Logistic — อินดิเคเตอร์และกลยุทธ์

Directional Logistic Oscillator | GainzAlgo Overview The Directional Logistic Oscillator (DLO) is a momentum-based indicator designed to measure directional market strength and identify potential trend reversals or mean-reversion opportunities. It builds on the classic Directional Movement Index (DMI) by transforming its components (+DI, -DI, and ADX) into probabilistic signals using logistic functions, then combining them into a bounded oscillator that oscillates between approximately -1 and +1. Unlike traditional oscillators like RSI or MACD, DLO emphasizes directional probability by estimating the likelihood of bullish or bearish dominance while factoring in overall trend strength (via ADX). This makes it particularly useful for: Spotting overbought/oversold conditions in ranging markets. Confirming trend shifts in trending markets. Generating reversal signals based on oscillator cycles. The oscillator is plotted as histogram bars (columns) for visual clarity, with color-coding to highlight strength and direction. Positive values indicate bullish momentum, negative values bearish, and crossings of key levels can signal trading opportunities. How It Works At its core, DLO processes DMI data through a logistic transformation to create "probabilities" of directional movement: 1. DMI Calculation : Uses the standard DMI with a user-defined length (default 14) to compute +DI (upward movement), -DI (downward movement), and ADX (trend strength). 2. Logistic Probability : Each DMI component is normalized against its long-term mean and passed through a logistic (sigmoid) function. This creates smooth probabilities between 0 and 1. The logistic function is defined as: logistic_prob(series, mean_lb, slope, smooth_len) => mean = ta.sma(series, mean_lb) z = (series - mean) * slope prob_raw = 1.0 / (1.0 + math.exp(-z)) ta.ema(prob_raw, smooth_len) This step makes the indicator adaptive to market conditions, with the "slope" controlling how sharply it reacts to deviations from the mean. 3. Net Directional Strength : Bullish minus bearish probability, scaled by ADX probability and a user-defined multiplier, then bounded using a hyperbolic tangent (tanh) function to keep values between -1 and +1. net_dir = prob_plus - prob_minus strength_raw = net_dir * prob_adx * osc_scale strength_bound = tanh(strength_raw) Tanh ensures smooth, bounded output without clipping extremes unnaturally. 4. Smoothing and Signals: The raw strength is smoothed with EMA, then further processed into SMA and EMA lines for signal generation. Percentile-based thresholds (adaptive over a lookback period) detect extreme zones for mean-reversion signals. The result is a visually intuitive oscillator: Green bars for bullish, red for bearish, with varying intensity based on momentum. Inputs DLO offers customizable settings grouped for ease of use. Defaults are tuned for balanced performance on daily charts. DMI Settings DI Length (default: 14): Controls DMI sensitivity. Shorter lengths react faster to price changes but add noise; longer lengths smooth signals for trends. Mean Lookback (default: 360): The period for calculating the long-term average of DMI components. Higher values provide a more stable baseline, reducing false signals from short-term volatility. Lower values make the indicator more responsive but noisier Difference between high and low mean lookback period, lower length can pick up on new trends faster but at the cost of increased noise. Logistic Probability Settings LR Slope (higher = steeper) (default: 0.18): Adjusts the steepness of the logistic curve. Lower values create gradual transitions (smoother oscillator); higher values make sharp shifts, emphasizing extremes. Probability Smoothing (EMA) (default: 3): Short EMA to reduce noise in probabilities. Keep low (1-5) for responsiveness; higher for smoothness. Oscillator Settings Oscillator Scale (pre-tanh) (default: 2.5): Multiplies net strength before bounding. Higher values increase sensitivity and amplitude (larger swings); lower values compress the range for subtler signals. Comparison of 4 different settings for Oscillator scale, showing that as the scale parameter increases, the oscillator output becomes more pronounced, exhibiting higher amplitude compression toward the bounds and spending more time saturated near the extreme values of +1 and −1. Oscillator Smoothing Length (default: 7): Period for SMA/EMA smoothing of the final oscillator. Longer = smoother, fewer signals; shorter = more reactive. Color & Display Settings Buy Color / Sell Color: Customize colors for bullish/bearish visuals. Plot Reversion Signals (default: true): Shows arrows for cycle reversals (local highs/lows). Plot Mean-Reversion Signals (default: true): Arrows for crossings from extreme percentile zones. Plot Oscillator MA (default: false): Overlays an SMA on the oscillator for additional confirmation. Allow Intrabar Updating (default: true): Enables real-time updates within incomplete bars (may cause minor repainting). Visuals Oscillator Histogram: Columns colored green (bullish) or red (bearish), with lighter shades for weaker momentum. Crosses above/below zero signal momentum shifts. Horizontal Lines: Zero (neutral), +0.5 (strong bullish), -0.5 (strong bearish). Background Highlights: Subtle green/red shading when in strong zones. Bar Colors: Mirrors oscillator direction on the price chart. Color-coded trend regimes: green/teal highlight strong and weak uptrends, red/purple mark strong and weak downtrends, while the oscillator histogram confirms direction and strength through its polarity and amplitude. Signals Mean-Reversion (MR) Signals : Triangles (▲/▼) when the smoothed oscillator crosses up from low percentiles (oversold) or down from high percentiles (overbought). These are adaptive, using historical data for dynamic extremes. Buy: Oscillator crosses above lower threshold (e.g., 10th/5th percentile). Sell: Crosses below upper threshold (e.g., 90th/95th percentile). Reversion Signals : Arrows (⬆/⬇) at local turning points in the oscillator cycle, indicating potential reversals. Zero-Line Crosses : Basic bullish/bearish momentum changes. Usage Tips Trend Confirmation: Use in trending markets—persistent positive/negative values confirm up/down trends. Pair with moving averages for entries. Mean-Reversion: In sideways markets, trade MR signals from extremes. Combine with support/resistance. Divergences: Look for price making new highs/lows while DLO doesn't for reversal setup. Alerts MR Buy/Sell: Extreme zone crosses (percentile-based). Reversion Up/Down: Cycle turning points. Osc Bullish/Bearish Cross: Zero-line crosses. Limitations Like all oscillators, DLO can lag in strong trends or produce false signals in choppy markets, use with confirmation. Percentile thresholds adapt over time but may vary by asset volatility. Not a standalone system; always combine with risk management.

