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Quantitative

Quantitative and Algorithmic Trading in the Global MarketIntroduction In the ever-evolving world of financial markets, quantitative and algorithmic trading have emerged as the twin engines powering modern investment and trading strategies. They represent the fusion of finance, mathematics, statistics, and computer science to create data-driven, rule-based systems capable of executing trades with precision and speed beyond human capability. Over the past three decades, these methods have transformed global trading dynamics — reshaping liquidity, price discovery, and even the structure of exchanges. Quantitative and algorithmic trading now dominate trading volumes in equities, forex, commodities, and derivatives markets worldwide. This essay explores the concepts, strategies, technologies, advantages, and risks associated with quantitative and algorithmic trading, as well as their impact on global financial markets. Understanding Quantitative and Algorithmic Trading Quantitative trading refers to the use of mathematical and statistical models to identify trading opportunities. It relies heavily on quantitative analysis, which involves collecting large sets of historical and real-time market data, identifying patterns, and forecasting potential price movements. Quantitative traders, often called “quants,” use sophisticated models to test hypotheses and develop systematic strategies for profit generation. Algorithmic trading (Algo trading), on the other hand, is the practical implementation of these quantitative models through computer algorithms that automatically execute trades. It involves predefined instructions that specify when, how, and how much to trade, based on parameters such as timing, price, volume, and market conditions. In simple terms, quantitative trading focuses on the “why” — the logic and mathematical framework — while algorithmic trading handles the “how” — the automation and execution of the strategy. Historical Evolution The roots of quantitative trading can be traced back to the 1970s when computers were first used for portfolio optimization and risk management. Pioneers like Edward Thorp, the author of Beat the Market, applied probability theory to stock trading and option pricing, laying the foundation for quant finance. The 1980s and 1990s witnessed the rise of electronic trading platforms, which enabled automated order matching. Firms like Renaissance Technologies and D.E. Shaw built statistical arbitrage models that consistently delivered high returns using advanced mathematics. By the 2000s, algorithmic trading became mainstream, aided by technological progress, faster data transmission, and regulatory changes such as the U.S. SEC’s approval of electronic communication networks (ECNs). High-Frequency Trading (HFT) — the fastest form of algorithmic trading — emerged, executing thousands of orders in milliseconds. Today, more than 70% of equity trades in developed markets like the U.S. and Europe are executed algorithmically. Core Components of Quantitative and Algorithmic Trading Data Acquisition and Management Data is the lifeblood of quantitative trading. Traders collect massive datasets — historical prices, order book information, news sentiment, economic indicators, and alternative data such as satellite images or social media trends. This data is cleaned, normalized, and stored for analysis using advanced databases and cloud computing systems. Model Development and Backtesting Quant models are developed using statistical and machine learning techniques to forecast price movements or detect inefficiencies. Backtesting evaluates these models on historical data to verify performance and robustness before deployment in live markets. Execution Algorithms Algorithms are designed to execute trades efficiently while minimizing market impact and transaction costs. Common execution algorithms include Volume Weighted Average Price (VWAP), Time Weighted Average Price (TWAP), and Percentage of Volume (POV). Risk Management Systems Every quantitative model includes strict risk controls — such as stop-loss mechanisms, position limits, and exposure checks — to protect against unforeseen market events and model failures. Infrastructure and Technology Cutting-edge hardware, low-latency networks, and co-location services (placing trading servers near exchange data centers) are essential for high-frequency and algorithmic trading. Millisecond delays can mean the difference between profit and loss. Types of Quantitative and Algorithmic Strategies Statistical Arbitrage This involves exploiting short-term price inefficiencies between related securities. For instance, pairs trading identifies two correlated assets — when their price relationship diverges, one is bought and the other is sold short, expecting reversion to the mean. Trend-Following Models These algorithms capitalize on persistent market trends using indicators like moving averages or momentum oscillators. When the price breaks above a defined resistance, a buy signal is triggered. Mean Reversion Strategies Based on the idea that prices tend to revert to their long-term average, these models look for overbought or oversold conditions. Market Making Algorithms Market makers continuously quote buy and sell prices, earning the bid-ask spread while providing liquidity. Algorithms dynamically adjust quotes based on volatility and order flow. High-Frequency Trading (HFT) HFT strategies execute thousands of trades per second to exploit micro-inefficiencies. Techniques include latency arbitrage and order anticipation. Machine Learning-Based Strategies Modern quants increasingly use artificial intelligence and deep learning models to analyze nonlinear patterns in large datasets, from news sentiment to macroeconomic variables. Event-Driven Trading Algorithms react to real-time events such as earnings announcements, mergers, or