What makes the engine proprietary
The proprietary edge does not come from claiming that one indicator predicts the market. It comes from how market data, indicator families, signal filters, trade-type context, and risk framing are assembled into a repeatable workflow. That system design is the intellectual property, not the fact that common indicators exist.
- The same public indicators can produce very different outputs depending on how they are combined
- Signal quality depends on filtering, confirmation, and invalidation logic
- The platform is designed around reviewable signals rather than raw indicator noise
The indicators and market inputs we openly use
We do publish the indicator families that help power the engine so users know the system is grounded in familiar market structure rather than vague magic. The published set includes RSI, VWAP, moving averages, MACD, volume and OBV, Supertrend, ADX, Bollinger Bands, and ATR, along with price action and multi-timeframe context.
Publishing the indicators helps traders understand the framework. It does not disclose the proprietary scoring, weighting, threshold, or validation system that sits on top of them.
- Trend context from moving averages, MACD, Supertrend, and ADX
- Momentum context from RSI and related signal families
- Volatility and range context from Bollinger Bands and ATR
- Participation context from volume and OBV
How signals are built in layers
The engine is structured in layers so each signal passes through more than one lens before it reaches the dashboard. First comes market data normalization. Then the system reads indicator context across relevant timeframes. After that, it combines setup quality, trade-style context, and risk framing into a structured signal that can be monitored in the main application.
- Layer 1: market data normalization and price context
- Layer 2: trend, momentum, volatility, and participation reads
- Layer 3: multi-timeframe confirmation and setup filtering
- Layer 4: central risk framing and final signal presentation
Why the exact formula stays private
We do not publish the proprietary formula because the copyable part of a trading product is rarely the indicator name. It is the exact weighting, thresholds, if-then combinations, consensus math, and validation rules that connect those inputs into a specific output. Publishing those details would make low-effort cloning easier without improving the user’s ability to judge the product responsibly.
- Exact weights and thresholds remain private
- Consensus and ranking logic remain private
- Validation and regime logic remain private
- Users still get framework-level transparency and clear risk framing
How to judge signal quality
AlgoTradingAI does not publish an accuracy percentage, a win rate, or any expected-return figure. Signal quality is assessed setup by setup against real market context: whether the setup was clearly defined before entry, whether the invalidation level held, and whether the stop-loss framing was respected. The public methodology page explains exactly how that review is carried out.
AlgoTradingAI does not publish accuracy or return figures. Signal quality is assessed setup by setup using the published methodology at /trade-accuracy-methodology, and outcomes vary by regime, instrument, execution, and trader settings.
- No blanket win rate or accuracy figure is claimed
- Quality is reviewed per setup, not as a single headline number
- Market regime, instrument, execution, and risk settings all change outcomes
- Read the full method at /trade-accuracy-methodology
FAQ
Do you publish the exact formula or indicators?
We do publish the indicators and signal families we use, such as RSI, VWAP, moving averages, MACD, volume, Supertrend, ADX, Bollinger Bands, and ATR. We do not publish the proprietary formula, weighting, thresholds, combination logic, or validation rules that turn those inputs into the final signal.
Do you publish an accuracy percentage or win rate?
No. AlgoTradingAI does not publish accuracy percentages, win rates, or expected returns. The methodology used to review signal quality is published at /trade-accuracy-methodology so traders can judge the process instead of a headline number.
Why keep the exact formula private?
Because the proprietary value sits in how the published inputs are combined, filtered, and validated. That logic is core product IP and is not necessary for a user to understand the workflow at a responsible level.
How should traders use this information?
As a way to understand the framework behind the product, not as a shortcut around judgment. The main app is designed for structured signal review, monitoring, and risk-aware decision support.
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