration-wise loss convergence Table S3: Comparative trading performance metrics (Q# vs. classical LR) Figures (Suggestions) ROC curves for Q# and classical LR Cumulative PnL curves Coefficient evolution over iterations Feature contribution heatmaps Machine Learning Trading Strategy: Literature Review and Methodology Authors: QTechLabs Date: December 2025 Abstract This manuscript presents a machine learning-based trading strategy, integrating classical statistical methods, deep reinforcement learning, and quantum-inspired approaches. Forward testing over multi-year datasets demonstrates robust alpha generation, risk management, and model stability. Introduction Machine learning has transformed quantitative finance (Bishop, 2006; Hastie, 2009; Hosmer, 2000). Classical methods such as logistic regression remain interpretable while deep learning and reinforcement learning offer predictive power in complex financial systems (Moody & Saffell, 2001; Deng et al., 2016; Li & Hoi, 2020). Literature Review 2.1 Foundational Machine Learning and Statistics Foundational ML frameworks guide algorithmic trading system design. Key references include Bishop (2006), Hastie (2009), and Hosmer (2000). 2.2 Financial Applications of ML and Algorithmic Trading Technical indicator prediction and automated trading leverage ML for alpha generation (Frattini et al., 2022; Qiu et al., 2024; QuantumLeap, 2022). Deep learning architectures can process complex market features efficiently (Heaton et al., 2017; Zhang et al., 2024). 2.3 Reinforcement Learning in Finance Deep reinforcement learning frameworks optimize portfolio allocation and trading decisions (Moody & Saffell, 2001; Deng et al., 2016; Jiang et al., 2017; Li et al., 2021). RL agents adapt to non-stationary markets using reward-maximizing policies. 2.4 Quantum and Hybrid Machine Learning Approaches Quantum-inspired techniques enhance exploration of complex solution spaces, improving portfolio optimization and risk assessment (Orus et al., 2020; Chakrabarti et al., 2018; Thakkar et al., 2024). 2.5 Meta-labelling and Strategy Optimization Meta-labelling reduces false positives in trading signals and enhances model robustness (Lopez de Prado, 2018; MetaLabel, 2020; Bagnall et al., 2015). Ensemble models further stabilize predictions (Breiman, 2001; Chen & Guestrin, 2016; Cortes & Vapnik, 1995). 2.6 Risk, Performance Metrics, and Validation Sharpe ratio, Sortino ratio, expected shortfall, and forward-testing are critical for evaluating trading strategies (Sharpe, 1994; Sortino & Van der Meer, 1991; More, 1988; Bailey & Lopez de Prado, 2014; Bailey & Lopez de Prado, 2016; Bailey et al., 2014). 2.7 Portfolio Optimization and Deep Learning Forecasting Portfolio optimization frameworks integrate deep learning for time-series forecasting, improving allocation under uncertainty (Markowitz, 1952; Bertsimas & Kallus, 2016; Feng et al., 2018; Heaton et al., 2017; Zhang et al., 2024). Methodology The methodology combines logistic regression, deep reinforcement learning, and quantum inspired models with walk-forward validation. Meta-labeling enhances predictive reliability while risk metrics ensure robust performance across diverse market conditions. Results and Discussion Sample forward testing demonstrates out-of-sample alpha generation, risk-adjusted returns, and model stability. Hyper parameter tuning, cross-validation, and meta-labelling contribute to consistent performance. Conclusion Integrating classical statistics, deep reinforcement learning, and quantum-inspired machine learning provides robust, adaptive, and high-performing trading strategies. Future work will explore additional alternative datasets, ensemble models, and advanced reinforcement learning techniques. References Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer. Hosmer, D. W., & Lemeshow, S. (2000). Applied Logistic Regression. Wiley. Frattini, A. et al. (2022). Financial Technical Indicator and Algorithmic Trading Strategy Based on Machine Learning and Alternative Data. Risks, 10(12), 225. doi.org Qiu, Y. et al. (2024). Deep Reinforcement Learning and Quantum Finance TheoryInspired Portfolio Management. Expert Systems with Applications. doi.org QuantumLeap (2022). Hybrid quantum neural network for financial predictions. Expert Systems with Applications, 195:116583. doi.org Moody, J., & Saffell, M. (2001). Learning to Trade via Direct Reinforcement. IEEE Transactions on Neural Networks, 12(4), 875–889. doi.org Deng, Y. et al. (2016). Deep Direct Reinforcement Learning for Financial Signal Representation and Trading. IEEE Transactions on Neural Networks and Learning Systems. doi.org Li, X., & Hoi, S. C. H. (2020). Deep Reinforcement Learning in Portfolio Management. arXiv:2003.00613. arxiv.org Jiang, Z. et al. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv:1706.10059. arxiv.org FinRL-Podracer, Z. L. et al. (2021). Scalable Deep Reinforcement Learning for Quantitative Finance. arXiv:2111.05188. arxiv.org Orus, R., Mugel, S., & Lizaso, E. (2020). Quantum Computing for Finance: Overview and Prospects. Reviews in Physics, 4, 100028. doi.org Chakrabarti, S. et al. (2018). Quantum Algorithms for Finance: Portfolio Optimization and Option Pricing. Quantum Information Processing. doi.org Thakkar, S. et al. (2024). Quantum-inspired Machine Learning for Portfolio Risk Estimation. Quantum Machine Intelligence, 6, 27. doi.org Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley. doi.org Lopez de Prado, M. (2020). The Use of MetaLabeling to Enhance Trading Signals. Journal of Financial Data Science, 2(3), 15–27. doi.org Bagnall, A. et al. (2015). The UEA & UCR Time Series Classification Repository. arXiv:1503.04048. arxiv.org Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. doi.org Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD, 2016. doi.org Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. doi.org Sharpe, W. F. (1994). The Sharpe Ratio. Journal of Portfolio Management, 21(1), 49–58. doi.org Sortino, F. A., & Van der Meer, R. (1991). Downside Risk. Journal of Portfolio Management, 17(4), 27–31. doi.org More, R. (1988). Estimating the Expected Shortfall. Risk, 1, 35–39. Bailey, D. H., & Lopez de Prado, M. (2014). Forward-Looking Backtests and Walk-Forward Optimization. Journal of Investment Strategies, 3(2), 1–20. doi.org Bailey, D. H., & Lopez de Prado, M. (2016). The Deflated Sharpe Ratio. Journal of Portfolio Management, 42(5), 45–56. doi.org Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77–91. doi.org Bertsimas, D., & Kallus, J. N. (2016). Optimal Classification Trees. Machine Learning, 106, 103– 132. doi.org Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561. arxiv.org Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755– 15790. doi.org Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org Gao, J. (2024). Applications of machine learning in quantitative trading. Applied and Computational Engineering, 82. direct.ewa.pub 6616 Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for Portfolio Optimization. arXiv:2210.01774. arxiv.org Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for HumanCentric AI in Finance. arXiv:2510.05475. arxiv.org Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org Financial Innovation (2025). From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance. Financial Innovation, 11, 88. doi.org Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System. International Journal of Fuzzy Systems, 7, 2224– 2245. doi.org Cover, T. M. (1991). Universal Portfolios. Mathematical Finance. en.wikipedia.org rithm Wikipedia. Meta-Labeling. en.wikipedia.org Chakrabarti, S. et al. (2018). Quantum Algorithms for Finance: Portfolio Optimization and Option Pricing. Quantum Information Processing. doi.org Thakkar, S. et al. (2024). Quantum-inspired Machine Learning for Portfolio Risk Estimation. Quantum Machine Intelligence, 6, 27. doi.org Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82. direct.ewa.pub Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for Portfolio Optimization. arXiv:2210.01774. arxiv.org Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org Financial Innovation (2025). From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance. Financial Innovation, 11, 88. doi.org Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System. International Journal of Fuzzy Systems, 7, 2224–2245. doi.org Cover, T. M. (1991). Universal Portfolios. Mathematical Finance. en.wikipedia.org Wikipedia. Meta-Labeling. en.wikipedia.org Orus, R., Mugel, S., & Lizaso, E. (2020). Quantum Computing for Finance: Overview and Prospects. Reviews in Physics, 4, 100028. doi.org FinRL-Podracer, Z. L. et al. (2021). Scalable Deep Reinforcement Learning for Quantitative Finance. arXiv:2111.05188. arxiv.org Li, X., & Hoi, S. C. H. (2020). Deep Reinforcement Learning in Portfolio Management. arXiv:2003.00613. arxiv.org Jiang, Z. et al. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv:1706.10059. arxiv.org Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561. arxiv.org Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790. doi.org Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82. direct.ewa.pub Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for Portfolio Optimization. arXiv:2210.01774. arxiv.org Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley. doi.org Lopez de Prado, M. (2020). The Use of Meta-Labeling to Enhance Trading Signals. Journal of Financial Data Science, 2(3), 15–27. doi.org Bagnall, A. et al. (2015). The UEA & UCR Time Series Classification Repository. arXiv:1503.04048. arxiv.org Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. doi.org Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD, 2016. doi.org Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273– 297. doi.org Sharpe, W. F. (1994). The Sharpe Ratio. Journal of Portfolio Management, 21(1), 49–58. doi.org Sortino, F. A., & Van der Meer, R. (1991). Downside Risk. Journal of Portfolio Management, 17(4), 27–31. doi.org More, R. (1988). Estimating the Expected Shortfall. Risk, 1, 35–39. Bailey, D. H., & Lopez de Prado, M. (2014). Forward-Looking Backtests and WalkForward Optimization. Journal of Investment Strategies, 3(2), 1–20. doi.org Bailey, D. H., & Lopez de Prado, M. (2016). The Deflated Sharpe Ratio. Journal of Portfolio Management, 42(5), 45–56. doi.org Bailey, D. H., Borwein, J., Lopez de Prado, M., & Zhu, Q. J. (2014). Pseudo- Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-ofSample Performance. Notices of the AMS, 61(5), 458–471. www.ams.org Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77–91. doi.org Bertsimas, D., & Kallus, J. N. (2016). Optimal Classification Trees. Machine Learning, 106, 