geopolitical developments. For example, a positive earnings surprise may trigger a buy signal. Index Arbitrage and ETF Strategies These exploit price differences between index futures, exchange-traded funds, and their underlying constituents. Quantitative and Algorithmic Trading in Major Global Markets United States The U.S. is the global hub of algorithmic trading, accounting for the majority of automated volume. Major exchanges like NASDAQ and NYSE provide low-latency access, and firms such as Citadel Securities, Renaissance Technologies, and Jane Street dominate market making and quant strategies. Europe European markets, regulated under MiFID II, emphasize transparency and fairness in algorithmic trading. London remains a major center for hedge funds and algorithmic firms. Asia-Pacific Algorithmic trading is rapidly expanding in markets like Japan, Singapore, Hong Kong, and India. In India, the National Stock Exchange (NSE) supports co-location and direct market access, making it one of the fastest-growing algorithmic ecosystems. Emerging Markets Countries such as Brazil, South Africa, and the Middle East are adopting algorithmic platforms, although liquidity and infrastructure remain developmental challenges. Benefits of Quantitative and Algorithmic Trading Speed and Efficiency Algorithms execute orders within microseconds, allowing traders to capture fleeting market opportunities impossible for humans to detect manually. Reduced Human Bias Trading decisions are based on predefined logic rather than emotion, minimizing psychological biases such as fear and greed. Lower Transaction Costs Smart order routing and optimal execution algorithms reduce slippage and market impact, enhancing profitability. Liquidity Enhancement Market-making algorithms continuously provide buy and sell orders, improving liquidity and narrowing bid-ask spreads. Scalability A single algorithm can manage thousands of securities across global markets simultaneously, offering unmatched scalability. Backtesting and Optimization Quantitative systems can be tested extensively on historical data, refining strategies before real-world application. Risks and Challenges Despite their advantages, quantitative and algorithmic trading come with significant risks: Model Risk Models are based on assumptions that may fail under changing market conditions. A small coding error or mis-specified model can cause massive losses. Overfitting and Data Snooping Over-optimization of models on historical data can produce unrealistic results that fail in live trading. Liquidity and Flash Crashes Excessive algorithmic activity can amplify volatility. The 2010 U.S. “Flash Crash” highlighted how algorithmic feedback loops could trigger rapid market collapses. Regulatory Risk Regulators globally are tightening oversight of algorithmic trading to prevent manipulation and ensure fairness. Compliance costs and monitoring requirements are rising. Technology Failures System outages, latency issues, or cyberattacks can disrupt trading and cause severe financial losses. Competition and Market Saturation As more participants adopt similar strategies, profit margins shrink, and edge becomes increasingly difficult to maintain. Regulatory Framework and Global Standards Regulators worldwide are implementing rules to govern algorithmic and high-frequency trading. In the United States, the SEC and CFTC monitor automated trading for fairness, requiring disclosure of algorithms and pre-trade risk checks. In Europe, MiFID II mandates firms to test algorithms, maintain kill-switch mechanisms, and provide detailed audit trails. In India, SEBI regulates algorithmic trading by requiring pre-approval, audit certification, and real-time risk management systems. These measures aim to balance innovation with market integrity and investor protection. Technological Advancements Driving the Future The next phase of quantitative and algorithmic trading will be shaped by technologies such as: Artificial Intelligence and Deep Learning – Algorithms that learn autonomously from new data, improving accuracy over time. Natural Language Processing (NLP) – Automated interpretation of news, tweets, and reports to derive trading signals. Quantum Computing – Offering unprecedented processing power for portfolio optimization and complex simulations. Blockchain Integration – Enhancing transparency, settlement efficiency, and security in algorithmic transactions. Cloud Computing and Big Data – Allowing scalable data storage and computation across global markets in real time. Impact on Global Market Dynamics Quantitative and algorithmic trading have profoundly reshaped market structure. They have enhanced liquidity, tightened spreads, and accelerated price discovery. However, they also contribute to short-term volatility and market fragmentation across multiple venues. Institutional investors now compete with sophisticated algorithms, while retail traders benefit indirectly through lower costs and better execution. Exchanges have evolved to accommodate high-speed connectivity, and data analytics has become a core asset for every financial institution. The global market, once driven by intuition and human judgment, is now governed largely by algorithms and machine intelligence. Conclusion Quantitative and algorithmic trading represent the pinnacle of financial innovation, combining mathematics, computation, and automation to redefine how markets operate. They have democratized access to efficient trading tools while challenging traditional notions of value, speed, and human decision-making. Yet, with great power comes great responsibility — ensuring transparency, ethical deployment, and robust regulation will determine the sustainable future of algorithmic trading. As artificial intelligence and data science advance further, quantitative trading will continue to evolve, shaping global markets that are faster, smarter, and more interconnected than ever before.
