Regimechange — Indicators and Strategies

Hurst Flow • @Capital.com Description Hurst Flow is a regime-adaptive analytical tool that measures the continuous intention force behind market behavior. It blends momentum and persistence analysis to quantify how strongly price movement aligns with trend continuation versus mean reversion. The output is a normalized continuous force line: Positive values indicate increasing long-side capital exposure — markets showing trend-persistence and momentum alignment. Negative values reflect strengthening short-side capital exposure — environments favoring mean reversion or fading moves. Internally, the indicator processes open-price rate-of-change dynamics through adaptive smoothing, persistence estimation, and standardized scaling, producing a stable and comparable signal across time frames and assets. Use Hurst Flow as a market regime compass — to gauge bias, filter trades, or allocate exposure intensity dynamically. Input descriptions TF — Timeframe used to compute the signal. Higher TF = smoother, less whipsaw, but more lag. ROC length (Open) — Lookback for Open-to-Open rate of change (base momentum horizon). EMA length — Smoothing for ROC; increases stability at the cost of responsiveness. Hurst window — Window for Hurst-style persistence estimate; governs regime sensitivity. Standartizatoin window — Period for standardization; makes values comparable across assets/timeframes. Scale factor (0..1) — Final gain applied to the standardized signal; use <1 to temper amplitude. Presets/Backtest Below is a list of presets that can be used to test indicators. The presets cover various asset classes and time frames, demonstrating versatility and high customizability. To do this, you can use a special strategy Target % Rebalancer Based Strategy on Intention Indicator . The entry signal for the strategy is the output signal of the indicator from the chart, which can be selected from a special drop-down list. A detailed description of the strategy can be found on a special page. The presets presented were created on instruments not included in the sample. Below are the basic presets for the strategy. Other configuration functions can be used to fine-tune the strategy. The strategy settings are the same for all of the presets listed. The time interval must be set for both the indicator and the chart. Strategy fine tuning Enable Hysteresis + Cooldown : Off Risk & costs Enable Max Daily Loss Halt : Off Commission : 0.1% ============== Pre-Sets for Hurst Flow Indicator ============================= Preset Gold Chart bar size: 3D Indicator settings TF : 3D ROC : 10 EMA : 22 Hurst : 16 Standardization window length : 8 Scale : 1 ==================================================== Preset Crude Oil:USOIL Chart bar size: 1D Indicator settings TF : 1D ROC : 70 EMA : 6 Hurst : 26 Standardization window length : 16 Scale : 1 Final Weight Cap : 1 ==================================================== Preset S&P500 index Chart bar size: 2D Indicator settings TF : 2D ROC : 26 EMA : 8 Hurst : 33 Standardization window length : 16 Scale : 1 ==================================================== Preset MSFT Chart bar size: 2D Indicator settings TF : 2D ROC : 16 EMA : 50 Hurst : 44 Standardization window length : 32 Scale : 1

Blockchain Fundamentals: PPT [CR]Blockchain Fundamentals: PPT A proprietary market positioning indicator that analyzes price behavior using percentile-based statistical methods. The PPT (Percentile Position Transform) provides a normalized oscillator view of market conditions, helping traders identify potential trend exhaustion and reversal zones through multi-timeframe statistical analysis. █ FEATURES Dual Signal Lines The indicator plots two distinct signals: - White Line — Primary signal representing the normalized, smoothed market position. This is the main signal used for trading decisions. - Red Line — Raw statistical measurement before final normalization. Useful for identifying divergences and signal development. Background Coloring Dynamic background colors provide at-a-glance market context: - Green Background — Indicates bullish positioning when the primary signal exceeds the buffer threshold. - Red Background — Indicates bearish positioning when the primary signal falls below the buffer threshold. - Gray Background — Neutral zone where no clear directional bias is present. Flip Buffer An adjustable threshold system designed to reduce noise and false signals: - Enable Flip Buffer — Toggle the buffer system on or off. - Buffer Size — Adjustable threshold level (default -0.1) that determines when background colors change. Higher values reduce sensitivity; lower values increase responsiveness. Reference Levels Three horizontal reference lines provide context: - Center line at 0 — Neutral market position. - Upper dashed line at +1 — Extreme bullish positioning threshold. - Lower dashed line at -1 — Extreme bearish positioning threshold. █ HOW TO USE Signal Interpretation The indicator operates as a mean-reversion oscillator within a normalized range: 1 — Values approaching +1 suggest extended bullish conditions where price may be overextended relative to recent history. 