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Catalogador Binarias Padron MHI DejaVuTradesQuadrant patterns with 1 minute cataloging. This script simulates the quadrants between candles from minute 1 to 5. set your time frame to 1M. Ready, now we have our quadrants and we will do the analysis of these patterns using them. Green circles indicate winning trades, and blue circles Martingale steps. Counting candles within the quadrants (time frame m1 ) MHI There are several variants of MHI . In our cataloguer we offer 3 variants. MHI 1, 2 and 3. The analysis is carried out from the last three candles in the last quadrant. And the entry is made: on the first candle of the current quadrant ( MHI 1), on the second candle of the current quadrant ( MHI 2) and on the third candle of the current quadrant ( MHI 3). MHI 1 Entry into the first candle after the quadrant analysis. First martingale (in case of loss) in the second candle and second martingale (in case of two losses) in the third candle. MHI 2 Entry into the second candle after the quadrant analysis. First martingale (in case of loss) on the third candle and second martingale (in case of two losses) on the fourth candle. MHI 3 Entry into the third candle after the quadrant analysis. First martingale (in case of loss) on the fourth candle and second martingale (in case of two losses) on the fifth candle. The million dollar pattern It is an analysis very similar to that of the MHI , with the difference that it is in the entire quadrant. The million analysis consists of looking at all the candles in the last quadrant and entering the first candle of the current quadrant saying that that first candle will be the same color as most of the last quadrant. In cases where we have the same number of candles (3 red and 3 green) we do not trade! And the cataloger will not count these operations. Three Neighbors Pattern The analysis of the pattern of the three neighbors will always be in the third candle of the quadrant. Either in M5 or M1 . And it consists of observing the third candle and entering the following ones saying that it will be the same. Pattern C3 The C3 pattern is also an analysis in the candlestick count. We will enter the first quadrant candle saying that it will be equal to the first candle of the last quadrant. In the case of 1 martingale we will enter the third candle saying that it will be equal to the third candle of the last quadrant. And finally, in need of a second martingale, the entry will be in the fifth candle saying that it will be similar to the fifth of the last candle. Moonwalker Pattern The moonwalker pattern is also a quadrant pattern. Our entries will be in the first, second and third candles of the current quadrant. The analysis is performed on the third, second and first candle of the last quadrant. The entry of the first candle will be observed in the third candle of the last quadrant, being the opposite color to that observed. Being necessary a martingale, we make our entry into the second candle that claims the opposite color to the second candle in the last quadrant. Finally, our second martingale will be on the third candle of the current quadrant with analysis in which the color will be opposite to the first candle of the last quadrant.
Indicatore Pine Script®
di DejaVuTrades_BettoLe
5
TradeMiner S9This is the first TradingView indicator EVER to include dynamic support and resistance lines from upper or lower diagonal highs and lows in real-time. Note: This indicator has been built using Pinescript V2 Like and Share for access and more awesome indicators! A blue arrow appears only in a red bar and under these conditions: Closing Score Trigger (CS < 50) On Balance Volume, Accumulation/Distribution, and Chaikin Money Flow Combination (OBV/AD /CMF > 0) Chaikin Money Flow (CMF <-0.05) A blue horizontal line will be drawn when CMF > 0.05 indicates a sale of the position. A red arrow appears only in a green bar and under these conditions: Closing Score Trigger (CS > 50) On Balance Volume, Accumulation/Distribution, and Chaikin Money Flow Combination (OBV/AD/CMF < 0) Chaikin Money Flow (CMF > 0.05) A red horizontal line will be drawn when CMF <-0.05 indicates a sale of the position. A new condition called " leaniency " has been added that allows all these conditions to be fulfilled within multiple bars so that the occurrence occurs more frequently. This will result in more signals appearing. Setting leniency to " 1 " means that all four conditions must occur in a single bar, while " 5 " means that all four conditions must occur within 5 bars. Find lifetime access to the indicator here: www.kenzing.com BTC
Indicatore Pine Script®
di GrantPeace
33
B3 HL2 Method Candle PainterThis script is similar to the "Hi-Lo" or "Clear" methods of painting bars. Instead of using the tips/edges of the candles like those two, the "(H+L)/2" method uses the change in (high+low)/2 to paint the bars. This gives you some similar results if you were to be binary with the candle coloring. However, my coloring scheme is not entirely binary. There are 5 possible colors: HL2>LastHigh = Bright Green HL2LastHL2 = Dull Green HL2<LastHL2 = Dull Red Bar Change (close - open) is going against the indicator = Gray Free to share and enjoy! ~B3
Indicatore Pine Script®
di HammondB3
uranium V ☭Now everyone has free access to the script until February 18th. This script FULLY automated to open a trade by many strategies and scripts: *Bollinger Bands *RSI *Fibo levels 61.8, 78.6 *NEEW 10.0 *Zones of support from the timeframe m15 and 1h *Candle patterns This script combines 3 strategies for different open trade conditions (sometimes opens can be the same) Signals of different colors: +Pink arrows I recommend to use only at specified intervals (from 4 to 8 UTC, and from 13 to 18 UTC). Expiration should be 5 minutes. +Green arrows can be used always, but greater efficiency will be at specified intervals. Expiration should be 3 minutes. +Blue stripes are the strongest signal, they can always be used, but greater efficiency will be at the specified intervals. Expiration should be 5 minutes. For best results, I recommend opening deals on the following criteria: the indicator is superimposed on the timeframe m1 and EURUSD. +Opening trade only at the specified time, when the blue signal and the pink or green signal coincide, or the blue signal is preceded by a pink signal, open trade at the place of closing the candle of the blue signal. +Avoid strong non-characteristic price impulses, as well as news outlets. +Do not open a trade with a next pink signal bar when there is a strong pulse. +Do not open a trade with a green signal following a pink or blue signal, if the green close lowest or bigest than pink or blue signal. +Do not use martingale strategy on failure, wait for the next signal. +Use money management By adhering to these conditions.
Indicatore Pine Script®
di 777-5.Tonopa
31
Wick Detection (1 and 0) - AYNETDetailed Scientific Explanation 1. Wick Detection Logic Definition of a Wick: A wick, also known as a shadow, represents the price action outside the range of a candlestick's body (the region between open and close). Upper Wick: Occurs when the high value exceeds the greater of open and close. Lower Wick: Occurs when the low value is lower than the smaller of open and close. Upper Wick Detection: pinescript Kodu kopyala bool has_upper_wick = high > math.max(open, close) This checks if the high price of the candle is greater than the maximum of the open and close prices. If true, an upper wick exists. Lower Wick Detection: pinescript Kodu kopyala bool has_lower_wick = low < math.min(open, close) This checks if the low price of the candle is less than the minimum of the open and close prices. If true, a lower wick exists. 2. Binary Representation The presence of a wick is encoded as a binary value for simplicity and computational analysis: Upper Wick: Represented as 1 if present, otherwise 0. pinescript Kodu kopyala float upper_wick_binary = has_upper_wick ? 1 : 0 Lower Wick: Represented as 1 if present, otherwise 0. This value is inverted (-1) for visualization purposes. pinescript Kodu kopyala float lower_wick_binary = has_lower_wick ? 1 : 0 3. Visualization with Histograms The plot function is used to create histograms for visualizing the binary wick data: Upper Wicks: Plotted as positive values with green columns: pinescript Kodu kopyala plot(upper_wick_binary, title="Upper Wick", color=color.new(color.green, 0), style=plot.style_columns, linewidth=2) Lower Wicks: Plotted as negative values with red columns: pinescript Kodu kopyala plot(lower_wick_binary * -1, title="Lower Wick", color=color.new(color.red, 0), style=plot.style_columns, linewidth=2) Features and Applications 1. Wick Visualization: Upper wicks are displayed as positive green columns. Lower wicks are displayed as negative red columns. This provides a clear visual representation of wick presence in historical data. 2. Technical Analysis: Wick formations often indicate market sentiment: Upper Wicks: Sellers pushed the price lower after buyers drove it higher, signaling rejection at the top. Lower Wicks: Buyers pushed the price higher after sellers drove it lower, signaling rejection at the bottom. 3. Signal Generation: Traders can use wick detection to build strategies, such as identifying key price levels or market reversals. Enhancements and Future Improvements 1. Wick Length Measurement Instead of binary detection, measure the actual length of the wick: pinescript Kodu kopyala float upper_wick_length = high - math.max(open, close) float lower_wick_length = math.min(open, close) - low This approach allows for thresholds to identify significant wicks: pinescript Kodu kopyala bool significant_upper_wick = upper_wick_length > 10 // For wicks longer than 10 units. bool significant_lower_wick = lower_wick_length > 10 2. Alerts for Long Wicks Trigger alerts when significant wicks are detected: pinescript Kodu kopyala alertcondition(significant_upper_wick, title="Long Upper Wick", message="A significant upper wick has been detected.") alertcondition(significant_lower_wick, title="Long Lower Wick", message="A significant lower wick has been detected.") 3. Combined Wick Analysis Analyze both upper and lower wicks to assess volatility: pinescript Kodu kopyala float total_wick_length = upper_wick_length + lower_wick_length bool high_volatility = total_wick_length > 20 // Combined wick length exceeds 20 units. Conclusion This script provides a compact and computationally efficient way to detect candlestick wicks and represent them as binary data. By visualizing the data with histograms, traders can easily identify wick formations and use them for technical analysis, signal generation, and volatility assessment. The approach can be extended further to measure wick length, detect significant wicks, and integrate these insights into automated trading systems.
Indicatore Pine Script®
di Aynet-London
Qualitative Smoothed Strength Index***RSI CHART BELOW IS FOR COMPARSION TO SHOW HOE THEY MAKE SIMILIAR PATTERNS*** IT IS NOT PART OF THE INDICATOR*** The Qualitative Smoothed Strength Index (QSSI) is a simplified momentum oscillator whose values will oscillate between 0 and 1 . By converting price differences into binary values and smoothing them with a moving average, it identifies qualitative strength of price movements. This simplification allows traders to easily interpret trends and reversals. The QSSI offers advantages such as noise reduction, clear trend identification, and early signal detection, resulting in less lag compared to traditional oscillators. Traders can customize the indicator based on their preferences and use it across various markets. QSSI Indicator uses the input function is used to define the input parameters of the indicator. In this case, there are two inputs: length: The number of periods used for calculating the differences (a, b, c) and their assigned values. Default value is 5. MAL: The length of the moving average used for smoothing the assigned values. Default value is 14. The next few lines calculate 'a', 'b', and 'c', which represent the differences between the high, low, and close prices, respectively, and their corresponding previous simple moving averages (SMAs) of specified length. These differences are used to identify price movements. The code assigns binary values (0 or 1) to a_assigned, b_assigned, and c_assigned, depending on whether the corresponding differences (a, b, c) are greater than 0. This step converts the differences into a binary representation, indicating upward or downward price movements. Average_assigned calculates the average of the assigned binary values of a, b, and c. This average value represents the overall strength of the price movement.ma_assigned calculates the 14-day moving average of average_assigned, which smoothens the indicator and helps traders identify trends more easily. The code plots the 14-day moving average (ma_assigned) on the chart as a blue line. It also plots the individual assigned values of a, b, and c as dots on the chart. a_assigned is shown in green, b_assigned in red, and c_assigned in black. These dots indicate the presence of upward or downward movements in the respective price components. By visualizing these dots on the chart, the trader can quickly identify the presence and direction of price movements for each of the price components. This information can be valuable for understanding how the different price elements (high, low, and close) are contributing to the overall trend and strength of the market. Traders can use this data to make more informed decisions, such as confirming the presence of trends, identifying potential reversals, or gauging the overall market sentiment based on the distribution of upward and downward movements across the price components. Finally, the code draws horizontal dotted lines at levels 0.70 (0.8)and 0.30 (0.2). These levels are typically used to identify overbought (above 0.70 or 0.8) and oversold (below 0.30 or 0.2) conditions in the market. The Qualitative Smoothed Strength Index (QSSI) provides traders with information about the strength and direction of price movements. By using assigned binary values, the indicator simplifies the interpretation of price data, making it easier to identify trends and potential reversals.