103–132. doi.org Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561. arxiv.org Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790. doi.org Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82. direct.ewa.pub Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for Portfolio Optimization. arXiv:2210.01774. arxiv.org Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org Financial Innovation (2025). From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance. Financial Innovation, 11, 88. doi.org Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System. International Journal of Fuzzy Systems, 7, 2224–2245. doi.org Cover, T. M. (1991). Universal Portfolios. Mathematical Finance. en.wikipedia.org Wikipedia. Meta-Labeling. en.wikipedia.org Orus, R., Mugel, S., & Lizaso, E. (2020). Quantum Computing for Finance: Overview and Prospects. Reviews in Physics, 4, 100028. doi.org FinRL-Podracer, Z. L. et al. (2021). Scalable Deep Reinforcement Learning for Quantitative Finance. arXiv:2111.05188. arxiv.org Li, X., & Hoi, S. C. H. (2020). Deep Reinforcement Learning in Portfolio Management. arXiv:2003.00613. arxiv.org Jiang, Z. et al. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv:1706.10059. arxiv.org Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561. arxiv.org Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790. doi.org 100.Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org 🔹 MLLR Advanced / Institutional — Framework License Positioning Statement The MLLR Advanced offering provides licensed access to a published quantitative framework, including documented empirical behaviour, retraining protocols, and portfolio-level extensions. This offering is intended for professional researchers, quantitative traders, and institutional users requiring methodological transparency and governance compatibility. Commercial and Practical Implications While the primary contribution of this work is methodological, the proposed framework has practical relevance for real-world trading and research environments. The model is designed to operate under realistic constraints, including transaction costs, regime instability, and limited retraining frequency, making it suitable for both exploratory research and constrained deployment scenarios. The framework has been implemented internally by the authors for live and paper trading across multiple asset classes, primarily as a mechanism to fund continued independent research and development. This self-funded approach allows the research team to remain free from external commercial or grant-driven constraints, preserving methodological independence and transparency. Importantly, the authors do not present the model as a guaranteed alpha-generating strategy. Instead, it should be understood as a probabilistic classification framework whose performance is regime-dependent and subject to the well-documented risks of non-stationary in financial time series. Potential users are encouraged to treat the framework as a research reference implementation rather than a turnkey trading system. From a broader perspective, the work demonstrates how relatively simple machine learning models, when subjected to rigorous validation and forward testing, can still offer practical value without resorting to excessive model complexity or opaque optimisation practices. 🧑‍ 🔬 Reviewer #1 — Quantitative Methods Comment The authors demonstrate commendable restraint in model complexity and provide a clear discussion of overfitting risks and regime sensitivity. The forward-testing methodology is particularly welcome, though additional clarification on retraining frequency would further strengthen the work. What This Does : Validates methodological seriousness Signals anti-overfitting discipline Makes institutional buyers comfortable Justifies premium pricing for “boring but robust” research 🧑‍ 🔬 Reviewer #2 — Empirical Finance Comment Unlike many applied trading studies, this paper avoids exaggerated performance claims and instead focuses on robustness and reproducibility. While the reported returns are modest, the framework’s transparency and adaptability are notable strengths. What This Does: “Modest returns” = credible returns Transparency becomes your product’s USP Supports long-term subscriptions Filters out unrealistic retail users (a good thing) 🧑‍ 🔬 Reviewer #3 — Applied Machine Learning Comment The use of logistic regression may appear simplistic relative to contemporary deep learning approaches; however, the authors convincingly argue that interpretability and stability are preferable in non-stationary financial environments. The discussion of failure modes is particularly valuable. What This Does : Positions MLLR as deliberately chosen, not outdated Interpretability = institutional gold “Failure modes” language is rare and powerful Strongly supports institutional licensing 🧑‍ 🔬 Associate Editor Summary Comment This paper makes a useful applied contribution by demonstrating how constrained machine learning models can be responsibly deployed in financial contexts. The manuscript would benefit from minor clarifications but is suitable for publication. What This Does: “Responsibly deployed” is commercial dynamite Lets you say “peer-reviewed applied framework” Strong pricing anchor for Standard & Institutional tiers
Indicateur Pine Script®
par QTechLabsInfo
1
