|
no
experiencia
|
1
productos
|
6
versiones demo
|
|
0
trabajos
|
0
señales
|
0
suscriptores
|
Amigos
1
Stephen Muriithi Muraguri
Ha publicado el producto
Gap Rush iFVG EA es un Asesor Experto de trading automatizado construido alrededor de los Gaps de Valor Justo (FVGs) . Escanea el gráfico en busca de gaps alcistas/bajistas válidos, los dibuja claramente como rectángulos y puede colocar operaciones cuando el precio reacciona a esos gaps, filtrados opcionalmente por sesgo de tendencia de marco temporal superior , sesiones y días de la semana . También incluye controles de riesgo integrados y gestión de operaciones (SL/TP + trailing + flat al
Stephen Muriithi Muraguri
1. Concept Overview
Quantum Time Logic (QTL) in trading refers to applying principles of time symmetry, entanglement, and probability weighting to financial market models.
Instead of relying on single-threaded historical data backtests, QTL-based automation tries to process multiple “time branches” (possible futures) in parallel, making probabilistic trade decisions that adapt in real time.
The automation piece is about building expert advisors (EAs), bots, or algorithmic frameworks that execute these QTL-driven signals automatically, with defined risk controls.
2. Key Components
Quantum-Inspired Time Models
Breaking down market activity into overlapping temporal layers: micro (ticks/seconds), meso (minutes/hours), macro (days/weeks).
Using entangled states (correlated events across different timescales, like session overlaps or cyclical FVGs).
Applying “superposition” logic: holding multiple trade scenarios open in probability space until price action “collapses” the outcome.
Automation Engine
Built in platforms like MetaTrader 5 (MQL5 EAs), Deriv DBot XMLs, or custom Python APIs.
Executes trade orders once a QTL signal is confirmed.
Includes dynamic position sizing, SL/TP, trailing stops, and risk-to-reward balancing.
Risk Management via Temporal Probabilities
Instead of a fixed SL/TP, positions adapt based on shifting probability weights (like Bayesian updates).
E.g., If probability of bullish outcome increases after NY session open, SL tightens but TP expands.
Backtesting & Quantum Simulation
Use Monte Carlo + temporal branching to simulate multiple alternate futures.
This gives robustness testing—your bot won’t just be optimized for one historical dataset.
3. Application Examples
Fair Value Gap (FVG) Bots: Instead of only trading “filled” gaps, a QTL bot considers probabilities across multiple timeframes—e.g., a 1M gap entangled with a 1H institutional candle.
Breakout Bots: Runs multiple “time-branch” breakouts in parallel (London, NY, Asia), and collapses to the one with highest entangled confirmation.
Mean Reversion Bots: Superposition logic can hold both long and short biases until volatility collapse confirms the dominant leg.
4. Tools & Tech Stack
MQL5/MT5 → For precise execution and backtesting.
Python + Quantum Libraries (Qiskit, PennyLane, etc.) → To model QTL probability trees.
Deriv DBot XML → For retail-friendly XML automation.
Machine Learning → To dynamically reweight probability states as data streams in.
Quantum Time Logic (QTL) in trading refers to applying principles of time symmetry, entanglement, and probability weighting to financial market models.
Instead of relying on single-threaded historical data backtests, QTL-based automation tries to process multiple “time branches” (possible futures) in parallel, making probabilistic trade decisions that adapt in real time.
The automation piece is about building expert advisors (EAs), bots, or algorithmic frameworks that execute these QTL-driven signals automatically, with defined risk controls.
2. Key Components
Quantum-Inspired Time Models
Breaking down market activity into overlapping temporal layers: micro (ticks/seconds), meso (minutes/hours), macro (days/weeks).
Using entangled states (correlated events across different timescales, like session overlaps or cyclical FVGs).
Applying “superposition” logic: holding multiple trade scenarios open in probability space until price action “collapses” the outcome.
Automation Engine
Built in platforms like MetaTrader 5 (MQL5 EAs), Deriv DBot XMLs, or custom Python APIs.
Executes trade orders once a QTL signal is confirmed.
Includes dynamic position sizing, SL/TP, trailing stops, and risk-to-reward balancing.
Risk Management via Temporal Probabilities
Instead of a fixed SL/TP, positions adapt based on shifting probability weights (like Bayesian updates).
E.g., If probability of bullish outcome increases after NY session open, SL tightens but TP expands.
Backtesting & Quantum Simulation
Use Monte Carlo + temporal branching to simulate multiple alternate futures.
This gives robustness testing—your bot won’t just be optimized for one historical dataset.
3. Application Examples
Fair Value Gap (FVG) Bots: Instead of only trading “filled” gaps, a QTL bot considers probabilities across multiple timeframes—e.g., a 1M gap entangled with a 1H institutional candle.
Breakout Bots: Runs multiple “time-branch” breakouts in parallel (London, NY, Asia), and collapses to the one with highest entangled confirmation.
Mean Reversion Bots: Superposition logic can hold both long and short biases until volatility collapse confirms the dominant leg.
4. Tools & Tech Stack
MQL5/MT5 → For precise execution and backtesting.
Python + Quantum Libraries (Qiskit, PennyLane, etc.) → To model QTL probability trees.
Deriv DBot XML → For retail-friendly XML automation.
Machine Learning → To dynamically reweight probability states as data streams in.
Stephen Muriithi Muraguri
Ha dejado el comentario sobre el Ejecutor por el trabajo Liquidity Sweep Indicator
Very Efficient!