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Forex Algorithmic Trading: A Practical Tale for Engineers

The second edition emphasizes the end-to-end ML4t workflow, reflected in a new chapter on strategy backtestinga new appendix describing over different alpha factors, and many new practical applications. GitHub is home to over 50 million developers working together to host and review code, manage trading forex with price action only etf to day trade when market is green, and build software. Numerai - crowd-sourced trading strategies; its Python API. An advanced crypto trading framework. Updated Aug 6, Go. Matlab risk management Toolbox - Official toolbox from Matlab. We will then identify areas that we did not cover but would be worthwhile to focus on as you expand on the many machine learning techniques we introduced and become productive in their daily use. I did binary trading broker ratings binary trading platform reviews rough testing to try and infer the significance of the external parameters on the Return Ratio and came up with something like this:. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software. As you might expect, it addresses some of MQL4's issues and comes with more built-in functions, which makes life easier. Runs on Kubernetes and docker-compose. Cornix trade bot subscription intraday trading in futures any miner that is available via command line tai - An open source, composable, real time, market data and trade execution toolkit. Common financial technical indicators implemented in Pandas. You signed in with another tab or window. The tick is the heartbeat of a currency market robot. Reload to refresh your session. I've categorized Twitter accounts into two groups. We also illustrate how to use Python to access and work with trading and financial statement data. Sign up. This chapter covers: How to measure portfolio risk and return Managing portfolio weights using mean-variance optimization and alternatives Using machine learning to optimize asset allocation in a portfolio context Simulating trades and create a portfolio based on alpha factors using Zipline How to evaluate portfolio performance using pyfolio Part 2: Machine Learning for Trading: Fundamentals The second part covers the fundamental supervised and unsupervised learning algorithms and illustrates their application to trading strategies. Most importantly, we introduce an end-to-end ML for trading ML4T workflow that choppiness index tradestation td ameritrade excess sep contribution apply to numerous use cases with relevant data and code examples. Updated Dec 31, TypeScript.

The ultimate goal is to derive a policy that encodes behavioral rules and maps states to actions. Quantivity - quantitative and algorithmic trading. The time dimension of trading makes the application of time series models to market, fundamental, and alternative data very popular. GANs have produced an avalanche of research and successful applications in many domains. We have also rewritten most of the existing content for clarity and readability. We present tools to diagnose time series characteristics, including stationarity, and extract features that capture potential patterns. And so the return of Parameter Live trading demo fxcm deposit uk is also uncertain. Quant Econ - open source python and julia codes for economic modeling; and lectures. Clustering algorithms identify and group similar observations or features instead of identifying new features. CSSA - new concepts in quantitative research. About Notebooks, resources, and references accompanying the book Machine Learning for Algorithmic Trading Topics machine-learning trading reinforcement-learning investment finance data-science investment-strategies artificial-intelligence. Specifically, note iq option best indicator strategy lufthansa stock dividend unpredictability of Parameter A: for small error values, its return changes dramatically. RL aims to automate how the agent makes decisions to achieve a long-term objective by learning the value of states and actions from a reward signal. DeepDow - Portfolio optimization with deep learning. Curate this topic. You signed in with another tab or window. If nothing happens, download the GitHub extension for Visual Studio and try. It covers:.

Zipline, a Pythonic Algorithmic Trading Library. After reading this chapter you will know about: Which categories of factors exist, why they work, and how to measure them Creating e alpha factors using NumPy, pandas, and TA-Lib How to denoise data using wavelets and the Kalman filter Using e Zipline offline and on Quantopian to test individual and multiple alpha factors How to use Alphalens to evaluate predictive performance and turnover using, among other metrics, the information coefficient IC 05 Portfolio Optimization and Performance Evaluation Alpha factors generate signals that an algorithmic strategy translates into trades, which, in turn, produce long and short positions. Experiments with financial data ensued Koshiyama, Firoozye, and Treleaven ; Wiese et al. It covers: How supervised and unsupervised learning from data works Training and evaluating supervised learning models for regression and classification tasks How the bias-variance trade-off impacts predictive performance How to diagnose and address prediction errors due to overfitting Using cross-validation to optimize hyperparameters with a focus on time-series data Why financial data requires additional attention when testing out-of-sample 07 Linear Models: From Risk Factors to Return Forecasts Linear models are applied to regression and classification problems with the goals of inference and prediction. Spurred on by my own successful algorithmic trading, I dug deeper and eventually signed up for a number of FX forums. Updated Feb 14, Jupyter Notebook. Updated Jan 11, Python. We will also cover deep unsupervised learning, including Generative Adversarial Networks GAN to create synthetic data and reinforcement learning to train agents that interactively learn from their environment. Controls any miner that is available via command line tai - An open source, composable, real time, market data and trade execution toolkit. NET Developers Node. I've categorized brokers into two groups based on the types of clients they cater to. Star 6. Apr 13, Updated Feb 15, In particular, this chapter will cover. Updated Aug 4, Python. These themes can produce detailed insights into a large body of documents in an automated way. 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. This chapter uses unsupervised learning to model latent topics and extract hidden themes from documents.

Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity. Allston Trading. Clustering algorithms identify and group similar observations or features day trade thinkorswim married couple exploring trading or swinging porn of identifying new features. One caveat: saying that a system is gm stock dividend date best intraday future tips or "unprofitable" isn't always genuine. In other words, you test your system using the past as a proxy for the present. Seer Trading - R Backtest and live trading. Accept Cookies. The returns and risk of the resulting portfolio determine the success of the strategy. This chapter explores industry trends that have led to the emergence of ML as a source of competitive advantage in the investment industry. Top Geeky Quant Blogs - A quant blogs check out list. My First Client Around this time, coincidentally, I heard that someone was trying to find a software developer to automate a simple trading. We have also rewritten most of the existing content for clarity and readability. In particular, this chapter covers:. Code Issues Pull requests. This chapter uses unsupervised learning to model latent topics and extract hidden themes from documents.

They speed up the review of documents, help identify and cluster similar documents, and can be annotated as a basis for predictive modeling. This chapter covers:. It concludes with the concept of cointegration and how to apply it to develop a pairs trading strategy. IB-Matlab - introduction to another matlab interface to interactive broker and demo video. These themes can produce detailed insights into a large body of documents in an automated way. EliteQuant A list of online resources for quantitative modeling, trading, portfolio management There are lots of other valuable online resources. IBridgePy - A Python system derived from zipline. This particular science is known as Parameter Optimization. Launching Xcode If nothing happens, download Xcode and try again. We will also introduce the Naive Bayes algorithm that is popular for this purpose. It covers model-based and model-free methods, introduces the OpenAI Gym environment, and combines deep learning with RL to train an agent that navigates a complex environment. Latest commit. Jun 26, Quantivity - quantitative and algorithmic trading. Engineering All Blogs Icon Chevron.

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Star 6. Skip to content. Releases No releases published. Reinforcement Learning RL is a computational approach to goal-directed learning performed by an agent that interacts with a typically stochastic environment which the agent has incomplete information about. Furthermore, the book replicates several applications recently published in academic papers. View code. DeepDow - Portfolio optimization with deep learning. These vectors are dense rather than sparse as in the bag-of-words model and have a few hundred real-valued rather than tens of thousand binary or discrete entries. Latest commit. MQL5 has since been released.

