Unlocking the Future of Trading: Market Insights & The Power of Quantitative Investment.

Hello, outstanding students of Diamond Ridge Financial Academy!

I’m Charles Hanover, and I’m excited to embark on this journey of smart investing with you in an era full of opportunities. Investing in yourself and continuously learning is the key to building wealth. Tonight, let’s start with the UK stock market, analyse market trends, and explore the secrets of quantitative trading.

Today, the UK stock market showed strong momentum. The FTSE 100 hit a new all-time high during the session, rising 1% and continuing its upward trend this week. This rally is not an isolated event but is driven by overall optimism in European markets. European stocks gained broadly, with the Stoxx 600 climbing to 537 points, boosting investor confidence. At the same time, UK domestic companies delivered impressive results, further supporting the market.

Notably, St. James’s Place PLC reported record-high assets under management, sending its stock up 8.9%, leading the market. Additionally, Airtel Africa PLC continued to rise on strong earnings, further lifting sentiment and demonstrating investors’ confidence in companies with steady growth.

Despite the overall optimism, some sectors still face challenges, particularly energy. Shell, despite reporting a sharp drop in profits, remains committed to rewarding shareholders, announcing a $3.5 billion stock buyback and a 4% dividend increase. However, the market reaction was muted, with the stock rising only 0.5%. Meanwhile, a Scottish court overturned Shell and Equinor’s oil field drilling permits, which could bring uncertainty to the UK’s future energy policies and industry outlook.

On a macro level, while the UK economy has not been directly affected by the Eurozone’s stalled growth, the European Central Bank’s 0.25% rate cut has led to expectations of looser global liquidity. This policy shift could influence capital flows into the UK market and may prompt the Bank of England to adjust its own monetary policy pace in the future.

At the same time, US GDP data came in weaker than expected, with growth slowing to 2.3%, strengthening market expectations for Fed rate cuts. This has become a key short-term driver for global markets.

In the US market, overall performance was choppy as investors weighed economic data, corporate earnings, and policy expectations. Although GDP growth came in lower than expected, personal consumption spending unexpectedly rose 4.2%, far exceeding the 3.1% forecast, showing that the US economy remains resilient.

In this context, the Fed kept interest rates unchanged, and the market is still digesting Powell’s policy comments. Compared to previous hawkish stances, the Fed’s tone this time was slightly more dovish. However, potential new tariffs from the Trump administration remain a major uncertainty, particularly regarding trade policies with Canada, Mexico, and China, which could disrupt global supply chains and inflation trends.

In terms of market sectors, the three major indexes showed mixed performance. Tech stocks were the focus, with Meta surging 4% to a new high after strong earnings. Meanwhile, Microsoft beat earnings expectations, but slower growth in its Azure cloud business caused its stock to drop 6.1%. IBM soared 13% to a record high, thanks to AI business growth exceeding market expectations. Also, INOD, which I recommended to you all on Tuesday, jumped 7% today, benefiting from tech industry trends and support from quantitative trading systems.

Notably, the energy market also drew attention. Oil prices rose 0.5% to $73.02 per barrel, while gold surged 1.5% to a new high of $2,797 per ounce, reflecting stronger risk-aversion sentiment. In a more volatile market, small-cap stocks stood out, with the Russell 2000 index jumping 1.3%, showing signs of partial capital inflows.

In this market environment, the importance of quantitative trading systems is more apparent than ever. As the public test ends, many students have actively shared their trading experiences and raised valuable questions.

Some want the system to cover more asset classes, such as stocks, forex, and gold, instead of just crypto. Others ask whether trade signals could be released earlier to allow more time for positioning. Many are also concerned about whether they can continue using the system after the public test, what the pricing model will be, and if there will be long-term subscription options.

Additionally, some students expressed interest in understanding the core mechanics of the system better to improve their trading skills and make more accurate market judgments. While the questions vary, at the end of the day, everyone shares the same goal—how to seize market opportunities more efficiently and accurately to achieve long-term, stable profits.

To answer these questions, we need to return to the core of the quantitative trading system.

First, regarding whether the system can support more trading assets, the answer is yes. The system’s underlying framework and algorithm design are highly adaptable, allowing it to adjust to different market conditions. Whether it’s crypto, stocks, forex, or commodities, all can be integrated into the system’s analysis framework.

The reason we chose crypto for this public test is primarily due to its unique market characteristics. Right now, the global economy is complex—the Fed’s monetary policy outlook remains uncertain, and Trump’s potential return adds even more market variables. Given this situation, we needed a highly volatile asset that trades 24/7, without market closure restrictions, to test the system’s responsiveness. Crypto perfectly meets all these conditions. Its round-the-clock trading allows the system to capture market signals at any time, verifying the effectiveness and stability of its algorithms.

Through this public test, the system has demonstrated strong data processing capabilities and signal recognition, making precise judgments even during sharp market swings. This confirms its stability and reliability.

