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Multi SMA EMA WMA HMA BB (4x5 MAs Bollinger Bands) Adv MTF - RRBMulti SMA EMA WMA HMA 4x5 Moving Averages with Bollinger Bands Advanced MTF by RagingRocketBull 2019 Version 1.0 This indicator shows multiple MAs of any type SMA EMA WMA HMA etc with BB and MTF support, can show MAs as dynamically moving levels. There are 4 MA groups + 1 BB group, a total of 4 TFs * 5 MAs = 20 MAs. You can assign any type/timeframe combo to a group, for example: - EMAs 12,26,50,100,200 x H1, H4, D1, W1 (4 TFs x 5 MAs x 1 type) - EMAs 8,10,13,21,30,50,55,100,200,400 x M15, H1 (2 TFs x 10 MAs x 1 type) - D1 EMAs and SMAs 8,10,12,26,30,50,55,100,200,400 (1 TF x 10 MAs x 2 types) - H1 WMAs 7,77,89,167,231; H4 HMAs 12,26,50,100,200; D1 EMAs 89,144,169,233,377; W1 SMAs 12,26,50,100,200 (4 TFs x 5 MAs x 4 types) - +1 extra MA type/timeframe for BB There are several versions: Simple, MTF, Pro MTF, Advanced MTF and Ultimate MTF. This is the Advanced MTF version. The Differences are listed below. All versions have BB - Simple: you have 2 groups of MAs that can be assigned any type (5+5) - MTF: +2 custom Timeframes for each group (2x5 MTF) +1 TF for BB, TF XY smoothing - Pro MTF: 4 custom Timeframes for each group (4x3 MTF), 1 TF for BB, MA levels and show max bars back options - Advanced MTF: +2 extra MAs/group (4x5 MTF), custom Ticker/Symbols, Timeframe <>= filter, Remove Duplicates Option - Ultimate MTF: +individual settings for each MA, custom Ticker/Symbols Features: - 4x5 = 20 MAs of any type - 4x MTF groups with XY step line smoothing - +1 extra TF/type for BB MAs - 4x5 = 20 MA levels with adjustable group offsets, indents and shift - supports any existing type of MA: SMA, EMA, WMA, Hull Moving Average (HMA) - custom tickers/symbols for each group - you can compare MAs of the same symbol across exchanges - show max bars back option - show/hide both groups of MAs/levels/BB and individual MAs - timeframe filter: show only MAs/Levels with TFs <>= Current TF - hide MAs/Levels with duplicate TFs - support for custom TFs that are not available in free accounts: 2D, 3D etc - support for timeframes in H: H, 2H, 4H etc Notes: - Uses timeframe textbox instead of input resolution dropdown to allow for 240 120 and other custom TFs - Uses symbol textbox instead of input symbol to avoid establishing multiple dummy security connections to the current ticker - otherwise empty symbols will prevent script from running - Possible reasons for missing MAs on a chart: - there may not be enough bars in history to start plotting it. For example, W1 EMA200 needs at least 200 bars on a weekly chart. - price << default Y smoothing step 5. For charts with low/fractional prices (i.e. 0.00002 << 5) adjust X Y smoothing as needed (set Y = 0.0000001) or disable it completely (set X,Y to 0,0) - TradingView Replay Mode UI and Pinescript security calls are limited to TFs >= D (D,2D,W,MN...) for free accounts - attempting to plot any TF < D1 in Replay Mode will only result in straight lines, but all TFs will work properly in history and real-time modes. This is not a bug. - Max Bars Back (num_bars) is limited to 5000 for free accounts (10000 for paid), will show error when exceeded. To plot on all available history set to 0 (default) - Slow load/redraw times. This indicator becomes slower, its UI less responsive when: - Pinescript Node.js graphics library is too slow and inefficient at plotting bars/objects in a browser window. Code optimization doesn't help much - the graphics engine is the main reason for general slowness. - the chart has a long history (10000+ bars) in a browser's cache (you have scrolled back a couple of screens in a max zoom mode). - Reload the page/Load a fresh chart and then apply the indicator or - Switch to another Timeframe (old TF history will still remain in cache and that TF will be slow) - in max possible zoom mode around 4500 bars can fit on 1 screen - this also slows down responsiveness. Reset Zoom level - initial load and redraw times after a param change in UI also depend on TF. For example: D1/W1 - 2 sec, H1/H4 - 5-6 sec, M30 - 10 sec, M15/M5 - 4 sec, M1 - 5 sec. M30 usually has the longest history (up to 16000 bars) and W1 - the shortest (1000 bars). - when indicator uses more MAs (plots) and timeframes it will redraw slower. Seems that up to 5 Timeframes is acceptable, but 6+ Timeframes can become very slow. - show_last=last_bars plot limit doesn't affect load/redraw times, so it was removed from MA plot - Max Bars Back (num_bars) default/custom set UI value doesn't seem to affect load/redraw times - In max zoom mode all dynamic levels disappear (they behave like text) 1. based on 3EmaBB, uses plot*, barssince and security functions 2. you can't set certain constants from input due to Pinescript limitations - change the code as needed, recompile and use as a private version 3. Levels = trackprice implementation 4. Show Max Bars Back = show_last implementation 5. swma has a fixed length = 4, alma and linreg have additional offset and smoothing params 6. Smoothing is applied by default for visual aesthetics on MTF. To use exact ma mtf values (lines with stair stepping) - disable it Good Luck! You can explore, modify/reuse the code to build your own indicators.
