Gate 1: Composite Threshold
Weighted composite score must be above +0.10 for BUY or below -0.10 for SELL.
Methodology and Transparency
This page documents exactly how Q Signals computes outputs so users can interpret results with context, not blind trust.
Signal Decision Framework
Gate 2: Vote Consensus
At least 60% of active modules must agree on direction, reducing one-module false positives.
Confidence
Confidence rises when score is farther beyond threshold and module agreement is stronger. Borderline setups show lower confidence.
Runtime and Fallback Transparency
DQN Runtime Mode
Primary mode is a Deep Q-Network (PyTorch) with 10 core signal inputs and BUY/HOLD/SELL Q-value output.
Fallback Mode
If DQN runtime dependencies are unavailable, tabular fallback mode is used so analysis remains available.
Operational Principle
Fallback behavior prioritizes continuity over perfect feature parity. Endpoints may return simplified or partial results under degraded conditions.
What the 23 Modules Measure
| Module | Primary Input |
|---|---|
| Fibonacci | Retracement and extension structure from recent price swings |
| Elliott Wave | Wave-pattern structure and trend phase |
| Swing Strategy | SMA crossover, RSI, Bollinger Band context |
| Sentiment / Social | News and social tone direction |
| Insider / Institutional / Congress | Ownership and disclosed transaction behavior |
| Supply Chain / Competitor | Peer stress and dependency signals |
| Volume + Multi-Timeframe | Volume confirmation and cross-timeframe trend alignment |
| Sector / Correlation / VIX Regime | Macro regime and cross-asset flow context |
| Economic / Analyst | Macro indicator direction and analyst consensus |
| Price Trend / Volatility / 52w / Moving Avg | Core technical direction and overextension |
| RL Agent (DQN) | Learned BUY/HOLD/SELL Q-values from historical outcomes |
Targets, Stops, and Holding Period
Targets and stop-loss values are ATR-scaled estimates, not guarantees. Every signal includes conservative, moderate, and aggressive targets plus a suggested hold duration that expands or contracts with volatility and selected horizon.
RL Training Stages
| Steps | Stage | Interpretation |
|---|---|---|
| 0-500 | Infant | Mostly exploratory behavior |
| 500-5,000 | Learning | Early pattern formation |
| 5,000-25,000 | Developing | Improving but still unstable by regime |
| 25,000-100,000 | Intermediate | Stronger pattern recognition and consistency |
| 100,000+ | Experienced | High training volume, still requires ongoing validation |
How KPI Pages Are Calculated
KPIs are computed from evaluated outcomes in signal history. Win rate, expectancy, and alpha are retrospective statistics and can change as more outcomes settle. Leaderboards are grouped by horizon and market regime over a rolling lookback window.
Signal Outcome Evaluation Rules
| Check | Rule |
|---|---|
| Targets | Each directional signal tracks three targets (T1/T2/T3). If any target is hit before deadline, outcome is WIN (with level recorded). |
| Stop | If stop-loss is breached before any target, outcome is LOSS (Stopped). |
| Expiration | If deadline passes without target/stop hit, outcome is LOSS (Expired). |
| Pending Window | Signals remain pending until enough time has passed for evaluation under selected horizon. |
Interpretation Guidance
Highest-conviction setups generally have: threshold-clearing composite, strong module agreement, acceptable risk/reward, and RL agreement. Even then, all results remain probabilistic. Treat outputs as structured research evidence, not guaranteed outcomes.
Markets are non-stationary. Methodology transparency improves interpretation but does not eliminate risk, uncertainty, or model drift.