Indicatore Pine Script®
di federalTacos5392b
Trend Gauge [BullByte]Trend Gauge Summary A multi-factor trend detection indicator that aggregates EMA alignment, VWMA momentum scaling, volume spikes, ATR breakout strength, higher-timeframe confirmation, ADX-based regime filtering, and RSI pivot-divergence penalty into one normalized trend score. It also provides a confidence meter, a Δ Score momentum histogram, divergence highlights, and a compact, scalable dashboard for at-a-glance status. ________________________________________ ## 1. Purpose of the Indicator Why this was built Traders often monitor several indicators in parallel - EMAs, volume signals, volatility breakouts, higher-timeframe trends, ADX readings, divergence alerts, etc., which can be cumbersome and sometimes contradictory. The “Trend Gauge” indicator was created to consolidate these complementary checks into a single, normalized score that reflects the prevailing market bias (bullish, bearish, or neutral) and its strength. By combining multiple inputs with an adaptive regime filter, scaling contributions by magnitude, and penalizing weakening signals (divergence), this tool aims to reduce noise, highlight genuine trend opportunities, and warn when momentum fades. Key Design Goals Signal Aggregation Merged trend-following signals (EMA crossover, ATR breakout, higher-timeframe confirmation) and momentum signals (VWMA thrust, volume spikes) into a unified score that reflects directional bias more holistically. Market Regime Awareness Implemented an ADX-style filter to distinguish between trending and ranging markets, reducing the influence of trend signals during sideways phases to avoid false breakouts. Magnitude-Based Scaling Replaced binary contributions with scaled inputs: VWMA thrust and ATR breakout are weighted relative to recent averages, allowing for more nuanced score adjustments based on signal strength. Momentum Divergence Penalty Integrated pivot-based RSI divergence detection to slightly reduce the overall score when early signs of momentum weakening are detected, improving risk-awareness in entries. Confidence Transparency Added a live confidence metric that shows what percentage of enabled sub-indicators currently agree with the overall bias, making the scoring system more interpretable. Momentum Acceleration Visualization Plotted the change in score (Δ Score) as a histogram bar-to-bar, highlighting whether momentum is increasing, flattening, or reversing, aiding in more timely decision-making. Compact Informational Dashboard Presented a clean, scalable dashboard that displays each component’s status, the final score, confidence %, detected regime (Trending/Ranging), and a labeled strength gauge for quick visual assessment. ________________________________________ ## 2. Why a Trader Should Use It Main benefits and use cases 1. Unified View: Rather than juggling multiple windows or panels, this indicator delivers a single score synthesizing diverse signals. 2. Regime Filtering: In ranging markets, trend signals often generate false entries. The ADX-based regime filter automatically down-weights trend-following components, helping you avoid chasing false breakouts. 3. Nuanced Momentum & Volatility: VWMA and ATR breakout contributions are normalized by recent averages, so strong moves register strongly while smaller fluctuations are de-emphasized. 4. Early Warning of Weakening: Pivot-based RSI divergence is detected and used to slightly reduce the score when price/momentum diverges, giving a cautionary signal before a full reversal. 5. Confidence Meter: See at a glance how many sub-indicators align with the aggregated bias (e.g., “80% confidence” means 4 out of 5 components agree ). This transparency avoids black-box decisions. 6. Trend Acceleration/Deceleration View: The Δ Score histogram visualizes whether the aggregated score is rising (accelerating trend) or falling (momentum fading), supplementing the main oscillator. 7. Compact Dashboard: A corner table lists each check’s status (“Bull”, “Bear”, “Flat” or “Disabled”), plus overall Score, Confidence %, Regime, Trend Strength label, and a gauge bar. Users can scale text size (Normal, Small, Tiny) without removing elements, so the full picture remains visible even in compact layouts. 8. Customizable & Transparent: All components can be enabled/disabled and parameterized (lengths, thresholds, weights). The full Pine code is open and well-commented, letting users inspect or adapt the logic. 9. Alert-ready: Built-in alert conditions fire when the score crosses weak thresholds to bullish/bearish or returns to neutral, enabling timely notifications. ________________________________________ ## 3. Component Rationale (“Why These Specific Indicators?”) Each sub-component was chosen because it adds complementary information about trend or momentum: 1. EMA Cross o Basic trend measure: compares a faster EMA vs. a slower EMA. Quickly reflects trend shifts but by itself can whipsaw in sideways markets. 2. VWMA Momentum o Volume-weighted moving average change indicates momentum with volume context. By normalizing (dividing by a recent average absolute change), we capture the strength of momentum relative to recent history. This scaling prevents tiny moves from dominating and highlights genuinely strong momentum. 3. Volume Spikes o Sudden jumps in volume combined with price movement often accompany stronger moves or reversals. A binary detection (+1 for bullish spike, -1 for bearish spike) flags high-conviction bars. 4. ATR Breakout o Detects price breaking beyond recent highs/lows by a multiple of ATR. Measures breakout strength by how far beyond the threshold price moves relative to ATR, capped to avoid extreme outliers. This gives a volatility-contextual trend signal. 5. Higher-Timeframe EMA Alignment o Confirms whether the shorter-term trend aligns with a higher timeframe trend. Uses request.security with lookahead_off to avoid future data. When multiple timeframes agree, confidence in direction increases. 6. ADX Regime Filter (Manual Calculation) o Computes directional movement (+DM/–DM), smoothes via RMA, computes DI+ and DI–, then a DX and ADX-like value. If ADX ≥ threshold, market is “Trending” and trend components carry full weight; if ADX < threshold, “Ranging” mode applies a configurable weight multiplier (e.g., 0.5) to trend-based contributions, reducing false signals in sideways conditions. Volume spikes remain binary (optional behavior; can be adjusted if desired). 7. RSI Pivot-Divergence Penalty o Uses ta.pivothigh / ta.pivotlow with a lookback to detect pivot highs/lows on price and corresponding RSI values. When price makes a higher high but RSI makes a lower high (bearish divergence), or price makes a lower low but RSI makes a higher low (bullish divergence), a divergence signal is set. Rather than flipping the trend outright, the indicator subtracts (or adds) a small penalty (configurable) from the aggregated score if it would weaken the current bias. This subtle adjustment warns of weakening momentum without overreacting to noise. 8. Confidence Meter o Counts how many enabled components currently agree in direction with the aggregated score (i.e., component sign × score sign > 0). Displays this as a percentage. A high percentage indicates strong corroboration; a low percentage warns of mixed signals. 9. Δ Score Momentum View o Plots the bar-to-bar change in the aggregated score (delta_score = score - score ) as a histogram. When positive, bars are drawn in green above zero; when negative, bars are drawn in red below zero. This reveals acceleration (rising Δ) or deceleration (falling Δ), supplementing the main oscillator. 10. Dashboard • A table in the indicator pane’s top-right with 11 rows: 1. EMA Cross status 2. VWMA Momentum status 3. Volume Spike status 4. ATR Breakout status 5. Higher-Timeframe Trend status 6. Score (numeric) 7. Confidence % 8. Regime (“Trending” or “Ranging”) 9. Trend Strength label (e.g., “Weak Bullish Trend”, “Strong Bearish Trend”) 10. Gauge bar visually representing score magnitude • All rows always present; size_opt (Normal, Small, Tiny) only changes text size via text_size, not which elements appear. This ensures full transparency. ________________________________________ ## 4. What Makes This Indicator Stand Out • Regime-Weighted Multi-Factor Score: Trend and momentum signals are adaptively weighted by market regime (trending vs. ranging) , reducing false signals. • Magnitude Scaling: VWMA and ATR breakout contributions are normalized by recent average momentum or ATR, giving finer gradation compared to simple ±1. • Integrated Divergence Penalty: Divergence directly adjusts the aggregated score rather than appearing as a separate subplot; this influences alerts and trend labeling in real time. • Confidence Meter: Shows the percentage of sub-signals in agreement, providing transparency and preventing blind trust in a single metric. • Δ Score Histogram Momentum View: A histogram highlights acceleration or deceleration of the aggregated trend score, helping detect shifts early. • Flexible Dashboard: Always-visible component statuses and summary metrics in one place; text size scaling keeps the full picture available in cramped layouts. • Lookahead-Safe HTF Confirmation: Uses lookahead_off so no future data is accessed from higher timeframes, avoiding repaint bias. • Repaint Transparency: Divergence detection uses pivot functions that inherently confirm only after lookback bars; description documents this lag so users understand how and when divergence labels appear. • Open-Source & Educational: Full, well-commented Pine v6 code is provided; users can learn from its structure: manual ADX computation, conditional plotting with series = show ? value : na, efficient use of table.new in barstate.islast, and grouped inputs with tooltips. • Compliance-Conscious: All plots have descriptive titles; inputs use clear names; no unnamed generic “Plot” entries; manual ADX uses RMA; all request.security calls use lookahead_off. Code comments mention repaint behavior and limitations. ________________________________________ ## 5. Recommended Timeframes & Tuning • Any Timeframe: The indicator works on small (e.g., 1m) to large (daily, weekly) timeframes. However: o On very low timeframes (<1m or tick charts), noise may produce frequent whipsaws. Consider increasing smoothing lengths, disabling certain components (e.g., volume spike if volume data noisy), or using a larger pivot lookback for divergence. o On higher timeframes (daily, weekly), consider longer lookbacks for ATR breakout or divergence, and set Higher-Timeframe trend appropriately (e.g., 4H HTF when on 5 Min chart). • Defaults & Experimentation: Default input values are chosen to be balanced for many liquid markets. Users should test with replay or historical analysis on their symbol/timeframe and adjust: o ADX threshold (e.g., 20–30) based on instrument volatility. o VWMA and ATR scaling lengths to match average volatility cycles. o Pivot lookback for divergence: shorter for faster markets, longer for slower ones. • Combining with Other Analysis: Use in conjunction with price action, support/resistance, candlestick patterns, order flow, or other tools as desired. The aggregated score and alerts can guide attention but should not be the sole decision-factor. ________________________________________ ## 6. How Scoring and Logic Works (Step-by-Step) 1. Compute Sub-Scores o EMA Cross: Evaluate fast EMA > slow EMA ? +1 : fast EMA < slow EMA ? -1 : 0. o VWMA Momentum: Calculate vwma = ta.vwma(close, length), then vwma_mom = vwma - vwma . Normalize: divide by recent average absolute momentum (e.g., ta.sma(abs(vwma_mom), lookback)), clip to . o Volume Spike: Compute vol_SMA = ta.sma(volume, len). If volume > vol_SMA * multiplier AND price moved up ≥ threshold%, assign +1; if moved down ≥ threshold%, assign -1; else 0. o ATR Breakout: Determine recent high/low over lookback. If close > high + ATR*mult, compute distance = close - (high + ATR*mult), normalize by ATR, cap at a configured maximum. Assign positive contribution. Similarly for bearish breakout below low. o Higher-Timeframe Trend: Use request.security(..., lookahead=barmerge.lookahead_off) to fetch HTF EMAs; assign +1 or -1 based on alignment. 2. ADX Regime Weighting o Compute manual ADX: directional movements (+DM, –DM), smoothed via RMA, DI+ and DI–, then DX and ADX via RMA. If ADX ≥ threshold, market is considered “Trending”; otherwise “Ranging.” o If trending, trend-based contributions (EMA, VWMA, ATR, HTF) use full weight = 1.0. If ranging, use weight = ranging_weight (e.g., 0.5) to down-weight them. Volume spike stays binary ±1 (optional to change if desired). 3. Aggregate Raw Score o Sum weighted contributions of all enabled components. Count the number of enabled components; if zero, default count = 1 to avoid division by zero. 4. Divergence Penalty o Detect pivot highs/lows on price and corresponding RSI values, using a lookback. When price and RSI diverge (bearish or bullish divergence), check if current raw score is in the opposing direction:  If bearish divergence (price higher high, RSI lower high) and raw score currently positive, subtract a penalty (e.g., 0.5).  