Financial Data and Key Metrics Changes - The company reported an 11% revenue growth to a record $219 million, including a $2 million benefit from foreign currency fluctuations, with a 10% growth excluding FX [21][22] - Diluted earnings per share increased by 11% to $1.91, or $2 per share excluding notable items, representing a 16% increase [21] - Commission revenue increased by 12% to a record $192 million, driven by strong market volumes and increased volatility [22][25] Business Line Data and Key Metrics Changes - Block trading in the client-initiated channel saw a 38% growth in average daily volume (ADV) across U.S. Credit, emerging markets, and Eurobonds [6][12] - Portfolio trading channel generated a 69% increase in total portfolio trading ADV [7] - Dealer-initiated channel experienced a 40% increase in dealer-initiated ADV [7] Market Data and Key Metrics Changes - U.S. High grade market share dropped to 10% in July from 12% in June, attributed to large trades moving to phone and chat [9] - The share of lock trades equal to or larger than $5 million increased to 47% in July, up from 42% in June [9] - U.S. High grade portfolio trading market share was over 19%, up 370 basis points year-over-year [14] Company Strategy and Development Direction - The company is focusing on becoming more protocol agnostic and expanding its addressable market through multiple trading protocols and automation tools [4] - A high-touch strategy was launched in U.S. Credit to target the phone and chat market, which has seen significant block trading activity [10] - The company is preparing to launch a new Midex solution in September for U.S. Credit, aimed at enhancing dealer-to-dealer trading [15][16] Management's Comments on Operating Environment and Future Outlook - Management expressed optimism about the supportive market backdrop and strong growth across new initiatives, despite disappointing U.S. High grade share numbers in July [19][20] - The company anticipates continued growth in U.S. Credit through progress in client-initiated, portfolio trading, and dealer-initiated channels [30][31] - Management highlighted the importance of strategic hires in driving future growth and enhancing product offerings [33][79] Other Important Information - The company generated $360 million in free cash flow over the trailing twelve months, with $145 million remaining on the share repurchase authorization as of July 31, 2025 [28] - Operating expenses increased by 6% year-over-year, driven by higher employee compensation and technology costs [26] Q&A Session Summary Question: Progress on new initiatives and July volumes - Management reported significant growth in key areas, with investment grade portfolio trading up 47% year-to-date and block activity up over 20% [37][39] - July market dynamics favored large portfolio trades, impacting market share [42][44] Question: Drivers of fee per million decline - The decline was attributed to new protocols and increased portfolio trading volumes, though July saw a slight recovery due to high-grade duration changes [49][50] Question: Update on block trading initiatives - Block trading volume in Eurobonds was up 54% in July, with expectations for continued growth as dealer content increases [56][62] Question: Performance in the muni business - The muni market saw a 23% increase in overall volume, with tax-exempt business growing by 34% [85][88] Question: Capital return priorities and M&A - The company remains focused on organic growth opportunities while being opportunistic with share repurchases and considering bolt-on acquisitions [92][95]

Core Insights - The integration of financial calendars with trading tools is essential for achieving a closed loop of "data - strategy - execution" [1] - Automation through APIs allows for real-time data-driven trading decisions, enhancing efficiency and reducing potential losses [3][4] Group 1: API-Driven Automated Trading System - Direct data connection and strategy triggers enable automatic adjustments in trading positions based on specific market conditions, such as unemployment claims and ETF holdings [3] - A quantitative fund successfully reduced its gold leverage from 3x to 1x, avoiding a potential loss of 12% during market fluctuations following non-farm payroll data [3] Group 2: Tool Matrix Collaboration - The use of smart Expert Advisors (EAs) in MT4/MT5 platforms allows for real-time monitoring and execution of trading strategies based on economic data releases [5] - A specific strategy generated a 1.8% daily return when the CPI exceeded expectations by 0.3% [5] Group 3: Dynamic Risk Management - Dynamic adjustments to volatility weights can significantly reduce potential losses; for instance, a fund shortened its holding period in response to a spike in gold volatility [7][8] - During the Federal Reserve's quiet period, market liquidity typically contracts, and smart order features can help manage risks effectively [9] Group 4: Efficiency Enhancement Strategies - Event arbitrage strategies can be constructed by leveraging multiple tools to capitalize on simultaneous economic announcements, such as ECB decisions and U.S. non-farm data [10] - A successful arbitrage model captured a 2% increase in euro-gold premiums following a major economic announcement [10] Group 5: AI-Assisted Expectation Capture - The integration of AI algorithms with financial calendars can provide alerts for high-sensitivity data, enhancing trading strategies based on historical performance metrics [11] - A specific strategy achieved a 2.3% daily return when core PCE data exceeded expectations [11] Conclusion - The synergy between financial calendars and trading tools transforms information advantages into execution advantages, significantly improving trading efficiency and risk management [12] - The 2025 practices indicate a 40% increase in trading efficiency for non-farm payroll events through automated data-strategy linkages, while risks in cryptocurrency markets can be reduced by 27% through smart contract integration [12]

