AI Trading Success: 86% of Trades

​Artificial intelligence (AI) has been making significant inroads into the financial markets, offering tools that enhance trading strategies and decision-making processes. One notable development is the integration of AI-powered trading bots, which have demonstrated remarkable performance metrics across various exchange-traded funds (ETFs). This article delves into the performance of these AI bots, the underlying Financial Learning Models (FLMs) that drive them, and provides insights tailored for day traders, swing traders, and long-term investors.​

AI Trading Bots: Unprecedented Success Rates

Recent advancements in AI-driven trading have yielded bots with exceptional success rates across multiple ETFs.​

QLD/QID Trades: 86% Win Rate

An AI trading bot focusing on leveraged ETFs, specifically the ProShares Ultra QQQ (QLD) and ProShares UltraShort QQQ (QID), achieved an impressive 86.6% win rate. Over a three-month period, the bot executed 67 trades, winning 58 of them. This performance underscores the potential of AI in navigating the complexities of leveraged ETFs. 

QQQ/QID Trades: 84% Success Rate

In another instance, an AI bot trading the Invesco QQQ Trust (QQQ) and ProShares UltraShort QQQ (QID) reported an 84% success rate. By leveraging advanced algorithms to analyze market trends and execute trades with precision, the bot demonstrated the efficacy of AI in ETF trading.

IXN/QID Trades: 72% Odds of Success 

The iShares Global Tech ETF (IXN) paired with QID saw an AI bot achieving a 72% win rate. This highlights the bot's capability to adapt to global tech market dynamics and make informed trading decisions.​

FTC/QID Trades: 79% Success Rate

Trading the First Trust Large Cap Growth AlphaDEX Fund (FTC) alongside QID, an AI bot recorded a 79% success rate. This performance reflects the bot's proficiency in managing large-cap growth assets in conjunction with inverse ETFs.​

VCR/SZK Trades: 88% Profit Rate

Perhaps most striking is the AI bot's 88% profit rate when trading the Vanguard Consumer Discretionary ETF (VCR) against the ProShares UltraShort Consumer Goods ETF (SZK). This achievement underscores AI's potential in the consumer discretionary sector.​

Understanding the Tickers and Their Roles

To fully appreciate the AI bots' performance, it's essential to understand the ETFs involved:

• QLD (ProShares Ultra QQQ): Seeks to provide twice (200%) the daily performance of the Nasdaq-100 Index.​
• QID (ProShares UltraShort QQQ): Aims for twice the inverse (-200%) of the daily performance of the Nasdaq-100 Index.​
• QQQ (Invesco QQQ Trust): Tracks the Nasdaq-100 Index, representing 100 of the largest non-financial companies listed on the Nasdaq.​
• IXN (iShares Global Tech ETF): Offers exposure to global technology stocks.​
• FTC (First Trust Large Cap Growth AlphaDEX Fund): Focuses on large-cap growth stocks selected based on specific investment criteria.​
• VCR (Vanguard Consumer Discretionary ETF): Invests in consumer discretionary sector stocks.​
• SZK (ProShares UltraShort Consumer Goods ETF): Seeks to achieve twice the inverse (-200%) of the daily performance of the Dow Jones U.S. Consumer Goods Index.​

Correlations and Anti-Correlations Among ETFs

Understanding the relationships between these ETFs is crucial for effective trading strategies:

• QLD and QID: These are leveraged ETFs with inverse objectives. When QLD aims to double the daily performance of the Nasdaq-100, QID seeks to double the inverse. Thus, they are highly negatively correlated.​
• QQQ and QID: QQQ tracks the Nasdaq-100, while QID inversely tracks it with leverage. This results in a strong negative correlation.​
• IXN and QID: IXN's focus on global tech means it shares some overlap with the Nasdaq-100 components, leading to a moderate negative correlation with QID.​
• FTC and QID: FTC's large-cap growth stocks may include tech companies, resulting in a nuanced correlation with QID, depending on specific holdings.​
• VCR and SZK: VCR invests in consumer discretionary stocks, while SZK inversely tracks consumer goods. While not direct opposites, they can exhibit negative correlations during specific market conditions.​

Financial Learning Models (FLMs): The Backbone of AI Trading

At the heart of these AI trading bots are Financial Learning Models (FLMs). Developed by companies like Tickeron, FLMs utilize machine learning to analyze vast amounts of financial data, identify patterns, and adapt to changing market conditions. By integrating FLMs with technical analysis, traders can spot patterns more accurately and make better-informed decisions. These models enhance control and transparency, especially in fast-moving markets. 

Tailored Strategies for Different Traders

The application of AI trading bots varies based on trading styles:

Day Traders

For day traders, AI bots offer real-time analysis and rapid trade execution, capitalizing on short-term market movements. The high success rates in ETFs like QLD/QID and QQQ/QID make these bots particularly appealing for intraday strategies.​

Swing Traders

Swing traders, who hold positions over several days or weeks, can benefit from AI bots' ability to identify medium-term trends. Bots trading ETFs like IXN and FTC provide insights into sector-specific movements, aligning with swing trading objectives.​

Long-Term Investors

While AI bots are typically associated with short to medium-term trading, long-term investors can utilize AI-driven insights for portfolio optimization. Understanding correlations and leveraging AI recommendations can enhance diversification and risk management over extended periods.

Disclaimers and Limitations