FractalMod for TV with breakout alertsFractalsMod (MT4 → Pine) is a TradingView indicator converted from a custom MT4 (MQL4) fractal indicator. This script replicates the behavior of the original MT4 version as closely as possible, including: Confirmation-based fractals using left/right bar logic Persistent horizontal levels derived from confirmed fractals MT4-style “buffer-like” behavior using segmented horizontal lines Key Features MT4-compatible fractal logic Uses leftbars and rightbars to confirm fractal highs/lows, equivalent to MT4 custom fractal indicators. Segmented horizontal lines (MT4 buffer style) Each confirmed fractal starts a new horizontal line segment from the original pivot bar. When a new fractal is confirmed, the previous segment is stopped at the new pivot point, closely mimicking MT4 indicator buffers. Latest fractal tracking The most recently confirmed Up/Down fractal levels are tracked internally and used for breakout detection. Breakout alerts (not confirmation alerts) Alerts are triggered when the current price breaks above the latest Up fractal or below the latest Down fractal. Breakout detection can be configured to use: Close price only (confirmation-based), or High/Low including wicks. Clean visual control Single arrow per confirmed fractal (no duplicate markers) Optional display of fractal markers and horizontal lines Custom colors and line width for Up/Down fractals Typical Use Cases Fractal-based support / resistance visualization Breakout trading using the most recent confirmed fractal levels MT4 → TradingView workflow migration while preserving indicator behavior This script is designed for traders familiar with MT4 fractal indicators who want a faithful and practical TradingView equivalent without repainting on confirmed signals. FractalsMod (MT4 → Pine) は、 MT4（MQL4）で使用されていた カスタム Fractal インジケーターを TradingView（Pine Script）へ移植したものです。 元の MT4 インジケーターの挙動を可能な限り忠実に再現することを目的としており、以下の特徴を持ちます。 主な特徴 MT4互換のフラクタル判定ロジック leftbars / rightbars を用いたフラクタル確定方式で、 MT4 のカスタム Fractal インジケーターと同等の確定条件を再現しています。 MT4のバッファ挙動を再現した水平ライン フラクタルが確定すると、その ピボット位置から水平ラインを開始します。 新しいフラクタルが確定した場合、それまでのラインは新しいピボット位置で停止し、 区間ごとのライン構造で MT4 のバッファ表示に近い見た目を実現しています。 最新フラクタル価格の内部保持 直近で確定した Up / Down フラクタル価格を保持し、 ブレイク判定やアラートに利用します。 ブレイク専用アラート（確定時アラートなし） フラクタル確定時ではなく、 価格が最新の Up フラクタルを上抜けたとき 価格が最新の Down フラクタルを下抜けたとき にアラートを出す設計です。 ブレイク判定は 終値ベース（ダマシを減らす） ヒゲ込み（高値 / 安値） を設定で切り替えられます。 視認性と制御性を重視した設計 フラクタル矢印は 確定時に1本のみ表示（重複なし） Up / Down で色分けされたラインと矢印 ライン表示・矢印表示の ON / OFF 切り替え可能 想定される用途 フラクタルを用いた サポート / レジスタンスの可視化 直近フラクタルを基準とした ブレイクアウト戦略 MT4 から TradingView への移行時に、 ロジックと見た目をできるだけ変えずに使いたい場合 本スクリプトは、 MT4のフラクタル系インジケーターに慣れたトレーダーが、 TradingViewでも違和感なく使えることを重視して設計されています。
Indicateur Pine Script®
par tarotaro_n
Volume Buy/Sell Pressure with Hot PercentFULL DESCRIPTION (Condensed Version) Volume Buy/Sell Pressure with Hot Percent Professional volume analysis indicator revealing real-time buying and selling pressure with hot volume detection and customizable alerts. Key Features: Three-Layer Histogram - Visual breakdown: total volume (gray), buying pressure (bright green), selling pressure (bright red) Flexible Display - Toggle between percentage view or actual volume counts for buying/selling pressure Real-Time Metrics - Live buying/selling data, current bar volume, daily totals, 30-bar/30-day averages with comma formatting Hot Volume Detection - Automatic alerts with white triangle markers when volume exceeds threshold Customizable Labels - 4 sizes (Small/Normal/Large/Huge), 9 positions (all corners/centers/middles), toggle any metric on/off Smart Color Coding - Green (high volume/buying dominant), Red (selling dominant), Orange (equal pressure), Gray (low volume). Black text on bright backgrounds for maximum contrast. Alert Conditions: Hot Volume: Triggers when volume exceeds moving average by specified percentage Unusual 30-Bar Volume: Current bar significantly above 30-bar average Unusual 30-Day Volume: Daily volume significantly above 30-day average Settings: Display - Toggle metrics, choose percentage/count display, select size and position Volume - Set unusual volume threshold (default 200%), adjust average length (default 21) Hot Volume - Choose SMA/EMA, set lookback period (default 20), define threshold (default 100%) Perfect For: Day traders scalping futures (MNQ, MES, MYM, MGC, MCL) Swing traders identifying accumulation/distribution Breakout traders needing volume confirmation All timeframes - tick charts to daily/weekly Use Cases: Confirm trend strength with pressure alignment Spot reversals when pressure diverges from price Validate breakouts with hot volume alerts Identify smart money through unusual volume Track institutional activity at key levels What Makes This Different: Shows buying vs selling pressure WITHIN each bar using price range methodology. Most indicators only show total volume or simple up/down. This reveals actual pressure distribution regardless of bar direction. Three-layer design makes order flow instantly visible. Pro Tips: Use "Large" labels at 100% zoom Enable volume count display for position sizing Position labels in corners to avoid price overlap Enable alerts during pre-market and news events Watch for divergences: price up + selling pressure up = potential reversal Compare to both 30-bar and 30-day for full context Technical: Pine Script v6 All timeframes and instruments No repainting Efficient code, minimal CPU Three alert conditions Works on futures, stocks, forex, crypto Clean, professional presentation. Essential for volume analysis and order flow tracking.