If bullish divergence (price lower low, RSI higher low) and raw score currently negative, add a penalty. o This reduces score magnitude to reflect weakening momentum, without flipping the trend outright. 5. Normalize and Smooth o Normalized score = (raw_score / number_of_enabled_components) * 100. This yields a roughly range. o Optional EMA smoothing of this normalized score to reduce noise. 6. Interpretation o Sign: >0 = net bullish bias; <0 = net bearish bias; near zero = neutral. o Magnitude Zones: Compare |score| to thresholds (Weak, Medium, Strong) to label trend strength (e.g., “Weak Bullish Trend”, “Medium Bearish Trend”, “Strong Bullish Trend”). o Δ Score Histogram: The histogram bars from zero show change from previous bar’s score; positive bars indicate acceleration, negative bars indicate deceleration. o Confidence: Percentage of sub-indicators aligned with the score’s sign. o Regime: Indicates whether trend-based signals are fully weighted or down-weighted. ________________________________________ ## 7. Oscillator Plot & Visualization: How to Read It Main Score Line & Area The oscillator plots the aggregated score as a line, with colored fill: green above zero for bullish area, red below zero for bearish area. Horizontal reference lines at ±Weak, ±Medium, and ±Strong thresholds mark zones: crossing above +Weak suggests beginning of bullish bias, above +Medium for moderate strength, above +Strong for strong trend; similarly for bearish below negative thresholds. Δ Score Histogram If enabled, a histogram shows score - score . When positive, bars appear in green above zero, indicating accelerating bullish momentum; when negative, bars appear in red below zero, indicating decelerating or reversing momentum. The height of each bar reflects the magnitude of change in the aggregated score from the prior bar. Divergence Highlight Fill If enabled, when a pivot-based divergence is confirmed: • Bullish Divergence : fill the area below zero down to –Weak threshold in green, signaling potential reversal from bearish to bullish. • Bearish Divergence : fill the area above zero up to +Weak threshold in red, signaling potential reversal from bullish to bearish. These fills appear with a lag equal to pivot lookback (the number of bars needed to confirm the pivot). They do not repaint after confirmation, but users must understand this lag. Trend Direction Label When score crosses above or below the Weak threshold, a small label appears near the score line reading “Bullish” or “Bearish.” If the score returns within ±Weak, the label “Neutral” appears. This helps quickly identify shifts at the moment they occur. Dashboard Panel In the indicator pane’s top-right, a table shows: 1. EMA Cross status: “Bull”, “Bear”, “Flat”, or “Disabled” 2. VWMA Momentum status: similarly 3. Volume Spike status: “Bull”, “Bear”, “No”, or “Disabled” 4. ATR Breakout status: “Bull”, “Bear”, “No”, or “Disabled” 5. Higher-Timeframe Trend status: “Bull”, “Bear”, “Flat”, or “Disabled” 6. Score: numeric value (rounded) 7. Confidence: e.g., “80%” (colored: green for high, amber for medium, red for low) 8. Regime: “Trending” or “Ranging” (colored accordingly) 9. Trend Strength: textual label based on magnitude (e.g., “Medium Bullish Trend”) 10. Gauge: a bar of blocks representing |score|/100 All rows remain visible at all times; changing Dashboard Size only scales text size (Normal, Small, Tiny). ________________________________________ ## 8. Example Usage (Illustrative Scenario) Example: BTCUSD 5 Min 1. Setup: Add “Trend Gauge ” to your BTCUSD 5 Min chart. Defaults: EMAs (8/21), VWMA 14 with lookback 3, volume spike settings, ATR breakout 14/5, HTF = 5m (or adjust to 4H if preferred), ADX threshold 25, ranging weight 0.5, divergence RSI length 14 pivot lookback 5, penalty 0.5, smoothing length 3, thresholds Weak=20, Medium=50, Strong=80. Dashboard Size = Small. 2. Trend Onset: At some point, price breaks above recent high by ATR multiple, volume spikes upward, faster EMA crosses above slower EMA, HTF EMA also bullish, and ADX (manual) ≥ threshold → aggregated score rises above +20 (Weak threshold) into +Medium zone. Dashboard shows “Bull” for EMA, VWMA, Vol Spike, ATR, HTF; Score ~+60–+70; Confidence ~100%; Regime “Trending”; Trend Strength “Medium Bullish Trend”; Gauge ~6–7 blocks. Δ Score histogram bars are green and rising, indicating accelerating bullish momentum. Trader notes the alignment. 3. Divergence Warning: Later, price makes a slightly higher high but RSI fails to confirm (lower RSI high). Pivot lookback completes; the indicator highlights a bearish divergence fill above zero and subtracts a small penalty from the score, causing score to stall or retrace slightly. Dashboard still bullish but score dips toward +Weak. This warns the trader to tighten stops or take partial profits. 4. Trend Weakens: Score eventually crosses below +Weak back into neutral; a “Neutral” label appears, and a “Neutral Trend” alert fires if enabled. Trader exits or avoids new long entries. If score subsequently crosses below –Weak, a “Bearish” label and alert occur. 5. Customization: If the trader finds VWMA noise too frequent on this instrument, they may disable VWMA or increase lookback. If ATR breakouts are too rare, adjust ATR length or multiplier. If ADX threshold seems off, tune threshold. All these adjustments are explained in Inputs section. 6. Visualization: The screenshot shows the main score oscillator with colored areas, reference lines at ±20/50/80, Δ Score histogram bars below/above zero, divergence fill highlighting potential reversal, and the dashboard table in the top-right. ________________________________________ ## 9. Inputs Explanation A concise yet clear summary of inputs helps users understand and adjust: 1. General Settings • Theme (Dark/Light): Choose background-appropriate colors for the indicator pane. • Dashboard Size (Normal/Small/Tiny): Scales text size only; all dashboard elements remain visible. 2. Indicator Settings • Enable EMA Cross: Toggle on/off basic EMA alignment check. o Fast EMA Length and Slow EMA Length: Periods for EMAs. • Enable VWMA Momentum: Toggle VWMA momentum check. o VWMA Length: Period for VWMA. o VWMA Momentum Lookback: Bars to compare VWMA to measure momentum. • Enable Volume Spike: Toggle volume spike detection. o Volume SMA Length: Period to compute average volume. o Volume Spike Multiplier: How many times above average volume qualifies as spike. o Min Price Move (%): Minimum percent change in price during spike to qualify as bullish or bearish. • Enable ATR Breakout: Toggle ATR breakout detection. o ATR Length: Period for ATR. o Breakout Lookback: Bars to look back for recent highs/lows. o ATR Multiplier: Multiplier for breakout threshold. • Enable Higher Timeframe Trend: Toggle HTF EMA alignment. o Higher Timeframe: E.g., “5” for 5-minute when on 1-minute chart, or “60” for 5 Min when on 15m, etc. Uses lookahead_off. • Enable ADX Regime Filter: Toggles regime-based weighting. o ADX Length: Period for manual ADX calculation. o ADX Threshold: Value above which market considered trending. o Ranging Weight Multiplier: Weight applied to trend components when ADX < threshold (e.g., 0.5). • Scale VWMA Momentum: Toggle normalization of VWMA momentum magnitude. o VWMA Mom Scale Lookback: Period for average absolute VWMA momentum. • Scale ATR Breakout Strength: Toggle normalization of breakout distance by ATR. o ATR Scale Cap: Maximum multiple of ATR used for breakout strength. • Enable Price-RSI Divergence: Toggle divergence detection. o RSI Length for Divergence: Period for RSI. o Pivot Lookback for Divergence: Bars on each side to identify pivot high/low. o Divergence Penalty: Amount to subtract/add to score when divergence detected (e.g., 0.5). 3. Score Settings • Smooth Score: Toggle EMA smoothing of normalized score. • Score Smoothing Length: Period for smoothing EMA. • Weak Threshold: Absolute score value under which trend is considered weak or neutral. • Medium Threshold: Score above Weak but below Medium is moderate. • Strong Threshold: Score above this indicates strong trend. 4. Visualization Settings • Show Δ Score Histogram: Toggle display of the bar-to-bar change in score as a histogram. Default true. • Show Divergence Fill: Toggle background fill highlighting confirmed divergences. Default true. Each input has a tooltip in the code. ________________________________________ ## 10. Limitations, Repaint Notes, and Disclaimers 10.1. Repaint & Lag Considerations • Pivot-Based Divergence Lag: The divergence detection uses ta.pivothigh / ta.pivotlow with a specified lookback. By design, a pivot is only confirmed after the lookback number of bars. As a result: o Divergence labels or fills appear with a delay equal to the pivot lookback. o Once the pivot is confirmed and the divergence is detected, the fill/label does not repaint thereafter, but you must understand and accept this lag. o Users should not treat divergence highlights as predictive signals without additional confirmation, because they appear after the pivot has fully formed. • Higher-Timeframe EMA Alignment: Uses request.security(..., lookahead=barmerge.lookahead_off), so no future data from the higher timeframe is used. This avoids lookahead bias and ensures signals are based only on completed higher-timeframe bars. • No Future Data: All calculations are designed to avoid using future information. For example, manual ADX uses RMA on past data; security calls use lookahead_off. 10.2. Market & Noise Considerations • In very choppy or low-liquidity markets, some components (e.g., volume spikes or VWMA momentum) may be noisy. Users can disable or adjust those components’ parameters. • On extremely low timeframes, noise may dominate; consider smoothing lengths or disabling certain features. • On very high timeframes, pivots and breakouts occur less frequently; adjust lookbacks accordingly to avoid sparse signals. 10.3. Not a Standalone Trading System • This is an indicator, not a complete trading strategy. It provides signals and context but does not manage entries, exits, position sizing, or risk management. • Users must combine it with their own analysis, money management, and confirmations (e.g., price patterns, support/resistance, fundamental context). • No guarantees: past behavior does not guarantee future performance. 10.4. Disclaimers • Educational Purposes Only: The script is provided as-is for educational and informational purposes. It does not constitute financial, investment, or trading advice. • Use at Your Own Risk: Trading involves risk of loss. Users should thoroughly test and use proper risk management. • No Guarantees: The author is not responsible for trading outcomes based on this indicator. • License: Published under Mozilla Public License 2.0; code is open for viewing and modification under MPL terms. ________________________________________ ## 11. Alerts • The indicator defines three alert conditions: 1. Bullish Trend: when the aggregated score crosses above the Weak threshold. 2. Bearish Trend: when the score crosses below the negative Weak threshold. 3. Neutral Trend: when the score returns within ±Weak after being outside. Good luck – BullByte
Indicatore Pine Script®
di BullByte
3
SML SuiteIntroducing the "SML Suite" Indicator The "SML Suite" is a powerful and easy-to-use trading indicator designed to help traders make informed decisions in the world of financial markets. Whether you're a seasoned trader or a novice, this indicator is your trusty sidekick for evaluating market trends. Key Features: Three Moving Averages: The indicator employs three different moving averages, each with a distinct length, allowing you to adapt to various market conditions. Customizable Parameters: You can easily customize the moving average lengths and source data to tailor the indicator to your specific trading strategy. Standard Deviation Multiplier: Adjust the standard deviation multiplier to fine-tune the indicator's sensitivity to market fluctuations. Binary Results: The indicator provides clear binary signals (1 or -1) based on whether the current price is above or below certain bands. This simplifies your decision-making process. SML Calculation: The SML (Short, Medium, Long) calculation is a smart combination of the binary results, offering you an overall sentiment about the market. Color-Coded Visualization: Visualize market sentiment with color-coded bars, making it easy to spot trends at a glance. Interactive Table: A table is displayed on your chart, giving you a quick overview of the binary results and the overall SML sentiment. With the "SML Suite" indicator, you don't need to be a coding expert to harness the power of technical analysis. Stay ahead of the game and enhance your trading strategy with this user-friendly tool. Make your trading decisions with confidence and clarity, backed by the insights provided by the "SML Suite" indicator.
