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구독자
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친구
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Stephen Muriithi Muraguri
출시돈 제품
Gap Rush iFVG EA is an automated trading Expert Advisor built around Fair Value Gaps (FVGs) . It scans the chart for valid bullish/bearish gaps, draws them clearly as rectangles , and can place trades when price reacts to those gaps—optionally filtered by higher-timeframe trend bias , sessions , and days of the week . It also includes built-in risk checks and trade management (SL/TP + trailing + end-of-day flat). Key advantages Automatic FVG detection: Identifies bullish and bearish fair value
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.