Machine Learning: Multiple Logistic Regression Multiple Logistic Regression Indicator The Logistic Regression Indicator for TradingView is a versatile tool that employs multiple logistic regression based on various technical indicators to generate potential buy and sell signals. By utilizing key indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend, the indicator aims to provide a systematic approach to decision-making in financial markets. How It Works: Technical Indicators: The script uses multiple technical indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend as input variables for the logistic regression model. These indicators are normalized to create categorical variables, providing a consistent scale for the model. Logistic Regression: The logistic regression function is applied to the normalized input variables (x1 to x6) with user-defined coefficients (b0 to b6). The logistic regression model predicts the probability of a binary outcome, with values closer to 1 indicating a bullish signal and values closer to 0 indicating a bearish signal. Loss Function (Cross-Entropy Loss): The cross-entropy loss function is calculated to quantify the difference between the predicted probability and the actual outcome. The goal is to minimize this loss, which essentially measures the model's accuracy. // Error Function (cross-entropy loss) loss(y, p) => -y * math.log(p) - (1 - y) * math.log(1 - p) // y - depended variable // p - multiple logistic regression Gradient Descent: Gradient descent is an optimization algorithm used to minimize the loss function by adjusting the weights of the logistic regression model. The script iteratively updates the weights (b1 to b6) based on the negative gradient of the loss function with respect to each weight. // Adjusting model weights using gradient descent b1 -= lr * (p + loss) * x1 b2 -= lr * (p + loss) * x2 b3 -= lr * (p + loss) * x3 b4 -= lr * (p + loss) * x4 b5 -= lr * (p + loss) * x5 b6 -= lr * (p + loss) * x6 // lr - learning rate or step of learning // p - multiple logistic regression // x_n - variables Learning Rate: The learning rate (lr) determines the step size in the weight adjustment process. It prevents the algorithm from overshooting the minimum of the loss function. Users can set the learning rate to control the speed and stability of the optimization process. Visualization: The script visualizes the output of the logistic regression model by coloring the SMA. Arrows are plotted at crossover and crossunder points, indicating potential buy and sell signals. Lables are showing logistic regression values from 1 to 0 above and below bars Table Display: A table is displayed on the chart, providing real-time information about the input variables, their values, and the learned coefficients. This allows traders to monitor the model's interpretation of the technical indicators and observe how the coefficients change over time. How to Use: Parameter Adjustment: Users can adjust the length of technical indicators (rsi_length, cci_length, etc.) and the Z score length based on their preference and market characteristics. Set the initial values for the regression coefficients (b0 to b6) and the learning rate (lr) according to your trading strategy. Signal Interpretation: Buy signals are indicated by an upward arrow (▲), and sell signals are indicated by a downward arrow (▼). The color-coded SMA provides a visual representation of the logistic regression output by color. Table Information: Monitor the table for real-time information on the input variables, their values, and the learned coefficients. Keep an eye on the learning rate to ensure a balance between model adjustment speed and stability. Backtesting and Validation: Before using the script in live trading, conduct thorough backtesting to evaluate its performance under different market conditions. Validate the model against historical data to ensure its reliability.

