Machine Learning for Algorithmic Trading
Machine Learning for Algorithmic Trading

Machine Learning for Algorithmic Trading

Most importantly, we introduce an end-to-end ML for trading (ML4T) workflow that we apply to numerous use cases with relevant data and code examples. (LocationĀ 631)

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It also includes simulating strategies on historical data using a backtesting engine and evaluating their performance. (LocationĀ 633)

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To this end, we present a framework that guides you through the ML4T process of the following: (LocationĀ 646)

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Sourcing, evaluating, and combining data for any investment objective Designing and tuning ML models that extract predictive signals from the data Developing and evaluating trading strategies based on the results (LocationĀ 647)

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Ultimately, the goal of active investment management is to generate alpha, defined as portfolio returns in excess of the benchmark used for evaluation. (LocationĀ 830)

Aggressive strategies include order anticipation or momentum ignition. Order anticipation, also known as liquidity detection, involves algorithms that submit small exploratory orders to detect hidden liquidity from large institutional investors and trade ahead of a large order to benefit from subsequent price movements. Momentum ignition implies an algorithm executing and canceling a series of ordersĀ to spoof other HFT algorithms into buying (or selling) more aggressively and benefit from the resulting price changes. (LocationĀ 892)

The momentum effect, discovered in the late 1980s by, among others, Clifford Asness, the founding partner of AQR, states that stocks with good momentum, in terms of recent 6-12 month returns, have higher returns going forward than poor momentum stocks with similar market risk. (LocationĀ 932)

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The three most important macro factors are growth, inflation, and volatility, in addition to productivity, demographic, and political risk. (LocationĀ 944)

Its secretive Medallion Fund, which is closed toĀ outsiders, has earned an estimated annualized return of 35 percent sinceĀ 1982. (LocationĀ 967)

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Point72, with $14 billion in assets, has been shifting about half of its portfolio managers to a human-plus-machine approach. (LocationĀ 1005)

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Alternative data is much broader and includes sources such as satellite images, credit card sales, sentiment analysis, mobile geolocation data, and website scraping, as well as the conversion of data generated in the ordinary course of business into valuable intelligence. It includes, in principle, any data source containing (potential) trading signals. (LocationĀ 1030)

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Typically, the datasets are large and require storage, access, and analysis using scalable data solutions for parallel processing, such as Hadoop and Spark. There are more than 1 billion websites with more than 10 trillion individual web pages, with 500 exabytes (or 500 billion gigabytes) of data, according to Deutsche Bank. And more than 100 million websites are added to the internet every year. (LocationĀ 1036)

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Algorithmic trading strategies are driven by signals that indicate when to buy or sell assets to generate superior returns relative to a benchmark, such as an index. (LocationĀ 2543)

Alpha factors are transformations of market, fundamental, and alternative data that contain predictive signals. (LocationĀ 2571)

Momentum investing is among the most well-established factor strategies, underpinned by quantitative evidence since Jegadeesh and Titman (1993) for the US equity market. It follows the adage: the trend is your friend or let your winners run. (LocationĀ 2608)

Over shorter, intraday horizons, market microstructure effects can also create price momentum as investors implement strategies that mimic their biases. (LocationĀ 2636)

Quality factors aim to capture the excess returns reaped by companies that are highly profitable, operationally efficient, safe, stable, and well-governedā€”in short, high quality. The markets also appear to reward relative earnings certainty and penalize stocks with high earnings volatility. (LocationĀ 2771)

Key tools that facilitate the transformation of data into factors include the Python libraries for numerical computing, NumPy and pandas, as well as the Python wrapper around the specialized library for technical analysis, TA-Lib. (LocationĀ 2814)

101 Formulaic Alphas, and implemented by the alphatools library. (LocationĀ 2816)

Recent examples include Rebellion Research, Sentient, and Aidyia, which rely on evolutionary algorithms and deep learning to devise fully automatic artificial intelligence (AI)-driven investment platforms. (LocationĀ 995)

Market microstructure studies how the institutional environment affects the trading process and shapes outcomes like price discovery, bid-ask spreads and quotes, intraday trading behavior, and transaction costs (Madhavan 2000; 2002). (LocationĀ 1243)