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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Algorithms for predicting a couple points in the future

I'm familiar with supervised learning algorithms like regression and neural networks which look at a bunch of input points and learn a function which outputs a value (the value varying depending on ...
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4answers
159 views

Backtest overfitting - in-sample vs out-of-sample

Recently, I read a great paper by De Prado et al. on backtest overfitting problem in Quantitative Finance titled Pseudo-Mathematics and Financial Charlatanism: the Effects of Backtest Overfitting on ...
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53 views

machine-learning method to predict PCA weights

I have been using certain linear-regression to extract the PCA (top 3) weights relating to a certain data-set. I was wondering, instead of using linear-regression to generate the weights, I can use ...
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284 views

Information Driven Bars (Advances in Financial Machine Learning)

My team and I are busy coding up a python implementation of the information driven bars (imbalance and run bars) mentioned in Chapter 2 of the text book Advances in Financial Machine Learning. There ...
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410 views

Getting over bid-ask bounce

One property of High-Frequency data is it's subject to bid-ask bounce. Description : Unlike traditional data based on just closing prices, tick data carry additional supply-and-demand information in ...
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348 views

Differential Sortino Ratio

I'm attempting to optimize a reinforcement learning system to maximize risk adjusted returns. I have currently defined the reward as the differential Sharpe ratio at each step: the influence of the ...
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0answers
283 views

Good books on predictive modeling (for alpha signal research)

In terms of books on predictive models, I find ESL (elements of statistical learning) trying to cover too much and serves more like a reference, instead of explaining and developing the theories for ...
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0answers
172 views

What are examples of boosting, bagging, stacking or subspace method in quantitative finance?

The above ensemble methods appear useful in several machine learning competitions, like Netflix prize or KDD. They work by diversifying between several model variants. Are they also useful in ...
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51 views

Using news to predict Stock Prices dataset

In order to build Regression or Deep Learning models for predicting the market, we need a bunch of historical data. Prices and technical indicators are easily accessible, but getting news from the ...
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118 views

Machine learning for portfolio optimization

What algorithms from machine learning, supervised learning or unsupervised learning have been recently used for asset allocation models as alternatives to the Markowitz mean-variance optimization ...
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28 views

Given historical performance of a financial index, how to categorise different historical periods depending on the market regime at the time?

We are trying to work on a Machine Learning application to attempt to predict market regime changes (bull, bear, stale?). Generally a ML algorithm needs well defined training data for establishing its ...
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31 views

Machine Learning Munging - order of transforms? + adding in econometric tests?

I have a list of possible transforms, and I've read some confusing/contradictory stuff about the preferred order in which these operations are performed. Maybe 1) the order is sometimes amorphous, ...
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1answer
41 views

Appropriate Encoding for Stock Technical Indicators ? RSI

happy new year and i am new to machine learning + python.. so recently i am doing a project on my own to use machine learning models on technical indicators.. I have my technical indicators data ...
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1answer
82 views

Why meta-labeling is is robust?

With all due respect, I saw this technique in the book , Advances in financial machine learning, but I found that it acts like a filter for the trades only. And it seems doing the job of overfitting ...
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32 views

Clusters evolution over time

I have a dataset of stock prices and I want to group stocks that share similar characteristics together using cluster analysis. I'm interested in following the evolution of each cluster over time, but ...
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283 views

How Machine Learning model addresses adverse action concerns -credit scorcard?

How to find the variables involved in the decision to report adverse action when the origination scorecard is developed using Machine Learning - XGBOOST with monotonic constraints (80 variables) ...
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52 views

Can Q-table learned by specific stock be applied to only that stock?

Let say I develop Q-learning strategy to predict IBM's stock price. So, it means that Q-table is created based on past IBM stock price data. In this case, this Q-table could be applied only to ...
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1answer
479 views

How to use exponential smoothing for trading?

I was wondering if there's a rule of thumb regarding the value of alpha used when performing exponential smoothing. I plan to use this technique to preprocess my data before feeding them into my ...