Indicatore Pine Script®
di kikfraben
Options Oscillator [Lite] IVRank, IVx, Call/Put Volatility Skew The first TradingView indicator that provides REAL IVRank, IVx, and CALL/PUT skew data based on REAL option chain for 5 U.S. market symbols. 🔃 Auto-Updating Option Metrics without refresh! 🍒 Developed and maintained by option traders for option traders. 📈 Specifically designed for TradingView users who trade options. 🔶 Ticker Information: This 'Lite' indicator is currently only available for 5 liquid U.S. market smbols : NASDAQ:TSLA AMEX:DIA NASDAQ:AAPL NASDAQ:AMZN and NYSE:ORCL 🔶 How does the indicator work and why is it unique? This Pine Script indicator is a complex tool designed to provide various option metrics and visualization tools for options market traders. The indicator extracts raw options data from an external data provider (ORATS), processes and refines the delayed data package using pineseed, and sends it to TradingView, visualizing the data using specific formulas (see detailed below) or interpolated values (e.g., delta distances). This method of incorporating options data into a visualization framework is unique and entirely innovative on TradingView. The indicator aims to offer a comprehensive view of the current state of options for the implemented instruments, including implied volatility (IV), IV rank (IVR), options skew, and expected market movements, which are objectively measured as detailed below. The options metrics we display may be familiar to options traders from various major brokerage platforms such as TastyTrade, IBKR, TOS, Tradier, TD Ameritrade, Schwab, etc. 🟨 The following data is displayed in the oscillator 🟨 We use Tastytrade formulas, so our numbers mostly align with theirs! 🔶 𝗜𝗩𝗥𝗮𝗻𝗸 The Implied Volatility Rank (IVR) helps options traders assess the current level of implied volatility (IV) in comparison to the past 52 weeks. IVR is a useful metric to determine whether options are relatively cheap or expensive. This can guide traders on whether to buy or sell options. IV Rank formula = (current IV - 52 week IV low) / (52 week IV high - 52 week IV low) IVRank is default blue and you can adjust their settings: 🔶 𝗜𝗩𝘅 𝗮𝘃𝗴 The implied volatility (IVx) shown in the option chain is calculated like the VIX. The Cboe uses standard and weekly SPX options to measure expected S&P 500 volatility. A similar method is used for calculating IVx for each expiration cycle. We aggregate the IVx values for the 35-70 day monthly expiration cycle, and use that value in the oscillator and info panel. We always display which expiration the IVx values are averaged for when you hover over the IVx cell. IVx main color is purple, but you can change the settings: 🔹IVx 5 days change % We are also displaying the five-day change of the IV Index (IVx value). The IV Index 5-Day Change column provides quick insight into recent expansions or decreases in implied volatility over the last five trading days. Traders who expect the value of options to decrease might view a decrease in IVX as a positive signal. Strategies such as Strangle and Ratio Spread can benefit from this decrease. On the other hand, traders anticipating further increases in IVX will focus on the rising IVX values. Strategies like Calendar Spread or Diagonal Spread can take advantage of increasing implied volatility. This indicator helps traders quickly assess changes in implied volatility, enabling them to make informed decisions based on their trading strategies and market expectations. Important Note: The IVx value alone does not provide sufficient context. There are stocks that inherently exhibit high IVx values. Therefore, it is crucial to consider IVx in conjunction with the Implied Volatility Rank (IVR), which measures the IVx relative to its own historical values. This combined view helps in accurately assessing the significance of the IVx in relation to the specific stock's typical volatility behavior. This indicator offers traders a comprehensive view of implied volatility, assisting them in making informed decisions by highlighting both the absolute and relative volatility measures. 🔶 𝗖𝗔𝗟𝗟/𝗣𝗨𝗧 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗦𝗸𝗲𝘄 𝗵𝗶𝘀𝘁𝗼𝗴𝗿𝗮𝗺 At TanukiTrade, Vertical Pricing Skew refers to the difference in pricing between put and call options with the same expiration date at the same distance (at tastytrade binary expected move). We analyze this skew to understand market sentiment. This is the same formula used by TastyTrade for calculations. We calculate the interpolated strike price based on the expected move, taking into account the neighboring option prices and their distances. This allows us to accurately determine whether the CALL or PUT options are more expensive. 🔹 What Causes Pricing Skew? The Theory Behind It The asymmetric pricing of PUT and CALL options is driven by the natural dynamics of the market. The theory is that when CALL options are more expensive than PUT options at the same distance from the current spot price, market participants are buying CALLs and selling PUTs, expecting a faster upward movement compared to a downward one . In the case of PUT skew, it's the opposite: participants are buying PUTs and selling CALLs , as they expect a potential downward move to happen more quickly than an upward one. An options trader can take advantage of this phenomenon by leveraging PUT pricing skew. For example, if they have a bullish outlook and both IVR and IVx are high and IV started decreasing, they can capitalize on this PUT skew with strategies like a jade lizard, broken wing butterfly, or short put. 🔴 PUT Skew 🔴 Put options are more expensive than call options, indicating the market expects a faster downward move (▽). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves downward, it could do so faster in velocity compared to a potential upward movement. 🔹 SPY PUT SKEW example: If AMEX:SPY PUT option prices are 46% higher than CALLs at the same distance for the optimal next monthly expiry (DTE). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves downward, it could do so 46% faster in velocity compared to a potential upward movement 🟢 CALL Skew 🟢 Call options are more expensive than put options, indicating the market expects a faster upward move (△). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves upward, it could do so faster in velocity compared to a potential downward movement. 🔹 INTC CALL SKEW example: If NASDAQ:INTC CALL option prices are 49% higher than PUTs at the same distance for the optimal next monthly expiry (DTE). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves upward, it could do so 49% faster in velocity compared to a potential downward movement . 🔶 USAGE example: The script is compatible with our other options indicators. For example: Since the main metrics are already available in this Options Oscillator, you can hide the main IVR panel of our Options Overlay indicator, freeing up more space on the chart. The following image shows this: 🔶 ADDITIONAL IMPORTANT COMMENTS 🔹 Historical Data: Yes, we only using historical internal metrics dating back to 2024-07-01, when the TanukiTrade options brand launched. For now, we're using these, but we may expand the historical data in the future. 🔹 What distance does the indicator use to measure the call/put pricing skew?: It is important to highlight that this oscillator displays the call/put pricing skew changes for the next optimal monthly expiration on a histogram. The Binary Expected Move distance is calculated using the TastyTrade method for the next optimal monthly expiration: Formula = (ATM straddle price x 0.6) + (1st OTM strangle price x 0.3) + (2nd OTM strangle price x 0.1) We interpolate the exact difference based on the neighboring strikes at the binary expected move distance using the TastyTrade method, and compare the interpolated call and put prices at this specific point. 🔹 - Why is there a slight difference between the displayed data and my live brokerage data? There are two reasons for this, and one is beyond our control. ◎ Option-data update frequency: According to TradingView's regulations and guidelines, we can update external data a maximum of 5 times per day. We strive to use these updates in the most optimal way: (1st update) 15 minutes after U.S. market open (2nd, 3rd, 4th updates) 1.5–3 hours during U.S. market open hours (5th update) 10 minutes before U.S. market close. You don’t need to refresh your window, our last refreshed data-pack is always automatically applied to your indicator, and you can see the time elapsed since the last update at the bottom of the corner on daily TF. ◎ Brokerage Calculation Differences: Every brokerage has slight differences in how they calculate metrics like IV and IVx. If you open three windows for TOS, TastyTrade, and IBKR side by side, you will notice that the values are minimally different. We had to choose a standard, so we use the formulas and mathematical models described by TastyTrade when analyzing the options chain and drawing conclusions. 🔹 - EOD data: The indicator always displays end-of-day (EOD) data for IVR, IV, and CALL/PUT pricing skew. During trading hours, it shows the current values for the ongoing day with each update, and at market close, these values become final. From that point on, the data is considered EOD, provided the day confirms as a closed daily candle. 🔹 - U.S. market only: Since we only deal with liquid option chains: this option indicator only works for the USA options market and do not include future contracts; we have implemented each selected symbol individually. Disclaimer: Our option indicator uses approximately 15min-3 hour delayed option market snapshot data to calculate the main option metrics. Exact realtime option contract prices are never displayed; only derived metrics and interpolated delta are shown to ensure accurate and consistent visualization. Due to the above, this indicator can only be used for decision support; exclusive decisions cannot be made based on this indicator. We reserve the right to make errors.This indicator is designed for options traders who understand what they are doing. It assumes that they are familiar with options and can make well-informed, independent decisions. We work with public data and are not a data provider; therefore, we do not bear any financial or other liability.
