Free MT5 RSI Research Indicator

Hidden-Markov RSI for MT5

RSI signals mean different things in bull, bear, panic, and sideways states.

Compiled file only. Install in MQL5/Indicators. Source code is not included.

Hidden-Markov RSI shown inside the six indicator RSI research cluster on an XAUUSD H1 MetaTrader 5 chart

What It Solves

Traders who want state probabilities around RSI instead of one fixed oscillator rule.

This is the "context brain" of the suite. It helps explain why the same RSI value can be bullish in one state, dangerous in another, and meaningless in chop.

Technical Method

The model refits on trailing historical observations and uses data through the prior bar for causal state estimation. Latent HMM states are mapped into readable market regimes, then RSI payoff functions are calculated per state and RSI bin.

Four-state Gaussian HMM learned from standardized return and volatility
Semantic mapping to Bull, Bear, Panic, and Sideways states
State probabilities plotted with RSI
State-specific RSI payoff and expected RSI-change diagnostics
Causal walk-forward refit interval
Buy and sell arrows gated by probability, expectancy, t-stat, and samples

How This Indicator Works In Practice

Hidden-Markov RSI is the regime layer of the suite. It learns four latent states from standardized return and realized volatility, then maps those states into readable Bull, Bear, Panic, and Sideways regimes. The indicator then evaluates RSI payoff inside each state.

This matters because RSI is context-sensitive. In a bull state, a high RSI can be continuation. In a panic state, a low RSI can still be dangerous. In a sideways state, classic mean-reversion logic may become more useful. Hidden-Markov RSI helps decide which RSI interpretation deserves trust.

Input Parameters Explained

Input Default Meaning Tuning Guidance
InpRSIPeriod 14 RSI period used inside the state-specific payoff model. Keep 14 for baseline. Use 21 for smoother swing state-payoff behavior.
InpRSIPrice PRICE_CLOSE Price source for RSI. Close is best for consistency.
InpRSIBins 10 Bins used for state-specific RSI payoff functions. Fewer bins are robust; more bins require more samples.
InpVolatilityPeriod 20 Rolling log-return volatility used by the HMM observation model. 10-20 intraday, 30-50 swing.
InpHMMTrainingBars 600 Trailing observations used for each HMM refit. 400-600 adapts faster. 800-1200 is more stable.
InpHMMMinTrainingBars 250 Minimum observations required before fitting. Raise if you want fewer weak early states.
InpRefitEveryBars 25 How often the HMM refits in walk-forward mode. 10-15 adapts faster but costs more CPU. 25 default. 50 smoother.
InpEMIterations 12 Maximum expectation-maximization iterations. Keep default unless optimizing CPU.
InpEMTolerance 0.00010 Convergence tolerance for EM fitting. Smaller is stricter, larger is faster.
InpVarianceFloor 0.05 Minimum variance for numerical stability. Avoid changing unless model becomes unstable.
InpTransitionPrior 0.20 Pseudo-count smoothing for state transitions. Higher gives smoother transitions.
InpInitialSelfTransition 0.94 Initial assumption that regimes persist. Higher creates stickier states, lower allows faster switching.
InpForwardHorizon 5 Bars ahead used for state-specific RSI payoff. 3-5 intraday, 10-20 swing.
InpUseLogForwardReturn true Uses log returns for payoff. Leave true for research consistency.
InpMinEffectiveSamples 20.0 Minimum weighted samples for a state-RSI bin. Raise to reduce noisy signals.
InpPayoffShrinkage 15.0 Shrinks RSI-bin payoff toward the state mean. Higher is conservative. Lower is more reactive.
InpMinStateProbability 55.0 Minimum dominant-state probability required for signals. 65-75 for high-confidence regime signals.
InpMinExpectedReturnBps 5.0 Minimum expected payoff required. Raise for fewer signals.
InpMinAbsTStat 0.75 Minimum payoff t-stat. 1.0-1.5 for conservative filtering.
InpSignalMode SIGNAL_STATE_ENTRY_ONLY Signals every qualifying bar or only state entry. Entry-only is best for practical use.
InpMaxCalculatedBars 1000 Maximum bars calculated. Increase for research, reduce for speed.
InpUpdateIntrabar false Allows intrabar updates. Keep false for speed and cleaner bar-close research.

Parameter Ideas By Trader Type

Regime Filter

Use before taking any RSI signal from another indicator.

Suggested direction: Training 600, refit 25, state probability 55-65, entry-only.

Conservative Trader

Needs high regime confidence and stronger payoff evidence.

Suggested direction: Training 800-1200, state probability 70+, min samples 30-50, t-stat 1.0+.

Fast Intraday Trader

Wants regimes to adapt faster during active sessions.

Suggested direction: Training 400-600, refit 10-15, volatility period 14-20, max bars 700-1000.

These are research starting points, not guaranteed profitable presets. Test each symbol and timeframe with realistic spread, commission, slippage, and session filters before using any RSI signal in live trading.

Where It Fits In The RSI Stack

This indicator is part of the AlgoSpecial RSI Research Cluster: six MT5 indicators designed to answer the questions a fixed RSI line cannot answer alone. The cluster covers percentile tails, volatility-conditioned thresholds, two-dimensional expectancy, oversold continuation risk, stressed-market rebound probability, and Hidden-Markov regime probabilities.

FAQ

Is this a buy and sell signal indicator?

It can plot arrows, but it is best treated as a research-grade RSI decision layer. Confirm entries with market structure, spread, session, volatility, and risk rules.

Does the free download include source code?

No. The free download is the compiled MT5 .ex5 file only. This keeps the proprietary research logic protected while still letting traders test the indicator.

Can I use it on XAUUSD, forex, crypto, or indices?

Yes, it is symbol based and can be attached to MT5 charts. The included chart image uses XAUUSD H1 because gold exposes RSI regime changes very clearly, but the logic is not limited to gold.

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