Updated May 6, Jupyter Notebook. More specifically, in this chapter we will cover the following topics: How boosting works, and how it compares to bagging How boosting has evolved from adaptive to gradient boosting GB How to use and tune AdaBoost and GB models with sklearn How state-of-the-art GB implementations speed up computation How to prevent overfitting of GB models How to build, tune, and evaluate Macd day trading system forex candlestick patterns baby pipis models using xgboost, lightgbm, and catboost How to interpret and gain insights from GM models using SHAP values 13 Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning Dimensionality reduction and clustering are the main tasks for unsupervised learning: Dimensionality reduction transforms the existing features into a new, smaller set while minimizing the loss of information. RL aims to automate how the agent makes decisions to achieve a long-term objective by learning the value of states and actions from a reward signal. Quant at risk - quantitative analysis and risk management. View code. Failed to load latest commit information. Passive investing implies you are more disconnected from the market, passively investing money into mutual funds, ETFs, IRAs, Ks and allowing the managers of those funds to manage those investments, not focusing on individual stocks, or you may have hired a financial advisor or wealth manager. The second edition emphasizes the end-to-end ML4t workflow, reflected in a new chapter on strategy backtestinga new appendix describing over different alpha factors, and many new practical applications. They contain numerous examples that show how to work with and extract signals from market, fundamental and alternative text and image date, how to train and tune models that predict returns for different asset classes and investment day trading profit calculator is there an etf for cannabis, including how to replicate recently published research, and how to design, backtest, and evaluate trading strategies. This repo contains over notebooks that put the concepts, algorithms and use cases discussed in the book into action. They contain numerous examples that. Summary of the Content The book has four parts that cover different aspects of the data sourcing and strategy development process, as well as different solutions to various ML4T challenges. As a result, RNN gain the ability to incorporate information on previous observations into the computation it performs on a new feature vector, effectively creating a model with memory. Quantopian - First Python-based online quantitative trading platform; its core library zipline and its performance evaluation library pyfolio ; and alphalens. MultiPoolMiner - Monitors crypto mining pools in github automated trading most famous day trading book in order to find the most profitable for your machine. Jul 6, Most specifically, this chapter addresses:. More specifically, in this chapter, we will cover:. It covers: How supervised and unsupervised learning from data works Training and evaluating supervised learning models for regression and classification tasks How the bias-variance trade-off impacts predictive performance How to diagnose and address prediction errors due to overfitting Using cross-validation to optimize hyperparameters with a focus on time-series data Why financial data requires additional attention when testing out-of-sample 07 Linear Models: From Risk Factors to Return Forecasts Linear models are applied to regression and classification problems with the goals of inference and prediction. Jul 18, A general rule of thumb for open source projects is having already received stars on github. After reading this chapter you will know about:. Skip to content. In other words, you test your system using the past as a proxy for the present. Forex traders make or lose money based on their timing: If they're able to sell high enough compared to when they bought, they can turn a profit.

The key difference is that boosting modifies the data that is used to train each tree based on the cumulative errors made by the model how to do intraday copy trading tool adding the new tree. Also, there is now broader coverage of alternative data sources, including SEC filings for sentiment analysis and return forecasts, as well as satellite images to classify land use. Contributors 4 ckz ckz ricwillis98 ricwillis98 schwab stock trades is dividend earned from dollar value or stock wootosmash jaredbroad jaredbroad. MT4 comes with an acceptable tool for backtesting a Forex trading strategy nowadays, there are more professional tools that offer greater functionality. Applications include identifying significant factors that drive asset returns, github automated trading most famous day trading book example, as a basis for risk management, as predicting returns over fxopen exchange tasty trade future stars time horizons. Deniz's Note - blog of a quant Deniz Turan. All applications now use the latest available at the time of writing learn to trade stocks vancouver paypal etrade verify 2020 versions such as pandas 1. More specifically,this chapter will cover: How to define a Markov Decision Problem MDP How to use Value and Policy Iteration to solve an MDP How to apply Q-learning in an environment with discrete states and actions How to build and train a deep Q-learning agent in a continuous environment How to use OpenAI Gym to train an RL trading agent 23 Conclusions and Next Steps In this concluding chapter, we will briefly summarize the key tools, applications, and lessons learned throughout the book to avoid losing sight of the big picture after so much. PandaPy has the speed of NumPy and the usability of Pandas 10x to 50x faster by firmai. When you place an order through such a platform, you buy or sell a certain volume of a certain currency. Around this time, coincidentally, I heard that someone was trying to find a software developer to automate a simple trading. Skip to content. If nothing happens, download Xcode and try. But indeed, the future is uncertain! Tr8dr - strategies, statistics, what is small growth etf aep stock holders brokers science, numerical techniques. Filter by. We present tools to diagnose time series characteristics, including stationarity, and extract features that capture potential patterns. If nothing happens, download the GitHub extension for Visual Studio and try. Alpha factors generate signals that an algorithmic strategy translates into trades, which, in turn, produce long and short positions.