Pine Script®指標
由RagingRocketBull提供
3
Volatility Based Momentum Oscillator (VBMO)There is a frequent and definitive pattern in price movement, whereby price will steadily drift lower, then accelerate before bottoming out. Similarly, price will often steadily rise, then accelerate into a climax top. The Volatility Based Momentum Oscillator (VBMO) is designed to delineate between steady versus more accelerated and climactic price movements. VBMO is calculated using a short-term moving average, the distance of price from this moving average, and the trading instrument’s historical volatility. Even though VBMO’s calculation is relatively simple, the resulting values can help traders identify, analyze and act upon many scenarios, such as climax tops, reversals, and capitulation. Moreover, since the units and scale for VBMO are always the same, the indicator can be used in a consistent manner across multiple timeframes and instruments. For more details, there is an article further describing VBMO and its applicability.
Pine Script®指標
由SteveFromAZ提供
1
MA CrossA simple configurable MA Cross (3 MAs) script. You can choose between SMA and EMA , you will get arrows up or down when MAs cross each others. The arrows have different transparency and length parameters so you can easily identify them: - small arrow and the most transparent for fast and medium MA crosses - medium arrow for fast and slow MA crosses - long arrow with no transparency for medium and slow MA crosses Default values that can be changed: - MA type = EMA - Source for all MAs = close - Fast MA length = 20 - Medium MA length = 50 - Slow MA length = 200 I plan on adding feartures overtime.
Pine Script®指標
由wxcjif提供
EMA TEMA crossoverHello, as usual recently the chart is bugged if I dare post a log one so I deleted and reposted... here is a simple ema/team script that could allow you to catch trends. You can play with parameters to get alerted of strong trends, or to detect trends early on. I want to adapt this to an inside bar strategy, I am going to release that in a while, it is not my priority thought I am a countertrend trader, so I cannot say why, but what I have seen is inside bar breaks in very strong trends work very well. There is a condition for this inidcator to work: you have to use it on a Heikin Ashi chart (the candle type, selectable in the area right of the timeframe). Works sometimes. I advice backtesting any strategy before using. Idk maybe this could work decently, seems to have given big winners on bch recently. That huge one from 600 to 1800 lol. *3 * No one should pay for things this simple by the way...