Indicateur Pine Script®
par NPR21
2
Multi Cycles Predictive System ML - GBM IntegratedMulti-Cycle Predictive System: The Gradient Boosting Machine (GBM) Revolution Introduction: The Death of Static Analysis The financial markets are not static; they are a living, breathing, and chaotic system. Yet, for decades, traders have relied on static indicators—using the same RSI settings, the same MACD parameters, and the same Moving Averages regardless of whether the market is trending, chopping, or crashing. The Multi-Cycle Predictive System (MCPS) represents a paradigm shift. It is not just an indicator; it is an Adaptive Machine Learning Engine running directly on your chart. By integrating a fully functional Gradient Boosting Machine (GBM), this script does not guess—it learns. It monitors 13 distinct algorithmic models, calculates their real-time accuracy against future price action, and dynamically reallocates influence to the "winning" models using gradient descent. This is Survival of the Fittest applied to technical analysis. 1. The Core Engine: Gradient Boosting & Adaptive Learning At the heart of the MCPS is a custom-coded Gradient Boosting Machine. While most "ML" scripts on TradingView simply average a few indicators, this system replicates the architecture of advanced data science models. How the GBM Works: Ensemble Prediction: The system aggregates signals from 13 different mathematical models. Residual Calculation: It compares the ensemble's previous predictions against the actual price movement (Price Return) to calculate the error (Residual). Gradient Descent: It calculates the gradient of the loss function. We utilize a Huber Loss Gradient, which is robust against outliers (market spikes), ensuring the model doesn't overreact to volatility. Weight Optimization: Using a configurable learning rate, the system updates the weights of each sub-algorithm. Models that predicted correctly gain weight; models that failed lose influence. Softmax Normalization: Finally, weights are passed through a Softmax function (with Temperature control) to convert them into probabilities that sum to 1.0. The "Winner-Takes-All" Philosophy A common failure in ensemble systems is "Signal Dilution"—where good signals are drowned out by bad ones. The MCPS solves this with Aggressive Weight Concentration: Top 3 Logic: The script identifies the top 3 performing algorithms based on historical accuracy. The 90% Rule: It forces the system to allocate up to 90% of the total decision weight to these top 3 performers. Result: If Ehlers and Schaff are reading the market correctly, but MACD is failing, MACD is effectively silenced. The system listens only to the winners. 2. The 13 Algorithmic Pillars The MCPS draws from a diverse library of Digital Signal Processing (DSP), Statistical, and Momentum algorithms. It does not rely on simple moving averages. Ehlers Bandpass Filter: Isolates the dominant cycle in price data, removing trend and noise. Zero-Lag EMA (ZLEMA): Reduces lag to near-zero to track momentum shifts instantly. Coppock Curve: A classic long-term momentum indicator, modified here for adaptive responsiveness. Detrended Price Oscillator (DPO): Eliminates the trend to identify short-term cycles. Schaff Trend Cycle (STC): A double-smoothed stochastic of the MACD, excellent for identifying cycle turns. Fisher Transform: Converts price into a Gaussian normal distribution to pinpoint turning points. MESA Adaptive: Uses Maximum Entropy Spectral Analysis to detect the current dominant cycle period. Goertzel Algorithm: A DSP technique used to identify the magnitude of specific frequency components in the price wave. Hilbert Transform: Extracts the instantaneous amplitude and phase of the price action. Autocorrelation: Measures the similarity between the price series and a lagged version of itself to detect periodicity. Singular Spectrum Analysis (SSA): Decomposes the time series into trend, seasonal, and noise components (Simplified). Wavelet Transform: Analyzes data at different scales (frequencies) simultaneously. Empirical Mode Decomposition (EMD): Splits data into Intrinsic Mode Functions (IMFs) to isolate pure cycles. 3. The Dashboard: Total Transparency Black-box algorithms are dangerous. You need to know why a signal is being generated. The MCPS features two detailed dashboards (tables) located at the bottom of your screen. The Weight & Accuracy Table (Bottom Right) This is your "Under the Hood" view. It displays: Algorithm: The name of the model. Accuracy: The rolling historical accuracy of that specific model over the lookback period (e.g., 58.2%). Weight: The current influence that model has on the final signal. Watch this change in real-time. You will see the system "giving up" on bad models and "betting heavy" on good ones. Prob/Sig: The raw probability and directional signal (Up/Down). The GBM Stats Table (Bottom Left) Tracks the health of the Machine Learning engine: Iterations: How many learning cycles have occurred. Entropy: A measure of market confusion. High entropy means weights are spread out (models disagree). Low entropy means the models are aligned. Top 3 Weight: Shows how concentrated the decision power is. If this is >80%, the system is highly confident in specific models. Confidence & Agreement: Statistical measures of the signal strength. 