Report Industry Investment Rating - Not provided in the content Core Viewpoints of the Report - The report systematically studies the construction method of futures automated trading strategies, emphasizing the core advantages of automated trading in efficiency, discipline, and data processing, and points out that successful strategy construction requires developers to have comprehensive capabilities such as market cognition, programming skills, and psychological qualities. It also provides a complete risk control framework and a gradual implementation plan from simulation to live trading, and believes that AI - driven and compliance - transparent will be the main future development directions [3]. Summary by Relevant Catalogs 1. Big Data Era's Automated Trading Revolution 1.1 Market Background and Development Status of Automated Trading - In the era of big data and AI, the proportion of automated trading in the global foreign exchange market is rising. The daily average trading volume of the global foreign exchange market is $7.5 trillion, with 65% of transactions conducted electronically. Barclays plans to increase the proportion of automated spot foreign exchange transactions. Automated trading improves efficiency, reduces manual intervention, and has a significant speed advantage over manual trading, with an average execution delay of 300 - 500 milliseconds for manual trading and less than 5 milliseconds for automated systems [6]. 1.2 Core Advantage Analysis of Automated Trading - Automated trading has discipline advantages as it follows preset rules without being affected by emotions, avoiding behavior biases like over - trading after consecutive losses. It can also monitor multiple markets and thousands of varieties 24/7. In terms of data processing, modern quantitative systems can process TB - level market data daily, providing a basis for trading decisions [7]. 2. Core Competency System for Building Automated Trading 2.1 Market Cognition and Market Judgment Ability - Developers need multi - dimensional professional capabilities, including understanding of variety characteristics, participant structures, and price formation mechanisms. For example, trading crude oil futures requires knowledge of OPEC policies, geopolitical factors, and inventory data, as well as technical analysis skills [8]. 2.2 Programming and Quantitative Analysis Skills Requirements - Python is the industry - standard programming language, and statistical modeling involves advanced techniques such as time - series analysis and machine learning. For instance, a simple mean - reversion strategy may need ADF tests and Z - score standardization [9]. 2.3 Psychological Quality and Risk Management Ability - Psychological quality is crucial. During strategy development, developers face a 3 - 6 - month trial - and - error period, and in live trading, they need to maintain emotional stability. Professional traders often establish psychological training mechanisms [10]. 3. Tool Selection and Platform Evaluation 3.1 Comparison of Mainstream Automated Trading Platforms - There are three types of automated trading tools: retail - level platforms (e.g., MT5, TradingView), professional - level platforms (e.g., Infinite Easy, MultiCharts), and institutional - level systems (e.g., QuantConnect, AlgoTrader), each with different features [11]. 3.2 Data Interface and Execution Efficiency Evaluation - The quality of data interfaces affects strategy performance. The CTP interface of SHFE can process over 5000 orders per second, and the penetration - style regulatory interface balances data richness and compliance. Different platforms have different order round - trip times (RTT), and developers should choose tools according to strategy types [12]. 4. Strategy Development Process and Practice Guide 4.1 Methodology and Trap Avoidance of Historical Backtesting - Strategy development starts with historical backtesting. Reliable backtesting needs to address issues like survivorship bias, look - ahead bias, and slippage. Backtesting has limited reference value for high - frequency strategies [13]. 4.2 Construction Principles of Risk Control System - A complete risk control module includes fund management, position control, circuit - breaker mechanisms, and exception handling. It should be tested under extreme market conditions, and the risk control system needs continuous optimization in live trading [14]. 5. Live Trading Deployment and Continuous Optimization 5.1 Key Transition from Simulation to Live Trading - It is recommended to use a three - stage transition method: 3 - month simulation trading, 1 - month trial with 10% of live - trading funds, and then gradually increase the position to the target level [15]. 