T3movingaverage — Индикаторы и стратегии

Adaptive MAD Supertrend | GForge Adaptive MAD Supertrend | GForge The Adaptive MAD Supertrend is a trend-following indicator built on the classic Supertrend framework, but with two core innovations that address well-known weaknesses in the original design: how volatility is measured, and how the indicator behaves across different market conditions. The Problem With Standard Supertrend The classic Supertrend uses ATR (Average True Range) as its volatility measure and a fixed multiplier. ATR squares its deviation calculations internally, which means a single spike candle — a news wick, a liquidation cascade — can temporarily blow the bands wide and either trigger a false flip or delay a valid one. On top of that, a fixed multiplier means the indicator behaves identically in a clean trending market and a choppy ranging one. It has no awareness of what the market is actually doing. Innovation 1 — MAD Replaces ATR This indicator uses Mean Absolute Deviation as its volatility measure instead of ATR or Standard Deviation. MAD = mean( |close − mean(close, n)| , n ) The key difference is linearity. Each bar contributes its deviation to the average equally, without squaring. A spike candle influences the band width, but proportionally — it cannot disproportionately dominate the calculation the way it can in ATR or StdDev. The result is a more stable, consistent band width that responds to genuine volatility without overreacting to outlier bars. An optional EMA smoothing layer can be applied to the raw MAD output before it scales the bands, which further stabilizes band width during volatile periods. Innovation 2 — Kaufman Efficiency Ratio Scales the Multiplier The Efficiency Ratio (ER), developed by Perry Kaufman, measures how efficiently price is moving: ER = |net price change over n bars| / sum(|bar-to-bar changes|, n) ER → 1.0: price moved efficiently in one direction — a clean trend ER → 0.0: price moved a lot but went nowhere — chop and noise The adaptive multiplier uses ER to scale band width dynamically: adaptive_multiplier = Multiplier_Chop − ER × (Multiplier_Chop − Multiplier_Trend) During a clean trend, the multiplier contracts toward your Trend setting — bands tighten and the trailing stop follows price closely, capturing more of the move. During choppy conditions, the multiplier expands toward your Chop setting — bands widen and the stop absorbs noise without flipping unnecessarily. This means the indicator automatically adjusts its sensitivity to what the market is doing, rather than applying the same fixed behaviour to every bar. Basis MA The band centre line (basis) is fully configurable. Rather than raw hl2 like the classic Supertrend, any moving average from the menu can serve as the anchor — the band is built outward from it. The default is T3, a Tillson triple-smoothed MA that provides an extremely clean centre line with minimal lag overshoot. Other useful options include DEMA and TEMA for faster response, VWMA for volume-weighted anchoring, or RMA for higher timeframes. How to Read It Line colour: green/up colour = bullish trend, red/down colour = bearish trend Trend fill: shaded area between price and the trailing stop — visual confirmation of which side of the line price is on Inactive band: the faint dotted line on the opposite side shows where a flip would trigger if price reaches it Signal diamonds: markers at each trend flip — below bar for long entries, above bar for short/cash exits Notes Optimised and tested on Bitcoin 1D. Performs well on trending instruments across higher timeframes (4H and above). The MAD + ER combination is theoretically complementary: MAD handles what the band width is, ER handles how much of it to apply. They solve orthogonal problems. As with all trend-following tools, performance degrades in prolonged sideways markets — the Chop multiplier setting mitigates this but does not eliminate it. ⚠️ Disclaimer This indicator is a technical analysis tool provided for informational and educational purposes only. It is not financial advice, and nothing presented here should be construed as a recommendation to buy, sell, or hold any asset. Past performance does not guarantee future results. Developed by GForge

