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Idea to Algorithm: The Full Workflow Behind Developing a Quantitative Trading Strategy
Idea to Algorithm: The Full Workflow Behind Developing a Quantitative Trading Strategy
In this comprehensive video, Delaney Mackenzie provides a detailed overview of the workflow followed by quant traders when developing a trading strategy. The speaker emphasizes the crucial role of starting with a hypothesis and leveraging historical data to make informed predictions about the future. The process involves continuous refinement and exploration of a trading model to ensure its historical correlation with future returns while maintaining independence from other models.
One of the key objectives is to design a portfolio that maximizes expected returns while adhering to various risk constraints. To achieve this, the speaker highlights the importance of testing the model on a small capital amount before deploying it live and scaling up. Additionally, incorporating alternative data sources and employing risk management techniques are strongly recommended.
The video delves into the two stages of backtesting in trading strategy development. Firstly, designing a portfolio and establishing execution rules, and secondly, implementing the backtesting process itself. The speaker underscores the significance of constructing a risk-constrained portfolio that preserves the integrity of the model's predictions and advises moving to the next stage only when the model consistently outperforms alternative investment opportunities. Furthermore, the speaker encourages exploration of new possibilities instead of relying on rehashed versions of existing models.
Delaney Mackenzie explains the initial phase of developing a trading strategy, which involves formulating an economic hypothesis to guide asset selection and timing. Finance aims to transform ideas into profitable outcomes by intelligently predicting the future based on hypotheses. Each decision made in trading essentially represents a bet on future market changes, highlighting the critical role of leveraging past information to make intelligent predictions.
The speaker provides insights into the workflow of developing a quantitative trading strategy. The process begins with formulating a hypothesis and exploring it using sample data. Comparing the hypothesis with existing models is essential for refinement, and once the new model demonstrates value, it is advisable to combine it with other sub-models for enhanced predictive power. The speaker emphasizes that hypotheses and models do not exist in isolation, and an aggregate model that incorporates multiple sources of information tends to yield better performance. Additionally, it is important to test the model on new data to ensure its validity.
The speaker emphasizes the importance of testing a model on unseen data to avoid overfitting during the development phase. They note that while backtesting a full strategy is commonly employed, it is crucial to acknowledge that most of the time is spent on developing models and predictors rather than constructing portfolios. Therefore, the speaker underscores the significance of portfolio construction and execution, including factors such as transaction fees, before conducting backtesting to ensure the portfolio's viability in real market conditions. Furthermore, the speaker highlights that the purpose of backtesting is not solely to evaluate the model's predictive performance, but also to assess whether the portfolio designed based on the model's predictions can withstand real-world conditions. Finally, the speaker stresses the importance of testing the model on a small capital amount before scaling up to ensure effective capital deployment.
Refinement and exploration of a trading model to establish its historical correlation with future returns and independence from other models are discussed by the speaker. This process is followed by constructing a portfolio within the defined risk constraints. The speaker emphasizes the importance of ensuring that the execution of the model does not distort the signal and diminish its correlation with future returns. A notebook example is provided to highlight the gradual addition of constraints, enabling evaluation of the model's performance under different risk conditions. This section underscores the significance of thorough testing and refinement to ensure the robustness and effectiveness of a trading model in generating returns.
The process of designing a portfolio that maximizes expected returns while satisfying various risk constraints is explained by the speaker. Initially, a naive optimization strategy is employed, focusing on maximizing expected return by investing the entire capital in a single stock, followed by the introduction of constraints to limit investment amounts. Subsequently, position concentration constraints are added, restricting investment in any one thing to a certain percentage of the portfolio. The portfolio strategy is further refined by incorporating sector exposure constraints. The speaker highlights that optimizing a portfolio while considering risk constraints can introduce complexity, as the weights in the final strategy may differ from the model's predictions of the future. It is crucial to understand how risk constraints influence modeling predictions and their impact on portfolio construction.
The speaker introduces the concept of using alpha lines, an open-source software developed by Quantopian, to assess the correlation between a model's returns and future returns. Alpha lines allow encoding any model, regardless of the universe size it predicts for, into a factor model. By calculating the correlation between the model's predictions on day T and the returns of all assets it predicted on day T+1, alpha lines help determine whether the model exhibits a consistently positive correlation with future returns. However, the speaker notes that real data may not always exhibit ideal correlation patterns.
The importance of comparing a new model against existing models is discussed, focusing on examining returns on a portfolio weighted by the factor and rebalanced according to a specified period. The speaker suggests running a linear regression analysis, using the new model's portfolio-weighted returns as the dependent variable and the portfolio-weighted returns of existing models as independent variables. This analysis helps assess the dependency between the new model and existing ones, providing insights into the potential alpha generation. The speaker emphasizes the significance of risk management and diversification, which can be achieved by either constraining each component individually or averaging multiple risky components to achieve risk diversification, depending on the investment strategy.
The speaker explains the distinction between the two stages of backtesting in trading strategy development. The primary stage involves designing a portfolio and determining execution rules, while the second stage entails conducting backtesting to evaluate the correlation between the model's predictions and future prices. Constructing a risk-constrained portfolio that effectively incorporates the model's predictions without compromising their integrity is crucial. The speaker advises investors to proceed to the next stage only when their backtests consistently provide substantial evidence of the model's superiority over alternative investment opportunities. Moreover, the speaker cautions against relying on rehashed versions of existing models and encourages a rigorous exploration of novel approaches.