Logistic RSI, STOCH, ROC, AO, ... by DGT Experimental attemt of applying Logistic Map Equation for some of widly used indicators. With this study "Awesome Oscillator (AO)", "Rate of Change (ROC)", "Relative Strength Index (RSI)", "Stochastic (STOCH)" and a custom interpretation of Logistic Map Equation is presented Calculations with Logistic Map Equation makes sense when the calculated results are iterated many times within the same equation. Here is the Logistic Map Equation : Xn+1 = r * Xn * (1 - Xn) Where, the value of r is the key for this equation which changes amazingly the behaviour of the Logistic Map. The value we have asigned for r is less then 1 and greater than 0 ( 0 < r < 1) and in this case the iterations performed with the maximum number of output series allowed by Pine is quite enough for our purpose and thanks to arrays we can easiliy store them for further processing What we have as output: Each iteration result is then plotted (excluding plotting the first iteration), as circles or line based on user preference Values above and below zero level (0) are coloured differently to emphasis bull and bear power Finally Standard Deviation of Array's Elements is ploted as line. Users may choose to display this line only So where it comes the indicators "Awesome Oscillator (AO)", "Rate of Change (ROC)", "Relative Strength Index (RSI)", "Stochastic (STOCH)". Those are the indicators whose values are assigned to our key varaiable in the Logistic Map equation forulma which is r Further details regarding Logistic Map can found under the description of “Logistic EMA w/ Signals by DGT” study Disclaimer: Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script

Logistic EMA w/ Signals by DGT Logistic Map Equation - The logistic map connects fluid convection, neuron firing, the Mandelbrot set and so much more. This study is an attempt to apply Logistic Map Equation in Trading Logistic Map Equation Xn+1 = r * Xn * (1 - Xn) Where, r - growth rate Xn - percentage of theoretical maximum of measured event (from 0 to 1) (1 - Xn) - represents constraints of the environment, presents the idea of negative feedback For trading the measured event will be the price of the instrument (price is commonly reffered as source in mathematicall forumlations), hence r - growth rate can be expressed as => change(source, length) / source, expressing r in such manner mades the equation dynamic with regards to the growth rate Xn - percentage of theoretical maximum of the price for given duration can be expressed as => source / highest(length) Putting pieces together we are ready to plot Printed alone does not seem to provide much useful visualization for trading, in fact not easy to interpret especially when the market is an uptrend What it has numerically, Provides a ratio, where sudden changes are much more reflected thanks to negative feedback nature of the logistic equation. As we know moving average indicators are lagging and the logistic map may fit here to reduce the lag With this study you will find application of Logistic Map Equation with combination of Exponential Moving Average (EMA) Logistic EMA (LEMA) and LEMA COLORS one line with user defined periods of length, where the colors of the line will change automatically depending where the value is compared to 50-100-200 moving average Multiple LEMAs : optional – three fixed lenght of 50-100-200 period lines LEMA Signals Various signals are added by using LEMA and applying some common market approaches. Use with caution and with conjunction of other indicators Thanks to @allanster for the idea A fascinating YouTube video explaining the logistic map - “This equation will change how you see the world (the logistic map)” Disclaimer: Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script