Next, the quantitative trading system will enter a full optimisation and upgrade phase. Once the final model tuning and strategy improvements are completed, we will gradually expand its coverage to include US stocks, forex, and precious metals. This will allow investors to use the system across a broader market environment, seizing more trading opportunities.

Beyond asset expansion, many students are particularly interested in whether the system can send trading signals earlier.

During the public test, the signal timing was carefully calculated to ensure both accuracy and timeliness. In future official versions, the system will further optimise signal delivery by providing different time-frame signals, including short-term, mid-term trend, and long-term investment signals.

For example:

• Short-term traders will receive signals a few minutes to half an hour before key market turning points, ensuring they can enter positions in time.

• Trend traders will get early forecasts of potential market shifts, giving them enough time for position management and risk control.

• Additionally, the system will factor in market news and policy changes, providing early warnings about major events that could cause market volatility, allowing investors to prepare in advance.

During times of high market volatility, the quantitative trading system has another key function—cross-market arbitrage. Many investors focus solely on the price movements of a single market and overlook the connections between different markets. In reality, the price movement of one asset often appears first in related assets, creating arbitrage opportunities.

For example, when positive news hits the market, BTC is usually the first to react, while tech stocks and the forex market take slightly longer to follow. This means that if you can spot BTC’s uptrend early, you can enter tech stocks before they start moving, wait for the market to catch up, and lock in steady profits.

Take yesterday’s Fed rate decision as an example. The moment Powell started speaking, BTC’s price had already jumped, but the Nasdaq didn’t react until five minutes later. A few minutes might not seem like much, but for a quantitative trading system, this represents a perfect arbitrage opportunity. The system quickly analyses market sentiment when BTC rises, evaluates how other assets are linked, and generates trading signals.

For example, if the system finds that in similar rate decision events, BTC’s rise has led to a tech stock rally over 95% of the time, this creates a chance to enter early. That way, even if the overall market trend isn’t clear yet, traders can still use this cross-market arbitrage strategy to secure steady profits.

Of course, beyond arbitrage, the system also has powerful features such as economic data tracking, technical chart analysis, and market sentiment detection. It’s not just a simple trade signal tool—it’s an advanced market analysis system.

The system tracks and analyses key macroeconomic indicators in real time, such as Fed rate decisions, non-farm payrolls, and GDP growth. It then compares them to historical data to predict market reactions.

For example, if the system finds that in the last 10 rate cuts, BTC went up 9 times and remained flat once, it will generate a buy signal with a 90% probability after a new rate cut is announced. This data-driven approach is far more precise than relying on market sentiment or gut feeling.

Traditional investors usually rely on basic indicators such as MACD and moving averages, but these often lag behind and can lead to poor trades. The quantitative trading system, however, scans massive amounts of historical data, identifies recurring patterns, and extracts the most useful insights.

For example, if the system detects that BTC’s current price movement closely matches a past pattern where the market surged afterwards, it will generate a similar trade signal to alert traders to a potential price move. This pattern recognition technology significantly improves decision-making accuracy and reduces market noise.

Many students in the public test have personally experienced how powerful the quantitative trading system is, and they have all expressed a strong desire to keep using it. Some even have their funds ready, hoping I can guide them to continue making profits in the market. My answer to this demand is, of course, yes. After all, such a large group of students is not only the system’s potential user base but also a key force in promoting quantitative trading strategies in the future.

On this topic, I want to clarify one thing first: the original purpose of launching Diamond Ridge Financial Academy’s online course was to recruit more investors for the public test for free. The goal was to let everyone witness the power of the quantitative trading system in real market conditions and encourage more investors to purchase the system once it’s fully upgraded, thereby supporting the growth of the quantitative trading fund. In other words, this wasn’t just a technical test—it was also an opportunity for investors to truly understand quantitative trading.

Regarding pricing, the annual service fee for the quantitative trading system is currently set at £69K. In the financial industry, professional investment advisors, institutional research reports, and trading strategy consultations are not cheap. The real value of this system lies in the fact that it’s not just a simple analysis tool—it’s an intelligent system with fully automated trading decision-making. It monitors market trends in real time, processes massive amounts of data, captures investment opportunities with precision, and executes trades at the optimal moment. Without AI and big data, achieving this level of trading ability would require an entire team of senior analysts.

Many students understand that the quantitative trading system has undergone a long and rigorous development process. This system not only incorporates the investment expertise of thousands of top analysts but also integrates cutting-edge AI technology to achieve data-driven, precise trading.

For some students, the £69K price tag may seem high. However, in terms of value, this system is equivalent to the work of at least three senior analysts. Relying solely on human analysis requires significant manpower, and it is nowhere near as fast or accurate as the system. Considering that a professional analyst earns around £100K per year, the cost-effectiveness of this system is already extremely high.