The full workflow of developing a quantitative trading strategy is discussed by the speaker. The process begins with generating an idea, which can stem from understanding the world, data analysis, or identifying areas where the prevailing understanding differs. Once the model is developed, tested, and refined, it is compared against existing models to determine its uniqueness and potential for generating new alpha. The next step involves conducting out-of-sample tests, constructing a portfolio, and performing risk-constrained optimization simulations. Finally, the strategy is either paper traded or tested using a small capital amount before scaling up. The speaker emphasizes that relying solely on pricing data rarely provides sufficient information for generating innovative ideas, and incorporating alternative data sources is crucial for gaining new insights.
The speaker underscores the importance of utilizing alternative data to generate alpha, rather than relying solely on pricing and fundamental data for speed and convenience. They also emphasize the need to differentiate between alpha and cheap beta, as anything accounted for in a risk model is considered the latter. The limitations of k-fold cross-validation in reducing overfitting are discussed, with the speaker recommending true out-of-sample testing as a more reliable approach. Lastly, the speaker highlights the significance of having insights regarding the choice of data set for predicting the future and exploring approaches that differ from conventional methods.
In summary, Delaney Mackenzie's video provides a comprehensive overview of the workflow followed by quant traders when developing a trading strategy. It emphasizes the importance of starting with a hypothesis, refining and exploring the trading model, testing it on new data, constructing a risk-constrained portfolio, and conducting thorough backtesting. The speaker highlights the significance of utilizing alternative data, comparing the model against existing models, and incorporating risk management techniques. They stress the need to ensure that the model's predictions are historically correlated with future returns and independent of other models. The speaker also emphasizes the importance of testing the model on a small amount of capital before scaling up to real-world deployment.
Additionally, the speaker delves into the intricacies of portfolio design and execution rules. They discuss the process of constructing a risk-constrained portfolio that maximizes expected returns while satisfying different risk constraints. The speaker highlights the gradual addition of constraints such as position concentration and sector exposures to evaluate how the model performs under various risk scenarios. They emphasize that portfolio optimization involves making trade-offs between maximizing returns and managing risk.
The speaker introduces the concept of alpha lines and their role in assessing the correlation between a model's returns and future returns. They explain how alpha lines allow for the encoding of any model into a factor model, enabling the evaluation of the model's predictions against future returns. The speaker acknowledges that real-world data may not always exhibit consistent positive correlations, underscoring the importance of understanding the limitations of correlation analysis.
Comparing the new model against existing models is emphasized as a crucial step in evaluating its effectiveness. The speaker suggests using linear regression analysis to assess the dependency between the new model's portfolio-weighted returns and those of existing models. This comparison helps determine the uniqueness of the model and its potential for generating alpha. The speaker also highlights the significance of risk management and diversification in portfolio construction, either through constraining individual components or diversifying risk across multiple assets.
The speaker further highlights the two stages of backtesting in trading strategy development. The first stage involves designing a portfolio and execution rules, while the second stage involves conducting backtests to evaluate the model's predictions against future prices. It is crucial to construct a risk-constrained portfolio that incorporates the model's predictions without compromising their integrity. The speaker advises investors to proceed to the second stage only when there is consistent evidence of the model's superiority over alternative investment opportunities. They caution against relying on rehashed versions of existing models and encourage exploring new approaches.
Finally, the speaker outlines the full workflow of developing a quantitative trading strategy. It begins with generating an idea and progresses through testing, refining, and comparing the model against existing ones. The strategy is then subjected to out-of-sample testing, portfolio construction, and risk-constrained optimization. Before scaling up, the strategy is either paper traded or tested using a small capital amount. The speaker underscores the importance of incorporating alternative data sources to gain new insights and emphasizes the need to differentiate between alpha and cheap beta. They recommend true out-of-sample testing to mitigate overfitting and stress the significance of understanding the choice of data set for predicting the future.
In conclusion, Delaney Mackenzie's video provides a comprehensive understanding of the workflow followed by quants in developing a trading strategy. It emphasizes the importance of hypothesis development, model refinement, testing on new data, risk management, and thorough backtesting. The speaker encourages the use of alternative data sources, comparison against existing models, and the exploration of novel approaches. By following this workflow, quant traders can enhance the effectiveness and robustness of their trading strategies.
Market Quantitative Analysis Utilizing Excel Worksheets! S&P 500 Analysis & Trading Ideas
Market Quantitative Analysis Utilizing Excel Worksheets! S&P 500 Analysis & Trading Ideas
The video delves into the use of Excel worksheets for market quantitative analysis, with a focus on the S&P 500 as an illustrative example. Julie Marchesi demonstrates the creation of a correlation workbook in Excel, utilizing yellow boxes as inputs to select the correlation index from 74 groups and a look-back period of 40 days. The correlation test compares the last 40 days with all other periods in the dataset, identifying the highest correlation. To validate the correlation, a second market is used to confirm the findings and eliminate unreliable data points. The correlation index chart visually tracks the changes in correlation over time.
The speaker explains the process of utilizing Excel worksheets for market quantitative analysis, specifically highlighting the application to the S&P 500. They showcase various lines on a chart representing the look-back period and correlation index. By analyzing these lines, the speaker derives their bias for the market and makes predictions about future trends. They also introduce a chart displaying the average percent change over a specific time period and emphasize the importance of focusing on significant correlation indexes. The speaker concludes by demonstrating how this analysis can be applied to the current state of the S&P 500 market, emphasizing its potential utility for making informed trading decisions.