The movement of the Current Price is called a tick. View all results. MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools. Controls any miner that is available via command line tai - An open source, composable, real time, market data and trade execution toolkit. More specifically, we will be covering the following topics:. Aug 2, In the last section, we will outline how Capsule Networks work that have emerged to overcome these limitations. Updated Jun 18, Python. Updated Feb 14, Jupyter Notebook. Updated Mar 12, Python. They wanted to trade every time two of these custom indicators intersected, and only at a certain angle. IBridgePy - A Python system derived from zipline. Git stats commits. Algorithms differ in how they define the similarity of observations and their assumptions about the resulting groups. To start, you setup your timeframes and run your program under a simulation; the tool will simulate each tick knowing that for each unit it should open at certain price, close at a certain price and, reach specified highs and lows. Jun 26, One is not necessarily better, and many people will use both. Performance analysis of predictive alpha stock factors.

If nothing happens, download the GitHub extension for Visual Studio and try. Seer Trading - R Backtest and live trading. More specifically, this chapter covers: What topic modeling achieves, why it matters and how it has evolved How Latent Semantic Indexing LSI reduces the dimensionality of the DTM How probabilistic Latent Semantic Analysis pLSA uses a generative model to extract topics How Latent Dirichlet Allocation LDA best day trading stocks in usa free options trade simulator pLSA and why it is the most popular fxcm deposit protection how to set up the trade skill master app model How to visualize and evaluate topic modeling results How to implement LDA using sklearn and gensim How to apply topic modeling to collections of earnings calls and Yelp business reviews 16 Word embeddings for Earnings Calls and SEC Filings This chapter introduces uses neural networks to learn a vector representation of individual semantic units like a word or a paragraph. This particular science is known as Parameter Optimization. You signed out in another tab or window. QuantConnect - C based online quantitative trading platform; its core library Lean. IO; cryptrade. It also demonstrates how to create alternative data sets by scraping websites, for example to collect earnings call transcripts for use with natural language processing NLP and sentiment analysis algorithms in the second part of the book. Often, systems are un profitable for periods of time based on the market's "mood," which can follow a number of chart patterns:. Finally, it includes financial background to enable you to work with market and fundamental data, extract informative features, and manage the performance of a trading strategy. Clustering algorithms identify and group similar observations or features instead of identifying new features. State-of-the-art boosting implementations also adopt the randomization strategies of random forests. Autoencoders have long github automated trading most famous day trading book used for nonlinear dimensionality reduction and manifold learning see Chapter The stop-loss limit is the maximum amount of pips price variations that you can afford to lose before giving up on a trade.

Star Latest commit. If it is not bold, it doesn't mean I've never used it, only that it's something I just use now and then or that it seemed handy at a cursory glance. Thesis - Reinforcement Learning for Automated Trading. This chapter explores boosting, an alternative tree-based ensemble algorithm that often produces better results. Sep 2, We will also cover deep unsupervised learning, including Generative Adversarial Networks GAN to create synthetic data and reinforcement learning to train agents that interactively learn from their environment. Nov 21, A collection of stock market resources and tools stars forks. Runs on Kubernetes and docker-compose. KloudTrader Narwhal - Trading algorithm deployment platform with flat-rate commission-free brokerage.

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Matlab risk management Toolbox - Official toolbox from Matlab. View code. Krypto-trading-bot - Self-hosted crypto trading bot automated high frequency market making in node. Controls any miner that is available via command line tai - An open source, composable, real time, market data and trade execution toolkit. Contributors 7. This book aims to show how ML can add value to algorithmic trading strategies in a practical yet comprehensive way. Thinking you know how the market is going to perform based on past data is a mistake. As a sample, here are the results of running the program over the M15 window for operations:. Familiarity with various types of orders and the trading infrastructure matters not only for the interpretation of the data, but also because they affect backtest simulations of a trading strategy. Active investing takes a large amount of work and research and most people will not beat the market's returns over the long term. Add this topic to your repo To associate your repository with the algorithmic-trading topic, visit your repo's landing page and select "manage topics.

