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.

Filter by
Sorted by
Tagged with
1
vote
0answers
18 views

How to use multi-periods and mult-factors to predict stock price by linear regression?

Give data in $t_n$ denoted by $[x_1^n, x_2^n, ... x_d^n]$ and label $y_n$ to be predicted. We can just train a $d$-dimensional linear regression $y_n=\sum b_ix_i^n$ to make a prediction. However, I ...
1
vote
0answers
61 views

Meta Labeling for trading opportunities

In Advances in Financial Machine Learning, Lopez explains how we should build a primary exogenous model (binary classifier) to identify trading opportunities and a secondary meta model to filter out ...
2
votes
2answers
83 views

Does the non-causal nature of quant models limit their applicability?

I understand that to describe financial data, we build stochastic models and calibrate their parameters to past data. When coming up with new algorithms, we rely on rigorous backtesting to convince ...
0
votes
1answer
88 views

Random Forest on financial time-serie?

Is it okay to apply Random Forest to a non-stationary financial serie? Or would it be correct to first difference the serie and then apply Random Forest to the new serie?
4
votes
4answers
207 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 ...
1
vote
1answer
112 views

How can I approximate Dollar Bars from Minute Data instead of Tick Data?

Having been influenced by de Prado's Advances in Machine learning book, I've set out to build the dollar bars (in which each bar represents a set dollar amount of transactions in the security) that he ...
1
vote
1answer
56 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 ...
0
votes
3answers
209 views

What's a good resource of book for Python programming in relation to quantitative finance?

I know some of base Python, but I have only briefly used numpy, pandas, etc... I was wondering what's a good resource to learn Python specifically for quantitative finance. I know of plenty of books/...
5
votes
1answer
189 views

How to use neural network for technical analysis?

I am working on building a Neural network for technical analysis of stocks. The input I have is the open price and two (so far) technical indicators : RSI and William's R - for the past 2 years. I can ...
0
votes
1answer
93 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 ...
1
vote
0answers
56 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 ...
1
vote
0answers
140 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 ...
2
votes
0answers
54 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 ...
2
votes
1answer
82 views

Dollar bars in Advances in Financial Machine Learning book

Does anyone have use the dollar bars for building a strategy? I would like to know what ways you guys might be interested to set the dollar bars' parameter ( the dollar value ). I have thought of one ...
0
votes
1answer
55 views

Sample uniqueness and sample weight in AFML book

With reference to AFML ("Advances in Financial Machine Learning" book by Marcos Lopez de Prado). Are sample uniqueness and sample weight pointing towards to the same thing? I am confused on the term ...
2
votes
1answer
64 views

Labeling and excluding specific market conditions

I'm going through "Advances in Financial ML" book and got stuck with something which is not covered there (correct me if I'm wrong). Let's assume I labeled data to 0, 1, 2 according to triple barrier ...
2
votes
2answers
97 views

Flexible horizon in Triple Barrier Method

I'm going through "Advances in Financial ML" book and I really like the ideas behind Triple Barrier Method and using a flexible horizontal threshold based on volatility. What bothers me is that an ...
0
votes
0answers
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 ...
2
votes
1answer
95 views

Choosing best expressions from all possible combinations on variables, unary operators and binary operators along with hyper parameters

I have a few financial variables of a stock universe like OHLC prices, volume, and other fundamentals with varying time-frequency. Using this set I'm creating an expression that gives the weights to ...
2
votes
0answers
46 views

Conceptual help - Machine Learning on finance data set [closed]

I am working on Anomaly detection model problem for a finance data set - set of gift card activation transactions. My team member suggested an idea that " First train the model with normal instances ...
1
vote
1answer
76 views

Random Forests - Trees vs Predictors

This question relates to the use of random forests in finance and the relationship between the number of features, the observations, and the number of trees. Consider the relation between an RF, the ...
2
votes
1answer
120 views

Building a semi-discretionary system

I've been investing for the last 15 years in a weird Buffett/Soros way. For the last few years I've been toying with the idea of modeling myself. I want to build a 'stock screener' that will be able ...
3
votes
3answers
207 views

Getting sets of random correlated variables

For the training of a machine learning model I need to add additional features (macro variables), and these features are correlated. I need to run the model N times, and for each time I have to add ...
1
vote
1answer
90 views

Modeling mortgage loan defaults

I have a machine learning model trained with a list of mortgage features that include macro variables where the field to predict (the label) is "Mortgage Defaulted" = 1 or 0 (Yes or No). Now, I need ...
1
vote
1answer
139 views

How can stationary time series data be used as input in an ML model?