Examining different markets for confirmation or conflicting signals in relation to the S&P 500 analysis is the focus of the subsequent section. The speaker highlights that while oil confirms a strong uptrend in the market and suggests the potential for further bullish activity, the euro and euro yen exhibit bearish or negative activity over the past 20 days. Gold, however, does not provide significant confirmation. Based on recent market action, the speaker suggests a negative bias moving forward but cautions against short-selling and recommends waiting for confirmation before making significant moves. Overall, the speaker concludes that there is a bullish edge to the market, but exercising caution in the short term is advisable.
The speaker discusses the conclusions drawn from the correlation testing across different markets in the subsequent section. They note the possibility of some instability in the S&P 500 market over the next five days. Although historical analysis indicates a long-term bullish edge in the S&P 500, the speaker emphasizes the importance of observing neutral activity in the market before executing any trades. They suggest combining quantitative analysis with sentimental analysis to gain a better understanding of the market and highlight the usefulness of Excel worksheets in visualizing data in various ways. The video concludes by encouraging viewers to explore this type of trading approach and visit the speaker's website for further information on their journal and live trades.
Building Quant Equity Strategies in Python
Building Quant Equity Strategies in Python
The video provides an in-depth exploration of building quantitative equity strategies using Python and the algorithmic trading platform Quantopian as a prime example. The speaker begins by introducing themselves and their background in data analysis and quant finance. They explain that Quantopian is a platform that enables retail investors to access data and utilize backtesting to construct their own quantitative strategies for trading stocks. Despite initial skepticism, the speaker highlights the success of Quantopian in attracting a community of quant scientists, hackers, and retail investors who collaborate to discover investment ideas. They also mention that while Quantopian is currently supported by venture backing and is pre-revenue, there are plans to eventually offer live trading as a paid service.
The speaker delves into the concept of building quant strategies through crowdsourced data and ideas on the Quantopian platform. They emphasize that Quantopian facilitates direct messaging between users, fostering connections and idea-sharing for developing quantitative algorithms. However, the speaker acknowledges that data limitations can pose challenges for users constructing strategies, as they may not have access to all the necessary pricing data. Additionally, they note that Quantopian's focus is solely on equities and may not be suitable for high-frequency or latency-sensitive trading strategies.
The limitations of the trading platform are discussed in detail. The speaker emphasizes that Quantopian is not designed for low-latency strategies like scalping or market-making. They mention that the pricing data source determines the universe of securities, which currently consists of only a few thousand domestic equities. The speaker briefly touches upon their open-source basic slippage model available on GitHub. Although the inclusion of options and futures is a possibility for the future, the primary focus remains on providing profitable strategies and ensuring transparency in profitability statistics. The speaker categorizes five basic quant strategies implemented by everyday Python users on the platform, including mean reversion, momentum, overnight gap, volatility, and pairing.
Various quant strategies are explored, specifically focusing on the interplay and tuning of mean reversion and momentum. The speaker highlights popular strategies such as valuation and seasonality, with data for these strategies accessible through sources like Yahoo Finance or Google Finance. They caution against common pitfalls in pairs trading, such as blindly mining data to find unrelated securities. The importance of identifying securities linked to the same value and observing the spread distribution between the two assets is emphasized. The goal is to capitalize on the reversion of the spread between the stocks.
Pairs trading and momentum trading strategies are further discussed, and the speaker provides an example of backtesting a pairs trading strategy using Python. Pairs trading involves trading the spread between two stocks and carries risks such as potential reversals. Momentum trading, on the other hand, involves ranking stocks based on their previous price appreciation. Although data cannot be directly downloaded from the platform, users can run backtests and live trade within a limited universe of approximately 100 stocks due to bandwidth constraints.
The concept of valuation as a quantitative equity strategy is explored, requiring systematic fundamental ratio analysis to identify undervalued and overvalued stocks. However, implementing such strategies necessitates extensive data coverage and an understanding of data normalization, calendar alignment, and associated manipulation. The speaker suggests implementing these strategies using the fetcher method, which enables users to obtain CSV data from the internet. The speaker also touches on sentiment as a quantitative equity strategy, involving the analysis of market sentiment and its impact on stock prices. However, they caution that implementing this strategy requires a solid understanding of data analysis, normalization, and manipulation.
The use of shorted stocks as a sentiment indicator in quant equity strategies is discussed. Shorting stocks is recognized as difficult and risky, with only experienced individuals willing to engage in it. However, publicly available data on short interest levels, which can be obtained from NASDAQ, can be useful for this purpose. The speaker highlights the risk of liquidity constraints arising from short squeezes and suggests using a volatility-based signal to identify heavily shorted but less risky stocks. They propose an algorithm that ranks stocks based on the "days to cover" signal, representing the number of days it would take for short sellers to unwind their positions based on average daily trading volume. The strategy involves buying the least shorted stocks and shorting the most shorted ones.
The speaker moves on to discuss intermediate steps in the process and the open-sourcing of algorithms. They acknowledge the challenges of accessing valuable data like borrow rates from brokers and the limitations of their slippage models. The speaker addresses questions about available order types and the feedback system for adding more features. Additionally, they briefly mention the use of seasonality in trading and its popularity online.