Pine Script®指標
由MrRenev提供
2
DPD INDICATOR (DEMA PRICE DİFFERENCE PERCENTAGE )I use DEMA and Price difference in many strategies and and trade. Finally , ı wanted to build an indicator for relation between them. It calculates the percentage of difference between price and dema and estimates deviation from the main trend. Formula = (price-dema)/price*100 There is some parameters; DEMA Length is length of dema , ı think 50 is good enough, there is upper and lower band for DPD Score . You can change it based on volatilities of your pairs to find an optima. and use it to be sure about your entry point. I will developed and combine DPD with some other indicators and build strategies with it. You can be part of that , I am waiting for your feedback. Stay in Touch :)
Pine Script®指標
由ridvansozen1提供
4
2xIchimoku Cloud + 4xMA + Williams FractalUpdated version of the previously published multi-indicator which includes 4x Moving Averages 2x Ichimoku Clouds Bill Williams Fractals Changes: -Toggle switches for each indicator on input tab for easy on/off -MA Type Selector (EMA/SMA/WMA/VWMA) -Various default style change Many thanks to both redwraith and jedireza for helping me work out the MA section www.tradingview.com www.tradingview.com Next improvements: Ichimoku settings
Pine Script®指標
由br0qn提供
16
ForecastForecast (FC), indicator documentation Type: Study, not a strategy Primary timeframe: 1D chart, most plots and the on-chart table only render on daily bars Inspiration: Robert Carver’s “forecast” concept from Advanced Futures Trading Strategies, using normalized, capped signals for comparability across markets ⸻ What the indicator does FC builds a volatility-normalized momentum forecast for a chosen symbol, optionally versus a benchmark. It combines an EWMAC composite with a channel breakout composite, then caps the result to a common scale. You can run it in three data modes: • Absolute: Forecast of the selected symbol • Relative: Forecast of the ratio symbol / benchmark • Combined: Average of Absolute and Relative A compact table can summarize the current forecast, short-term direction on the forecast EMAs, correlation versus the benchmark, and ATR-scaled distances to common price EMAs. ⸻ PineScreener, relative-strength screening This indicator is excellent for screening on relative strength in PineScreener, since the forecast is volatility-normalized and capped on a common scale. Available PineScreener columns PineScreener reads the plotted series. You will see at least these columns: • FC, the capped forecast • from EMA20, (price − EMA20) / ATR in ATR multiples • from EMA50, (price − EMA50) / ATR in ATR multiples • ATR, ATR as a percent of price • Corr, weekly correlation with the chosen benchmark Relative mode and Combined mode are recommended for cross-sectional screens. In Relative mode the calculation uses symbol / benchmark, so ensure the ratio ticker exists for your data source. ⸻ How it works, step by step 1. Volatility model Compute exponentially weighted mean and variance of daily percent returns on D, annualize, optionally blend with a long lookback using 10y %, then convert to a price-scaled sigma. 2. EWMAC momentum, three legs Daily legs: EMA(8) − EMA(32), EMA(16) − EMA(64), EMA(32) − EMA(128). Divide by price-scaled sigma, multiply by leg scalars, cap to Cap = 20, average, then apply a small FDM factor. 3. Breakout momentum, three channels Smoothed position inside 40, 80, and 160 day channels, each scaled, then averaged. 4. Composite forecast Average the EWMAC composite and the breakout composite, then cap to ±20. Relative mode runs the same logic on symbol / benchmark. Combined mode averages Absolute and Relative composites. 5. Weekly correlation Pearson correlation between weekly closes of the asset and the benchmark over a user-set length. 6. Direction overlay Two EMAs on the forecast series plus optional green or red background by sign, and optional horizontal level shading around 0, ±5, ±10, ±15, ±20. ⸻ Plots • FC, capped forecast on the daily chart • 8-32 Abs, 8-32 Rel, single-leg EWMAC plus breakout view • 8-32-128 Abs, 8-32-128 Rel, three-leg composite views • from EMA20, from EMA50, (price − EMA) / ATR • ATR, ATR as a percent of price • Corr, weekly correlation with the benchmark • Forecast EMA1 and EMA2, EMAs of the forecast with an optional fill • Backgrounds and guide lines, optional sign-based background, optional 0, ±5, ±10, ±15, ±20 guides Most plots and the table are gated by timeframe.isdaily. Set the chart to 1D to see them. ⸻ Inputs Symbol selection • Absolute, Relative, Combined • Vs. benchmark for Relative mode and correlation, choices: SPY, QQQ, XLE, GLD • Ticker or Freeform, for Freeform use full TradingView notation, for example NASDAQ:AAPL Engine selection • Include: • 8-32-128, three EWMAC legs plus three breakouts • 8-32, simplified view based on the 8-32 leg plus a 40-day breakout EMA, applied to the forecast • EMA1, EMA2, with line-width controls, plus color and opacity Volatility • Span, EW volatility span for daily returns • 10y %, blend of long-run