AI可以预测市场吗？发现交易机器人如何使用高级算法来自动交易并可能产生利润

自从出现以来，AI已重塑了许多行业，并且继续这样做。金融市场就是其中之一，特别是发生了很大的变化

Artificial intelligence (AI) has quickly changed many industries since its appearance and it continues to do so. One industry that saw a big change with the introduction of AI is the financial market.

自从出现以来，人工智能（AI）已迅速改变了许多行业，并且继续这样做。一个人AI的引入是一个很大变化的行业是金融市场。

This market is particularly interesting as it’s constantly evolving and influenced by various factors, presenting a challenge for traditional algorithmic trading. However, trading bots powered by AI could overcome this.

这个市场特别有趣，因为它不断发展并受到各种因素的影响，这对传统算法交易构成了挑战。但是，由AI支持的交易机器人可以克服这一点。

These bots use machine learning, deep learning, and predictive analytics to identify trading opportunities and execute trades at blazing speed (one could say it’s even ludicrous speed). Unlike traditional algorithmic trading, AI-based systems continuously learn from new data and adapt to changing market conditions, making them powerful tools for traders.

这些机器人使用机器学习，深度学习和预测分析来确定交易机会并以猛烈的速度执行交易（人们可以说这甚至是荒谬的速度）。与传统的算法交易不同，基于AI的系统不断从新数据中学习并适应不断变化的市场条件，从而使它们成为交易者的强大工具。

However, using AI for market prediction faces challenges and limitations.

但是，使用AI进行市场预测面临挑战和局限性。

Predicting price movements with certainty remains difficult due to the inherent complexity of financial markets, external economic influences, and sudden, unpredictable events (which, considering human nature, is quite often). Let’s just say, the technology just isn’t quite there yet, or rather, people haven’t figured out all the kinks and nuances.

由于金融市场的固有复杂性，外部经济影响以及突然的，不可预测的事件（考虑到人性，经常是经常），因此可以确定性地预测价格变动。可以说，这项技术还不到那儿，或者说，人们还没有弄清楚所有的纠结和细微差别。

Why AI Struggles with Market Prediction

为什么AI在市场预测中挣扎

As one might have gathered by now, predicting financial markets is far from straightforward, probably even more so today with the crypto industry in the mix. Multiple hurdles limit the effectiveness of AI-powered trading systems, starting with the nature of financial markets.

正如人们现在可能收集到的那样，预测金融市场远非直接，而今天的加密货币行业也可能更重要。从金融市场的性质开始，多个障碍限制了AI驱动的交易系统的有效性。

They are complicated and influenced by a combination of several elements, that is, macroeconomic factors, geopolitical events, investor psychology, market sentiment, high-frequency trading, and institutional manipulation.

它们是由几个要素的组合（即宏观经济因素，地缘政治事件，投资者心理学，市场情绪，高频交易和机构操纵）组合的组合所影响的。

A key issue is the lack of structured rules; markets lack fixed patterns and are often swayed by unforeseeable events. For instance, a sudden crackdown on crypto exchanges in China or a major economic crisis can drastically shift market trends, which AI struggles to anticipate.

一个关键问题是缺乏结构化规则。市场缺乏固定的模式，通常会被不可预见的事件所挥之不去。例如，突然镇压中国的加密交流或重大经济危机可能会大大改变市场趋势，而AI努力预期。

The next set of challenges are data limitations and bias. AI models require vast amounts of high-quality data for precise predictions. However, financial data often contains biases, missing information, or manipulated data that can mislead models.

下一组挑战是数据限制和偏见。 AI模型需要大量的高质量数据才能进行精确的预测。但是，财务数据通常包含可能误导模型的偏见，丢失的信息或操纵数据。

To give you an example, an AI model trained only on bull market data might perform poorly during a sudden market downturn because it has never encountered such conditions before. Similarly, historical data may not always reflect current market realities due to evolving economic policies and investor behaviors.

举一个例子，只有在牛市数据中训练的AI模型在突然的市场衰退中可能表现较差，因为它以前从未遇到过这种情况。同样，由于经济政策和投资者的行为不断发展，历史数据可能并不总是反映当前的市场现实。

Then, there are overfitting and model risks. At first glance, this doesn’t sound like an issue, but overfitting is a common problem in AI trading. It refers to a situation when an AI model performs exceptionally well on historical data but fails in live trading.