Indicatore Pine Script®
di TanukiTrade
Options Oscillator [PRO] IVRank, IVx, Call/Put Volatility Skew𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗧𝗿𝗮𝗱𝗶𝗻𝗴𝗩𝗶𝗲𝘄 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿 𝘁𝗵𝗮𝘁 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝘀 𝗥𝗘𝗔𝗟 𝗜𝗩𝗥𝗮𝗻𝗸, 𝗜𝗩𝘅, 𝗮𝗻𝗱 𝗖𝗔𝗟𝗟/𝗣𝗨𝗧 𝘀𝗸𝗲𝘄 𝗱𝗮𝘁𝗮 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗥𝗘𝗔𝗟 𝗼𝗽𝘁𝗶𝗼𝗻 𝗰𝗵𝗮𝗶𝗻 𝗳𝗼𝗿 𝗼𝘃𝗲𝗿 𝟭𝟲𝟱+ 𝗺𝗼𝘀𝘁 𝗹𝗶𝗾𝘂𝗶𝗱 𝗨.𝗦. 𝗺𝗮𝗿𝗸𝗲𝘁 𝘀𝘆𝗺𝗯𝗼𝗹𝘀 🔃 Auto-Updating Option Metrics without refresh! 🍒 Developed and maintained by option traders for option traders. 📈 Specifically designed for TradingView users who trade options. 🔶 Ticker Information: This indicator is currently only available for over 165+ most liquid U.S. market symbols (eg. SP:SPX AMEX:SPY NASDAQ:QQQ NASDAQ:TLT NASDAQ:NVDA , etc.. ), and we are continuously expanding the compatible watchlist here: www.tradingview.com 🔶 How does the indicator work and why is it unique? This Pine Script indicator is a complex tool designed to provide various option metrics and visualization tools for options market traders. The indicator extracts raw options data from an external data provider (ORATS), processes and refines the delayed data package using pineseed, and sends it to TradingView, visualizing the data using specific formulas (see detailed below) or interpolated values (e.g., delta distances). This method of incorporating options data into a visualization framework is unique and entirely innovative on TradingView. The indicator aims to offer a comprehensive view of the current state of options for the implemented instruments, including implied volatility (IV), IV rank (IVR), options skew, and expected market movements, which are objectively measured as detailed below. The options metrics we display may be familiar to options traders from various major brokerage platforms such as TastyTrade, IBKR, TOS, Tradier, TD Ameritrade, Schwab, etc. 🟨 The following data is displayed in the oscillator 🟨 We use Tastytrade formulas, so our numbers mostly align with theirs! 🔶 𝗜𝗩𝗥𝗮𝗻𝗸 The Implied Volatility Rank (IVR) helps options traders assess the current level of implied volatility (IV) in comparison to the past 52 weeks. IVR is a useful metric to determine whether options are relatively cheap or expensive. This can guide traders on whether to buy or sell options. IV Rank formula = (current IV - 52 week IV low) / (52 week IV high - 52 week IV low) IVRank is default blue and you can adjust their settings: 🔶 𝗜𝗩𝘅 𝗮𝘃𝗴 The implied volatility (IVx) shown in the option chain is calculated like the VIX. The Cboe uses standard and weekly SPX options to measure expected S&P 500 volatility. A similar method is used for calculating IVx for each expiration cycle. We aggregate the IVx values for the 35-70 day monthly expiration cycle, and use that value in the oscillator and info panel. We always display which expiration the IVx values are averaged for when you hover over the IVx cell. IVx main color is purple, but you can change the settings: 🔹 IVx 5 days change % We are also displaying the five-day change of the IV Index (IVx value). The IV Index 5-Day Change column provides quick insight into recent expansions or decreases in implied volatility over the last five trading days. Traders who expect the value of options to decrease might view a decrease in IVX as a positive signal. Strategies such as Strangle and Ratio Spread can benefit from this decrease. On the other hand, traders anticipating further increases in IVX will focus on the rising IVX values. Strategies like Calendar Spread or Diagonal Spread can take advantage of increasing implied volatility. This indicator helps traders quickly assess changes in implied volatility, enabling them to make informed decisions based on their trading strategies and market expectations. Important Note: The IVx value alone does not provide sufficient context. There are stocks that inherently exhibit high IVx values. Therefore, it is crucial to consider IVx in conjunction with the Implied Volatility Rank (IVR), which measures the IVx relative to its own historical values. This combined view helps in accurately assessing the significance of the IVx in relation to the specific stock's typical volatility behavior. This indicator offers traders a comprehensive view of implied volatility, assisting them in making informed decisions by highlighting both the absolute and relative volatility measures. 🔶 𝗖𝗔𝗟𝗟/𝗣𝗨𝗧 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗦𝗸𝗲𝘄 𝗵𝗶𝘀𝘁𝗼𝗴𝗿𝗮𝗺 At TanukiTrade, Vertical Pricing Skew refers to the difference in pricing between put and call options with the same expiration date at the same distance (at tastytrade binary expected move). We analyze this skew to understand market sentiment. This is the same formula used by TastyTrade for calculations. We calculate the interpolated strike price based on the expected move, taking into account the neighboring option prices and their distances. This allows us to accurately determine whether the CALL or PUT options are more expensive. 🔹 What Causes Pricing Skew? The Theory Behind It The asymmetric pricing of PUT and CALL options is driven by the natural dynamics of the market. The theory is that when CALL options are more expensive than PUT options at the same distance from the current spot price, market participants are buying CALLs and selling PUTs, expecting a faster upward movement compared to a downward one . In the case of PUT skew, it's the opposite: participants are buying PUTs and selling CALLs , as they expect a potential downward move to happen more quickly than an upward one. An options trader can take advantage of this phenomenon by leveraging PUT pricing skew. For example, if they have a bullish outlook and both IVR and IVx are high and IV started decreasing, they can capitalize on this PUT skew with strategies like a jade lizard, broken wing butterfly, or short put. 🔴 PUT Skew 🔴 Put options are more expensive than call options, indicating the market expects a faster downward move (▽). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves downward, it could do so faster in velocity compared to a potential upward movement. 🔹 SPY PUT SKEW example: If AMEX:SPY PUT option prices are 46% higher than CALLs at the same distance for the optimal next monthly expiry (DTE). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves downward, it could do so 46% faster in velocity compared to a potential upward movement 🟢 CALL Skew 🟢 Call options are more expensive than put options, indicating the market expects a faster upward move (△). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves upward, it could do so faster in velocity compared to a potential downward movement. 🔹 INTC CALL SKEW example: If NASDAQ:INTC CALL option prices are 49% higher than PUTs at the same distance for the optimal next monthly expiry (DTE). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves upward, it could do so 49% faster in velocity compared to a potential downward movement . 🔶 USAGE example: The script is compatible with our other options indicators. For example: Since the main metrics are already available in this Options Oscillator, you can hide the main IVR panel of our Options Overlay indicator, freeing up more space on the chart. The following image shows this: 🔶 ADDITIONAL IMPORTANT COMMENTS 🔹 Historical Data: Yes, we only using historical internal metrics dating back to 2024-07-01, when the TanukiTrade options brand launched. For now, we're using these, but we may expand the historical data in the future. 🔹 What distance does the indicator use to measure the call/put pricing skew?: It is important to highlight that this oscillator displays the call/put pricing skew changes for the next optimal monthly expiration on a histogram. The Binary Expected Move distance is calculated using the TastyTrade method for the next optimal monthly expiration: Formula = (ATM straddle price x 0.6) + (1st OTM strangle price x 0.3) + (2nd OTM strangle price x 0.1) We interpolate the exact difference based on the neighboring strikes at the binary expected move distance using the TastyTrade method, and compare the interpolated call and put prices at this specific point. 🔹 - Why is there a slight difference between the displayed data and my live brokerage data? There are two reasons for this, and one is beyond our control. ◎ Option-data update frequency: According to TradingView's regulations and guidelines, we can update external data a maximum of 5 times per day. We strive to use these updates in the most optimal way: (1st update) 15 minutes after U.S. market open (2nd, 3rd, 4th updates) 1.5–3 hours during U.S. market open hours (5th update) 10 minutes before U.S. market close. You don’t need to refresh your window, our last refreshed data-pack is always automatically applied to your indicator, and you can see the time elapsed since the last update at the bottom of the corner on daily TF. ◎ Brokerage Calculation Differences: Every brokerage has slight differences in how they calculate metrics like IV and IVx. If you open three windows for TOS, TastyTrade, and IBKR side by side, you will notice that the values are minimally different. We had to choose a standard, so we use the formulas and mathematical models described by TastyTrade when analyzing the options chain and drawing conclusions. 🔹 - EOD data: The indicator always displays end-of-day (EOD) data for IVR, IV, and CALL/PUT pricing skew. During trading hours, it shows the current values for the ongoing day with each update, and at market close, these values become final. From that point on, the data is considered EOD, provided the day confirms as a closed daily candle. 🔹 - U.S. market only: Since we only deal with liquid option chains: this option indicator only works for the USA options market and do not include future contracts; we have implemented each selected symbol individually. Disclaimer: Our option indicator uses approximately 15min-3 hour delayed option market snapshot data to calculate the main option metrics. Exact realtime option contract prices are never displayed; only derived metrics and interpolated delta are shown to ensure accurate and consistent visualization. Due to the above, this indicator can only be used for decision support; exclusive decisions cannot be made based on this indicator. We reserve the right to make errors.This indicator is designed for options traders who understand what they are doing. It assumes that they are familiar with options and can make well-informed, independent decisions. We work with public data and are not a data provider; therefore, we do not bear any financial or other liability.