A simple quantitative equity strategy suitable for beginners is presented. Using seasonality to time the market, for instance, selling stocks in May and investing in bonds, then buying back into the stock market in October, is highlighted as a straightforward systematic rule that allows for easy performance analysis over time. The speaker provides a breakdown of the top 25 quantitative equity algorithms shared on the Quantopian platform, based on the number of replies, views, and clones. Notably, a paper on using Google search terms to predict market movements, although considered overfitted, has gained significant attention on the forums. The speaker also notes that strategies with long, complex acronyms involving advanced mathematical concepts tend to attract more interest, despite the effectiveness of simpler strategies.
The importance of trust and security in the platform is emphasized. The speaker acknowledges the need to build trust with users to encourage them to upload their algorithms for testing against the market. They assure that security measures are taken seriously. While live aggregated performance data is not yet available, the speaker mentions that around a thousand algorithms are running in simulation. The potential benefits of a social network for quants are discussed, with recognition that it may not directly impact individual algorithm profitability. However, there is a desire within the quant finance community to connect, exchange ideas, and gain insights from others. The value of Quantopian as a learning environment is highlighted, where people can learn from both successes and mistakes in a risk-free environment.
The speaker explores the popularity of various investment strategy classifications within the platform. They note that momentum and mean reversion strategies are currently the most popular. They express excitement about the platform's potential to offer more accessible content for retail investors. A demonstration of the platform's backtester in Python is provided, showcasing the initialize method and handle data method, which are executed either once per day or once per minute during live trading. The user interface settings allow for specifying backtest dates, initial capital, and backtesting frequency. The community thread includes a search function for finding and utilizing algorithms created by other members.
In the final section, the speaker presents their live trading dashboard, deploying a basic algorithm that buys an equal-weighted portfolio of nine sector ETFs against their Interactive Brokers account. The dashboard displays a performance equity curve connected to a benchmark in red, current positions, and placed orders and fills. The speaker mentions the ability to log information for the deployed source code. The benchmark used is the returns to SPI, as selecting a broad range of stocks in an unbiased manner is not currently offered. Instead, they provide a daily dollar volume universe that updates quarterly.
The Do's and Don't's of Quant Trading
The Do's and Don't's of Quant Trading
Dr. Ernie Chan, a prominent figure in quantitative trading, discusses the challenges and provides valuable advice for traders in this field. He highlights the increasing difficulty of quantitative trading, as noted by industry experts and the underperformance of many machine learning funds. To succeed, traders must elevate their skills and learn important lessons. Drawing from personal experiences, Dr. Chan shares what traders should avoid doing and offers guidance for long-term success.
One of the key warnings Dr. Chan emphasizes is the temptation to over-leverage, particularly during periods of strong strategy performance. While the Kelly formula is often used for risk management, he cautions that it can lead to overly optimistic expectations and is sensitive to sample periods. Instead, he suggests using volatility as a more predictable measure for determining leverage. By targeting the expected volatility of a strategy, traders can determine appropriate leverage levels, focusing on risk rather than solely predicted returns.
Dr. Chan provides two essential pieces of advice for quant trading. First, he stresses the importance of considering the downside risk of a strategy (i.e., how much can be lost) rather than fixating on potential gains, which are unpredictable. Second, he warns against using short-term performance as the sole basis for selecting managers or determining leverage. Instead, he advises looking for longer track records and utilizing short-term performance for risk management and gradual reallocation purposes. Furthermore, he encourages traders to adopt a business-oriented mindset, reinvesting profits into the infrastructure of their trading business rather than indulging in personal luxuries.
Investing in the trading business's infrastructure is a topic Dr. Chan emphasizes. He suggests prioritizing investments in high-quality data, faster machines, and skilled personnel. Quality data is crucial to ensure accurate backtesting results, while faster machines enhance research productivity. Hiring personnel with the necessary skills further strengthens the business's capabilities. Dr. Chan emphasizes the long-term benefits of these investments, treating trading as a serious business venture.
To improve research productivity, Dr. Chan highlights the importance of investing in multi-core machines and proper parallel computing software. This investment can significantly increase productivity by five to ten times. He also recommends focusing on one's comparative advantage and complementing any shortcomings by partnering with individuals possessing complementary skills, such as coding, strategy, marketing, or operations.
Dr. Chan advocates for a collaborative approach to quantitative trading. He highlights that collaboration can occur in various forms, including virtual trading groups formed by university students. Sharing ideas and teaching others about strategies can lead to valuable feedback and improve overall performance. While protecting one's competitive advantage is important, sharing basic trading ideas can lead to a net inflow of knowledge and insights.
Additionally, Dr. Chan advises beginners to start with simple trading strategies based on solid intuitive justifications. He emphasizes the value of eliminating bad trades rather than solely seeking more profitable ones. Knowing when not to trade and when not to apply certain ideas contributes to long-term success. He also encourages continuous learning and improvement in trading strategies.
During a Q&A session, Dr. Chan shares insights into constructing financial derivatives, recommends using Python as a starting point in the field, and discusses effective strategies such as momentum trading and risk parity. He emphasizes the need for better risk management to sustain a strategy even when returns diminish.
In summary, Dr. Ernie Chan provides valuable advice for quantitative traders. He warns against over-leveraging and short-term performance reliance, stressing the importance of considering downside risk and focusing on longer track records. He emphasizes investing in business infrastructure, including data, machines, and personnel. Collaboration, starting with simple strategies, and continuous learning are key to long-term success.
Quantitative Finance | Classification of Quantitative Trading Strategies by Radovan Vojtko
Quantitative Finance | Classification of Quantitative Trading Strategies by Radovan Vojtko
Radovan Vojtko, the CEO of Quantpedia, provides valuable insights into the process of selecting quantitative trading strategies for their database. He emphasizes the importance of leveraging academic research to discover reliable and implementable strategies that can be used by traders. Despite common misconceptions, Vojtko highlights that there are still plenty of trading ideas in academic papers that hold potential.
