Questions tagged [machine-learning]

Algorithms that allow computers to evolve behaviors based on empirical data. Approaches include genetic programming, artificial neural networks, decision trees, support vector machines, and cluster analysis.

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54 views

transforming a model to long short instead of long-only

I am currently trying to adapt a model to a long short portfolio strategy. The model is stated here: A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem by Jiang, Xu,...
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40 views

What is an appropriate Risk Metric for Portfolio Construction when returns are predicted instead of using mean returns?

I am trying to build a portfolio management system as my college project, and the approach I have chosen is that of combining machine learning and mathematical optimization. I am using weekly data. ...
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76 views

Stress testing by Banks

AFAIK typically banks stress test it trading portfolio by assuming stressed value of risk factors or by considering times series corresponding to some historical ...
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50 views

How to translate patterns in technical analysis into ML model?

Is there any working example/study that quantifies patterns seen in technical charts into tradeable signals for intraday regime? For example, I want to short a stock as it goes up to a 100-minute ...
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73 views

How to identify between Analytical, Numerical and ML Model based option pricing? [closed]

I am new to Quantitiative Finance. Coming from Computer Science domain, I wanted to clear the key distinguishing factor between analytical, numerical and ML based models for option pricing. As far as ...
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257 views

What kind of data cleansing/scrubbing are hedge funds doing?

It's a well-known fact that several hedge funds have a handful of PhDs just doing data cleansing. All day. Every day. What kind of data cleansing are they actually doing? Is it really that difficult? ...
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41 views

Triple barrier labeling for long-short strategy

Based on this previous question: Flexible horizon in Triple Barrier Method For a long-short strategy, should I develop one model to predict the direction of long or short (binary classification) and ...
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64 views

Applying day trading strategy into a quantitative strategy

I have been day trading US equities for a while successfully. I have a set of technical indicators and time frame that works for me plus profit taking and stop loss rules. I want to apply the rules ...
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51 views

larger sample weights for larger absolute returns?

In section 4.6 of Advances in Financial Machine Learning, Lopez de Prado writes In the previous section we learned a method to bootstrap samples closer to IID. In this section we will introduce a ...
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34 views

Algortihm for distributing volume for 1min candle

Context: I have historical 1min prices for stocks, including premarket. However, when importing real-time data, the standard practice in the financial data industry is to give only OHLC (open, high, ...
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37 views

Machine learning in stock price prediction [duplicate]

I am new and thinking to experiment in the stock price predication. There are many way like moving average but I am interested in using machine learning. Anyone can help me here to give pointer?
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83 views

stock price trend classification using Random Forest in sklearn

I have created a random forest classification model in skicit-learn, but I am unsure how to finalise my forecast. I have built the model and it is showing good results on the testing data. I get a ...
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49 views

How to create a local price index?

I have a set of real estate data; historic sales price, square meters, location (latitude, longitude), neighbourhood, city, sold date and bunch of other features. I have used a boosting model to ...
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59 views

Standardizing Sharpe Ratio or not when standardizing Features

I am currently trying to check the Feature Autocorrelation for a Trend Strategy. I am using XGBoost for that purpose. In addition I work with SHAP. In the first run I realized that without ...
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71 views

Train/test: why 80:20 split performed better than 90:10 split?

Playing with Random Forest Classifier, I am wondering what could cause in a 80:20 split the test results to perform better than in a 90:10 split? With 2000+ data points and: with 80:20 split, ...
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25 views

Target variable for a supervised learning approach for market sentiment index

My goal is to produce a signal going from -1 (negative) to +1 (positive) which corresponds to a sentiment index for USA. The index will be computed both based on headlines (taken from some free ...
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17 views

Which scikit learn module to use for bayesian nonlinear regression?

I have a nonlinear dataset and I am using GradientBoostingRegressor from scikit-learn. It gives me an r2 score of 96.9 after hyperparameter tuning. I want to use a bayesian model for this nonlinear ...
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107 views

Machine Learning approach for the probability estimation of certain events

I am planning a research project on estimating the probability of corporate takeovers. I think that different variables could be indicators to predict takeover bids. For example, price increases in ...
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23 views

How to decide which sentiment analyzer is the better model?

Assume one has trained different sentiment analysis models that assigns sentiment scores to the financial news or documents. How would one should approach testing the different models and decide which ...
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29 views

Minimize Composite Dispersion

Let's say that we have a composite of 10 fixed income portfolios, each with the same benchmark, the US Aggregate. Additionally, let's say that each portfolio has a position in Corporation ABC. The ...
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139 views

Backshifting Price Timeseries with Memory Preservation

In Advances in Financial Machine Learning the author makes a case for fractionally differentiated price returns in chapter 5. The reason is to both maintain memory and to generate a stationary time ...
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1answer
86 views

Using candlesticks for Stock price direction prediction

I am working on a college project wherein I want my machine learning model to predict the one-day-ahead direction of a given stock (i.e. whether the closing price of the stock would rise or fall as ...
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1answer
44 views

References on cashflow modelling for private equity

I would like to build a model to predict capital calls and distributions of a private equity fund. The first question is: does any of you can address me towards the state of art for it? also machine ...
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117 views

How are the values of the ARMA process linked in python

In the code below, you can see that 'ret' is an ARMA process, and I am trying to see how the ret[0], etc... ret3, ret4, etc. are linked to each other, and although I know the formula for the ARMA ...
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111 views

What data should I use for a machine learning model

I would like to ask you for an advice of any of you could help me with this information it would be really helpful. I am trying to build a reinforcement learning trading bot that based on the current ...
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100 views

What is the best way to impute missing values for financial data?