Indicatore Pine Script®
di TanukiTrade
1
RSI Pulsar [QuantraSystems]RSI Pulsar Introduction The RSI Pulsar is an advanced and multifaceted tool designed to cater to the varying needs of traders, from long-term swing traders to higher-frequency day traders. This indicator takes the Relative Strength Index (RSI) to new heights by combining several unique methodologies to provide clear, actionable signals across different market conditions. With its ability to analyze impulsive trend strength, volatility, and binary market direction, the RSI Pulsar offers a holistic view of the market that assists traders in identifying robust signals and rotational opportunities within a volatile market. The integration of dynamic color coding further aids in quick visual assessments, allowing traders to adapt swiftly to changing market conditions, making the RSI Pulsar an essential component in the arsenal of modern traders aiming for precision and adaptability in their trading endeavors. Legend The RSI Pulsar encapsulates various modes tailored to diverse trading strategies. The different modes are the: Impulse Mode: Focuses on strong outperformance, ideal for capturing movements in highly dynamic tokens. Trend Following Mode: A classical perpetual trend-following approach and provides binary long and short signal classifications ideal for medium term swing trading. Ribbon Mode: Offers quicker signals that are also binary in nature. Perfect for a confirmation signal when building higher frequency day trading systems. Volatility Spectrum: This feature projects a visual 'cloud' representing volatility, which helps traders spot emerging trends and potential breakouts or reversals. Compressed Mode: A condensed view that displays all signals in a clean and space-efficient manner. It provides a clear summary of market conditions, ideal for traders who prefer a simplified overview. Methodology The RSI Pulsar is built on a foundation of dynamic RSI analysis, where the traditional RSI is enhanced with advanced moving averages and standard deviation calculations. Each mode within the RSI Pulsar is designed to cater to specific aspects of the market's behavior, making it a versatile tool allowing traders to select different modes based on their trading style and market conditions. Impulse Mode: This mode identifies strong outperformance in assets, making it ideal for asset rotation systems. It uses a combination of RSI thresholds and dynamic moving averages to pinpoint when an asset is not just trending positively, but doing so with significant strength. This is in contrast to typical usage of a base RSI, where elevated levels usually signal overbought and oversold periods. The RSI Pulsar flips this logic, where more extreme values are actually interpreted as a strong trend. Trend Following Mode: Here, the RSI is compared to the midline (the default is level 50, but a dynamic midline can also be set), to determine the prevailing trend. This mode simplifies the trend-following process, providing clear bullish or bearish signals based on whether the RSI is above or below the midline - whether a fixed or dynamic level. Ribbon Mode: This mode employs a series of calculated values derived from modified Heikin-Ashi smoothing to create a "ribbon" that smooths out price action and highlights underlying trends. The Ribbon Mode is particularly useful for traders who need quick confirmations of trend reversals or continuations. Volatility Spectrum: The Volatility Spectrum takes a unique approach to measuring market volatility by analyzing the size and direction of Heikin-Ashi candles. This data is used to create a volatility cloud that helps traders identify when volatility is rising, falling, or neutral - allowing them to adjust their strategies accordingly. When the signal line breaks above the cloud, it signals increasing upwards volatility. When it breaks below it signifies increasing downwards volatility. This can be used to help identify strengthening and weakening trends, as well as imminent volatile periods, allowing traders to position themselves and adapt their strategies accordingly. This mode also works as a great volatility filter for shorter term day trading strategies. It is incredibly sensitive to volatility divergences, and can give additional insights to larger market turning points. Compressed Mode: In Compressed Mode, all the signals from the various modes are displayed in a simplified format, making it easy for traders to quickly assess the market's overall condition without needing to delve into the details of each mode individually. Perfect for only viewing the exact data you need when live trading, or back testing. Case Study I: Utilizing ALMA Impulse Mode in High-Volatility Environments Here, the RSI Pulsar is configured with an RSI length of 9 and an ALMA length of 2 in Impulse Mode. The chart example shows how this setup can identify significant price movements, allowing traders to enter positions early and capture substantial price moves. Despite the fast settings resulting in occasional false signals, the indicator's ability to catch and ride out major trends more than compensates, making it highly effective in volatile environments. This configuration is suitable for traders seeking to trade quick, aggressive movements without enduring prolonged drawdowns. In Impulse Mode, the RSI Pulsar seeks strong trending zones, providing actionable signals that allow for timely entries and exits. Case Study II: SMMA Trend Following Mode for Ratio Analysis The RSI Pulsar in Trend Following mode, configured with the SMMA with default length settings. This setup is ideal for analyzing longer-term trends, particularly useful in cryptocurrency pairs or ratio charts, where it’s crucial to identify robust directional moves. The chart showcases strong trends in the Solana/Ethereum pair. The RSI Pulsar’s ability to smooth out price action while remaining responsive to trend changes makes it an excellent tool for capturing extended price moves. The image highlights how the RSI Pulsar efficiently tracks the strength of two tokens against each other, providing clear signals when one asset begins to outperform the other. Even in volatile markets, the SMMA ensures that the signals are reliable, filtering out noise and allowing traders to stay in the trend longer without being shaken out by minor corrections. This approach is particularly effective in ratio trading in order to inform a longer term swing trader of the strongest asset out of a customized pair. Case Study III: Monthly Analysis with RSI Pulsar in Ribbon Mode This case study demonstrates the versatility and reliability of the RSI Pulsar in Ribbon mode, applied to a monthly chart of Bitcoin with an RSI length of 8 and a TEMA length of 14. This setup highlights the indicator’s robustness across multiple timeframes, extending even to long-term analysis. The RSI Pulsar effectively smooths out noise while capturing significant trends, as seen during Bitcoin bull markets. The Ribbon mode provides a clear visual representation of momentum shifts, making it easier for traders to identify trend continuations and reversals with confidence. Case Study IV: Divergences and Continuations with the Volatility Spectrum Identifying harmony/divergences can be hit-or-miss at times, but this unique analysis method definitely has its merits at times. The RSI Pulsar, with its Volatility Spectrum feature, is used here to identify critical moments where price action either aligns with or diverges from the underlying volatility. As seen in the Bitcoin chart (using default settings), the indicator highlights areas where price trends either continue in harmony with volatility or diverge, signaling potential reversals. This method, while not always perfect, provides significant insight during key turning points in the market. The Volatility Spectrum's visual representation of rising and falling volatility, combined with divergence and harmony analysis, enables traders to anticipate significant shifts in market dynamics. In this case, multiple divergences correctly identified early trend reversals, while periods of harmony indicated strong trend continuations. While this method requires careful interpretation, especially during complex market conditions, it offers valuable signals that can be pivotal in making informed trading decisions, especially if combined with other forms of analysis it can form a critical component of an investing system.
Indicatore Pine Script®
di QuantraSystems
1
[GYTS-Pro] Flux Composer🧬 Flux Composer (Professional Edition) 🌸 Confluence indicator in GoemonYae Trading System (GYTS) 🌸 The Flux Composer is a powerful tool in the GYTS suite that is designed to aggregate signals from multiple Signal Providers, apply advanced decaying functions, and offer customisable and advanced confluence mechanisms. This allows making informed decisions by considering the strength and agreement ("when all stars align") of various input signals. 🌸 --------- TABLE OF CONTENTS --------- 🌸 1️⃣ Main Highlights 2️⃣ Flux Composer’s Features Multi Signal Provider support Advanced decaying functions Customisable Flux confluence mechanisms Actionable trading experience Filtering options User-friendly experience Upgrades compared to Community Edition 3️⃣ User Guide Selecting Signal Providers Connecting Signal Providers to the Flux Composer Understanding the Flux Tuning the decaying functions Choosing Flux confluence mechanism Choosing sensitivity Utilising the filtering options Interpreting the Flux for trading signals 4️⃣ Limitations 🌸 ------ 1️⃣ --- MAIN HIGHLIGHTS --- 1️⃣ ------ 🌸 - Signal aggregation : Combines signals from multiple different 📡 Signal Providers, each of which can be tuned and adjusted independently. - Decaying function : Utilises advanced decaying functions to model the diminishing effect of signals over time, ensuring that recent signals have more weight. In addition to the decaying effect, the "quality" of the original signals (e.g. a "strong" GDM from WaveTrend 4D ) are accounted for as well. - Flux confluence mechanism : The aggregation of all decaying functions form the "Flux", which is the core signal measurement of the Flux Composer. Multiple mechanisms are available for creating the Flux and effectively using it for actionable trading signals. - Visualisation : Provides detailed visualisation options to help users understand and tune the contributions of individual Signal Providers and their decaying functions. - Backtesting : The 🧬 Flux Composer is a core component of the TradingView suite of the 🌸 GoemonYae Trading System (GYTS) 🌸. It connects multiple 📡 Signal Providers, such as the WaveTrend 4D, and processes their signals to produce a unified "Flux". This Flux can then be used by the GYTS "🎼 Order Orchestrator" for backtesting and trade automation. 🌸 ------ 2️⃣ --- FLUX COMPOSER'S FEATURES --- 2️⃣ ------ 🌸 Let's delve into more details... 💮 1. Multi Signal Provider support Using the name of the GYTS "🎼 Order Orchestrator" as an analogy: Imagine a symphony where each instrument plays its own unique part, contributing to the overall harmony. The Flux Composer operates similarly, integrating multiple Signal Providers to create a comprehensive and robust trading signal -- the "Flux". Currently, it supports up to four streams from the WaveTrend 4D's ’s Gradient Divergence Measure (GDM) and another four streams from the Quantile Median Cross (QMC). These can be either four "Professional Edition" Signal Providers or eight "Community Editions". Note that the GDM includes 2 different continuous signals and the QMC 3 different continuous signals (from different frequencies). This means that the Community Edition can handle 2*2 + 2*3 = 10 different continuous signals and the Professional Edition as much as 20. As GYTS evolves, more Signal Providers will be added; at the moment of releasing the Flux Composer, only WaveTrend 4D is publicly available. 💮 2. Advanced decaying functions A trading signal can be relevant today, less relevant tomorrow, and irrelevant in a week's time. In other words, its relevance diminishes, or decays , over time. The Flux Composer utilises decaying functions that ensure that recent signals carry more weight, while older signals fade away. This is crucial for accurate signal processing. The intensity and decay settings allow for precise control, allowing emphasising certain signals based on their strength and relevance over time. On top of that, unlike binary signals ("buy now"), the Flux Composer utilises the actual values from the Signal Providers, differentiating between the exact quality of signals, and thus offering a detailed representation of the trading landscape. We will illustrate this in a further section. 💮 3. Customisable Flux confluence mechanisms Another core component of the Flux Composer is the ability of intelligently combining the decaying functions. It offers four sophisticated confluence mechanisms: Amplitude Compression, Accentuated Amplitude Compression, Trigonometric, and GYTSynthesis. Each mechanism has its unique way of processing the Flux, tailored to different trading needs. For instance, the Amplitude Compression method scales the Flux based on recent values, much like the Stochastic Oscillator, while the Trigonometric method uses smooth functions to reduce outliers’ impact. The GYTSynthesis is a proprietary method, striking a balance between signal strength and discriminative power. We'll discuss this in more detail in the User Guide section. 