Aalto Tuottus analyzes market data in real time and uses forecasting models to identify situations where the risk increases in relation to the expected return. The system does not promise profits, but tries to reduce the size of the bet at the wrong moments.
The view describes a typical work environment: the analysis supports the decision, but the trader makes the final decision.
An example of price swings in the short term — a dark bar depicts a spike to which an emotional reaction often leads to an incorrect position.
The problem
Short-term price movements contain a lot of random variation, which tells nothing about the true direction of the instrument. A trader who reacts to every spike exposes his portfolio to repeated small losses that add up to a significant drawdown.
The biggest single obstacle to accumulating long-term returns is often not bad individual trades, but their multiplier effect on capital size. The deeper the bet, the more the next trade needs to restore the starting level.
Key metric: max drawdownThe solution
Aalto Tuottus combines historical price data, volatility metrics and market liquidity data into a single input that the model updates continuously. The result is not a single forecast, but a probability distribution for the movement of the next period.
The model does not aim to predict the exact price. It assesses how likely it is that the current movement will continue in the same direction versus that it is a temporary deviation. This estimate is used in the calculation of position size and stop-loss level.
Core function
A fixed stop-loss level ignores the fact that the normal swing of the market varies from time to time. The same percentage limit that makes sense on a calm day can trigger a position to be closed unnecessarily during periods of high volatility.
Aalto Tuottus calculates the stop-loss level in relation to the current volatility and the confidence level of the forecast model. As uncertainty increases, the boundary expands in a controlled manner; when the market calms down, the limit tightens. The goal is mathematical, not psychological: to reduce the expected value of the loss without the normal noise closing positions prematurely.
This is essential for the interest-for-interest principle. Capital that remains less damaged after periods of losses requires less return to recover — and is then able to grow faster.
Illustrative example: an adaptive limit smooths out loss spikes over an entire period.
Method
Price, volume and liquidity data are collected from multiple sources and synchronized to the same timestamp to keep the model input consistent regardless of market conditions.
The model estimates short-term direction and uncertainty using statistical methods. The result is expressed as a probability, not an absolute statement of the future price.
The system turns the estimate into a concrete proposal: position size, stop-loss level and possible alert. The merchant also sees the reasoning behind the proposal.
Usage situations
The risk parameters are calculated as relative values, not as fixed euro amounts. This means that the same logic works regardless of whether the capital to be managed is a few thousand or several hundred thousand euros — the sizing is scaled to the size of the portfolio.
When the confidence level of the forecast model falls below the set threshold during an open position, the system sends a notification about the proposed update of the stop-loss level. The notification includes a short explanation: what changed in volatility or liquidity.
When there are several open positions in the portfolio, the system looks at their combined risk, not just individual trades separately. This helps to detect situations where several positions would react to the same market factor at the same time.
Background
Aalto Tuottus is built to support decision making, not to replace it. The trader sees the estimate produced by the forecast model, the associated uncertainty and the proposed risk parameter — and makes the final decision himself.
The goal is to reduce the share of emotional decisions in trading by providing a consistent, repeatable framework for risk assessment.
Often asked
Price and volume data are retrieved from the interfaces offered by the marketplaces. The user's own state data and analysis history are stored encrypted and are not shared with third parties for marketing purposes.
The integration takes place through an interface that reads market data and, if necessary, can forward alerts to the existing trading environment. The system does not require trading account management rights to function as an analysis tool.
Each proposal is accompanied by a short explanation, which tells which variables — for example, an increase in volatility or a decrease in liquidity — had the greatest impact on the final result. The logic of the model is not a closed "black box" without any explanation.
The tool is designed for the user who understands the basic risks of trading and wants to add systematicity to his decision-making. It does not eliminate the risk associated with trading, but provides a structure for evaluating it.
The next step
Get started by learning how Aalto Tuottus calculates stop-loss levels and presents the reasoning behind them with your own data.
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