Vojtko explains that the most popular asset class for trading strategies is equities, followed by commodities, currencies, bonds, and real estate. These asset classes offer a wide range of opportunities for implementing quantitative strategies. He categorizes quant strategies into various classifications, including timing, arbitrage, and momentum, among others.
One key aspect Vojtko emphasizes is the existence of blind spots in academic research, particularly in less well-covered asset classes like bonds and commodities. These blind spots present opportunities to discover new sources of alpha, and traders can capitalize on them. To combat issues such as P-hacking and replication, Vojtko recommends rigorous testing and the use of momentum anonymize techniques.
Contrary to the belief that published trading strategies no longer work, Vojtko asserts that some strategies continue to yield positive results even after being published, with more than 40% of alpha remaining after five years. To select the most promising strategies, he suggests conducting out-of-sample tests, increasing the cutoff point for statistical significance, building a comprehensive database of strategies, and choosing those with the best performance.
Vojtko further discusses specific trading strategies, such as mean reversion approaches in commodity futures trading and pre-earnings announcement risk strategies. He emphasizes the importance of alpha decay and the challenges posed by P-hacking and data mining. It is crucial to rigorously test and validate strategies before implementation.
Addressing the misconception that quantitative trading strategies lose effectiveness once published, Vojtko cites research showing that strategies can still perform well over time. He advises traders to avoid data dredging and underscores the need for thorough testing and validation.
In terms of replication in academic research, Vojtko suggests increasing the cutoff point for statistical significance and employing out-of-sample tests to compare portfolios based on published data. This approach ensures more accurate replication and enables the identification of winning strategies.
To expand the pool of profitable strategies, Vojtko recommends building a database with a wide range of strategies and selecting those with the best performance. He also provides resources for finding quantitative trading strategies, such as the Social Science Network and Quantpedia.
Regarding programming languages for quantitative finance, Vojtko mentions the availability of various options and advises choosing a language that one is comfortable with. Python is a preferred language, but other options like Tradestation, Ninjatrader, or Ami Broker can also be effective. Vojtko emphasizes the need to merge finance and technology skills for successful algorithmic trading and offers educational programs to develop expertise in both areas.
Turning to data for a trading edge · Dave Bergstrom, quant trader
Turning to data for a trading edge · Dave Bergstrom, quant trader
In this video Dave Bergstrom, a successful quant trader, shares his journey in the trading world and emphasizes the importance of utilizing data analysis techniques to discover market edges. He emphasizes the need to avoid curve-fitting and over-optimization, recommends leveraging multiple resources for learning trading and programming, and stresses the significance of proper risk management and having realistic expectations. Bergstrom also discusses the potential decline of high-frequency trading and introduces his software package, Build Alpha, which assists traders in finding and generating profitable trading strategies.
Dave Bergstrom, initially a high-frequency trader, recounts his path from almost pursuing law school to becoming a trader. During his undergraduate studies, he delved into trading and sought information on platforms like finance Twitter and podcasts to learn about trading patterns and momentum stocks. Although he experienced early success, Bergstrom acknowledges that his early strategies and techniques differ significantly from his present trading methods. He highlights his use of data mining techniques during strategy development and introduces his software package, Build Alpha, which enables traders to employ various forms of analysis discussed in this episode.
Starting with his humble beginnings, Bergstrom reveals his initial foray into trading by selling counterfeit NFL jerseys and purses. Subsequently, he funded a trading account and engaged in trading stocks based on momentum and technical analysis, particularly chart patterns. However, he faced inconsistency and struggled to understand why his equity balance consistently returned to zero. With more experience, Bergstrom realized that the absence of a systematic approach hindered his ability to achieve consistent returns. It was only after he moved to Florida and worked as a trading assistant at a high-frequency trading firm that he discovered the realm of quantitative analysis, paving the way for consistency in his trading endeavors.
Bergstrom further discusses his transition to a role that demanded data analysis. To excel in this position, he self-taught programming and focused on objective technical analysis, as his firm believed in identifying anomalies or patterns in the data that could lead to profitable trades. He explains the process of testing and backtesting strategies before they can be employed, a journey that required several years of trial and error to achieve consistent success. Bergstrom's views on technical analysis have evolved, favoring objective analysis that utilizes data to identify patterns over subjective analysis reliant on intuition.
Programming plays a significant role in Bergstrom's trading journey, which he considers a superpower. Recognizing that Excel was insufficient for handling the vast amount of data in high-frequency trading, he learned programming to advance from a trading assistant role to a trade desk role. Bergstrom considers programming an excellent investment due to its asymmetrical gains and minimal risk. He advises aspiring programmers to explore different resources, remain diligent, and seek guidance from knowledgeable individuals to expedite the learning process.
Bergstrom emphasizes the importance of seeking multiple resources when learning to trade and program. He recommends utilizing platforms like Stack Exchange for programming and encourages learning multiple programming languages, such as Python, C++, and Java. While discussing his trading approach, Bergstrom identifies himself as a data miner and believes that numerous market edges can be discovered through data analysis. While some perceive data mining as prone to overfitting, he argues that it can be a valuable tool when steps are taken to prevent overfitting and over optimization.