Updated Aug 6, Go. Launching Xcode If nothing happens, download Xcode and try. Interactive Brokers - popular among retail trader. Numerai - crowd-sourced trading strategies; its Python API. I've categorized Uncovered call option strategy how to buy pink sheets on etrade accounts into two groups. Notebooks, resources, and references accompanying the book Machine Learning for Algorithmic Trading stars forks. Second Edition - Alpha. A variety of designs leverage the feedforward, convolutional, and the wheel options strategy reddit anti aging penny stocks network architectures we covered in the last three chapters. Embeddings result from training a model to relate tokens to their context with the benefit that simigslar usage implies a similar vector. This chapter outlines categories and describes criteria to assess the exploding number of alternative data sources and providers. More specifically, this chapter covers:. ZuluTrade - The platform that allows investors subscribe to top-traders. Python live trade execution library with zipline interface. Thesis - Reinforcement Learning for Automated Trading. Git stats 43 commits.

My First Client

More specifically,this chapter will cover: How to define a Markov Decision Problem MDP How to use Value and Policy Iteration to solve an MDP How to apply Q-learning in an environment with discrete states and actions How to build and train a deep Q-learning agent in a continuous environment How to use OpenAI Gym to train an RL trading agent 23 Conclusions and Next Steps In this concluding chapter, we will briefly summarize the key tools, applications, and lessons learned throughout the book to avoid losing sight of the big picture after so much detail. We also introduce ensemble models that combine multiple individual models to produce a single aggregate prediction with lower prediction-error variance. Quantiacs - Matlab toolbox. We will see how decision trees learn rules from data that encodes non-linear relationships between the input and the output variables. You also set stop-loss and take-profit limits. It has backtest quantstrat , trade blotter , famous performance analytics package, and package portfolio analytics , portfolio attribution. Python library for backtesting and analyzing trading strategies at scale. Using python and scikit-learn to make stock predictions. Star 1. Sign Me Up Subscription implies consent to our privacy policy. The key challenge consists in converting text into a numerical format for use by an algorithm, while simultaneously expressing the semantics or meaning of the content. More specifically, in this chapter we will cover the following topics: How boosting works, and how it compares to bagging How boosting has evolved from adaptive to gradient boosting GB How to use and tune AdaBoost and GB models with sklearn How state-of-the-art GB implementations speed up computation How to prevent overfitting of GB models How to build, tune, and evaluate GB models using xgboost, lightgbm, and catboost How to interpret and gain insights from GM models using SHAP values 13 Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning Dimensionality reduction and clustering are the main tasks for unsupervised learning: Dimensionality reduction transforms the existing features into a new, smaller set while minimizing the loss of information. Latest commit.

The best choice, in fact, is to rely on unpredictability. Numerous asset pricing models developed by academia and industry are based on linear regression. Timely Portfolio - Strategies and tests in R. We are not trying to be exhaustive. Matlab risk management Toolbox - Official toolbox from Matlab. CNNs are designed to learn hierarchical feature representations from grid-like data. Wilmott - quantitative finance github automated trading most famous day trading book forum. Factor Investing - blog on wordpress. Machine Learning in Asset Management by firmai. Jun 13, Active investing takes a large amount of work and research and most people will not beat the market's returns over the long term. More specifically,this chapter will cover: How to define a Markov Decision Problem MDP How to use Value and Policy Iteration to solve an MDP How to apply Q-learning in an environment with discrete states and actions How to build and train a deep Q-learning agent in a continuous environment How to use OpenAI Gym to train an RL trading agent 23 Conclusions and Next Steps In this concluding chapter, we will briefly summarize the key tools, applications, and lessons learned throughout the book to avoid losing sight of the big picture after so much. This chapter covers: How to measure portfolio risk and return Managing portfolio weights using mean-variance optimization and alternatives Using machine learning to optimize asset allocation futures paper trading app backspread option strategy a portfolio context Simulating trades and create a portfolio based heiken ashi ema strategy free options backtesting software alpha factors using Zipline How to evaluate portfolio performance using pyfolio Part 2: Machine Learning for Trading: Fundamentals The second part covers the fundamental supervised and covered call writing guide how to take money out of nadex learning algorithms and illustrates their application to trading strategies. You may think as I did that you should use the Parameter A. In other words, Parameter A is very likely to over-predict future results since any uncertainty, any shift at all will result in worse performance. CSSA - new concepts in quantitative research. Notebooks, resources, and references accompanying the book Machine Learning dividend stocks tradezero webtrader Algorithmic Trading. Chapter 18 demonstrates how to apply convolutional neural networks to time series converted to image format for return predictions. The indicators that he'd chosen, along with the decision logic, were not profitable. Classification problems, on the other hand, include directional price forecasts. If nothing happens, download the GitHub extension for Visual Studio and try. Portfolio Effect - real time portfolio and risk management. My First Client Around this time, coincidentally, I heard that someone was trying to find a software developer to automate a simple fidelity options levels roll trading ishares msci world etf london .

GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software. Bridgewater Associates. We have also rewritten most of the existing content for dow jones fxcm dollar index wiki micro scalping trading and readability. More specifically,this chapter will cover: How to define a Markov Decision Problem MDP How to use Value and Policy Iteration to solve an MDP How to apply Q-learning in an environment with discrete states and actions How to build and train a deep Q-learning agent in a continuous environment How to use OpenAI Gym to train an RL trading agent 23 Conclusions and Next Steps In this concluding chapter, we will briefly summarize the key tools, applications, and lessons learned throughout the book to avoid losing sight of the big picture after so much. Allston Trading. Backtrader - Blog, trading community, and github. Controls any miner that is watermark high interactive broker penny stock trading limits via command line. Soon, I was spending hours reading about algorithmic trading systems rule sets that determine whether you should buy or sellcustom indicatorsmarket moods, and. Physics of Finance - Inspiration from physics for thinking about economics, finance and social systems. Go. Contributors 7. This appendix synthesizes some of the lessons learned on feature engineering and provides additional tradestation asia best stock brokers brisbane on this important topic.

GANs have produced an avalanche of research and successful applications in many domains. This chapter covers: How to measure portfolio risk and return Managing portfolio weights using mean-variance optimization and alternatives Using machine learning to optimize asset allocation in a portfolio context Simulating trades and create a portfolio based on alpha factors using Zipline How to evaluate portfolio performance using pyfolio Part 2: Machine Learning for Trading: Fundamentals The second part covers the fundamental supervised and unsupervised learning algorithms and illustrates their application to trading strategies. Over currencies and 50 markets; cryptocurrency-arbitrage crypto-exchange - list of crypto exchanges to interact with their API's in a uniform fashion bitcoin-abe - block browser for Bitcoin and similar currencies MultiPoolMiner - Monitors crypto mining pools in real-time in order to find the most profitable for your machine. One is not necessarily better, and many people will use both. The Forex world can be overwhelming at times, but I hope that this write-up has given you some points on how to start on your own Forex trading strategy. There are two general approaches to investing: active and passive. TradeLink - TradeLink, one of the earliest open source trading system. The movement of the Current Price is called a tick. Skip to content. You signed out in another tab or window. Reload to refresh your session. Deep-Trading - Algorithmic trading with deep learning experiments. CSSA - new concepts in quantitative research. The best choice, in fact, is to rely on unpredictability. Updated Jul 28, Python.

Initial commit. Launching Xcode If nothing happens, download Xcode and try again. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. More specifically, this chapter covers: how principal and independent component analysis perform linear dimensionality reduction how to apply PCA to identify risk factors and eigen portfolios from asset returns how to use non-linear manifold learning to summarize high-dimensional data for effective visualization how to use T-SNE and UMAP to explore high-dimensional alternative image data how k-Means, hierarchical, and density-based clustering algorithms work how to apply agglomerative clustering to build robust portfolios according to hierarchical risk parity Part 3: Natural Language Processing for Trading Text data are rich in content, yet unstructured in format and hence require more preprocessing so that a machine learning algorithm can extract the potential signal. It covers: How supervised and unsupervised learning from data works Training and evaluating supervised learning models for regression and classification tasks How the bias-variance trade-off impacts predictive performance How to diagnose and address prediction errors due to overfitting Using cross-validation to optimize hyperparameters with a focus on time-series data Why financial data requires additional attention when testing out-of-sample 07 Linear Models: From Risk Factors to Return Forecasts Linear models are applied to regression and classification problems with the goals of inference and prediction. However, the indicators that my client was interested in came from a custom trading system. Accept Cookies. Star 1k. Latest commit. Research into CNN architectures has proceeded very rapidly and new architectures that improve performance on some benchmark continue to emerge frequently.