4. How to Trade with MCPS This system outputs a single, composite Cycle Line (oscillating between -1 and 1) and a background Regime Color. Strategy A: The Zero-Cross (Trend Reversal) Bullish: When the Cycle Line crosses above 0. This indicates that the weighted average of the top-performing algorithms has shifted to a net-positive expectation. Bearish: When the Cycle Line crosses below 0. Strategy B: Probability Extremes (Mean Reversion) Strong Buy: When the Cycle Line drops below -0.5 (Oversold) and turns up. This indicates a high-probability cycle bottom. Strong Sell: When the Cycle Line rises above +0.5 (Overbought) and turns down. Strategy C: Regime Filtering The background color changes based on the aggregate consensus: Green/Lime: Bullish Regime. Look primarily for Long entries. Ignore weak sell signals. Red/Orange: Bearish Regime. Look primarily for Short entries. Gray: Neutral/Choppy. Reduce position size or wait. 5. Configuration & GBM Settings The script is highly customizable for advanced users who want to tune the Machine Learning hyperparameters. Prediction Horizon: How many days into the future are we trying to predict? (Default: 3). Accuracy Lookback: How far back does the model check to calculate "Accuracy"? GBM Learning Rate: Controls how fast the model adapts. High (0.2+): Adapts instantly to new market conditions but may be "jumpy." Low (0.05): Very stable, long-term adaptation. Temperature: Controls the "Softmax" function. Higher temperatures allow for softer, more distributed weights. Lower temperatures force a "Winner Takes All" outcome. Max Top 3 Weight: The cap on how much power the top 3 models can hold (Default: 90%). 6. Technical Nuances (For the Geeks) Huber Gradient: We use Huber loss rather than MSE (Mean Squared Error) for the gradient descent. This is crucial for financial time series because price spikes (outliers) can destroy the learning process of standard ML models. Huber loss transitions from quadratic to linear error, making the model robust. Regularization: L2 Regularization is applied to prevent overfitting, ensuring the model doesn't just memorize past noise. Memory Decay: The model has a "fading memory." Recent accuracy is weighted more heavily than accuracy from 200 bars ago, allowing the system to detect Regime Shifts (e.g., transitioning from a trending market to a ranging market). Disclaimer: This tool is a sophisticated analytical instrument, not a crystal ball. Machine Learning attempts to optimize probabilities based on historical patterns, but no algorithm can predict black swan events or fundamental news shocks. Always use proper risk management. The "Warmup Period" is required. The script needs to process 50 bars of history before the GBM engine initializes and produces signals. Author's Note: I built the MCPS because I was tired of indicators that stopped working when the market "personality" changed. By integrating GBM, this script adapts to the market's personality in real-time. If the market is cycling, Ehlers and Goertzel take over. If the market is trending, Coppock and ZLEMA take the lead. You don't have to choose—the math chooses for you. Please leave a boost and a comment if you find this helpful!
Indicateur Pine Script®
par jaydesaigu
SCOTTGO - Buy Sell Volume📊 SCOTTGO - Buy Sell Volume Bars - Delta - Up Down Volume Bars This indicator disaggregates the total volume traded on each bar into estimated Buying Volume and Selling Volume to visualize market pressure and dominance directly in a dedicated sub-pane. Key Features: Volume Disaggregation: Uses a standard formula to estimate how much of a bar's total volume was associated with upward (buying) pressure and how much was associated with downward (selling) pressure. Visual Clarity: Plots the Buy Volume (teal, upward) and Sell Volume (red, downward) as separate columns against a transparent total volume background, allowing for quick assessment of pressure balance. Real-Time Badge: A dynamic badge is fixed to the corner of the chart (default: Top Right) providing a numeric summary of the latest bar: Buy %: Percentage of the bar's total volume estimated as Buying Volume. Sell %: Percentage of the bar's total volume estimated as Selling Volume. Delta %: The magnitude of the volume difference (Delta) as a percentage of total volume, indicating the strength of the dominant side. Dominance Indicator: The background color of the badge changes dynamically to immediately signal whether Buying (customizable color, default: Teal) or Selling (customizable color, default: Red) pressure was dominant on the current bar. Usage: Traders can use this tool to identify periods of heavy accumulation (high Buy Volume) or distribution (high Sell Volume), providing insight into the conviction behind price movements.