NASDAQ:TSLAEducation
by GlobalWolfStreet
Quantitative Trading Models in Forex: A Deep DiveQuantitative Trading Models in Forex: A Deep Dive Quantitative trading in forex harnesses advanced algorithms and statistical models to decode market dynamics, offering traders a sophisticated approach to currency trading. This article delves into the various quantitative trading models, their implementation, and their challenges, providing insights for traders looking to navigate the forex market with a data-driven approach. Understanding Quantitative Trading in Forex Quantitative trading, also known as quant trading, in the forex market involves using sophisticated quantitative trading systems that leverage complex mathematical and statistical methods to analyse market data and execute trades. These systems are designed to identify patterns, trends, and potential opportunities in currency movements that might be invisible to the naked eye. At the heart of these systems are quantitative trading strategies and models, which are algorithmic procedures developed to determine market behaviour and make informed decisions. These strategies incorporate a variety of approaches, from historical data analysis to predictive modelling, which should ensure a comprehensive assessment of market dynamics. Notably, in quantitative trading, Python and similar data-oriented programming languages are often used to build models. In essence, quantitative systems help decipher the intricate relationships between different currency pairs, economic indicators, and global events, potentially enabling traders to execute trades with higher precision and efficiency. Key Types of Quantitative Models Quantitative trading, spanning diverse markets such as forex, stocks, and cryptocurrencies*, utilises complex quantitative trading algorithms to make informed decisions. While it's prominently applied in quantitative stock trading, its principles and models are particularly significant in the forex market. These models are underpinned by quantitative analysis, derivative modelling, and trading strategies, which involve mathematical analysis of market movements and risk assessment to potentially optimise trading outcomes. Trend Following Models Trend-following systems are designed to identify and capitalise on market trends. Using historical price data, they may determine the direction and strength of market movements, helping traders to align themselves with the prevailing upward or downward trend. Indicators like the Average Directional Index or Parabolic SAR can assist in developing trend-following models. Mean Reversion Models Operating on the principle that prices eventually move back towards their mean or average, mean reversion systems look for overextended price movements in the forex market. Traders use mean reversion strategies to determine when a currency pair is likely to revert to its historical average. High-Frequency Trading (HFT) Models Involving the execution of a large number of orders at breakneck speeds, HFT models are used to capitalise on tiny price movements. They’re less about determining market direction and more about exploiting market inefficiencies at micro-level time frames. Sentiment Analysis Models These models analyse market sentiment data, such as news headlines, social media buzz, and economic reports, to gauge the market's mood. This information can be pivotal in defining short-term movements in the forex market, though this model is becoming increasingly popular for quantitative trading in crypto*. Machine Learning Models These systems continuously learn and adapt to new market data by incorporating AI and machine learning, identifying complex patterns and relationships that might elude traditional models. They are particularly adept at processing large volumes of data and making predictive analyses. Hypothesis-Based Models These models test specific hypotheses about market behaviour. For example, a theory might posit that certain economic indicators lead to predictable responses in currency markets. They’re then backtested and refined based on historical data to validate or refute the hypotheses. Each model offers a unique lens through which forex traders can analyse the market, offering diverse approaches to tackle the complexities of currency trading. Quantitative vs Algorithmic Trading While quant and algorithmic trading are often used interchangeably and do overlap, there are notable differences between the two approaches. Algorithmic Trading Focus: Emphasises automating processes, often using technical indicators for decision-making. Methodology: Relies on predefined rules based on historical data, often without the depth of quantitative analysis. Execution: Prioritises automated execution of trades, often at high speed. Application: Used widely for efficiency in executing repetitive, rule-based tasks. Quantitative Trading Focus: Utilises advanced mathematical and statistical models to determine market movements. Methodology: Involves complex computations and data analysis and often incorporates economic theories. Execution: May or may not automate trade execution; focuses on strategy formulation. Application: Common in risk management and strategic trade planning. Implementation and Challenges Implementing quantitative models in forex begins with the development of a robust strategy involving the selection of appropriate models and algorithms. This phase includes rigorous backtesting against historical data to validate their effectiveness. Following this, traders often engage in forward testing in live market conditions to evaluate real-world performance. Challenges in this realm are multifaceted. Key among them is the quality and relevance of the data used. Models can be rendered ineffective if based on inaccurate or outdated data. Overfitting remains a significant concern, where systems too closely tailored to historical data may fail to adapt to evolving market dynamics. Another challenge is the constant need to monitor and update models to keep pace with market changes, requiring a blend of technical expertise and market acumen. The Bottom Line In this deep dive into quantitative trading in forex, we've uncovered the potency of diverse models, each tailored to navigate the complex currency markets with precision. These strategies, rooted in data-driven analysis, may offer traders an edge in decision-making. *Important: At FXOpen UK, Cryptocurrency trading via CFDs is only available to our Professional clients. They are not available for trading by Retail clients. To find out more information about how this may affect you, please get in touch with our team. This article represents the opinion of the Companies operating under the FXOpen brand only. It is not to be construed as an offer, solicitation, or recommendation with respect to products and services provided by the Companies operating under the FXOpen brand, nor is it to be considered financial advice.
Education
by FXOpen
1
How to pick a benchmark for you portfolio and beat the market What is a benchmark? A benchmark is an index or a basket of assets used to evaluate the performance of an investment portfolio In the context of portfolio analysis the benchmark serves as a point of comparison to determine whether a fund a strategy or an investment is performing better worse or in line with the reference market. In the current chart, Bitcoin ( BINANCE:BTCUSDT ) is displayed with a solid and larger blue line in relation to other cryptocurrencies for the current period. Benchmarks are essential tools for institutional and private investors as they allow measuring the effectiveness of asset allocation choices and risk management Additionally they help determine the added value of an active manager compared to a passive market replication strategy. Benchmark analysis example: NASDAQ:TSLA - NASDAQ:NDX Benchmark analysis example: NASDAQ:TSLA - NASDAQ:AAPL - NASDAQ:NDX What is the purpose of a benchmark The use of a benchmark in portfolio analysis has several objectives 1) Performance Evaluation: Provides a parameter to compare the portfolio's return against the market or other funds 2) Risk Analysis: Allows comparing the volatility of the portfolio against that of the benchmark offering a measure of risk management 3) Performance Attribution: Helps distinguish between returns derived from asset selection and those linked to market factors 4) Expectation Management: Supports investors and managers in assessing whether a portfolio is meeting expected return objectives 5) Strategy Control: If a portfolio deviates excessively from the benchmark it may signal the need to review the investment strategy How to select an appropriate benchmark? The choice of the correct benchmark depends on several factors: 1) Consistency with Portfolio Objective: The benchmark should reflect the market or sector in which the portfolio operates 2) Representativeness of Portfolio Assets: The benchmark should have a composition similar to that of the portfolio to ensure a fair comparison 3) Transparency and Data Availability: It must be easily accessible and calculated with clear and public methodologies 4) Stability Over Time: A good benchmark should not be subject to frequent modifications to ensure reliable historical comparison 5) Compatible Risk and Return: The benchmark should have a risk and return profile similar to that of the portfolio Most used benchmarks There are different benchmarks based on asset type and reference market Here are some of the most common. Equity FRED:SP500 Representative index of the 500 largest US companies. NYSE:MSCI World Includes companies from various developed countries ideal for global strategies FTSE:FTSEMIB Benchmark for the Italian stock market NASDAQ:NDX Represents the largest technology and growth companies Bonds Barclays Global Aggregate Bond Index Broad benchmark for the global bond market JP Morgan Emerging Market Bond Index EMBI Benchmark for emerging market debt [* ]BofA Merrill Lynch US High Yield Index Representative of the high-yield bond market junk bonds Mixed or Balanced 6040 Portfolio Benchmark 60 equities SP 500 and 40 bonds Bloomberg US Aggregate used to evaluate balanced portfolios Morningstar Moderate Allocation Index Suitable for moderate-risk investment strategies Alternative HFRI Fund Weighted Composite Index Benchmark for hedge funds Goldman Sachs Commodity Index GSCI Used for commodity-related strategies Bitcoin Index CoinDesk BPI Benchmark for cryptocurrencies A reference benchmark is essential in portfolio analysis to measure performance manage risk and evaluate investment strategies The selection of an appropriate benchmark must be consistent with the strategy and market of the portfolio to ensure meaningful comparison. Understanding and correctly selecting the benchmark allows investors to optimize their decisions and improve long-term results.
Education
by thequantscience
6
New Strategy Testing Consolidation HypothesisIf you see previous trades in this account, you'll notice this strategy has never been used before. This strategy consists of new indicators I created through my own research and back tested it using Yahoo Finance Data. Today I finally coded the indicators into tradingview, however I will not be sharing the code. Basically, the indicator is reading the trend as it currently is. Determining it's a bullish trend if the blue line is above 0 and the opposite is true. Once the blue line reaches the limits, then it's considered a local minimum or maximum. These however are not always activated, so it's up to the user to determine if the movement is way too close to the limit and therefore should close the position. However, it can also be possible that a strong trend causes many consecutive maximums to appear. It's up to the user to determine if the maximums are just part of a strong trend or actually a maximum. This exception happens more on the upside than the downside, making minimum signals more reliable. Looking at how the SPX has behaved and seeing it come out of a slump and with the elections coming up it would seem reasonable to see investors skeptical about the future. Whoever wins the election will have a heavy impact on the price action, however, I doubt investors will make up their mind until then. Therefore, it's reasonable to assume the market may stall before continuing its growth. Also allowing the technicals to reset for a healthy bullish setup for the long term. Warning: This is the first live test of this strategy!! Estimate time for price to increase : 1months-6 months Expect price to stall within the drawn range. For the following weeks I don't expect any mayor price movements until the elections, unless a sudden international event happens.
CME_MINI:ES1!Long
by DarkMessiah777
Quantitative Finance: Mathematical and Financial ConceptsWhat is Quantitative Trading? The first step in understanding quantitative trading is understanding its definition. It's a type of trading that uses mathematical models to make transaction decisions. This is an extremely math-heavy field of trading and that is what makes it so effective. Traders then input certain parameters into their model, which then uses these parameters to make decisions based on mathematical calculations. Quantitative trading heavily relies on mathematical and statistical concepts. Here are some of the key types of math involved in quantitative trading: 1. Calculus: Calculus, especially differential calculus, is used to optimize trading strategies, calculate the sensitivity of prices to various factors (like in the Greeks of options pricing), and model the expected change in different variables. 2. Linear Algebra: Linear algebra is used in various areas of quantitative finance, including portfolio optimization, risk management, and structuring of derivatives. Machine learning algorithms, which are frequently used in quantitative trading, often rely on linear algebra as well. 3. Probability Theory and Statistics: These are fundamental to quant trading. They're used to create statistical models of market behavior, estimate the likelihood of different outcomes, evaluate the risk and return of different strategies, and test the effectiveness of different trading models. Concepts like probability distributions, regression analysis, hypothesis testing, correlation, and covariance are all crucial. 4. Time Series Analysis: This is a specialized field of statistics that deals with data points ordered in time. Financial data, like stock prices or trading volumes, are time series, so time series analysis is used to identify trends, cycles, or other patterns in the data, and to forecast future values. 5. Stochastic Calculus: This branch of mathematics is used to model random processes, like the movement of stock prices. It's fundamental to the pricing of derivatives, like options, and is used in risk management and portfolio optimization. 6. Optimization: This involves finding the best solution (maximizing or minimizing a function) given certain constraints, like finding the portfolio with the highest expected return for a given level of risk. Optimization techniques are heavily used in portfolio construction, risk management, and algorithm design. 7. Numerical Methods: These are techniques used to find numerical solutions to mathematical problems. For example, they're used in options pricing, where we often need to find numerical solutions to partial differential equations. 8. Machine Learning and Data Mining: While not strictly a branch of mathematics, these disciplines heavily rely on mathematical techniques and are used to analyze large datasets, make predictions, and develop trading strategies. A strong understanding of these mathematical fields is crucial for anyone considering a career in quantitative trading. However, it's also important to have a strong understanding of finance and economic principles, as these provide the context in which the math is applied. Here are some of the key financial and economic principles you need to know: 1. Financial Markets: Understanding how different markets operate is key. This includes knowledge of the stock market, forex market, futures market, options market, bond market, and commodities market. You should understand how these markets function, what drives price changes, and how different events can impact the markets. 2. Financial Instruments: This includes understanding different financial instruments like stocks, bonds, futures, options, swaps, and other derivatives. Each of these has its own characteristics and dynamics. 3. Risk and Return: An understanding of the risk-return tradeoff is crucial. This includes understanding how to measure risk (e.g., standard deviation, Value at Risk, etc.) and return (e.g., mean return, Sharpe ratio, etc.) and how to optimize the risk-return tradeoff (e.g., portfolio optimization). 4. Financial Statements and Ratio Analysis: While this is more important for strategies that use fundamental data, understanding financial statements (balance sheet, income statement, cash flow statement) and how to calculate and interpret financial ratios can be helpful. 5. Economic Indicators: Understanding various economic indicators like GDP, inflation, interest rates, unemployment rate, consumer sentiment, etc., and their impact on financial markets is important, especially for strategies that trade based on macroeconomic data. 6. Behavioral Finance: This involves understanding the psychological factors that affect market participants and can lead to various market anomalies. 7. Portfolio Theory: This includes understanding concepts like diversification, the efficient frontier, the Capital Asset Pricing Model (CAPM), and the concept of beta. 8. Derivatives Pricing Models: Understanding models like Black-Scholes for options pricing, or the concept of no-arbitrage pricing, can be very useful for strategies that involve derivatives. 9. Interest Rates and Fixed Income: Understanding the dynamics of interest rates, the term structure, yield curves, and how to price fixed income securities is crucial for strategies that involve bonds or interest rate derivatives. 10. Market Microstructure: This involves understanding how trades are executed in the market, what factors determine the bid-ask spread, what causes price impact, and other nuances of how trading actually works. These are just some of the many financial and economic concepts that can be important in quantitative finance. The specifics will depend on what type of strategies you are interested in (e.g., high-frequency trading vs. long-term asset allocation, equities vs. fixed income, etc.). In conclusion, delving into the world of quantitative finance requires a solid understanding of various mathematical and financial concepts. From probability and statistics to calculus, linear algebra, and optimization techniques, each piece of knowledge plays a crucial role in analyzing financial data, managing risk, and developing effective trading strategies. By mastering these essential mathematical tools, you gain a competitive edge over the majority of traders. While algorithmic trading can be quite challenging at times, it is extremely reliable and effective and I suggest every serious trader learns about it and how it works. Hope this helped!
AMEX:SPYEducation
by BiggWigg
USDJPY (Hedge Idea) With all financial markets preparing for the upcoming summer rate hikes, I predict markets will consolidate within a larger than usual range presenting great opportunities for investments. Next Hike: June 15-16, 2022. Hedge Idea (Scale / Intraday): Short: Scale into positions when price breaches 130.000 handle up to the top third end of the range (131.500) Long: Scale into positions when price breaches 128.250 handle & below to the bottom end of the range (127.000) POST FOMC HIKES (Mid-Term Forecast): LONG Target Price: 140.000 Target Date: End of July / Beginning of August
by brandonmattis
How much of the Japanese stock market does the BOJ own?The Bank of Japan (BOJ), unlike any of its peers, has become a huge player in the country’s stock market. What began as a monetary policy experiment has turned into what some economists describe as a caveat for policymakers about the extent of intervention a central bank may take in propping up capital markets. Over the past decade, the BOJ managed to gobble up 80% of Japan’s exchange-traded funds (ETFs), accounting for about 7% of the country’s $6 trillion stock market, according to Bloomberg. Based on the Government Pension Investment Fund’s annual report for fiscal 2020 ended March 2021, the government held more than 47 trillion yen worth of Japanese stocks. GPIF is Japan’s largest public fund investor by assets. While ETFs in other parts of the world are used to monitor the performance of certain stocks according to industries, Japan has used its ETF investments to control inflation with the goal of spurring economic growth. The BOJ started employing this strategy in the later part of 2010 when it began acquiring shares listed on Japanese exchanges via ETFs as part of its quantitative and qualitative easing program. The program to buy ETFs began as a part of the central bank’s purchase of Japanese government bonds, until the BOJ tested stock-fund buying, hoping to boost stock prices, which in turn encouraged companies to spend more on expansions, create more jobs and push inflation higher. However, six years into the ETF-buying program, the BOJ still wasn’t able to reach its inflation target, prompting Governor Haruhiko Kuroda to introduce negative interest rates to prevent a strong yen that was hurting the country’s export-heavy economy. As it stands, the Japanese yen is trading at 130 per USD, a 20-year low for the currency, and could be heading for weaker territory without intervention. While a weaker yen has been welcomed by Kuroda, Reuters reported that Japan could be considering currency intervention to stem further weakness in the yen. The Reuters report helped the USDJPY push above a month’s long resistance of 129 per USD. Aside from stocks, the BOJ has also racked up large amounts of Japanese government bonds totaling 521 trillion yen as of the end of 2021. The level of bond holdings, however, has fallen for the first time in 13 years as the BOJ sought to taper its bond-buying program due to concerns of a looming financial risk. Where to from here? Fast forward to 2022, the BOJ is still stuck with a huge amount of bonds and stocks that the central bank may not be able to easily decrease as a sell-off would have adverse effects on the country’s capital markets. “The bank was surrounded by dead ends. They were cornered into a place where they couldn’t do anything else,” Izuru Kato, president at Totan Research, was quoted by Bloomberg as saying. Back in 2019, Kuroda defended the BOJ’s ETF-buying program, dismissing concerns that it is distorting influence. "At present, I don’t think our ETF buying is having any effect on market function… But we continue to watch out to make sure there are no negative side effects,” Kuroda was quoted by the Financial Times as saying. Most recently in March, as concerns over its stock holdings grew, the BOJ governor said it was premature to debate an exit from quantitative easing including how the central bank could pare its ETF holdings as inflation has yet to sustainably hit 2%. Kuroda had also hinted that in the event the BOJ decides to wind down its stock holdings, it will employ a strategy that would minimize the BOJ’s losses and any financial market disruption. "They cannot sell now. Shares will fall for sure... The negative impact would be pretty huge,” Tetsuo Seshimo, portfolio manager at Saison Asset Management, said earlier this month.
by BlackBull Markets
Your FAQ About Quant Trading I have received many questions about quantitative analysis/quant trading. This post is to address these FAQ I receive and point you in the right direction if this is something you are interested in! If I missed any questions, please leave them in the comments and I will add an addendum! Q: What is quantitative analysis/quant data? A : Quantitative analysis is the practice of applying mathematics and statistics to stock trading data. It involves the process of data mining and drawing statistic inferences between related and unrelated variables to look for correlations in data that can be used to predict future stock movement. Q: What is a “quant”? A: There are two types of quants or quant traders. This is more applicable to hedge funds and banks who employ these people, but essentially, there are quantitative modellers and quant developers/programmers. Quantitative modellers (which is essentially, what I am) are generally statisticians who have a degree in applied mathematics or statistics. They employ statistical theories to develop working mathematical models of stocks and attempt to quantify stock behaviour into mathematical formulas and determine probability of meeting certain conditions (i.e. price). Quant developers/programmers generally have degrees in computer science or computer engineering and software development. They take these models from the statisticians and program them into software to create high frequency trading algorithms and longer-term trading algorithms. They will also use this data to develop software to manage and view risk quantitatively. Q: Is quantitative analysis the same as technical analysis? A: No. Technical analysts apply a type of qualitative data analysis. While technical analysis attempts to, loosely, base itself on mathematical principles, it is an attempt to qualitatively represent quantitative data. As such, technical analysis is slightly more susceptible to biases. Whereas one TA may view a Fibonacci level as indicating bullish movement, another may view the same level as indicating bearish movement. It is dependent on the TA’s own sentiment and their ability to recognize sentiment and context. Contrast this to a QA, the range that one QA comes up with will likely be very similar to the range of the other QA. That is because QAs all apply the same statistical strategies and tests to identify the data and trends. Biases for QAs are generally counter-intuitive to the process. QAs should not care about what the context or sentiment is, they simply follow the algorithmic processes which are characterized as “If – Then” statements. Q: How does “quant” trading work? A: Traditional quant trading and the quant trading done by hedge funds and banks are accomplished through computers that execute algorithms directly with exchanges. They do not operate through brokerages, they have a direct link to the exchange where they can quickly enter and exit trades that have satisfied the algorithmic conditions. For retail quants like myself, it varies. As I am a quant modeller and not a programmer, I must execute my own trades based on the conditions being met. This introduces the