More importantly, in a fast-changing market, human traders are often influenced by emotions, leading to impulsive decisions. The quantitative trading system, on the other hand, is entirely data and algorithm-driven, remaining calm and objective at all times. This fundamentally reduces the risk of human errors and ensures more stable investment returns.

Of course, when investors evaluate the price of a trading tool, the more important factor is its potential to generate returns. The value of a trading system is not determined by its price, but by how much extra profit it can generate for investors.

For example, if an investment account has £1M, with the help of the quantitative trading system, it could earn at least £500K per year—or even more—while significantly reducing investment risks. In this case, the £69K fee seems very reasonable.

On the other hand, if the account balance is only £10K, even if the system has a high success rate, the fee would seem disproportionately high. Therefore, this system is best suited for investors seeking long-term stable returns or professional traders looking to scale their capital and enhance investment returns in the future.

However, I’ve also noticed that many students want to continue receiving guidance from the quantitative trading system but are hesitant to bear the high upfront cost—especially since the system hasn’t been officially launched yet, and they still need more time to familiarise themselves with how it works. This is completely understandable. Even if people are willing to buy it now, there is no finished product available yet, as the system is still in the optimisation and upgrade phase.

Although this round of public testing has ended, we still need to further refine the system to ensure it has stronger adaptability and profitability in real market conditions.

Given the high recognition of this system and the desire to expand its user base before the official release, I have already applied to the project team to continue using the quantitative trading system to provide free market guidance until the official version is launched.

This means that, before the system is finalised and available for subscription, I will continue to use it to provide trading strategy analysis and help everyone seize investment opportunities in the market.

At the same time, we are now officially accepting preorders for the quantitative trading system. During this valuable practical opportunity, you can also achieve higher trading returns and decide whether to purchase the system when it is officially launched in the future.

Next, my focus will not only be on guiding everyone through trades, but more importantly, on explaining in depth how the system operates in real-world scenarios. This will ensure that every student can grasp market rhythms and maximise their profits.

The core value of the quantitative trading system is not just to provide trading signals, but to guide investors in developing a completely new way of thinking—an investment decision-making model based on data, logic, and probability. In the upcoming trading guidance, I will combine real-time market data to explain in detail how the system analyses data, identifies market sentiment, and builds portfolios, helping everyone gain a true understanding of quantitative trading.

For example, during the trading process, we will break down the system’s signal generation logic.

• When sudden news breaks, the system can assess its impact in real-time and quickly calculate the likely market reaction based on historical data.

• When technical patterns emerge in the market, the system searches for similar patterns in its database and estimates the probability distribution of future market movements based on past trends.

Behind these trading signals, there is strict logic, and it is this logic that enables quantitative trading to maintain a high win rate in the long term. If everyone can truly understand this logic, even without using the system in the future, they will be able to develop their own trading strategies and make more scientific and accurate investment decisions.

Additionally, some students have asked how to handle the 30% profit earned during this public test. First, let me clarify that this profit is a reward from the public test and belongs entirely to your personal assets, which you are free to manage.

• Some students have withdrawn their profits and used them to enhance their lives, such as treating friends to a meal and enjoying the hard-earned trading gains.

• Others have chosen to reinvest their profits, using them as starting capital to continue trading and leverage the quantitative trading system to achieve even higher returns.

Regardless of the choice, the key is how to maximise the use of this capital and ensure it works effectively.

I would also like to remind you that when transferring funds or making withdrawals in the crypto market, it is advisable to use mainstream cryptocurrencies such as Ethereum (ETH) or Solana (SOL). The advantages are clear—lower transaction fees and faster processing speeds.

• ETH transaction fees are currently relatively low, and ETH is used directly to pay miner fees.

• If you use USDT for transfers, miner fees may require ETH or TRX, which increases costs and complicates the process.

Selecting the right cryptocurrency can effectively reduce transaction costs and improve capital efficiency.

Finally, I encourage everyone to actively participate in real-world trading during this period. The goal of the quantitative trading system is not just to serve as a money-making tool, but to help everyone develop a systematic investment mindset—one that prevents traders from making emotional decisions and allows them to make rational, data-driven investments. The root cause of many investors’ losses is not simply a bad market, but rather flawed trading methods. Many traders are driven by emotions, blindly follow trends, and lack logical support. The greatest value of the quantitative trading system is to help investors establish rigorous investment logic, ensuring that every trade is based on a clear rationale and that long-term, stable profits are achieved through a probability advantage.

The end of this large-scale public test does not signify the end of everything—on the contrary, it marks the beginning of a new investment journey.

Before the system is officially launched, we still have ample time to explore its full potential, validate its trading strategies, and continuously accumulate real-world experience. This period allows us to enhance investment understanding and increase market profits.

In today’s global economic turbulence and the rapid advancement of technology, market volatility presents both risk and immense opportunity. For true investors, opportunities do not simply appear out of thin air—they belong to those who understand market rhythms and utilise the most cutting-edge investment tools.

Let’s work together to succeed in this new investment era and maximise our profits.