2 — Values approaching -1 suggest extended bearish conditions where price may be oversold relative to recent history. 3 — Crosses of the center line (0) indicate shifts in the underlying statistical trend. Trading Applications While specific trading strategies will vary by individual approach and market conditions: - Consider the extremes (+1 and -1 levels) as potential areas of interest for mean-reversion setups. - Background color changes can help identify when market positioning shifts from one regime to another. - Divergences between the white and red lines may provide early warning of potential trend changes. - The buffer zone (gray background) represents areas where market positioning is relatively neutral. █ LIMITATIONS - The indicator requires sufficient historical data to function properly. In assets with limited price history, the statistical measurements may be less reliable during early data periods. - As a percentile-based system, the indicator is relative to recent history. Changing market regimes may require interpretation adjustments. - Not designed for high-frequency or scalping strategies due to its daily data dependency. - Background colors are visual aids and should not be used as standalone trading signals without additional confirmation. █ NOTES This indicator is part of the Blockchain Fundamentals suite and represents proprietary research into statistical market positioning analysis. Users should experiment with the buffer settings to match their risk tolerance and trading style. More conservative traders may prefer larger buffer values to reduce signal frequency, while active traders might benefit from smaller buffers that provide earlier warnings.

Markov Chain Regime & Next‑Bar Probability Forecast ✨ What it is A regime-aware, math-driven panel that forecasts the odds for the very next candle. It shows: • P(next r > 0) • P(next r > +θ) • P(next r < −θ) • A 4-bucket split of next-bar outcomes (>+θ | 0..+θ | −θ..0 | <−θ) • Next-regime probabilities: Calm | Neutral | Volatile 🧠 Why the math is strong • Markov regimes: Markets cluster in volatility “moods.” We learn a 3-state regime S∈{Calm, Neutral, Volatile} with a transition matrix A, where A = P(Sₜ₊₁=j | Sₜ=i). • Condition on the future state: We estimate event odds given the next regime j— q_pos(j)=P(rₜ₊₁>0 | Sₜ₊₁=j), q_gt(j)=P(rₜ₊₁>+θ | Sₜ₊₁=j), q_lt(j)=P(rₜ₊₁<−θ | Sₜ₊₁=j)— and mix them with transitions from the current (or frozen) state sNow: P(event) = Σⱼ A · q(event | j). This mixture-of-regimes view (HMM-style one-step prediction) ties next-bar outcomes to where volatility is likely headed. • Statistical hygiene: Laplace/Beta smoothing, minimum-sample gating, and unconditional fallbacks keep estimates stable. Heavy computations run on confirmed bars; “Freeze at close” avoids intrabar flicker. 📊 What each value means • Regime label & background: 🟩 Calm, 🟧 Neutral, 🟥 Volatile — quick read of market context. • P(next r > 0): Directional tilt for the very next bar. • P(next r > +θ): Odds of an outsized positive move beyond θ. • P(next r < −θ): Odds of an outsized negative move beyond −θ. • Partition row: Distributes next-bar probability across four intuitive buckets; they ≈ sum to 100%. • Next Regime Probs: Likelihood of switching to Calm/Neutral/Volatile on the next bar (row of A for the current/frozen state). • Samples row: How many next-bar samples support each next-state estimate (a confidence cue). • Smoothing α: The Laplace prior used to stabilize binary event rates. ⚙️ Inputs you control • Returns: Log (default) or % • Include Volume (z-score) + lookback • Include Range (HL/PrevClose) • Rolling window N (transitions & estimates) • θ as percent (e.g., 0.5%) • Freeze forecast at last close (recommended) • Display toggles (plots, partition, samples) 🎯 How to use it • Volatility awareness & sizing: Rising P(next regime = Volatile) → consider smaller size, wider stops, or skipping marginal entries. • Breakout preparation: Elevated P(next r > +θ) highlights environments where range expansion is more likely; pair with your setup/trigger. • Defense for mean-reversion: If P(next r < −θ) lifts while you’re late long (or P(next r > +θ) lifts while late short), tighten risk or wait for better context. • Calibration tip: Start θ near your market’s typical bar size; adjust until “>+θ” flags truly meaningful moves for your timeframe. 📝 Method notes & limits Activity features (|r|, volume z, range) are standardized; only positive z’s feed the composite activity score. Estimates adapt to instrument/timeframe; rare regimes or small windows increase variance (hence smoothing, sample gating, fallbacks). This is a context/forecast tool, not a standalone signal—combine with your entry/exit rules and risk management. 🧩 Strategies too We also develop full strategy versions that use these probabilities for entries, filters, and position sizing. Like this publication if you’d like us to release the strategy edition next. ⚠️ Disclaimer Educational use only. Not financial advice. Markets involve risk. Past performance does not guarantee future results.