possibility of bias on my part and this bias has gotten me into trouble before! However, other quant traders that are more on the computer programming side, develop their own trading algorithms that will automatically execute their trades, etc. To do this, you need a broker that allows third party integration, in order to integrate your trading platform directly with your developed software. I have no idea how to do this, but I know there are brokers out there that allow this to happen and I know quant retail traders who do, do this. Q: What do I need to be a quant trader? A: Generally, you need a solid understanding of statistics and/or computer programming. In order to effectively develop a working model of a stock, you really need to have a strong understanding of statistics; however, I do know some quants that apply machine learning to their modelling which works okay from what they tell me and can avoid the hassle of developing complex mathematical models of stocks (which takes a long time, I speak from experience!). You also need software and to have a working understanding of a programming language (knowing Excel as a programming language is sufficient!). You need either some form of statistical analysis software or programming software. Software that I frequently see advertised being used at quant firms and banks (at least in Canada) include MATLAB, C++ and Python. I personally use SPSS (in lieu of SAS and MATLAB) and Excel (in lieu of Python/C++). Python is much more powerful generally than Excel and even MATLAB, equally as powerful as SPSS and SAS in its ability to analyze statistical problems and has the ability to actually do more critical appraisals of information than SPSS, SAS, MATLAB and Excel can do. However, for mathematical modelling, I tend to prefer SAS or SPSS combined with Excel but this is mostly because I am a statistician and this software presents the results in a way that I am familiar with (I’m an old dog with no interest in new tricks). A software engineer or programmer would most likely prefer Python. Specifically Anaconda has the same functionality as MATLAB (or so I am told). Q: Is there a cost to the software? A: So, Python is free! So if you know how to use Python or you are interested in learning, you can download it free online! It is open source and very powerful! If your novice, I recommend downloading Python Anaconda, it has everything you need! Excel and SPSS (what I use) tend to be costly. Excel is the cheaper alternative, I think it costs me about 75$ a year (however, I am still a student so I get the student discount, not sure full price). SPSS, MATLAB, SAS are extremely expensive. In excess of over 2,000 USD. There is an option to do an annual licensing agreement for less, but the price would add up. Q: Do I need a degree in mathematics or computer programming? A: NO! You don’t. You can learn this stuff from books and reading. Having a degree doesn’t even guarantee you that you will understand this stuff. I speak to some of my classmates about what I do, and they still don’t understand what I am doing (despite also having MScs in statistics hahaha). It all comes down to your critical application of knowledge! Education is very important, IMO, but its not everything and everyone has the potential to learn if they are truly motivated to! My background was I started as a nurse with a bachelors of science. I fell in love with mathmatics and statistics in my undergrad and ended up pursuing higher education in mathematics, specifically applied statistics. Q: How is quant trading different then technical trading? A: So, as I wrote above, technical trading is the qualitative appraisal of quantitative data. I am not a technical trader and can’t speak too indepth about this process. But I can contrast a little bit, which I will do below! A technical trader may look to see that a particular price point was respected and not surpassed over a number of days. They would likely label this as strong support and would assume that a break of this support would lead to more sustained selling. Contrast to a quant trader, I do not pay attention to any one specific price point. Price action tends to be more on the random side. So I rely on all of the data over many years of trading to develop working ranges and variances between the data. From this, I can determine the range that a stock likes to operate in (whether it be +/- 10 points or, if its TSLA, +/- 30 to 60 points). From here, I can use previous day data to predict a likely range for the next day. When I have that range, I can then express my hypothesis in conditional algebraic forms, like: IF Condition 1 met THEN statement 1 correct AND statement 2 incorrect; or IF Condition 2 met then statement 2 correct and statement 1 incorrect. I then follow linear algebraic principles to identify those conditions and subsequents. For example, for today, SPY opened around 420.28. The range that I calculated for SPY today was 415 to 427. So, the problem that I needed to solve mathematically was: If Condition X met then SPY = 427; OR If Condition Y met then SPY = 415. Then I must use algebra and statistics to determine what Condition X and what Condition Y are. If you read my ideas, you will notice that I express my ideas in linear algebraic form. For my post about SPY today, this is what I had wrote: A break above 424 would indicate bullish sentiment and likely continuation towards 427. A break below 418 would indicate bearish sentiment and likely continuation towards 415.11. If you notice, this can be expressed as a conditional (algebraic) statement: IF X > 424 THEN 427 is met; OR IF X < 418 THEN 415 is met. Now I don’t manually do this because it would be to labour intensive. Which is why I say you need to know a programming language. You can program Python, Excel, MATLAB, C++, etc. to do this for you and identify those ranges. But you need to have the theory in order to understand how to get there and how to give Excel, Python, C++ or MATLAB what it needs to solve the problem for you. Q: Can you recommend books or videos on quant trading? A: So, I have not found any quantitative retail traders on youtube. There are 2 quant developers that actively post on YouTube who have okay content, one being Trading Jesus and the other is Korean Yuppie (who is still kind of novice and hasn’t posted much). Both are from the perspective of quantitative developers; however, this is a completely different skillset from a quantitative modeller. But equally interesting and informative! In terms of books, I would recommend general statistics books and books on programming language like Python or even books on Excel. Excel is generally an under-rated platform that is capable of quite advanced data analysis. Don’t under-estimate it! Excel is involved in my trade planning, execution and profit taking process. It is the thing that dictates what I should do and where I should enter/exit. You also need a solid understanding of the market, how it is organized and how it functions. So general books about market theory and trading are also useful. I have no real recommendations as I haven’t read any books, aside from The Trading Zone, which I found insightful but not helpful. Most of the information you need is available for free online. I wouldn’t invest a huge amount of money in books that are mostly fluff, especially books on day trading. Equally, avoid courses! Don’t buy people’s courses and don’t trust trading “gurus” from YouTube. Hope this answers all of your questions, again please let me know if you have any that I have not addressed! Thank you, take care and as always, trade safe!
