AI可以預測市場嗎？發現交易機器人如何使用高級算法來自動交易並可能產生利潤

自從出現以來，AI已重塑了許多行業，並且繼續這樣做。金融市場就是其中之一，特別是發生了很大的變化

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.

自從出現以來，人工智能（AI）已迅速改變了許多行業，並且繼續這樣做。一個人AI的引入是一個很大變化的行業是金融市場。

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.

這個市場特別有趣，因為它不斷發展並受到各種因素的影響，這對傳統算法交易構成了挑戰。但是，由AI支持的交易機器人可以克服這一點。

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.

這些機器人使用機器學習，深度學習和預測分析來確定交易機會並以猛烈的速度執行交易（人們可以說這甚至是荒謬的速度）。與傳統的算法交易不同，基於AI的系統不斷從新數據中學習並適應不斷變化的市場條件，從而使它們成為交易者的強大工具。

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

但是，使用AI進行市場預測面臨挑戰和局限性。

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.

由於金融市場的固有復雜性，外部經濟影響以及突然的，不可預測的事件（考慮到人性，經常是經常），因此可以確定性地預測價格變動。可以說，這項技術還不到那兒，或者說，人們還沒有弄清楚所有的糾結和細微差別。

Why AI Struggles with Market Prediction

為什麼AI在市場預測中掙扎

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.

正如人們現在可能收集到的那樣，預測金融市場遠非直接，而今天的加密貨幣行業也可能更重要。從金融市場的性質開始，多個障礙限制了AI驅動的交易系統的有效性。

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.

它們是由幾個要素的組合（即宏觀經濟因素，地緣政治事件，投資者心理學，市場情緒，高頻交易和機構操縱）組合的組合所影響的。

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.

一個關鍵問題是缺乏結構化規則。市場缺乏固定的模式，通常會被不可預見的事件所揮之不去。例如，突然鎮壓中國的加密交流或重大經濟危機可能會大大改變市場趨勢，而AI努力預期。

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.

下一組挑戰是數據限制和偏見。 AI模型需要大量的高質量數據才能進行精確的預測。但是，財務數據通常包含可能誤導模型的偏見，丟失的信息或操縱數據。

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.

舉一個例子，只有在牛市數據中訓練的AI模型在突然的市場衰退中可能表現較差，因為它以前從未遇到過這種情況。同樣，由於經濟政策和投資者的行為不斷發展，歷史數據可能並不總是反映當前的市場現實。

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.

然後，存在過度擬合和模型風險。乍一看，這聽起來並不是一個問題，但是過度擬合是AI交易中的常見問題。它指的是AI模型在歷史數據上表現出色但實時交易失敗的情況。

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.

當模型記住過去的趨勢而不是識別可概括的模式時，就會發生過度擬合。最重要的是，大型機構交易者積極調整其戰略來抵消AI驅動的零售交易，從而進一步降低了預測模型的可靠性。

How AI Trading Bots Analyze Markets

AI交易機器人如何分析市場

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

儘管面臨上述挑戰，但AI交易機器人仍然有用，因為它們使用各種技術來產生市場預測。僅舉幾例：

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組件（例如監督學習，增強學習和神經網絡）允許AI從標記的過去交易數據中學習以進行未來的預測。通過這些結合，AI從標記的過去的交易數據中學習並將其應用於未來的預測，同時它通過模擬交易的反饋不斷地改善策略。

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，深度學習技術識別價格模式，幫助AI檢測趨勢。總而言之，這些模型分析了歷史價格變動，交易量和波動性，以預測潛在的價格行動。

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).

這個名字聽起來可能很複雜，但基本上涉及AI機器人掃描新聞文章，財務報告和社交媒體來評估市場情緒。然後，通過分析文本數據，NLP模型儀表投資者的前景（看漲或看跌）。

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，社交媒體上對比特幣的積極情緒的藍色增長可能表明即將出現的價格上漲。另一方面，恐慌驅動的討論可能表明市場低迷。 NLP了解這些對話的上下文，分析段落中句子中單詞之間的單詞關係以獲得含義。

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.

這本質上是更具技術性的，並且由於AI驅動的交易機器人依賴於許多技術指標，因此更加複雜。其中包括移動平均值（MA，EMA），相對強度指數（RSI），移動平均收斂差異（MACD），Bollinger頻段和流動性分析。

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:

如果您不熟悉這些術語，那麼您現在可能會閱讀一堆Gibberish。簡而言之，這些信號幫助AI通過以下方式確定交易的潛在進入和退出點

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.

最後但並非最不重要的一點是，AI機器人使用和分析替代數據源以推測。這可能是帶有鏈交易，鯨魚運動和加密市場的Defi活動的區塊鏈數據。此外，它採用期權市場數據，其中開放興趣和交易量有助於預測投資者的情緒。

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，特別是Google趨勢和Web流量數據。它可以在搜索特定的加密貨幣或可能表明即將發生的市場轉移的股票時尋找峰值。

Using AI Wisely: Potential vs. Pitfalls

明智地使用AI：潛在與陷阱

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

值得記住的是，AI確實是一個強大的工具，