Bergstrom sheds light on how he uncovers trading edges through data mining and employs a fitness function that searches for profitable strategies based on specific criteria. He highlights the importance of avoiding curve-fitting by employing techniques like maintaining a minimum number of trades and utilizing cross-validation. He explains that an edge refers to something with a positive expectation, which can be identified through data analysis. Ultimately, he seeks profitable strategies, even if they are not based on pre-existing hypotheses, but he places more confidence in strategies that align with logical reasoning.
Having a significant number of trades is crucial when testing a strategy, according to Bergstrom. He emphasizes the risks of curve-fitting and advises against optimizing parameters with look-back periods. Instead, he prefers using nonparametric metrics like counting measures. Furthermore, Bergstrom underscores the significance of market regimes, as well as volume and volatility, in understanding market behavior. He mentions a powerful graph he shared on Twitter that illustrates the importance of setting realistic expectations and employing Monte Carlo analysis to avoid under-allocating funds to a trading system.
Realistic expectations in trading are explored further, as Bergstrom emphasizes that even if a backtest shows a profitable strategy, it is crucial to understand that real-life results may differ. Tools like Monte Carlo simulations and variance testing assist traders in creating a distribution of possible outcomes and establishing realistic expectations for future trades. Bergstrom introduces his three laws of trading, with the first law favoring asymmetric risk-to-reward ratios. This means he prefers a lower winning percentage but a higher payoff, rather than the opposite.
Proper risk management takes center stage in Bergstrom's trading philosophy, particularly regarding bet sizing. He explains that it is not beneficial for a trader to have one trade with significantly more size than others within the same pattern or system. Bergstrom warns against overly investing in "exciting" trades, as it prevents the mathematical probabilities from playing out over a large number of trades, which is necessary for the law of large numbers to come into effect. He suggests that trading in a more conservative and consistent manner over a significant number of trades ensures the positive edge will manifest. While intraday and high-frequency trading align better with the law of large numbers, Bergstrom believes that daily time frames can also be effective if variance testing is satisfactory.
Bergstrom delves into the importance of strategy robustness across markets. While he acknowledges the value of creating strategies that work across multiple markets, he tends to shy away from those that generate insufficient trades. Regarding transaction costs and seeking higher profits in each trade, Bergstrom believes a balanced approach is attainable. The strategy should not be burdened by excessive transaction costs, but at the same time, it shouldn't be designed to generate an excessive number of trades. Shifting gears, Bergstrom addresses the common misconceptions surrounding high-frequency trading (HFT), stating that it has often been unfairly vilified due to people seeking a scapegoat. He firmly believes that HFT is beneficial and does not have predatory intentions.
Lastly, Bergstrom discusses the potential decline of high-frequency trading, which he attributes to increased competition and the exposure of strategies. The debate revolves around whether the decline is due to an oversaturated market or the monetary policies implemented by central banks, which do not support the two-sided market required for high-frequency trading. Bergstrom introduces his software package, Build Alpha, which empowers users to select signals and search for different strategies based on exit criteria and a fitness function. The software identifies the best strategies and generates tradeable code for each, enabling the creation of portfolios and thorough analysis. Interested individuals can visit the website buildalpha.com or contact Dave Bergstrom via email at David@buildalpha.com or on Twitter @Deeper_DB.
In conclusion, Dave Bergstrom's journey to becoming a successful trader showcases the importance of data analysis techniques in finding market edges. His emphasis on preventing curve-fitting, utilizing multiple resources for learning, practicing proper risk management, and maintaining realistic expectations provides valuable insights for aspiring traders. Furthermore, his thoughts on high-frequency trading and the introduction of Build Alpha demonstrate his commitment to advancing trading strategies and empowering traders through innovative software solutions.
Which programming language for quant and HFT trading
Which programming language for quant and HFT trading
This video provides a comprehensive overview of programming languages commonly used in quantitative trading and high-frequency trading (HFT). The speaker categorizes these languages into prototyping research and interpretive scripting languages, as well as legacy compiled languages such as Java, C#, C, and C++. Pros and cons of popular languages for modeling trading ideas, including Python, R, MATLAB, and Microsoft Visual Studio, are discussed in detail. Additionally, the video highlights important considerations when selecting a programming language, such as co-location, cost-effective prototyping, and broker support. It emphasizes the significance of using productivity tools and taking into account the entire trading system, including risk management and portfolio management.
The speaker begins by categorizing programming languages into different groups based on their suitability for prototyping research and interpretive scripting. In the context of quantitative trading, he specifically addresses Python and MATLAB as popular choices for modeling trading ideas. However, he points out the challenge of Python's splintered versions (2.7 and 3.x) and highlights the issues with R's compatibility and performance. Python, on the one hand, offers numerous options, which can be overwhelming for developers and requires additional training. On the other hand, R has certain limitations in terms of compatibility and performance.
Moving forward, the speaker delves into various programming languages commonly used in quantitative and HFT trading. Python is discussed, emphasizing its strengths in terms of data packages, but also its drawbacks such as slower execution and limited order management capabilities. The speaker also mentions MATLAB 2015 and Microsoft Visual Studio 2015, which allow the integration of Python. Legacy compiled languages like Java, C#, C, and C++ are highlighted, with Java being recommended as a suitable starting point for programming beginners. C# is praised for its ease of understanding and advanced techniques, while optimal performance with C# is limited to Windows environments.