Blockunity Regime Monitoring (BRM)Efficiently analyze market conditions and detect overheating zones. Regime Monitoring (BRM) is here to help you analyze the behavior of financial markets. The oscillator allows you to observe when an asset’s trend is likely to reverse. The trend is also given by the indicator, as is the phase the market is in (trending or congested). The BRM also provides the state of the Choppiness Index, indicating whether or not the asset is about to enter a more volatile phase. The Idea The goal is to provide the community with a comprehensive tool for tracking market conditions, with a visual approach to identifying overheating zones. How to Use This tool consists of 3 main components: An oscillator, which we describe in detail below. Bar color to transcribe oscillator information directly onto the graph. To activate Bar Color, make sure the first option is checked in the settings. You must also uncheck "Borders" and "Wick" in your Chart Settings. A panel that summarizes the status of various indicator information. Elements The Regime Monitoring oscillator The oscillator provides several information points. First, it gives the market trend of the asset: Green: Bullish trend. Red: Bearish trend. Blue: Contested trend. It then indicates areas of overheating, where it is considered statistically probable that we will see a change in trend dynamics. These moments are shown in yellow. This market trend is also indicated in the table. If you see that the oscillator is above or below these limits, but not yellow, this is because we use a Choppiness Index to filter this information. The "Enable Choppiness Index Filter" is enabled by default in the settings. So, if the Chop is discharged (under 38.2), then the oscillator's overheating state is ignored. You can see the difference in the images below, the first with the filter and the other without: Market Phase We use a Vertical Horizontal Filter (VHF) to define the market phase the asset is in. This phase can have two values: Trending: Assets evolve within a trend. Congestion: The asset is in a moment of congestion. Chop State Visualize the Choppiness Index, indicating whether an asset is gearing up to enter a phase of increased volatility. It can be: Charged: Chop is considered to indicate to be entering a stable phase. Neutral: Chop is neutral and does not provide any specific information. Discharged: Chop is considered to indicate a continuation of the trend. In addition, with the "Show Choppiness Index" option, you can plot the Chop on the oscillator: Other Settings You can also modify the standard Regime Monitoring parameters (Lookback, Smoothing, Limits), display or hide certain components, and change all the colors. How it Works Regime Monitoring's main oscillator is established as follows: We calculate the percentage of times the closing price was higher than the opening price. This is then divided by a lookback period, which in this case defaults to 20. This calculation gives a probability of the current regime.