然后，存在过度拟合和模型风险。乍一看，这听起来并不是一个问题，但是过度拟合是AI交易中的常见问题。它指的是AI模型在历史数据上表现出色但实时交易失败的情况。

Overfitting occurs when models memorize past trends rather than recognizing generalizable patterns. On top of that, large institutional traders actively adapt their strategies to counteract AI-driven retail trading, further diminishing the reliability of predictive models.

当模型记住过去的趋势而不是识别可概括的模式时，就会发生过度拟合。最重要的是，大型机构交易者积极调整其战略来抵消AI驱动的零售交易，从而进一步降低了预测模型的可靠性。

How AI Trading Bots Analyze Markets

AI交易机器人如何分析市场

Despite the challenges above, AI trading bots can still be useful as they use various techniques to generate market predictions. To name a few:

尽管面临上述挑战，但AI交易机器人仍然有用，因为它们使用各种技术来产生市场预测。仅举几例：

Core AI components like supervised learning, reinforcement learning, and neural networks allow AI to learn from labeled past trading data for future predictions. Through a combination of these, AI learns from labeled past trading data and applies it to future predictions, all the while it continuously improves upon strategies via feedback from simulated trading.

核心AI组件（例如监督学习，增强学习和神经网络）允许AI从标记的过去交易数据中学习以进行未来的预测。通过这些结合，AI从标记的过去的交易数据中学习并将其应用于未来的预测，同时它通过模拟交易的反馈不断地改善策略。

In addition, deep learning techniques recognize price patterns, helping AI detect trends. In summary, these models analyze historical price movements, trading volume, and volatility to forecast potential price actions.

此外，深度学习技术识别价格模式，帮助AI检测趋势。总而言之，这些模型分析了历史价格变动，交易量和波动性，以预测潜在的价格行动。

The name perhaps sounds complicated, but it basically involves AI bots scanning news articles, financial reports, and social media to assess market sentiment. Then, by analyzing text data, NLP models gauge investor outlook (bullish or bearish).

这个名字听起来可能很复杂，但基本上涉及AI机器人扫描新闻文章，财务报告和社交媒体来评估市场情绪。然后，通过分析文本数据，NLP模型仪表投资者的前景（看涨或看跌）。

For instance, an out-of-the-blue increase in positive sentiment about Bitcoin on social media might indicate an impending price surge. On the other hand, panic-driven discussions may signal a market downturn. NLP understands the context of these conversations, analyzing word relationships between words in a sentence across paragraphs to get the meaning.

例如，社交媒体上对比特币的积极情绪的蓝色增长可能表明即将出现的价格上涨。另一方面，恐慌驱动的讨论可能表明市场低迷。 NLP了解这些对话的上下文，分析段落中句子中单词之间的单词关系以获得含义。

This is more technical in nature and is a bit more complicated as AI-powered trading bots rely on a bunch of technical indicators. These include moving averages (MA, EMA), relative strength index (RSI), moving average convergence divergence (MACD), Bollinger Bands, and liquidity analysis.

这本质上是更具技术性的，并且由于AI驱动的交易机器人依赖于许多技术指标，因此更加复杂。其中包括移动平均值（MA，EMA），相对强度指数（RSI），移动平均收敛差异（MACD），Bollinger频段和流动性分析。

If you’re not familiar with the terms, you’ve likely read a bunch of gibberish now. Put simply, these signals help AI determine potential entry and exit points for trades by:

如果您不熟悉这些术语，那么您现在可能会阅读一堆Gibberish。简而言之，这些信号帮助AI通过以下方式确定交易的潜在进入和退出点

Last but not least, AI bots use and analyze alternative data sources to speculate. This could be blockchain data with on-chain transactions, whale movements, and DeFi activity for crypto markets. Also, it employs options market data where open interest and trading volumes help predict investor sentiment.

最后但并非最不重要的一点是，AI机器人使用和分析替代数据源以推测。这可能是带有链交易，鲸鱼运动和加密市场的Defi活动的区块链数据。此外，它采用期权市场数据，其中开放兴趣和交易量有助于预测投资者的情绪。

Moreover, AI even uses Google, specifically Google Trends and web traffic data. It can look for spikes in searches for specific cryptocurrencies or stocks that may indicate upcoming market movements.

此外，AI甚至使用Google，特别是Google趋势和Web流量数据。它可以在搜索特定的加密货币或可能表明即将发生的市场转移的股票时寻找峰值。

Using AI Wisely: Potential vs. Pitfalls

明智地使用AI：潜在与陷阱

It’s worth remembering that AI indeed is a powerful tool,

值得记住的是，AI确实是一个强大的工具，