RSI-Adaptive T3 + Squeeze Momentum Strategy ✅ Strategy Guide: RSI-Adaptive T3 + Squeeze Momentum Strategy 📌 Overview The RSI-Adaptive T3 + Squeeze Momentum Strategy is a dynamic trend-following strategy based on an RSI-responsive T3 moving average and Squeeze Momentum detection . It adapts in real-time to market volatility to enhance entry precision and optimize risk. ⚠️ This strategy is provided for educational and research purposes only. Past performance does not guarantee future results. 🎯 Strategy Objectives The main objective of this strategy is to catch the early phase of a trend and generate consistent entry signals. Designed to be intuitive and accessible for traders from beginner to advanced levels. ✨ Key Features RSI-Responsive T3: T3 length dynamically adjusts according to RSI values for adaptive trend detection Squeeze Momentum: Combines Bollinger Bands and Keltner Channels to identify trend buildup phases Visual Triggers: Entry signals are generated from T3 crossovers and momentum strength after squeeze release 📊 Trading Rules Long Entry: When T3 crosses upward, momentum is positive, and the squeeze has just been released. Short Entry: When T3 crosses downward, momentum is negative, and the squeeze has just been released. Exit (Reversal): When the opposite condition to the entry is triggered, the position is reversed. 💰 Risk Management Parameters Pair & Timeframe: BTC/USD (30-minute chart) Capital (simulated): $30,00 Order size: `$100` per trade (realistic, low-risk sizing) Commission: 0.02% Slippage: 2 pips Risk per Trade: 5% Number of Trades (backtest period): 181 📊 Performance Overview Symbol: BTC/USD Timeframe: 30-minute chart Date Range: January 1, 2024 – July 3, 2025 Win Rate: 47.8% Profit Factor: 2.01 Net Profit: 173.16 (units not specified) Max Drawdown: 5.77% or 24.91 (0.79%) ⚙️ Indicator Parameters Indicator Name: RSI-Adaptive T3 + Squeeze Momentum RSI Length: 14 T3 Min Length: 5 T3 Max Length: 50 T3 Volume Factor: 0.7 BB Length: 27 (Multiplier: 2.0) KC Length: 20 (Multiplier: 1.5, TrueRange enabled) 🖼 Visual Support T3 slope direction, squeeze status, and momentum bars are visually plotted on the chart, providing high clarity for quick trend analysis and execution. 🔧 Strategy Improvements & Uniqueness Inspired by the RSI Adaptive T3 by ChartPrime and Squeeze Momentum Indicator by LazyBear , this strategy fuses both into a hybrid trend-reversal and momentum breakout detection system . Compared to traditional trend-following methods, it excels at capturing early trend signals with greater sensitivity . ✅ Summary The RSI-Adaptive T3 + Squeeze Momentum Strategy combines momentum detection with volatility-responsive risk management. With a strong balance between visual clarity and practicality, it serves as a powerful tool for traders seeking high repeatability. ⚠️ This strategy is based on historical data and does not guarantee future profits. Always use appropriate risk management when applying it.

STD-Adaptive T3 [Loxx]STD-Adaptive T3 is a standard deviation adaptive T3 moving average filter. This indicator acts more like a trend overlay indicator with gradient coloring. What is the T3 moving average? Better Moving Averages Tim Tillson November 1, 1998 Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years. Introduction "Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done." This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced. "Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator. Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus. We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes: It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values. It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits. The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade! Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how: Two Interesting Moving Averages We will examine two benchmark moving averages based on Linear Regression analysis. In both cases, a Linear Regression line of length n is fitted to price data. I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data. We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA . Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present. However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line. These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot. A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2. Filter Techniques Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag. There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by: L' = L(time series) + L(time series - L(time series)) That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to: 2L-L(L) This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994). In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator. We will use these two techniques to construct our better moving average, after we explore the first one a little more closely. Fixing Overshoot An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2. Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once. This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible. The solution to the overshoot problem is to recall what we are doing with twicing: DEMA (n) = EMA (n) + EMA (time series - EMA (n)) The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block: EMA (n) + EMA (time series - EMA (n))*.7; This is algebraically the same as: EMA (n)*1.7-EMA( EMA (n))*.7; I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is: GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v, Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data: T3(n) = GD ( GD ( GD (n))) In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves. Included Bar coloring Loxx's Expanded Source Types

T3 Super Guppy A Tillson T3 moving average implemented variation of the CM Super Guppy indicator by @FritzMurphy The T3 moving average was developed by Tom Tilson which combines multiple EMAs into a single moving average. it is smoother and more responsive compared to traditional moving averages. The disadvantage is that it can overshoot price. █ Description T3 Super Guppy consists of 20 T3 moving averages: • 7 fast T3 MAs • 13 slow T3 MAs Visuals: • Compact view available for chart minimalists • In compact view only 10 of the fastest T3 moving averages will be displayed • Compact view will not affect how the colour scales with trend movement • Ribbon transparency will automatically scale based on the display mode chosen Colour Gradient • The more T3 MAs that cross above or below their slower counterparts will result in how deep the chosen upTrend(Blue) or downTrend(Red) colour is displayed • Helps to spot weakening trends or reversal signals when indicator colour starts converging into the opposite colour • Single colour mode is available if you find the colour gradient distracting █ Credits @ChrisMoody original guppy idea: @FritzMurphy super guppy format: █ Examples compact view: full view: