Algorithmic trading strategies are made to adjust to changes in the market and unanticipated events, such as geopolitical events and economic news releases. Algorithmic trading strategies can adapt to these changes in the following ways:

1. Real-Time Data Analysis: Algorithmic trading strategies continuously track and assess market data in real-time, including price changes, order book depth, trade volumes, and news feeds. The trading system immediately takes in and analyses information from unexpected events, such as a significant economic news release or a geopolitical development, to identify potential opportunities or risks.
2. Reaction to Price Movements: Algorithmic trading strategies often include thresholds or triggers based on price changes. The strategy may automatically change its trading parameters or execute trades when a market event drives prices beyond set limits. For instance, the strategy may cause a stop-loss order to be placed if a stock's price drops significantly as a result of unfavorable news, thereby limiting potential losses.
3. Sentiment Analysis: Some algorithmic trading strategies use sentiment analysis and natural language processing to analyze information from news articles, social media posts, and other sources. The strategy can make wise trading decisions by evaluating the sentiment and market impact of news events. The sentiment analysis element of the strategy can assist in determining whether unexpected news is a good or bad development and adjusting trading positions accordingly.
4. Risk Management: Risk management strategies are frequently incorporated into algorithmic trading strategies to protect against unfavorable market changes. These controls may take the form of position size restrictions, predetermined stop-loss orders, or dynamic risk models that modify exposure in response to market conditions. The strategy may tighten risk controls, scale back position sizes, or even temporarily halt trading in response to unanticipated events to avoid excessive losses.
5. Backtesting and Optimization: Algorithmic trading strategies are often backtested and improved by using past market data. The best parameters and regulations for various market conditions can be identified through this process. Traders can modify and improve their strategies based on new information and market insights. Unexpected events can provide new information that can be used to inform changes to the strategy's parameters and rules to enhance its performance in future conditions.

It's crucial to remember that, despite their ability to adjust to shifting market conditions and unforeseen events, algorithmic trading strategies are not risk-free.

Extreme market movements or unexpected events with a big market impact could still pose problems and create unexpected results. A key component of reducing these risks is risk management, which also involves ongoing evaluation of the effectiveness of the strategy.