Volatility Bands by DGT Volatility represents how large an asset's prices swing around the mean price, the degree of variation of a trading price over time, and is commonly measured with beta (β) coefficients, standard deviations (σ) of returns where tools such as Average True Range, Bollinger Bands, Keltner Channel, Squeeze Indicator, etc presents volatility concept Volatility often refers to the amount of uncertainty or risk related to the size of changes in a security's value. The higher the volatility, the riskier the security - the price of the security can change dramatically over a short time period in either direction. A lower volatility - security's value does not fluctuate dramatically, and tends to be more steady This study, Volatility Bands , attempts to present a way to measure and visualize volatility , using standard deviations (σ) and average true range indicator, and aims to point out areas that might indicate potential trading opportunities I will try to explain the usage with examples, same setup with different option selected as you may observe from the examples different setting may have advantages and disadvantages over one another, it is recommended to verify a trading setup with different available options. Additionally, It is recommended to use this indicator in conjunction with other technical indicators, or verify using chart/candle patterns. Below is an usage example using in conjunction with other indicator, in the given example “Neglected Volume by DGT” is selected Similarities and Differences Bollinger Bands depicts two standard deviations above and below a simple moving average, and Keltner Channel depicts two times average true range (ATR) above and below an exponential moving average Volatility Bands study combines the approach of both Bollinger Bands and Keltner Channel, with different settings and different visualization Default settings are one standard deviations and one time average true range (ATR) above and below 13 period exponential moving average. Setting can be adjusted by users but let me remind all testes are performed with the default settings. Mathematically expressed as Upper band area between “ema + stdev” and “ema + atr” Lower band area between “ema – stdev” and “ema – atr” A different display is added with the inspiration I get from one of the @quantgym ‘s study, many thanks @quantgym 😉 When difference band display is selected the study will reflect the area between “ema + stdev – atr” and “ema – stdev + atr”. As shown in the examples above Note: standard deviation calculation can be adjusted based on price action or its moving average. Other differentiation between BB and KC is with V-BANDS mostly we look for trade opportunities when price action move out of the bands and in most cases we assume market is consolidating when the price action is within the bands The other indicator that presents similarities to Volatility Bands is Squeeze Indicator, which measures the relationship between Bollinger Bands and Keltner's Channels to help identify consolidations and signal when prices are likely to break out. Mainly Volatility Bands is different version of Squeeze indicator, in fact the purpose is almost same but visualization is completely different. Additionally Volatility Bands Offers trading opportunities whereas Squeeze indicator only presents market states unless a momentum indicator is adapted to Squeeze indicator. Disclaimer: Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script

Logistic Difference (ps4)This is an PS4 update to the Logistic Difference Indicator. It uses logistic function (sigmoid), which stabilizes the variance of data. The logistic function resembles the inverse Fisher transform. This version has a repaint/non-repaint switch and a scaling feature.

Logistic Difference Logistic Difference Indicator uses logistic function (sigmoid), which stabilizes the variance of data.

Logistic Correlation Logistic Correlation is a correlation oscillator using a logistic function. A Logistic Function is a Sigmoid Function who stabilize the variance of data.The logistic function have the same function as the inverse fisher transform but with an advantage over it, the k constant can control the steepness of the curve, lowers k's will preserve the original form of the data while highers one will transform it into a more square shaped form. 10 k 20 k