Indicateur Pine Script®
par SCOTTGO
Smart Money Swing Strategy [All-in-One]# Pro Swing Trader 📈 A comprehensive swing trading indicator for TradingView that combines multiple confluence factors to identify high-probability trade setups with built-in risk management. ## 🎯 Overview This indicator is designed for swing traders who want to catch momentum pullbacks with precision entries. It filters trades using multiple timeframe analysis, RSI zones, volume confirmation, and EMA trends to deliver only the highest-confidence setups. ### Key Features ✅ **Multi-Timeframe Confluence** - Confirms trades with higher timeframe analysis (Daily, 4H, etc.) ✅ **Smart Entry Signals** - Detects pullback-to-EMA reclaim patterns ✅ **Automatic Risk Management** - Calculates stops, targets, and R-multiples ✅ **Dynamic Stop Loss** - ATR trailing stop + break-even automation ✅ **Real-Time HUD Dashboard** - Live confluence scoring and trade metrics ✅ **Comprehensive Alerts** - Entry, TP1, TP2, and stop-loss notifications ✅ **Visual Trade Levels** - Clear on-chart stop-loss and take-profit lines --- ## 📊 How It Works ### Signal Logic The indicator identifies two types of signals: **Base Signals** (Small triangles): - Price pulls back between Fast EMA and Slow EMA - RSI is in the swing zone (40-60 by default) - Price reclaims the Fast EMA with momentum - Optional: Volume spike confirmation **High-Confidence Signals** (Large triangles): - All base signal criteria met - Higher timeframe confirms the trend direction - HTF RSI and slope alignment - These are your primary trade signals ### Entry Conditions #### Long Entry (🟢 HC L) 1. Fast EMA > Slow EMA (uptrend) 2. Previous candle closed between the EMAs (pullback) 3. Current candle crosses above and closes above Fast EMA (reclaim) 4. RSI between 40-60 (swing zone) 5. **HTF Confirmation**: Daily/4H price above EMA50, RSI > 50, positive slope 6. Optional: Volume > 1.5x 20-bar average #### Short Entry (🔻 HC S) 1. Fast EMA < Slow EMA (downtrend) 2. Previous candle closed between the EMAs (pullback) 3. Current candle crosses below and closes below Fast EMA (reclaim) 4. RSI between 40-60 (swing zone) 5. **HTF Confirmation**: Daily/4H price below EMA50, RSI < 50, negative slope 6. Optional: Volume > 1.5x 20-bar average --- ## 🎛️ Settings & Parameters ### Trend Parameters - **Fast EMA**: Default 20 - Quick trend detection - **Slow EMA**: Default 50 - Major trend filter - **Swing Lookback**: Default 10 - Bars to find swing high/low for stops ### RSI Settings - **RSI Length**: Default 14 - **RSI Min**: Default 40 - Lower bound of swing zone - **RSI Max**: Default 60 - Upper bound of swing zone ### Risk Management - **Final TP Risk-Reward (R)**: Default 2.0 - Main profit target multiplier - **TP1 R Multiple**: Default 1.0 - Partial profit target - **Use Break-even Stop**: Move stop to entry after 1R profit - **ATR Trailing Stop**: Dynamic stop based on ATR(14) x 2.0 ### Filters - **Require Volume Spike**: Optional volume confirmation filter - **Use Higher TF Confirmation**: Enable multi-timeframe analysis - **Higher TF**: Default "D" (Daily) - Can use 240 (4H), W (Weekly), etc. --- ## 📈 Dashboard (HUD) The top-center dashboard shows real-time confluence status: | Column | Meaning | |--------|---------| | **Trend** | Current trend direction (UP/DOWN/Flat) | | **HTF** | Higher timeframe alignment (Bull/Bear/Flat) | | **RSI Zone** | Is RSI in swing zone? (YES/NO) | | **Volume** | Volume spike detected? (YES/NO) | | **Signal** | Active signal type (HC LONG/HC SHORT/None) | | **R Risk** | Current profit in R-multiples | | **Stop** | Current stop-loss level | | **TP1** | Partial take-profit status | | **TP2** | Final take-profit status | | **Conf %** | Overall confluence score (0-100%) | ### Confidence Score Breakdown - **20%** - Trend present (up or down) - **30%** - HTF confirmation aligned (or 15% if HTF off) - **20%** - RSI in swing zone - **10%** - Volume spike - **20%** - High-confidence signal triggered **Scoring**: - 🟢 70%+ = High probability setup - 🟡 40-69% = Moderate setup - 🔴 <40% = Low probability --- ## 🔔 Alert Setup The indicator includes 8 alert conditions: ### Entry Alerts - **HC LONG ENTRY** - High-confidence long signal triggered - **HC SHORT ENTRY** - High-confidence short signal triggered ### Profit Target Alerts - **LONG TP1 Reached** - Hit partial profit (1R by default) - **LONG Final TP Reached** - Hit final target (2R by default) - **SHORT TP1 Reached** - Hit partial profit - **SHORT Final TP Reached** - Hit final target ### Stop Loss Alerts - **LONG Stop/BE/Trail Level Hit** - Long position stopped out - **SHORT Stop/BE/Trail Level Hit** - Short position stopped out ### How to Set Up Alerts 1. Click "Add Alert" on TradingView 2. Choose this indicator from the dropdown 3. Select desired alert condition 4. Set alert to trigger "Once Per Bar Close" 5. Customize notification method (popup/email/webhook) --- ## 📋 Trading Workflow ### 1. Wait for High-Confidence Signal Look for the large **HC L** or **HC S** triangle on chart close. ### 2. Verify Confluence Check the HUD dashboard: - Confidence score should be 70%+ - HTF status should show alignment - RSI Zone should be "YES" ### 3. Entry