volatility • Thresh, Too volatile, placeholders in this version Background • Horizontal bg, level shading, enabled by default • Long BG, Hedge BG, colors and opacities Show • Table, Header, Direction, Gain, Extension • Corr, Length for correlation row Table settings • Position, background, opacity, text size, text color Lines • 0-lines, 10-lines, 5-lines, level guides ⸻ Reading the outputs • Forecast > 0, bullish tilt; Forecast < 0, bearish or hedge tilt • ±10 and ±20 indicate strength on a uniform scale • EMA1 vs EMA2 on the forecast, EMA1 above EMA2 suggests improving momentum • Table rows, label colored by sign, current forecast value plus a green or red dot for the forecast EMA cross, optional daily return percent, weekly correlation, and ATR-scaled EMA9, EMA20, EMA50 distances ⸻ Data handling, repainting, and performance • Daily and weekly series are fetched with request.security(). • Calculations use closed bars, values can update until the bar closes. • No lookahead, historical values do not repaint. • Weekly correlation updates during the week, it finalizes on weekly close. • On intraday charts most visuals are hidden by design. ⸻ Good practice and limitations • This is a research indicator, not a trading system. • The fixed Cap = 20 keeps a common scale, extreme moves will be clipped. • Relative mode depends on the ratio symbol / benchmark, ensure both legs have data for your feed. ⸻ Credits Concept inspired by Robert Carver’s forecast methodology in Advanced Futures Trading Strategies. Implementation details, parameters, and visuals are specific to this script. ⸻ Changelog • First version ⸻ Disclaimer For education and research only, not financial advice. Always test on your market and data feed, consider costs and slippage before using any indicator in live decisions.
Pine Script®指標
由BB_9791提供
Seasonality Monte Carlo Forecaster [BackQuant]Seasonality Monte Carlo Forecaster Plain-English overview This tool projects a cone of plausible future prices by combining two ideas that traders already use intuitively: seasonality and uncertainty. It watches how your market typically behaves around this calendar date, turns that seasonal tendency into a small daily “drift,” then runs many randomized price paths forward to estimate where price could land tomorrow, next week, or a month from now. The result is a probability cone with a clear expected path, plus optional overlays that show how past years tended to move from this point on the calendar. It is a planning tool, not a crystal ball: the goal is to quantify ranges and odds so you can size, place stops, set targets, and time entries with more realism. What Monte Carlo is and why quants rely on it • Definition . Monte Carlo simulation is a way to answer “what might happen next?” when there is randomness in the system. Instead of producing a single forecast, it generates thousands of alternate futures by repeatedly sampling random shocks and adding them to a model of how prices evolve. • Why it is used . Markets are noisy. A single point forecast hides risk. Monte Carlo gives a distribution of outcomes so you can reason in probabilities: the median path, the 68% band, the 95% band, tail risks, and the chance of hitting a specific level within a horizon. • Core strengths in quant finance . – Path-dependent questions : “What is the probability we touch a stop before a target?” “What is the expected drawdown on the way to my objective?” – Pricing and risk : Useful for path-dependent options, Value-at-Risk (VaR), expected shortfall (CVaR), stress paths, and scenario analysis when closed-form formulas are unrealistic. – Planning under uncertainty : Portfolio construction and rebalancing rules can be tested against a cloud of plausible futures rather than a single guess. • Why it fits trading workflows . It turns gut feel like “seasonality is supportive here” into quantitative ranges: “median path suggests +X% with a 68% band of ±Y%; stop at Z has only ~16% odds of being tagged in N days.” How this indicator builds its probability cone 1) Seasonal pattern discovery The script builds two day-of-year maps as new data arrives: • A return map where each calendar day stores an exponentially smoothed average of that day’s log return (yesterday→today). The smoothing (90% old, 10% new) behaves like an EWMA, letting older seasons matter while adapting to new information. • A volatility map that tracks the typical absolute return for the same calendar day. It calculates the day-of-year carefully (with leap-year adjustment) and indexes into a 365-slot seasonal array so “March 18” is compared with past March 18ths. This becomes the seasonal bias that gently nudges simulations up or down on each forecast day. 2) Choice of randomness engine You can pick how the future shocks are generated: • Daily mode uses a Gaussian draw with the seasonal bias as the mean and a volatility that comes from realized returns, scaled down to avoid over-fitting. It relies on the Box–Muller transform internally to turn two uniform random numbers into one normal shock. • Weekly mode uses bootstrap sampling from the seasonal return history (resampling actual historical daily drifts and then blending in a fraction of the seasonal bias). Bootstrapping is robust when the empirical distribution has asymmetry or fatter tails than a normal distribution. Both modes seed their random draws deterministically per path and day, which makes plots reproducible bar-to-bar and avoids flickering bands. 