Trade Decision MatrixTrade Decision Matrix (TDM)
Trade Decision Matrix (TDM) is a professional-grade, multi-phase market intelligence indicator designed to assist traders in understanding market structure, regime behavior, capital confidence, and execution readiness using a systematic, probabilistic framework.
This indicator does not generate trade signals. Instead, it provides a structured decision matrix similar to institutional trading desks, combining regime analytics, entropy confidence, Bayesian reliability, capital allocation logic, and scenario interpretation.
🔹 Core Architecture
TDM is built using a nine-phase institutional decision pipeline:
Phase 1 — Market Context
Spot–future basis, volatility normalization, and structural slope detection.
Phase 2 — Regime Engine
Probabilistic classification of Trend, Breakout, Range, or Mean Reversion environments.
Phase 3 — Orthogonal Model Cores
Independent statistical, trend, breakout, and mean-reversion cores.
Phase 4 — Bayesian Reliability Engine
Adaptive reliability scoring for each core using Bayesian reinforcement.
Phase 5 — Capital Engine
Capital confidence and capital mode based on opportunity quality, regime clarity, entropy confidence, and risk filters.
Phase 6 — Decision Matrix
Bias, participation level, and trade quality grading.
Phase 7 — Scenario Engine
Contextual scenario interpretation such as Trend Expansion, Breakout Failure, Range Compression, etc.
Phase 8 — Execution Gate
Execution readiness filter based on capital and model alignment.
Phase 9 — Reversal Engine
Probabilistic reversal risk estimation using multi-factor logic.
🔹 Regime Entropy Confidence
TDM uses Shannon entropy to measure regime uncertainty and converts it into a confidence score.
Lower entropy = higher regime confidence.
Higher entropy = unstable or transitional market state.
This prevents over-confidence in noisy conditions.
🔹 Institutional Commentary Engine
A professional commentary layer interprets all internal engines and outputs institutional-style guidance such as:
• Institutional Alignment
• Capital Protection Mode
• Regime Uncertainty
• Momentum Continuation
• Structural Breakout
• Volatility Coiling
• Reversal Risk Elevated
This commentary is designed for situational awareness, not signal generation.
🔹 Dashboard
The dark-theme dashboard provides a compact institutional decision panel:
• Regime
• Entropy Confidence
• Scenario
• Bias
• Strength
• Capital Confidence
• Capital Mode
• Trade Quality
• Execution State
• Commentary
• Reversal Risk
All values are color-coded with heat shading for instant visual interpretation.
🔹 How To Use
TDM is best used as a decision support layer alongside your own trading strategy.
Typical workflow:
Identify regime and entropy confidence.
Observe capital confidence and capital mode.
Check scenario and bias alignment.
Confirm execution readiness.
Monitor reversal risk before entering or holding positions.
This tool is ideal for:
• Intraday traders
• Swing traders
• Options traders
• Index traders
• Systematic discretionary traders
🔹 Important Notes
• This indicator does NOT produce buy/sell signals.
• It is a decision intelligence framework.
• It should not be used as a standalone trading system.
• Always apply personal risk management.
🔹 Disclaimer
This indicator is provided for educational and informational purposes only.It does not constitute financial advice or investment recommendations.Trading involves risk. Users are responsible for their own trading decisions.
Decision
FunctionSMCMCLibrary "FunctionSMCMC"
Methods to implement Markov Chain Monte Carlo Simulation (MCMC)
markov_chain(weights, actions, target_path, position, last_value) a basic implementation of the markov chain algorithm
Parameters:
weights : float array, weights of the Markov Chain.
actions : float array, actions of the Markov Chain.
target_path : float array, target path array.
position : int, index of the path.
last_value : float, base value to increment.
Returns: void, updates target array
mcmc(weights, actions, start_value, n_iterations) uses a monte carlo algorithm to simulate a markov chain at each step.
Parameters:
weights : float array, weights of the Markov Chain.
actions : float array, actions of the Markov Chain.
start_value : float, base value to start simulation.
n_iterations : integer, number of iterations to run.
Returns: float array with path.
RSI with an Opinion (UO)This RSI has a very clear idea about when to buy and sell. It plots buy and sell signals. It is an expert system. Yes, of course, it can make some errors. You should have used stop-loss