Trendgetter: Trend Detection, Regime Change, Bias Filter by [CR]Trendgetter: Trend Detection, Regime Change, Bias Filter by Cryptorhythms “If you are not a trend setter, at least be able to exploit the ones you see.” ― Jeffrey Fry Intro Cryptorhythms back again with a members only indicator for trend capture this time! Trendgetter is not crypto specific and can be applied to a variety of timeframes, markets, and tickers. Its meant to be a general purpose trading aid and bias filter, providing reliable trend, bias and regime change information. Introduction This indicator relies upon various methods related to probabilities/statistics, digital signal processing and data science to predict optimal fair local price given any financial time series data. The goal was to create a tool that isolates trends and captures their bias, making it easier to follow a noisy market. The focus is making high hit rate uncorrelated returns to your base market. The way in which this indicator is constructed is not based upon any previous public work, and was researched and refined over a period of 6 months of trading and testing based on my own personal trading experiences and observations of the market. I use novel techniques I developed in house to denoise the data and determine a local fair price. Description The parameters in this indicator are mostly fixed and do not lend themselves to overfitting. So when you find good settings, its probably legit and not a false positive. They were pre-determined based on my own testing and research to handle almost all possible combinations of price action for determining trends. By fixing some parameters, you automatically reduce the chances of overfitting to historical data. The pre determined levels were carefully chosen after many options were considered. Not just a bias filtooor, fair price predictooor and regime change detectoooor though! TG also provides a price envelope feature which shows a likely fair price range that price will distribute itself upon. Above or below the envelope indicates the presence of a very strong trend . Within the envelope indicates consolidation , but still conforming to the bias. TG then uses a statistics-based approach to display a likely range that price could potentially travel over the near term which we called a price envelope. An additional option provides background coloration when there is the potential for a regime change on the trend bias. This can be used as a feature to help you manage your trades risk. This is simply measured by an internal (non exposed) script value returning to a mean which triggers the color to appear. Further Explanation of Settings -Timeframe : Change the timeframe the indicator is calculated on allowing you to for instance use the 15m Trendgetter output while remaining on the 5 minute chart. -Trend Capture : This is the "type" of trend you are trying to follow. The different options will attempt to find the trends at various levels of noise cancellation within the lookback period you specify. "Reactive" means it will quickly change its bias and capture smaller trends. "Slow" means it will filter more noise and capture larger trends. "Adaptive" is completely in its own class of behavior and was my attempt to mix both a slow and reactive profile into one setting, it uses a few market metrics like volume and volatility to adjust parameters on the fly. -Sample Length : Bars to consider in the calculation. Using large numbers here is not going to help, but rather hurt your results. Generally 10-100 is the range you should use for the best results. The exact value will depend on the timeframe, volatility and market/asset you are trading, and you should experiment to find it. (There is no "one size fits all" for potential trading situations) -Source : Data series used for calculation. I recommend hlcc4 or hl2 or hlc3 instead of just "close." This will help to pre process a noisy data series for the rest of the algo. -Certainty Level : This setting effects how easily the indicator will confirm a new trend and change its bias. " Reactive" does just as it says and will confirm new regimes faster, but can also lead to false signals or "flip flop" in certain types of price action. "Slow" will change biases less frequently or in conjunction with large moves - but this level of certainty requires the sacrifice of reactivity meaning its a bit laggy (but thats ok when you are following a larger trend). "Medium" is as you would expect the middle ground between reactive and slow. Lastly "Adaptive" tends to fall between reactive and medium in its behavior typically, but it will somewhat adjust itself to suit the variability of market conditions. -Price Envelope : -----My own personally created price distribution spread (not monte carlo based) -----Above or below the envelope indicates the presence of a very strong trend. You should not be fading a trend when its in this position! -----Within the envelope indicates consolidation, but still conforming to the bias. User Requests : Of course we also listen to the needs of our members and added these features upon request. -Added dark mode and light mode themes. ----Dark Mode is for dark/black charts and uses lighter colorations ----Light mode is for light/white charts and uses darker colorations -More updates to display and color selection options such as background colors and fill colors.