💮 4. Actionable trading experience While the mathematical abilities might seem overwhelming, the goal of the Flux Composer is to transform complex signal data into actionable trading signals. When the Flux reaches certain thresholds, it generates clear bullish or bearish signals, making it easy for traders to interpret. The inclusion of upper and lower thresholds (UT and LT) helps in identifying strong signals visually and should be a familiar behaviour similar to how many other indicators operate. Furthermore, the Flux Composer can plot trading signals directly on the oscillator, showing triangle shapes for buy or sell signals. This visual aid is complemented by the possibility to setup TradingView alerts. 💮 5. Filtering options The Professional Edition also offers filtering options to possibly further improve the quality of Flux signals. Signal streams can be divided into “Signal Flux” and “Filter Flux.” The Filter Flux acts as a gatekeeper, ensuring that only signals meeting the Filter's criteria (which consist of similar UT/LT thresholds) are considered for trading. This dual-layer approach enhances the reliability of trading signals, reducing the chances of false positives. 💮 6. User-friendly experience GYTS is all about sophisticated, robust methods but also "elegance". One of the interpretations of the latter, is that the users' experience is very important. Despite the Flux Composer's mathematical underpinnings, it offers intuitive settings that with omprehensive tooltips to help with a smooth setup process. For those looking to fine-tune their signals, the Flux Composer allows the visualisation of individual decaying functions. This feature helps users understand the impact of each setting and make informed adjustments. Additionally, the background of the chart can be coloured to indicate the trading direction suggested by the Filter Flux, providing an at-a-glance overview of market conditions. 💮 7. Upgrades compared to Community Edition Number of signal streams -- At the moment of writing, the Professional Edition works with 4x GDM and 4x QMC signal streams from WaveTrend 4D Signal Provider , while Community Edition (CE) Flux Composer (FC) only works with 2x GDM and 2x QMC signal streams. Flux confluence mechanism -- CE includes the Amplitude Compression and Trigonometric confluence mechanisms, while the Pro Edition also includes the Accentuated Amplitude Compression and the GYTSynthesis mechanisms. Signal streams as filters -- The Pro Edition can use Signal Providers as filters. 🌸 ------ 3️⃣ --- USER GUIDE --- 3️⃣ ------ 🌸 💮 1. Selecting Signal Providers The Flux Composer’s foundation lies in its Signal Providers. When starting with the Flux Composer, using a single Signal Provider can already provide significant value due to the nature of decaying functions. For instance, the WaveTrend 4D signal provider includes up to 5 signal types (GDM and QMC in different frequencies) in a single direction (long/short). Moreover, the various confluence mechanisms that enhance the resulting Flux result in improved discrimination between weak and strong signals. This approach is akin to ensemble learning in machine learning, where multiple models are combined to improve predictive performance. While using a single Signal Provider is beneficial, the true power of the Flux Composer is realised with multiple Signal Providers. Here are two general approaches to selecting Signal Providers: Diverse Behaviours Use Signal Providers with different behaviours, such as WaveTrend 4D on various assets/timeframes or entirely different Signal Providers. This approach leverages diversification to achieve robustness, rooted in the principle that varied sources enhance the overall signal quality. To explain this with an analogy, this strategy aligns with the theory of diversification in portfolio management, where combining uncorrelated assets reduces overall risk. Similarly, combining uncorrelated signals can mitigate the risk of signal failure. A practical example can be integrating a mean-reversion signal with a trend-following signal -- these can balance each other out, providing more stable outputs over different market conditions. Enhancing a Single Provider If you consider a particular Signal Provider highly effective, you could improve its robustness by using multiple instances with slight variations. These variations could include different sources (e.g., close, HL2, HLC3), data providers (same asset across different brokers/exchanges), or parameter adjustments. This method mirrors Monte Carlo simulations, often used in risk management and derivative pricing, which involve running many simulations with varied inputs to estimate the probability of different outcomes. By applying similar principles, the strategy becomes less susceptible to overfitting, ensuring the signals are not overly dependent on specific data conditions. 💮 2. Connecting Signal Providers to the Flux Composer Moving on to practicalities: how do you connect Signal Providers with the Flux Composer? You may have noticed that when you open the drawdown of a data source in a TradingView indicator (with "open", "high", "low", etc.), you also see names from other indicators on your chart. We call these "streams", and the Signal Providers are designed such that they output this stream in a way that the Flux Composer can interpret it. Thus, to connect a Signal Provider with the Flux Composer, you should first have that Signal Provider on your chart. Obviously you should set it up an a way that it seems to provide good signals. After that, in the Data Stream dropdown in the Flux Composer, you can select the stream that is outputted by your Signal Provider. This will always be with a prefix of "🔗 STREAM" (after the Signal Provider's indicator name). See the chart below. There is one important nuance: when you have multiple (similar) Signal Providers on your chart, it may be hard to select the correct data stream in the Flux Composer as the names of the streams keep repeating when you use identical indicators. So be sure to be attentive as you might end up using the same signals multiple times. Also, the Signal Providers have an "Indicator name" parameter (and another parameter to repeat this name) that is handy to use when you have multiple Signal Providers on your screen. It is handy to give names that describe the unique settings of that Signal Provider so you can better differentiate what you are looking at on your screen. 💮 3. Understanding the Flux Let's understand how the Signal Provider's signals are processed. In the chart below, you see we have one Signal Provider (WaveTrend 4D) connected to the Flux Composer and that it gives a bearish QMC signal. The Flux Composer converts this into a decaying function. You can show these functions per Signal Provider when the option "Show decaying function of Signal Provider" is enabled (as it is in the chart). In our opinion, of crucial importance is the ability to process the quality of signals, rather than just any signal. In mathematical terms, we are interested in continuous signals as these provide a spectrum of values. These signals can reflect varying degrees of market sentiment or trend strength, offering richer information than binary signals, which offer only two states (e.g., buy/sell). Especially in the context of the Flux Composer, where you aggregate multiple signals, it makes a big difference whether you combine 10 weak signals or 10 strong signals. To illustrate this principle, look at the chart below where there are 4 signals of different strengths. As you can see, each of the signals affects the Flux with different intensities. 💮 4. Tuning the decaying functions As previously mentioned, the decaying functions are a way to give more importance to recent signals while allowing older ones to fade away gradually. This mimics the natural way we assess information, giving more weight to recent events. The decaying functions in the Flux Composer are highly customisable while remaining easy to use. You can adjust the initial intensity , which sets the starting strength of a signal, and the decay rate, which determines how quickly this signal diminishes over time. Let's look at specific examples. If we add 3 Flux Composers on the chart, connect the same Signal Provider, keep all settings the same with one exception, we get the chart below. Here we have changed the "intensity" parameter of the specific signal. As you can see, the decaying functions are different. The intensity determines the initial strength of the decayed function. Adjusting the intensity allows you to emphasise certain signal types based on their perceived reliability or importance. Let's now keep the intensity the same ("normal"), but change the "decay" parameter. As you can see in the image below, the decay controls how quickly the signal’s strength diminishes over time. By adjusting the decay, you can model the longevity of the signal’s impact. A faster decay means the signal loses its influence quickly, while a slower decay means it remains relevant for a longer period. So how do multiple signals interact? You can see this as a simple "stacking of decaying functions" (although there is more to it, see next section). In the chart below we different strenghts of signals and different decay rates to illustrate how the Flux is constructed. Hopefully this helps with developing some intuition how signals are converted to decaying functions, how you can control them, and how the Flux is constructed. When tuning these parameters, use the visualisation options to see how individual decaying functions contribute to the overall Flux. This helps in understanding and refining the parameters to achieve the desired trading signal behaviour. 💮 5. Choosing Flux confluence mechanism While we mentioned that the Flux is a "stacking of individual decaying functions", in the back-end, that is not exactly that simple. Like previously mentioned, for GYTS, "elegance" is very important. One of the interpretations is "user friendliness" and the Flux confluence mechanism is one of the essential developments for this characteristic. The Flux confluence mechanism is critical in synthesising the aggregated signals into the Flux. The choice of mechanism affects how the signals are combined and the resulting trading signals. The Professional Edition offers four distinct mechanisms, each with its strengths. The Amplitude Compression mechanism is intuitive, scaling the Flux based on recent values, intuitively not unlike the method of the well-known Stochastic Oscillator. The Accentuated Amplitude Compression method takes this a step further, giving more weight to strong Flux values. The Trigonometric mechanism smooths the Flux and reduces the impact of outliers, providing a balanced approach. Finally, the GYTSynthesis mechanism, a proprietary approach, balances signal strength and discriminative power, making it easier to tune and generalise. It's difficult to convey the workings of the Flux confluence mechanism in a chart, but let's take the opportunity to show how the Flux would look like when connecting both one WaveTrend 4D Signal Provider signals to four Flux Composers with default settings, except the Flux confluence mechanism: You may notice subtle differences between the four methods. They react differently to different values and their overall shape is slightly be different. The Amplitude Compression is more "pointy" and GYTSynthesis doesn't react to low values. There are many nuances, especially in combination with tuning the sensitivity and upper/lower threshold (UT/LT) parameters. 💮 6. Choosing sensitivity Speaking of the sensitivity , this parameters fine-tunes how responsive the Flux is to the input signals. Higher sensitivity results in more pronounced responses, leading to more frequent trading signals. Lower sensitivity makes the Flux less responsive, resulting in fewer but potentially more reliable signals. You might think that changing the upper/lower threshold (UT/LT) parameters would be equivalent, but that's not the case. The sensitivity In case of the Amplitude Compression mechanisms, changing the sensitivity would change the relative Flux shape over time, and with the Trigonometric and GYTSynthesis mechanisms, the Flux shape itself (independent of time) would change. In other words, these are all good parameters for tuning. 💮 7. Utilising the filtering options When choosing the signal stream of a Signal Provider, you can also change the default "Signal" category of that Signal Provider to a "Filter". In the example below, two Signal Providers are connected; the second is set as a filter. You can see that a second row of a Flux is shown in the Flux Composer (this visualisation can be disabled), corresponding with the signals of the second Signal Provider. Logically, only when the Filter Flux gives a signal in a certain direction, signals from the regular Signal Flux are registered. Generally speaking, for this use case it is handy to set the thresholds for the Filter Flux low and possibly to decrease the decay rate so that the filtering is active for a long enough time. 💮 8. Interpreting the Flux for trading signals Lastly, the Signal Flux gives buy and sell signals when it crosses the upper/lower thresholds (UT/LT), when the filter allows it (if enabled). This can be visualised with the triangles as you may have seen in the charts in the previous sections. For people using TradingView's alerts -- these would work too out of the box. And finally, for backtesting and possibly trade automation, we will have the GYTS "🎼 Order Orchestrator" that connects with the Flux Composer. 🌸 ------ 4️⃣ --- LIMITATIONS --- 4️⃣ ------ 🌸 Only 🌸 GYTS 📡 Signal Providers are supported, as there is a specific method to pass continuous (non-binary) data in the data stream At the moment of release, only the WaveTrend 4D Signal Provider is available. Other Signal Providers will be gradually released.
