生活支援制度 is an analysis platform that uses AI to analyze market data in real time and prevents unrealized losses from expanding using smart stop losses. Algorithmic discipline that does not rely on discretionary judgment helps minimize drawdowns.
When the market changes suddenly, even if you keep looking at the screen, your judgment will be delayed for a moment, and the amount of loss will change greatly. Emotions such as fear and expectations can make your loss-cutting standards vague and cause you to postpone following the rules you originally set. 生活支援制度's basic design concept is to compensate for the limitations of human reaction speed and psychological load.
Three technological elements work together to process everything from market data acquisition to execution decisions.
Unlike a fixed-width stop loss, the stop loss line dynamically adjusts according to price volatility and changes in trading volume. It is designed to quickly respond to signs of a trend change while suppressing erroneous execution due to temporary noise. As a result, we aim to reduce both unnecessary loss cuts and fatal loss expansion even in the same downturn.
It processes multiple sources of information in parallel, including not only prices and technical indicators, but also news distribution, volume bias, and order flow. Reduce the risk of misjudgment caused by reliance on a single metric and prioritize signals only when multiple lines of evidence are consistent. This prevents us from overreacting to short-term information distortions.
Once a decision has been made, execution can be completed without human intervention. On the other hand, we also have a semi-automatic mode where you only receive notifications before execution and make the final decision yourself. Users decide how much to automate depending on their trading method and risk tolerance.
Rather than leaving the internal processing as a black box, we break it down into three steps and explain it.
Continuously collects data from multiple sources such as price, volume, market information, news, etc. in milliseconds. Data that is missing or delayed will be excluded from processing.
The collected data is passed through a predictive model to calculate possible scenarios and confidence levels. Outputs that are inconsistent with historical data are filtered out during the validation process.
The final decision will be presented in the form of an update to the stop loss level or an execution proposal. You can choose automatic execution or notification only for each transaction.
Regardless of the degree of automation, all decisions are logged and can be reviewed later. This allows you to verify after the fact why the stop loss line was set.
Even though the foundation is the same, the parameters to be set differ depending on the time axis and purpose.
The stop loss line is updated frequently to accommodate price movements ranging from seconds to minutes. We prioritize capital preservation even in times of sudden change such as a flash crash.
Assuming that the stock is held for a few days to a few weeks, we widen the tolerance range so that it does not react to short-term noise. Raise the stop loss line only when the trend clearly changes.
It monitors correlations across multiple stocks and alerts you when they are biased towards a particular sector. Decisions are made based on the risk of the entire asset, not on individual stocks.
I will answer technical concerns and data handling without exaggeration.
Internal processing from data acquisition to execution decisions is typically completed in milliseconds. However, the actual execution speed depends on the securities company's system and network environment, so our processing speed alone does not guarantee execution.
The data used for analysis is stored in an encrypted state and is not provided to third parties. The account connection authority is limited to the range necessary for setting and executing stop losses, and the design does not grant operational authority such as withdrawals.
If it supports APIs provided by major securities companies, it can be operated in conjunction with existing accounts. Compatibility status varies by securities company, so we check whether connectivity is possible on an individual basis before implementation.
The output of the predictive model is checked for consistency with historical data before being presented. The system is designed to automatically suspend judgments where the level of reliability does not meet a certain level and only notify the user. We do not recommend operations that rely on all judgments unconditionally.