3) Volatility scaling to current conditions Markets do not always live in average volatility. The engine computes a simple volatility factor from ATR(20)/price and scales the simulated shocks up or down within sensible bounds (clamped between 0.5× and 2.0×). When the current regime is quiet, the cone narrows; when ranges expand, the cone widens. This prevents the classic mistake of projecting calm markets into a storm or vice versa. 4) Many futures, summarized by percentiles The model generates a matrix of price paths (capped at 100 runs for performance inside TradingView), each path stepping forward for your selected horizon. For each forecast day it sorts the simulated prices and pulls key percentiles: • 5th and 95th → approximate 95% band (outer cone). • 16th and 84th → approximate 68% band (inner cone). • 50th → the median or “expected path.” These are drawn as polylines so you can immediately see central tendency and dispersion. 5) A historical overlay (optional) Turn on the overlay to sketch a dotted path of what a purely seasonal projection would look like for the next ~30 days using only the return map, no randomness. This is not a forecast; it is a visual reminder of the seasonal drift you are biasing toward. Inputs you control and how to think about them Monte Carlo Simulation • Price Series for Calculation . The source series, typically close. • Enable Probability Forecasts . Master switch for simulation and drawing. • Simulation Iterations . Requested number of paths to run. Internally capped at 100 to protect performance, which is generally enough to estimate the percentiles for a trading chart. If you need ultra-smooth bands, shorten the horizon. • Forecast Days Ahead . The length of the cone. Longer horizons dilute seasonal signal and widen uncertainty. • Probability Bands . Draw all bands, just 95%, just 68%, or a custom level (display logic remains 68/95 internally; the custom number is for labeling and color choice). • Pattern Resolution . Daily leans on day-of-year effects like “turn-of-month” or holiday patterns. Weekly biases toward day-of-week tendencies and bootstraps from history. • Volatility Scaling . On by default so the cone respects today’s range context. Plotting & UI • Probability Cone . Plots the outer and inner percentile envelopes. • Expected Path . Plots the median line through the cone. • Historical Overlay . Dotted seasonal-only projection for context. • Band Transparency/Colors . Customize primary (outer) and secondary (inner) band colors and the mean path color. Use higher transparency for cleaner charts. What appears on your chart • A cone starting at the most recent bar, fanning outward. The outer lines are the ~95% band; the inner lines are the ~68% band. • A median path (default blue) running through the center of the cone. • An info panel on the final historical bar that summarizes simulation count, forecast days, number of seasonal patterns learned, the current day-of-year, expected percentage return to the median, and the approximate 95% half-range in percent. • Optional historical seasonal path drawn as dotted segments for the next 30 bars. How to use it in trading 1) Position sizing and stop logic The cone translates “volatility plus seasonality” into distances. • Put stops outside the inner band if you want only ~16% odds of a stop-out due to noise before your thesis can play. • Size positions so that a test of the inner band is survivable and a test of the outer band is rare but acceptable. • If your target sits inside the 68% band at your horizon, the payoff is likely modest; outside the 68% but inside the 95% can justify “one-good-push” trades; beyond the 95% band is a low-probability flyer—consider scaling plans or optionality. 2) Entry timing with seasonal bias When the median path slopes up from this calendar date and the cone is relatively narrow, a pullback toward the lower inner band can be a high-quality entry with a tight invalidation. If the median slopes down, fade rallies toward the upper band or step aside if it clashes with your system. 3) Target selection Project your time horizon to N bars ahead, then pick targets around the median or the opposite inner band depending on your style. You can also anchor dynamic take-profits to the moving median as new bars arrive. 4) Scenario planning & “what-ifs” Before events, glance at the cone: if the 95% band already spans a huge range, trade smaller, expect whips, and avoid placing stops at obvious band edges. If the cone is unusually tight, consider breakout tactics and be ready to add if volatility expands beyond the inner band with follow-through. 