AMEX:SPY
by Steversteves
3
$DIDI - The Demise of an IPO (Qualitative Judgement)The current news and development suggest that DIDI will continue to go down. 1. The growth expectation is shattered by delisting the app. 2. The company has a lot of debt, just like any tech companies. 3. Earning growth will be slumpped until they figure out how to get past the hurddles. THE MOST IMPORTANT 4. Management's dishonesty was well documented with the listing process. My current valuation is, This will be a $1 stock soon. With a huge law suit to bear on the backbone, which they will definetely lose. (Clearly documented) The CEO's dishonesty and importance of personal gain rather than the investor's interest (not adding value to the company) is a big big big turn off for me. 2 Things must be noticed or tracked. Let the law suit settle, Unless you see a huge retained earnings that can compensate for the law suit. Want to see the CEO change, or atleast change his attitude and increase his holding by 20% (He just got paid $3,000,000,000 from the IPO). 20% is a conservative number. There are other great options for the money. $POSH will be something I have to evaluate net week. IF YOU TAKE THE POSITION, YOU HAVE TO EVALUATE THE QUALITATIVE NATURE EVERY WEEK
DShort
by UDAY_C_Santhakumar
1
11/8 Weekly Earnings Calendar Spreads (SYY, DHI, CAH, DIS)Description: Some potentially attractive Calendar Spreads I'm looking at putting on based off of the close on Friday. CAH looking especially attractive. Announced Earnings Dates SYY 11/9 DHI 11/9 CAH 11/9 DIS 11/10 Long Call Calendar Spread Levels, break-evens, and R/R will be updated when positions are filled. The boxes on the charts right now are the profit ranges at expiration for ATM Calls You could always spread the puts instead of the calls if you want a slight bearish bias on the stock post earnings. Criteria to enter: At least 4:1 R/R, measured from max profit to debit required to enter. Break-evens outside the expected move Intend to close directly following earnings. *Stops based off underlying stock price, not mark to market loss Only invest what you are willing to lose Break-evens and R/R vary on fill
NYSE:DIS
by BarnardR
RBLX Earnings PlayDescription: Earnings after close today, taking advantage of high IV on same week options and covering with next week's (Calendar Spread). Long Call Calendar Spread Levels on Chart Break-evens 91.34 +16% 69.01, -12% R/R: ~4:1 Positive R/R, stop loss levels built into position. Intend to close before near term expiration. *Stops based off underlying stock price, not mark to market loss The Trade BUY 11/19 79C SELL 11/12 79C Only invest what you are willing to lose Break-evens and R/R vary on fill
NYSE:RBLX
by BarnardR
DXY | DOLLAR INDEX - SHORT Commitment of Traders shows that central banks have been shorting the dollar since June, the slash in 1% slash in interest rates by the FEDs is doing the Dollar any good either. Technically we have a good ol' Cup & Handle, we broke the handle's ascending channel quite impulsively, we are now creating another ascending channel. FUNDAMENTALS MANUFACTURING NUMBERS - MIXED FACTORY ORDERS - GOOD BUT NOT GREAT OIL INVENTORIES - WEAK PMI's - GOOD BUT NOT GREAT EMPLOYMENT - WEAK OVERALL STRENGTH BASED ON NEWS Taking into consideration the market movers, oil and employment I say the fundamentals indicate a weak dollar thus far, tomorrow is a critical day as well. So keep stock of your fundamental releases and assess them for dollar strength or weakness this weekend.
TVC:DXYShort
by Austin_Palmer
BTCUSD Outlook March - April In that Orange Box I expect to see ranging for the rest of the week, then a continuation breakout ending into the 39-40K area the following week. What I would like to see after is price holding the level of 40K for a couple weeks in terms of its daily and weekly closures. The zone at the very bottom is a bonus area for additional spikes we can expect to see. If all goes to plan, We should be seeing healthy recovery PA and a relief around US tax season which would lead us into newer highs in the following months. Thank you sirs and madams! - CB
BITSTAMP:BTCUSD
by CBFXNebula
3