Indicatore Pine Script®
di GoemonYae
[GYTS-CE] Flux Composer🧬 Flux Composer (Community Edition) 🌸 Confluence indicator in GoemonYae Trading System (GYTS) 🌸 The Flux Composer is a powerful tool in the GYTS suite that is designed to aggregate signals from multiple Signal Providers, apply customisable decaying functions, and offer customisable and advanced confluence mechanisms. This allows making informed decisions by considering the strength and agreement ("when all stars align") of various input signals. 🌸 --------- TABLE OF CONTENTS --------- 🌸 1️⃣ Main Highlights 2️⃣ Flux Composer’s Features Multi Signal Provider support Advanced decaying functions Customisable Flux confluence mechanisms Actionable trading experience User-friendly experience 3️⃣ User Guide Selecting Signal Providers Connecting Signal Providers to the Flux Composer Understanding the Flux Tuning the decaying functions Choosing Flux confluence mechanism Choosing sensitivity Interpreting the Flux for trading signals 4️⃣ Limitations 🌸 ------ 1️⃣ --- MAIN HIGHLIGHTS --- 1️⃣ ------ 🌸 - Signal aggregation : Combines signals from multiple different 📡 Signal Providers, each of which can be tuned and adjusted independently. - Decaying function : Utilises advanced decaying functions to model the diminishing effect of signals over time, ensuring that recent signals have more weight. In addition to the decaying effect, the "quality" of the original signals (e.g. a "strong" GDM from WaveTrend 4D with GDM ) are accounted for as well. - Flux confluence mechanism : The aggregation of all decaying functions form the "Flux", which is the core signal measurement of the Flux Composer. Multiple mechanisms are available for creating the Flux and effectively using it for actionable trading signals. - Visualisation : Provides detailed visualisation options to help users understand and tune the contributions of individual Signal Providers and their decaying functions. - Backtesting : The 🧬 Flux Composer is a core component of the TradingView suite of the 🌸 GoemonYae Trading System (GYTS) 🌸. It connects multiple 📡 Signal Providers, such as the WaveTrend 4D, and processes their signals to produce a unified "Flux". This Flux can then be used by the GYTS "🎼 Order Orchestrator" for backtesting and trade automation. 🌸 ------ 2️⃣ --- FLUX COMPOSER'S FEATURES --- 2️⃣ ------ 🌸 Let's delve into more details... 💮 1. Multi Signal Provider support Using the name of the GYTS "🎼 Order Orchestrator" as an analogy: Imagine a symphony where each instrument plays its own unique part, contributing to the overall harmony. The Flux Composer operates similarly, integrating multiple Signal Providers to create a comprehensive and robust trading signal -- the "Flux". Currently, it supports up to two streams from the WaveTrend 4D’s Gradient Divergence Measure (GDM) and another two streams from the WaveTrend 4D's Quantile Median Cross (QMC) . Note that the GDM includes 2 different continuous signals and the QMC 3 different continuous signals (from different frequencies). This means that the Community Edition can handle 2*2 + 2*3 = 10 different continuous signals. As GYTS evolves, more Signal Providers will be added; at the moment of releasing the Flux Composer, only WaveTrend 4D with GDM and with QMC are publicly available. 💮 2. Advanced decaying functions A trading signal can be relevant today, less relevant tomorrow, and irrelevant in a week's time. In other words, its relevance diminishes, or decays , over time. The Flux Composer utilises decaying functions that ensure that recent signals carry more weight, while older signals fade away. This is crucial for accurate signal processing. The intensity and decay settings allow for precise control, allowing emphasising certain signals based on their strength and relevance over time. On top of that, unlike binary signals ("buy now"), the Flux Composer utilises the actual values from the Signal Providers, differentiating between the exact quality of signals, and thus offering a detailed representation of the trading landscape. We will illustrate this in a further section. 💮 3. Customisable Flux confluence mechanisms Another core component of the Flux Composer is the ability of intelligently combining the decaying functions. It offers two sophisticated confluence mechanisms: Amplitude Compression and Trigonometric. Each mechanism has its unique way of processing the Flux, tailored to different trading needs. The Amplitude Compression method scales the Flux based on recent values, much like the Stochastic Oscillator, while the Trigonometric method uses smooth functions to reduce outliers’ impact We'll discuss this in more detail in the User Guide section. 💮 4. Actionable trading experience While the mathematical abilities might seem overwhelming, the goal of the Flux Composer is to transform complex signal data into actionable trading signals. When the Flux reaches certain thresholds, it generates clear bullish or bearish signals, making it easy for traders to interpret. The inclusion of upper and lower thresholds (UT and LT) helps in identifying strong signals visually and should be a familiar behaviour similar to how many other indicators operate. Furthermore, the Flux Composer can plot trading signals directly on the oscillator, showing triangle shapes for buy or sell signals. This visual aid is complemented by the possibility to setup TradingView alerts. 💮 5. User-friendly experience GYTS is all about sophisticated, robust methods but also "elegance". One of the interpretations of the latter, is that the users' experience is very important. Despite the Flux Composer's mathematical underpinnings, it offers intuitive settings that with omprehensive tooltips to help with a smooth setup process. For those looking to fine-tune their signals, the Flux Composer allows the visualisation of individual decaying functions. This feature helps users understand the impact of each setting and make informed adjustments. 🌸 ------ 3️⃣ --- USER GUIDE --- 3️⃣ ------ 🌸 💮 1. Selecting Signal Providers The Flux Composer’s foundation lies in its Signal Providers. When starting with the Flux Composer, using a single Signal Provider can already provide significant value due to the nature of decaying functions. For instance, the WaveTrend 4D signal provider includes up to two GDM and three QMC signals in a single direction (long/short). Moreover, the various confluence mechanisms that enhance the resulting Flux result in improved discrimination between weak and strong signals. This approach is akin to ensemble learning in machine learning, where multiple models are combined to improve predictive performance. While using a single Signal Provider is beneficial, the true power of the Flux Composer is realised with multiple Signal Providers. Here are two general approaches to selecting Signal Providers: Diverse Behaviours Use Signal Providers with different behaviours, such as WaveTrend 4D on various assets/timeframes or entirely different Signal Providers. This approach leverages diversification to achieve robustness, rooted in the principle that varied sources enhance the overall signal quality. To explain this with an analogy, this strategy aligns with the theory of diversification in portfolio management, where combining uncorrelated assets reduces overall risk. Similarly, combining uncorrelated signals can mitigate the risk of signal failure. A practical example can be integrating a mean-reversion signal with a trend-following signal -- these can balance each other out, providing more stable outputs over different market conditions. Enhancing a Single Provider If you consider a particular Signal Provider highly effective, you could improve its robustness by using multiple instances with slight variations. These variations could include different sources (e.g., close, HL2, HLC3), data providers (same asset across different brokers/exchanges), or parameter adjustments. This method mirrors Monte Carlo simulations, often used in risk management and derivative pricing, which involve running many simulations with varied inputs to estimate the probability of different outcomes. By applying similar principles, the strategy becomes less susceptible to overfitting, ensuring the signals are not overly dependent on specific data conditions. 💮 2. Connecting Signal Providers to the Flux Composer Moving on to practicalities: how do you connect Signal Providers with the Flux Composer? You may have noticed that when you open the drawdown of a data source in a TradingView indicator (with "open", "high", "low", etc.), you also see names from other indicators on your chart. We call these "streams", and the Signal Providers are designed such that they output this stream in a way that the Flux Composer can interpret it. Thus, to connect a Signal Provider with the Flux Composer, you should first have that Signal Provider on your chart. Obviously you should set it up an a way that it seems to provide good signals. After that, in the Data Stream dropdown in the Flux Composer, you can select the stream that is outputted by your Signal Provider. This will always be with a prefix of "🔗 STREAM" (after the Signal Provider's indicator name). See the chart below. There is one important nuance: when you have multiple (similar) Signal Providers on your chart, it may be hard to select the correct data stream in the Flux Composer as the names of the streams keep repeating when you use identical indicators. So be sure to be attentive as you might end up using the same signals multiple times. Also, the Signal Providers have an "Indicator name" parameter (and another parameter to repeat this name) that is handy to use when you have multiple Signal Providers on your screen. It is handy to give names that describe the unique settings of that Signal Provider so you can better differentiate what you are looking at on your screen. 💮 3. Understanding the Flux Let's understand how the Signal Provider's signals are processed. In the chart below, you see we have one Signal Provider (WaveTrend 4D) connected to the Flux Composer and that it gives a bearish QMC signal. The Flux Composer converts this into a decaying function. You can show these functions per Signal Provider when the option "Show decaying function of Signal Provider" is enabled (as it is in the chart). In our opinion, of crucial importance is the ability to process the quality of signals, rather than just any signal. In mathematical terms, we are interested in continuous signals as these provide a spectrum of values. These signals can reflect varying degrees of market sentiment or trend strength, offering richer information than binary signals, which offer only two states (e.g., buy/sell). Especially in the context of the Flux Composer, where you aggregate multiple signals, it makes a big difference whether you combine 10 weak signals or 10 strong signals. To illustrate this principle, look at the chart below where there are 4 signals of different strengths. As you can see, each of the signals affects the Flux with different intensities. 💮 4. Tuning the decaying functions As previously mentioned, the decaying functions are a way to give more importance to recent signals while allowing older ones to fade away gradually. This mimics the natural way we assess information, giving more weight to recent events. The decaying functions in the Flux Composer are highly customisable while remaining easy to use. You can adjust the initial intensity , which sets the starting strength of a signal, and the decay rate, which determines how quickly this signal diminishes over time. Let's look at specific examples. If we add 3 Flux Composers on the chart, connect the same Signal Provider, keep all settings the same with one exception, we get the chart below. Here we have changed the "intensity" parameter of the specific signal. As you can see, the decaying functions are different. The intensity determines the initial strength of the decayed function. Adjusting the intensity allows you to emphasise certain signal types based on their perceived reliability or importance. Let's now keep the intensity the same ("normal"), but change the "decay" parameter. As you can see in the image below, the decay controls how quickly the signal’s strength diminishes over time. By adjusting the decay, you can model the longevity of the signal’s impact. A faster decay means the signal loses its influence quickly, while a slower decay means it remains relevant for a longer period. So how do multiple signals interact? You can see this as a simple "stacking of decaying functions" (although there is more to it, see next section). In the chart below we use different "intensity" and "decay" parameters to discuss how the Flux is created. Hopefully this helps with developing some intuition how signals are converted to decaying functions, how you can control them, and how the Flux is constructed. When tuning these parameters, use the visualisation options to see how individual decaying functions contribute to the overall Flux. This helps in understanding and refining the parameters to achieve the desired trading signal behaviour. 💮 5. Choosing Flux confluence mechanism While we mentioned that the Flux is a "stacking of individual decaying functions", in the back-end, that is not exactly that simple. Like previously mentioned, for GYTS, "elegance" is very important. One of the interpretations is "user friendliness" and the Flux confluence mechanism is one of the essential developments for this characteristic. The Flux confluence mechanism is critical in synthesising the aggregated signals into the Flux. The choice of mechanism affects how the signals are combined and the resulting trading signals. The Community Edition offers two distinct mechanisms, each with its strengths. The Amplitude Compression mechanism is intuitive, scaling the Flux based on recent values, intuitively not unlike the method of the well-known Stochastic Oscillator. On the other hand, the Trigonometric mechanism smooths the Flux and reduces the impact of outliers, providing a balanced approach. It's difficult to convey the workings of the Flux confluence mechanism in a chart, but let's take the opportunity to show how the Flux would look like when connecting both GDM and QMC signals to two Flux Composers with default settings, except the Flux confluence mechanism: You can notice that the upper Flux Converter (FC) triggered two signals while the other FC triggered only one. There are more nuances, especially in combination with tuning the sensitivity and upper/lower threshold (UT/LT) parameters. 💮 6. Choosing sensitivity Speaking of the sensitivity , this parameters fine-tunes how responsive the Flux is to the input signals. Higher sensitivity results in more pronounced responses, leading to more frequent trading signals. Lower sensitivity makes the Flux less responsive, resulting in fewer but potentially more reliable signals. You might think that changing the upper/lower threshold (UT/LT) parameters would be equivalent, but that's not the case. The sensitivity In case of the Amplitude Compression mechanism, changing the sensitivity would change the relative Flux shape over time, and with the Trigonometric mechanism, the Flux shape itself (independent of time) would change. In other words, these are all good parameters for tuning. 💮 8. Interpreting the Flux for trading signals Lastly, the Signal Flux gives buy and sell signals when it crosses the upper/lower thresholds (UT/LT) This can be visualised with the triangles as you may have seen in the charts in the previous sections. For people using TradingView's alerts -- these would work out of the box. And finally, for backtesting and possibly trade automation, we will have the GYTS "🎼 Order Orchestrator" that connects with the Flux Composer. 🌸 ------ 4️⃣ --- LIMITATIONS --- 4️⃣ ------ 🌸 Only 🌸 GYTS 📡 Signal Providers are supported, as there is a specific method to pass continuous (non-binary) data in the data stream At the moment of release, only WaveTrend 4D with GDM and with QMC are available. Other Signal Providers will be gradually released.
Indicatore Pine Script®
di GoemonYae