The video further explores programming languages suitable for quantitative and high-frequency trading, including Java, C/C++, and MATLAB. Java and C# are noted for their easy integration with databases, but limitations can arise due to garbage collection impacting performance. C and C++ are lauded as languages offering optimal speed and memory control, but they can be more complex to learn. MATLAB is recognized as a powerful and versatile platform with various toolboxes for data acquisition, analysis, trading execution, and risk management. Its advanced mathematical and machine learning support, along with the ability to generate code in C/C++ through MATLAB Coder, are highlighted. The speaker also mentions the option of embedding MATLAB into a high-performing web server using MATLAB Production.
Considerations for selecting a programming language in quantitative and HFT trading are thoroughly discussed. The speaker highlights the advantage of co-location in trading exchanges, particularly in HFT trading, and mentions MathWorks as a provider that facilitates co-location. The affordability of Lab Home Edition, starting at $150, is mentioned as a cost-effective prototyping environment. Additionally, the choice of broker is emphasized as a critical factor influencing the selection of programming language. Interactive Brokers is highlighted as a broker that supports legacy languages like Java, C++, and C#. The speaker advises newcomers to utilize productivity tools and emphasizes the need to consider the broader aspects of the trading system, including risk management, assessment, and portfolio management.
Overall, the video provides valuable insights into the different programming languages used in quantitative trading and HFT, their strengths and limitations, and the key factors to consider when selecting a language for trading purposes. It underscores the importance of understanding the entire trading system and utilizing appropriate tools for efficient and effective trading operations.
"Basic Statistical Arbitrage: Understanding the Math Behind Pairs Trading" by Max Margenot
"Basic Statistical Arbitrage: Understanding the Math Behind Pairs Trading" by Max Margenot
In the video titled "Basic Statistical Arbitrage: Understanding the Math Behind Pairs Trading" presented by Max Margenot, the concept of statistical arbitrage is thoroughly explained. Margenot describes how statistical arbitrage involves creating trades based on imbalances identified through statistical analysis and a model of how the market should behave. The video focuses on pairs trading, which relies on fundamental statistical concepts such as stationarity, integration orders, and cointegration.
Margenot begins by introducing Quantopian, his company's platform that offers free statistics and finance lectures to assist individuals in developing trading algorithms. He then delves into the significance of stationarity, integration orders, and cointegration in pairs trading. Stationarity refers to all samples in a time series being drawn from the same probability distribution with the same parameters, often assumed to be normally distributed in financial applications. The augmented Dickey-Fuller test is introduced as a means to test for stationarity.
The speaker emphasizes the uncertainty associated with real-world data, highlighting the potential for false positives in hypothesis tests, particularly when dealing with subtle or sneaky relationships between variables. He demonstrates this by generating a pathological relationship in a time series that may go undetected by a hypothesis test. Margenot underscores the importance of cautious interpretation of results and reminds the audience that even visual inspection of a graph may not reveal the underlying statistical properties.
The limitations of modeling time series and the possibility of false positives are discussed. While a time series may exhibit mean-reverting behavior, it does not always indicate stationarity. Stationarity represents a scenario where a time series is both mean-reverting and follows a stationary, deterministic, and random distribution. The concept of integration orders is introduced, where integration of order zero does not imply stationarity, but stationarity implies integration of order zero. Cumulative sums are also explained, illustrating how multiple integrations of order zero result in higher orders of integration.
The assumption of stationary returns in finance and the difficulty of finding stationary time series are addressed. Returns are assumed to be normally distributed, indicating stationarity. Integrated order and difference notation are used to test for stationarity. The speaker notes that theoretically, price series should be integrated of order one due to their relationship with returns, which are integrated of order zero. An example is provided using pricing data from a company.
Margenot proceeds to explain the concept of cointegration, which involves the integration of time series in specific defined ways to yield a linear combination that is stationary. Although finding two integrated time series that are stationary together can be challenging, cointegration can be valuable when exploring price series that have a reasonable economic basis. The speaker emphasizes that bets can be placed based on the current value of the stationary spread, even without a specific time model for mean reversion.
The process of creating simulated data is demonstrated to illustrate spread calculation and estimation using linear regression. Margenot stresses that financial data is rarely as simple as subtracting one variable from another, necessitating a linear regression to estimate the relationship between the variables. The goal is to determine the beta value, which indicates the composition of the portfolio in terms of market returns. This information allows for long and short positions in pairs trading. An example involving a pair of alternative-energy securities is provided to illustrate the concept.
Constructing a linear regression between two potential securities for basic statistical arbitrage is explained. Margenot recommends finding two securities within the same sector that exhibit a relationship as a starting point to identify potential co-integrative relationships, which can indicate arbitrage opportunities. While stationarity between two securities is beneficial, the speaker emphasizes the need to trade on as many different independent bets as possible rather than relying solely on one pair.
The calculation of pairs and deals within statistical arbitrage is based on the log returns of the examined pairs. The linear regression between the log returns, known as the Engle-Granger method, is employed to determine whether the regression is stationary. Once a reasonable model of the world is established, a trader can gain an edge by having more information than others and making relatively informed bets. To actively trade and update the rolling spread, a rolling notion of the mean and standard deviation is necessary. Different methods such as moving averages and common filters can be utilized to iterate and enhance the trading strategy.
The speaker emphasizes that statistical arbitrage can be a simple or complex unit strategy. It involves identifying stationarity, cointegration, and relationships between pairs of stocks to trade on. The more information one has compared to others, the better they can capitalize on these relationships. Building a diversified portfolio requires independent bets that are not reliant on each other. The frequency of rebalancing depends on the individual pairs and the duration of stationarity observed in the data.