Enhanced Sigma by Cryptorhythms [CR] - Advanced Enhanced Sigma - Advanced Volatility-Normalized Returns Analysis The Enhanced Sigma indicator measures volatility-adjusted price returns to identify statistical extremes, regime changes, and mean reversion opportunities. By normalizing returns against their standard deviation, the indicator reveals when markets are experiencing unusual moves relative to recent volatility—highlighting potential exhaustion points, capitulation events, and statistical anomalies. █ OVERVIEW Sigma (σ) represents how many standard deviations current returns deviate from normal behavior. Readings beyond ±2.0 indicate statistically unusual moves, while extreme readings beyond ±3.0 occur less than 1% of the time under normal distribution. This version adds multi-layered analysis including regime detection, multi-timeframe confirmation, and adaptive thresholds. █ KEY FEATURES Return Calculation Methods The indicator supports multiple return calculation approaches: - Simple Returns — Standard percentage change calculation - Log Returns — Logarithmic returns for better statistical properties - Geometric Returns — Square root based calculation - Percentage Returns — Scaled percentage format Volume Weighting Optional volume weighting emphasizes high-volume moves while dampening low-volume noise. When enabled, returns are weighted by normalized volume to give more significance to moves occurring on institutional participation. Threshold Methods Two threshold calculation approaches: - Fixed Thresholds — Manual static levels - Percentile-Based — Adaptive thresholds calculated from historical distribution Percentile-based thresholds automatically adjust to changing volatility regimes over configurable lookback periods. Statistical Significance Testing Built-in t-test functionality determines whether current sigma readings are statistically significant or merely random noise. Configurable confidence levels (80-99.9%) filter out insignificant moves. Non-significant readings are visually dimmed. Multi-Timeframe Analysis Analyzes sigma across multiple timeframes simultaneously. Confluence signals trigger when all timeframes (current, 3x, 5x) align at extremes—indicating stronger conviction and reduced false signals. MTF sigma values are plotted as reference lines. Regime Detection Automatically classifies current volatility environment into four regimes: - Low Volatility — Market in compression - Normal — Standard volatility conditions - High Volatility — Elevated movement - Extreme — Unusual volatility expansion Regime classification uses percentile ranking and statistical deviation analysis. Adaptive Threshold System Three methods for automatic threshold adjustment: - ATR-Based — Adjusts based on Average True Range ratio - Volatility Regime — Tightens in low volatility, widens in high volatility - Range Expansion — Responds to recent sigma range changes When enabled, thresholds dynamically scale with market conditions to maintain consistent sensitivity. Cluster Analysis Detects and scores clusters of threshold breaches within rolling windows. High cluster scores can indicate capitulation or exhaustion phases. Visual labels "C" show active clusters. Mean Reversion Signals Generates entry and exit signals based on extreme sigma readings returning to mean: - Long Entry — Triggered on oversold extremes - Short Entry — Triggered on overbought extremes - Exit Signals — When sigma normalizes or time-based exit reached (marked as gray "X") Optional statistical significance filter ensures only high-probability setups generate signals. Position tracking displays unrealized P&L. Distribution Histogram Displays sigma value distribution as a horizontal histogram showing Point of Control (POC)—the most frequently occurring sigma level. Configurable bin size and lookback period. Statistics Dashboard Real-time table displaying: - Current sigma value and percentile rank - Statistical measures (mean, std dev, skewness, kurtosis) - Volatility regime classification - Extreme event frequency - Bars since last extreme - Position status and P&L (when mean reversion enabled) █ VISUAL ELEMENTS Threshold Lines - Aqua/Cyan lines — Positive (high) thresholds - Fuchsia/Magenta lines — Negative (low) thresholds - Solid lines — Short-term dynamic thresholds - Circles — Long-term dynamic thresholds - Crosses — Percentile-based static thresholds (when enabled) Sigma Histogram Primary histogram uses momentum-based coloring: - Purple shades — Negative sigma (declining) - Cyan shades — Positive sigma (advancing) - Darker shades indicate weakening momentum - Brighter shades indicate strengthening momentum Statistically significant threshold breaches are highlighted with intensified colors. Shape Markers - Small triangles — Statistically significant threshold breaches - Diamonds — Multi-timeframe confluence signals - Large triangles with text — Mean reversion entry signals (LONG/SHORT) - X markers — Mean reversion exit signals - Labels — Cluster detection alerts █ ALERTS The indicator includes comprehensive alert conditions. Use confirm on bar close if repainting is enabled. █ HOW TO USE Identifying Extremes Sigma readings beyond ±2.0 indicate unusual moves. Look for: - Threshold breaches with statistical significance markers (triangles) - Multi-timeframe confluence (diamonds) for stronger signals - Cluster formation indicating potential exhaustion Regime-Aware Trading Use regime detection to adjust expectations: - In Low Volatility regimes, smaller sigma moves may be significant - In Extreme regimes, higher thresholds prevent overtrading Mean Reversion Strategy Enable mean reversion signals for systematic entries: - LONG signals appear after statistically significant oversold extremes - SHORT signals appear after statistically significant overbought extremes - Exit signals trigger on return to mean or time-based stops Distribution Analysis Use the histogram to identify: - POC level — Where sigma spends most time (equilibrium) - Current position relative to distribution