I've been tasked with imputing missing values for a dataset of ca. 4000 firms and 225 key metrics (e.g. revenue, net income, EPS, PE etc.). Since I haven't found a thread on here which answers my ...
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115 views

Steps to fit a Machine learning model for prediction of up and down market movement

I have around 5 years of data of an index containing many features on a daily basis. I want to classify whether the index will move up or down the next trading day (up or down movement is determined ...
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Why are there no papers about stock prediction with machine learning in leading financial journals?

I'm writing my master's thesis about stock price prediction using machine learning methods. During my literature review, I noticed that a lot of research produced on this topic is of poor quality, ...
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advances in financial machine learning - problem regarding to insufficient number of financial data to train ML algorithm

After reading 'Advances in Financial Machine Learning' by Marcos Lopez de Prado, I wonder how can we train machine learning algorithm with too few financial data. If we use cumsum filter etc the ...
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65 views

How to use 'purging' in predicting stock price tomorrow based on information today?

Q1. How to create an 'overlap' when we predict a stock price tomorrow based on information today? According to the book 'Advances in Financial Machine Learning' written by Marcos Lopez de Prado, the ...
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94 views

In Lopez de Prado's Advances in Financial Machine Learning, what is meant by "unnecessary labels"?

In Lopez de Prado's Advances in Financial Machine Learning, Chapter 3, Prof. Lopez de Padro talks about dropping rare labels: ...
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16 views

Understanding additive profit of risky and risk free asset

I was going through paper "Learning to Trade via Direct Reinforcement" by Moody and Saffell. It explains additive profit as follows Additive profits are appropriate to consider if each ...
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30 views

Computing statistics from historical returns

I'm reading age 35 of "Advances in Machine Learning" by de Prado. Consider an IID multivariate Gaussian process characterized by a vector of means μ, of size Nx1, and a covariance matrix V, ...
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89 views

Cannot achieve generalization of machine learning model

I'm working on a balanced, binary classification problem in a time-series (financial) dataset. I am using K-fold cross validation that is adapted for time-series (so that I'm never using future data ...
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1answer
74 views

House price inflation modelling

I have a data set of house prices and their corresponding features (rooms, meter squared, etc). An additional feature is the sold date of the house. The aim is to create a model that can estimate the ...
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50 views

How to use machine learning to generate optimal allocations for an instrument?

What is the idea behind using Machine Learning in finance? Let's assume that we have just one instrument given by its prices. At a given moment of time, we can "compress" the available ...
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118 views

Leakage and bias in XGBoost trading strategy

I apologize for my persistence, i'm on a course of study and doubts increase every day. My goal is "just" to code a profitable forex trading strategy with machine learning. I'm trying to ...
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199 views

Bug found in Optimal Number of Clusters algorithm - from de Prado and Lewis (2018)

I believe I have found a bug in Optimal Number of Clusters (ONC) from the paper "Detection of False Investment Strategies Using Unsupervised Learning Methods". ...
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107 views

Machine/Deep Learning for Exotic Option Pricing - Reference Request

Exotic options, in general, have very time-consuming valuation models. I believe in recent years there has been some research done on using supervised machine/deep learning to predict the valuation ...
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540 views

Deep Reinforcement Learning in Quant Finance?

I've been struggling to find engaging papers on the application of deep reinforcement learning in quantitative risk analysis, portfolio management, algorithmic trading and/or options pricing. What are ...
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304 views

Forecasting non-maturity deposits with machine learning

I need to forecast non-maturity deposits in a bank. My intent is to use Recurrent Neural Networks (aka deep learning) to model time series. The model will learn from past bank data and macroeconomic ...
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1answer
155 views

Fair Value Regression Methods

Recently we had an invited talk at our university (I'm Ph.D. student in ML department, so I'm sorry if my question is stupid, since I do not have quantitative finance background), where one researcher ...
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33 views

How to deal with outliers when normalizing price related input in algorithmic trading

I am training a CNN model for trading using indicator and MA lines to compose a 2D array as input. I want to normalize MA data(ema, sma...) into range between -1 and 1, I have tried several techniques ...
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65 views

Optimising returns weighted by Sharpe ratio in the context of Supervised Learning

In the Kaggle Jane Street market prediction competition we are put in a Supervised Learning Framework to deal with 'trade opportunities'. That is, we are given instances of previous trade ...
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199 views

Markov Property in Optimal Execution?

After reading papers on reinforcement learning with respect to the problem of optimal execution (Nevmyvaka et al (2006), Ning et al (2018), etc), I was wondering if the Markov property assumed in all ...
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286 views

Limit order book modeling based on computational statistics

Is someone aware of publications that try to model limit order book (and market mircostructure) in general using CS tools (such as online machine learning, game theory ecc...) and not stochastic ...
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173 views

Machine Learning model forecasting on real time data in python

I’m building a Forex trading system based on machine learning with Python and brokers API. I get price time series data + fundamental data and then i train the model on that. Model means SVM, RF, ...
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215 views

Reinforcement learning in finance

In brief, what are some mainstream and recent applications of reinforcement learning in finance that fall outside of the usual scope of agent-based modeling?
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115 views

Spectral clustering in finance

What are some examples of applying spectral clustering to financial times series data or other areas of finance? Why spectral clustering was used for each application rather than other types of ...
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95 views

How can deep learning methods measure implied volatility?

Why and how should we utilize deep learning methods to calculate implied vol of options? I've also heard that finding the fair price of the option is not nearly as important as finding a numerical ...

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