Clozen Glenor connects your data sources, runs predictive models on market data and delivers concrete action suggestions — without manually setting up rule sets or indicators.
A day trader typically monitors several markets, news feeds and technical indicators at the same time. As the amount of data increases, the average decision quality decreases — not because the trader becomes less competent, but because working memory has a fixed limit.
The consequence is delayed execution, inconsistent risk management and decisions based on partial information rather than the full data set.
Clozen Glenor retrieves price data, volume and selected market indicators from your existing accounts via secure, read-only access.
You specify risk tolerance and exposure limits. The model calibrates its recommendations according to these parameters — not the other way around.
The system starts generating real-time suggestions based on: if X data pattern is observed, then Y action is recommended within defined risk limits.
Technical note: The predictive models are trained on historical and real-time market data and are continuously updated. The models provide probability-weighted suggestions — not guaranteed outcomes — and all suggestions include a risk score that you can reject or adjust manually.
The dashboard shows an overall overview of open positions, exposure per asset class and deviation from your defined risk limit. Updates occur continuously rather than at set intervals, so changes in volatility are reflected in the recommendations with minimal delay.
Each recommendation is displayed with the underlying data source and the calculated confidence level so you can assess the basis before execution.
Three key metrics form the basis for the decision optimization:
Clozen Glenor does not publish specific return figures because market conditions vary and because historical performance is not a guarantee of future results. Instead, we describe what the models do and what data they are based on.
The models combine time series data, order book depth and volatility metrics from connected market data sources. Each recommendation goes through an internal validation process that compares the model's output with current risk limits before the suggestion is shown to the user.
The data sources are continuously updated, and the model's parameters are adjusted periodically based on deviations between predicted and observed market behavior.
Access is via read-only API connections to supported brokers and data sources. Clozen Glenor cannot execute trades without your explicit approval in each case, unless you enable automatic execution yourself.
No. The models deliver probability-weighted recommendations based on historical patterns and real-time data. All trading involves risk and past data patterns are no guarantee of future market behavior.
The models are continuously recalibrated based on deviation between predicted and actual market movements. The update frequency depends on market volatility and the update cycle of the data sources.
Yes. The risk profile can be changed at any time, and changes affect subsequent recommendations immediately. Already open positions are not automatically affected by a changed profile.
The risk index is calculated based on volatility, exposure concentration per asset class and correlation between open positions. The calculation is updated with each new data input.
The 60 seconds cover the initial connection of data sources and selection of risk profile. More complex portfolios with multiple asset classes may require additional configuration of specific limits after initial setup.
Setup takes less than 60 seconds. You can adjust risk limits and data sources continuously, without losing access to previous recommendations.