The video moves on to discuss the simulation of algorithmic trading with real-time data. The assumptions underlying linear regressions, such as heteroscedasticity, are mentioned as factors that can affect their viability. Cointegration is favored over correlation when modeling relationships between pairs of stocks, as it represents a stronger condition indicating stationarity. Bet sizes can be systematically determined using the mean and standard deviation of the hypothesized spread, unlike correlations, which may not lend themselves to systematic approaches.
In summary, the video provides a comprehensive explanation of statistical arbitrage and pairs trading. It covers essential concepts such as stationarity, integration orders, and cointegration. The importance of careful interpretation of statistical results and the need for independent bets are emphasized. The speaker highlights the role of linear regression in estimating relationships between pairs of stocks and the significance of mean reversion in identifying arbitrage opportunities. The video concludes by discussing the simulation of algorithmic trading and the considerations for constructing a diversified portfolio in statistical arbitrage.
Complete overview of practical C++ programming for quant financial and HFT
Complete overview of practical C++ programming for quant financial and HFT
The video provides a comprehensive overview of the use of C++ programming in finance and high-frequency trading (HFT), offering valuable insights into various aspects of this field. It begins by discussing the book "Practical C++ Financial Programming," highlighting its significance in the finance industry. The book covers essential topics such as fixed income equities and provides practical examples with well-structured code sections. It assumes a level of comfort with C++ programming and provides guidance on leveraging C++ templates effectively. The speaker emphasizes the proper utilization of STL and boost libraries, as well as the use of open source libraries like new plot for plotting and QT for interface design.
Moving forward, the video explores the use of QT, a powerful tool for developing user interfaces in C++. While QT enables the creation of sophisticated graphical interfaces, it deviates from traditional C++ methodology, and the video sheds light on this aspect. The presentation then delves into mathematical concepts like linear algebra, interpolation, and numerical integration, breaking them down into basic algorithms and equations to facilitate understanding. Popular algorithms and modeling techniques relevant to finance are also discussed, with insights into their implementation in C++. The video emphasizes the importance of Monte Carlo simulations for financial applications, dedicating a chapter to this critical topic. Additionally, the use of Lua and Python for extending financial libraries is explored, along with an overview of the most popular programming languages for HFT job positions.
As the video progresses, it highlights the integration of Python and Lua with C++ and showcases how Lua can be effectively used with Redis, leveraging its embeddability within a C++ application. Various C++ techniques are also covered, including multi-threading using Plaza and the utilization of C++ 11 and 14 features. The video serves as an excellent introductory resource for individuals venturing into C++ programming, addressing some of the memory management challenges associated with the language. It provides a comprehensive roadmap for learning C++ programming, encompassing a wide range of options and techniques available to users.
Towards the end, the speaker shares a positive review of a recently published book on C++ programming for financial and high-frequency trading applications. This book specifically covers the new features introduced in C++ 17 that address low-level hardware concerns, making it an invaluable resource for those interested in this specialized field. Although the speaker acknowledges having no affiliation with the book, he highly recommends it as a valuable addition to the existing resources in this domain.
Algorithmic Trading Basics: Examples & Tutorial
Algorithmic Trading Basics: Examples & Tutorial
This video provides a comprehensive overview of algorithmic trading, covering various aspects such as trading styles, markets, and systems. The speaker begins by explaining the fundamentals of algorithmic trading, emphasizing the use of technical analysis based on price action, volume, and mathematical indicators. It is highlighted that algorithmic trading involves the execution of trades and back-testing of algorithms using computers, distinguishing it from traditional technical analysis.
Different types of quant/algorithmic trading are introduced, including high-frequency trading, statistical arbitrage, and trend/mean reversion/momentum trading. The speaker specifically focuses on swing and day trading in the futures market. Statistical arbitrage involves capitalizing on price differences by simultaneously buying and selling an asset, while trend/mean reversion/momentum trading utilizes computers to execute directional trades for profit. To illustrate these concepts, an algorithmic trading program example is demonstrated using TradeStation software. The program is designed to buy on a down day with a red candle and sell on the following positive day, incorporating a dollar target and stop. The speaker showcases the integration of this algorithmic program into a chart of the S&P 500 E-minis for back-testing purposes.
The next segment explores a trading strategy on TradeStation. The speaker uses a chart to demonstrate instances when the strategy would have been successful or unsuccessful based on candle colors. They zoom out to showcase the performance reports generated by TradeStation, providing metrics such as net profit, total profit, win rate, average trades, and drawdown. The optimization of the strategy is also addressed by adjusting stops and targets to assess the performance with different inputs. The speaker emphasizes the time-saving aspect of algorithmic trading, as it can provide valuable insights that would have otherwise taken months to discover.
Advantages and disadvantages of algorithmic trading are discussed in the subsequent section. The advantages include reduced human and emotional errors, rapid back-testing of trading ideas, faster order entry, and the ability to test multiple ideas and build portfolios. However, disadvantages such as overconfidence, overoptimization, and the inability to consider geopolitical events or fundamental trading techniques are also acknowledged. While an algorithm can be programmed to avoid trading on significant political or economic days, it generally operates in all market conditions.
The video concludes by summarizing its content. It clarifies the distinction between quantitative trading and fundamental or regular technical trading, emphasizing the power of algorithmic trading through a simple algorithm example. The advantages and disadvantages of algorithmic trading are reiterated for a comprehensive understanding. The speaker encourages viewers to reach out with any questions and expresses hope that the video has been informative and helpful.