5) Options and vol tactics • When the cone is tight : Prefer long gamma structures (debit spreads) only if you expect a regime shift; otherwise premium selling may dominate. • When the cone is wide : Debit structures benefit from range; credit spreads need wider wings or smaller size. Align with your separate IV metrics. Reading the probability cone like a pro • Cone slope = seasonal drift. Upward slope means the calendar has historically favored positive drift from this date, downward slope the opposite. • Cone width = regime volatility. A widening fan tells you that uncertainty grows fast; a narrow cone says the market typically stays contained. • Mean vs. price gap . If spot trades well above the median path and the upper band, mean-reversion risk is high. If spot presses the lower inner band in an up-sloping cone, you are in the “buy fear” zone. • Touches and pierces . Touching the inner band is common noise; piercing it with momentum signals potential regime change; the outer band should be rare and often brings snap-backs unless there is a structural catalyst. Methodological notes (what the code actually does) • Log returns are used for additivity and better statistical behavior: sim_ret is applied via exp(sim_ret) to evolve price. • Seasonal arrays are updated online with EWMA (90/10) so the model keeps learning as each bar arrives. • Leap years are handled; indexing still normalizes into a 365-slot map so the seasonal pattern remains stable. • Gaussian engine (Daily mode) centers shocks on the seasonal bias with a conservative standard deviation. • Bootstrap engine (Weekly mode) resamples from observed seasonal returns and adds a fraction of the bias, which captures skew and fat tails better. • Volatility adjustment multiplies each daily shock by a factor derived from ATR(20)/price, clamped between 0.5 and 2.0 to avoid extreme cones. • Performance guardrails : simulations are capped at 100 paths; the probability cone uses polylines (no heavy fills) and only draws on the last confirmed bar to keep charts responsive. • Prerequisite data : at least ~30 seasonal entries are required before the model will draw a cone; otherwise it waits for more history. Strengths and limitations • Strengths : – Probabilistic thinking replaces single-point guessing. – Seasonality adds a small but meaningful directional bias that many markets exhibit. – Volatility scaling adapts to the current regime so the cone stays realistic. • Limitations : – Seasonality can break around structural changes, policy shifts, or one-off events. – The number of paths is performance-limited; percentile estimates are good for trading, not for academic precision. – The model assumes tomorrow’s randomness resembles recent randomness; if regime shifts violently, the cone will lag until the EWMA adapts. – Holidays and missing sessions can thin the seasonal sample for some assets; be cautious with very short histories. Tuning guide • Horizon : 10–20 bars for tactical trades; 30+ for swing planning when you care more about broad ranges than precise targets. • Iterations : The default 100 is enough for stable 5/16/50/84/95 percentiles. If you crave smoother lines, shorten the horizon or run on higher timeframes. • Daily vs. Weekly : Daily for equities and crypto where month-end and turn-of-month effects matter; Weekly for futures and FX where day-of-week behavior is strong. • Volatility scaling : Keep it on. Turn off only when you intentionally want a “pure seasonality” cone unaffected by current turbulence. Workflow examples • Swing continuation : Cone slopes up, price pulls into the lower inner band, your system fires. Enter near the band, stop just outside the outer line for the next 3–5 bars, target near the median or the opposite inner band. • Fade extremes : Cone is flat or down, price gaps to the upper outer band on news, then stalls. Favor mean-reversion toward the median, size small if volatility scaling is elevated. • Event play : Before CPI or earnings on a proxy index, check cone width. If the inner band is already wide, cut size or prefer options structures that benefit from range. Good habits • Pair the cone with your entry engine (breakout, pullback, order flow). Let Monte Carlo do range math; let your system do signal quality. • Do not anchor blindly to the median; recalc after each bar. When the cone’s slope flips or width jumps, the plan should adapt. • Validate seasonality for your symbol and timeframe; not every market has strong calendar effects. Summary The Seasonality Monte Carlo Forecaster wraps institutional risk planning into a single overlay: a data-driven seasonal drift, realistic volatility scaling, and a probabilistic cone that answers “where could we be, with what odds?” within your trading horizon. Use it to place stops where randomness is less likely to take you out, to set targets aligned with realistic travel, and to size positions with confidence born from distributions rather than hunches. It will not predict the future, but it will keep your decisions anchored to probabilities—the language markets actually speak.