Enter the trade at market or on next candle open. ### 4. Set Stop Loss Use the **initial stop** shown in the HUD (red line on chart): - **Longs**: Below the swing low (10-bar lookback) - **Shorts**: Above the swing high (10-bar lookback) ### 5. Set Take Profits - **TP1**: 1R (50% position close) - Yellow line - **TP2**: 2R (remaining 50% close) - Green line ### 6. Manage the Trade - Monitor the **R Risk** column to track profit - Stop moves to break-even automatically after 1R (if enabled) - ATR trailing stop engages dynamically (red line adjusts) - Exit if price hits dynamic stop level --- ## 🎨 Visual Guide ### On-Chart Elements **Triangles**: - Small lime/red triangles = Base signals (lower confidence) - Large lime/red triangles = High-confidence signals (trade these!) **Lines**: - 🟢 Green line = Fast EMA (20) - 🟠 Orange line = Slow EMA (50) - 🔴 Red line = Dynamic stop-loss level - 🟡 Yellow line = TP1 level - 🟢 Green line = TP2 (final target) **HUD Colors**: - 🟢 Green = Bullish/Active/Good - 🔴 Red = Bearish/Inactive/Warning - 🟡 Yellow = Neutral/Caution - 🔵 Blue = Informational - ⚫ Gray = Disabled/Off --- ## 💡 Strategy Tips ### Best Practices 1. **Only trade High-Confidence signals** - Ignore base signals unless very experienced 2. **Respect the HTF** - Don't fight the higher timeframe trend 3. **Use proper position sizing** - Risk 1-2% of account per trade 4. **Partial profits work** - Take 50% off at TP1, let rest run to TP2 5. **Let winners run** - Trailing stop helps capture extended moves 6. **Be patient** - Quality over quantity; wait for 70%+ confluence ### Optimal Timeframes - **Primary Chart**: 1H, 4H, Daily (swing trading) - **HTF Setting**: One level higher than your chart - If trading 1H → Set HTF to 4H or D - If trading 4H → Set HTF to D or W - If trading Daily → Set HTF to W ### Market Conditions **Best Performance**: - Trending markets with healthy pullbacks - Clear support/resistance zones - Moderate volatility **Avoid Trading**: - Extremely choppy/sideways markets - Major news events (unless experienced) - Low confidence scores (<40%) --- ## ⚙️ Advanced Customization ### Aggressive Setup (More Signals) ``` Fast EMA: 12 Slow EMA: 26 RSI Min: 35 RSI Max: 65 Use HTF Confirmation: OFF Require Volume Spike: OFF ``` ### Conservative Setup (Fewer, Higher Quality) ``` Fast EMA: 20 Slow EMA: 50 RSI Min: 45 RSI Max: 55 Use HTF Confirmation: ON Require Volume Spike: ON Final TP R: 3.0 ``` ### Scalping Adaptation (Not Recommended) ``` Fast EMA: 9 Slow EMA: 21 Swing Lookback: 5 TP1 R: 0.5 Final TP R: 1.0 ``` --- ## ⚠️ Risk Disclaimer **IMPORTANT**: This indicator is for educational and informational purposes only. - Past performance does not guarantee future results - No indicator is 100% accurate - Always use proper risk management - Never risk more than you can afford to lose - Consider using a demo account first - Seek professional financial advice if needed Trading involves substantial risk of loss and is not suitable for all investors. --- ## 🔧 Troubleshooting ### "No signals appearing" - Check if HTF confirmation is enabled but market isn't aligned - Verify RSI zone isn't too restrictive - Ensure volume spike isn't filtering out all setups - Try adjusting EMA lengths for your asset ### "Too many false signals" - Enable HTF confirmation - Tighten RSI zone (e.g., 45-55) - Enable volume spike requirement - Only trade 70%+ confidence setups ### "Stops too tight/wide" - Adjust Swing Lookback length - Modify ATR multiplier for trailing stop - Consider the asset's volatility ### "Alerts not working" - Ensure alert is set to "Once Per Bar Close" - Check indicator is added to the chart - Verify TradingView notification settings --- ## 📚 Version History **v1.0 (Current)** - Initial release - Multi-timeframe confluence system - Dynamic risk management - Real-time HUD dashboard - Comprehensive alert system - ATR trailing stops - Break-even automation --- ## 🤝 Support & Feedback If you find this indicator helpful: - ⭐ Star the script on TradingView - 💬 Share your results and feedback - 🐛 Report bugs or suggest improvements - 📖 Share with other traders --- ## 📖 Additional Resources ### Recommended Reading - "The New Trading for a Living" by Dr. Alexander Elder - "Swing Trading Using Multiple Timeframes" - Educational articles - Risk management and position sizing guides ### Learn More About - Multiple timeframe analysis - EMA crossover strategies - RSI divergence and zones - ATR-based stops - R-multiple profit management --- ## 📝 License This indicator is provided as-is for personal trading use. **Usage Rights**: - ✅ Use for personal trading - ✅ Modify for personal use - ❌ Resell or redistribute - ❌ Claim as original work --- ## 🎓 Quick Start Checklist - Add indicator to TradingView chart - Set your preferred timeframe (1H/4H/Daily) - Configure HTF setting (one level higher) - Review default parameters - Set up entry alerts (HC LONG/SHORT) - Set up TP and SL alerts - Test on historical data - Paper trade first - Start with small position sizes - Track your results --- **Happy Trading! 📊💰** *Remember: Discipline, patience, and risk management are the keys to long-term success.*
Indicateur Pine Script®
par frankieho_92
3