5.2 Wrong - Order Handling and System Monitoring Mechanism - The wrong - order handling system should have multi - level protection, including syntax checking, rationality verification, and emergency processing. A complete log system should be established to record order life cycles for strategy optimization [16]. 6. Typical Case Analysis and Strategy Evolution 6.1 Implementation Path of Market - Maker Strategy - A complete market - maker system includes order - book analysis, quote generation, and risk - hedging modules. For copper futures, factors such as the price difference between SHFE and LME copper and spot premium/discount need to be considered. The income from market - making is gradually decreasing, and higher requirements are placed on speed and strategy [17]. 6.2 Modern Evolution of Trend - Following Strategy - Traditional double - moving - average strategies are being replaced by LSTM - based waveform prediction models. For example, adding a volatility - adaptive mechanism to the iron ore futures breakout system can increase the return - risk ratio by over 15% [18]. 7. Conclusion and Outlook 7.1 Double - Edged Sword Characteristic of Automated Trading - Automated trading has both advantages in execution efficiency and scale and risks such as technical failures and strategy homogenization. Developers should maintain awe of the market and establish a human - machine collaboration mechanism [19]. 7.2 Future Development Directions and Technological Trends - The future development directions of automated trading are AI - driven, multi - modal integration, and compliance - transparent. Individual developers are advised to start with simple rule - based strategies and continuously learn and adapt [20].

Summary of MarketAxess Conference Call Company Overview - **Company**: MarketAxess - **Participants**: Chris Concannon (CEO), Ilene Bieler (CFO), Alex Kramm (Managing Director - Equity Research) Key Industry Insights - **Market Focus**: The company is focused on regaining market share in the U.S. credit business, with significant investments in technology made in 2024 aimed at execution in 2025 [5][7] - **Trading Channels**: MarketAxess operates through three main channels: client to dealer, portfolio trading, and dealer to dealer [8][12] - **Market Trends**: Electronic trading is increasing turnover in the market, with portfolio trading contributing significantly to this growth [13][43] Core Business Strategies - **Investment in Technology**: The company has made substantial investments in technology to enhance trading tools and analytics, which are expected to yield results in 2025 [7][10] - **Client to Dealer Channel**: MarketAxess has a dominant RFQ platform for corporate bonds in the U.S., with a focus on improving liquidity for clients [8][9] - **Portfolio Trading**: The company is targeting the block trading market, which represents a significant growth opportunity, as it is largely still conducted via phone and chat [19][20][45] - **Dealer to Dealer Solutions**: A new mid-market solution is expected to launch in Q2, aimed at providing dealers with efficient liquidity options [17][18][32] Financial Performance and Market Share - **Market Share Goals**: The company aims to capture a larger share of the block trading market, which is estimated to be 50% of the total market [19][20] - **Performance Metrics**: The portfolio trading tool has seen over $1 billion in bond blocks traded, indicating strong performance [16] - **Fee Structure**: The company is not currently facing significant price competition in its core offerings, focusing instead on enhancing features and functionality [77][80] Macro Market Conditions - **Market Environment**: The current low volatility and tight spreads are challenging for the business, but the company is well-positioned to benefit from increased volatility and wider spreads [60][61] - **Electronic Trading Growth**: The macro environment is favorable for electronic trading, with attractive yields and a growing fixed income market [41][42] Emerging Markets - **Growth in EM**: MarketAxess is a leading player in the emerging markets (EM) space, with a focus on converting manual trading to electronic trading [54][58] - **Market Share in EM**: The company is experiencing growth in market share in EM, with electronic adoption still under 5% of the total market [59] Automation and Future Outlook - **Automation Trends**: The company is investing in automation, with expectations that 2025 will see significant advancements in this area [48][51] - **Long-term Vision**: The CEO expressed optimism about the future of electronic trading, predicting that as traders become accustomed to electronic platforms, adoption will continue to grow [46][47] Expense Management - **Investment Strategy**: The company has maintained a disciplined approach to investment, focusing on targeted areas that will yield the highest returns [88][89] - **CapEx Plans**: MarketAxess plans to invest $65 million to $70 million in capital expenditures, primarily for software and development [86][91] Conclusion - MarketAxess is strategically positioned to capitalize on growth opportunities in electronic trading, particularly in the block trading and emerging markets. The company is focused on enhancing its technology and trading solutions while managing expenses effectively to drive future growth.