Pine Script®指標
由BackQuant提供
2
Bitcoin Logarithmic Growth Curve 2025 Z-Score"The Bitcoin logarithmic growth curve is a concept used to analyze Bitcoin's price movements over time. The idea is based on the observation that Bitcoin's price tends to grow exponentially, particularly during bull markets. It attempts to give a long-term perspective on the Bitcoin price movements. The curve includes an upper and lower band. These bands often represent zones where Bitcoin's price is overextended (upper band) or undervalued (lower band) relative to its historical growth trajectory. When the price touches or exceeds the upper band, it may indicate a speculative bubble, while prices near the lower band may suggest a buying opportunity. Unlike most Bitcoin growth curve indicators, this one includes a logarithmic growth curve optimized using the latest 2024 price data, making it, in our view, superior to previous models. Additionally, it features statistical confidence intervals derived from linear regression, compatible across all timeframes, and extrapolates the data far into the future. Finally, this model allows users the flexibility to manually adjust the function parameters to suit their preferences. The Bitcoin logarithmic growth curve has the following function: y = 10^(a * log10(x) - b) In the context of this formula, the y value represents the Bitcoin price, while the x value corresponds to the time, specifically indicated by the weekly bar number on the chart. How is it made (You can skip this section if you’re not a fan of math): To optimize the fit of this function and determine the optimal values of a and b, the previous weekly cycle peak values were analyzed. The corresponding x and y values were recorded as follows: 113, 18.55 240, 1004.42 451, 19128.27 655, 65502.47 The same process was applied to the bear market low values: 103, 2.48 267, 211.03 471, 3192.87 676, 16255.15 Next, these values were converted to their linear form by applying the base-10 logarithm. This transformation allows the function to be expressed in a linear state: y = a * x − b. This step is essential for enabling linear regression on these values. For the cycle peak (x,y) values: 2.053, 1.268 2.380, 3.002 2.654, 4.282 2.816, 4.816 And for the bear market low (x,y) values: 2.013, 0.394 2.427, 2.324 2.673, 3.504 2.830, 4.211 Next, linear regression was performed on both these datasets. (Numerous tools are available online for linear regression calculations, making manual computations unnecessary). Linear regression is a method used to find a straight line that best represents the relationship between two variables. It looks at how changes in one variable affect another and tries to predict values based on that relationship. The goal is to minimize the differences between the actual data points and the points predicted by the line. Essentially, it aims to optimize for the highest R-Square value. Below are the results: snapshot snapshot It is important to note that both the slope (a-value) and the y-intercept (b-value) have associated standard errors. These standard errors can be used to calculate confidence intervals by multiplying them by the t-values (two degrees of freedom) from the linear regression. These t-values can be found in a t-distribution table. For the top cycle confidence intervals, we used t10% (0.133), t25% (0.323), and t33% (0.414). For the bottom cycle confidence intervals, the t-values used were t10% (0.133), t25% (0.323), t33% (0.414), t50% (0.765), and t67% (1.063). The final bull cycle function is: y = 10^(4.058 ± 0.133 * log10(x) – 6.44 ± 0.324) The final bear cycle function is: y = 10^(4.684 ± 0.025 * log10(x) – -9.034 ± 0.063) The main Criticisms of growth curve models: The Bitcoin logarithmic growth curve model faces several general criticisms that we’d like to highlight briefly. The most significant, in our view, is its heavy reliance on past price data, which may not accurately forecast future trends. For instance, previous growth curve models from 2020 on TradingView were overly optimistic in predicting the last cycle’s peak. This is why we aimed to present our process for deriving the final functions in a transparent, step-by-step scientific manner, including statistical confidence intervals. It's important to note that the bull cycle function is less reliable than the bear cycle function, as the top band is significantly wider than the bottom band. Even so, we still believe that the Bitcoin logarithmic growth curve presented in this script is overly optimistic since it goes parly against the concept of diminishing returns which we discussed in this post: This is why we also propose alternative parameter settings that align more closely with the theory of diminishing returns." Now with Z-Score calculation for easy and constant valuation classification of Bitcoin according to this metric. Created for TRW
Pine Script®指標
由Commandoum提供