I am halfway through "Advances in Financial Machine Learning" by Marcos Lopez de Prado. I understand that a time series like stock prices can be transformed to make it sufficiently stationary. ...
1
vote
1answer
151 views

Which machine learning model rely on the normality assumption?

In the machine learning project, when the target variable is skewed, we need to use box-cox transformation to turn that into a normal distribution. But why do we need to do that? I mean, besides the ...
2
votes
1answer
138 views

Calculating PD of commercial bank loan

I have two main options to calculate PD of a loan in a commercial bank; with and without machine learning. On one hand, there are traditional methods such as Merton or KVM. On the other hand, I could ...
1
vote
2answers
90 views

Lower MSE results in less profit when using Machine Learning

When using Machine Learning for predicting stocks, can a lower Mean Squared Error result in less profit after Backtesting or is there a mistake in the experiment?
1
vote
1answer
84 views

Combine fundamental and market data into one ML model

What are the best tested ways to preprocess data with very different frequencies such as fundamental and market data into same ML model for quant trading?
2
votes
2answers
154 views

When to stop training?

I have built a deep reinforcement learning based portfolio optimisation agent. At a high level it is using macro economic data, valuations of the assets and a few technical indicators as the features. ...
2
votes
0answers
308 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 ...
3
votes
1answer
155 views

Risk-return ratio using ML default probability

I have access to a very large bond database (>20m rows) where 50% of the set are matured bonds for which a dummy variable identifies whether the bond defaulted or not. The remaining 50% are 'live' ...
8
votes
2answers
292 views

Defining an objective function for machine learning task of trading

A simplified example. Given: asset's price time series fixed distances to stop and target. A function of these inputs has two possible output values: $1$ if price is likely to hit the target ...
4
votes
1answer
264 views

The “Universal Model” by Justin Sirignano and Rama Cont

In the nicely written article https://arxiv.org/abs/1803.06917 by Justin Sirignano and Rama Cont, they explained that their model is universal and stationary. I am a bit confused about some questions. ...
3
votes
1answer
252 views

Algorithmic Trading: Normalization and Selection of Technical Indicators for Artificial Neural Networks [closed]

I study on algorithmic trading for a while based on technical indicators. I started to learn about neural networks and want to use technical trading indicators in this approach. However, I am not ...
1
vote
0answers
90 views

Machine Learnign for Factor Model python [closed]

I have read several articles about Factor Model using Deep Learning or machine learning, but none of them post the code. Where can I find the python code for anything similar?
3
votes
2answers
118 views

Any research on label/target variable design for ML training?

is there any discussion or paper about how to define/design the labels for the ML training? Intuitively I can think of: Net return of the next future day Net return using the max candle-high value of ...
2
votes
0answers
439 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 ...
3
votes
1answer
361 views

Why are there so few published research papers that apply Deep Learning to Algorithmic Trading?

The only related papers I can find are: Financial Trading as a Game: A Deep Reinforcement Learning Approach (2018) Deep Neural Networks in High Frequency Trading (2018) MACHINE LEARNING FOR TRADING (...
0
votes
2answers
128 views

Imputation of missing returns

I'm trying to calculate a historical VaR for a portfolio of futures, however there are certain days for which some assets are missing prices. Since the portfolio consists of many spread positions, the ...
1
vote
1answer
173 views

Should there be a relation between stocks when used as input data for integrating Technical Analysis with Machine Learning?

I'm integrating Technical Analysis with Deep Learning for the first phase of my research. I wanted to know how should I pick (or group) stocks as input data and whether there should be relation ...
0
votes
2answers
595 views

Hedging with machine learning

I’ve been thinking about an interesting problem lately: Suppose I have a position in an exotic derivative. How can I automate the hedging process? Traditionally, one build a pricing model and ...
1
vote
2answers
113 views

Measuring correlation between random variables when they are not normally distributed?

I want to perform some analysis on portfolio that consists of hedge funds (thus fund of hedge funds) In particular, I want to know the relationship between the funds during the downmarket. The ...
4
votes
1answer
361 views

Limit and Market Order for training a ML model

Goal : Using deep learning to build a ML model which would predict the right places where a stock price will increase, decrease or stay stable. For the current question, assume the labels are well ...
4
votes
4answers
518 views

Determine the right order size with market making strategy

In a market market strategy https://web.stanford.edu/class/msande448/2017/Final/Reports/gr4.pdf, how can we determine the right order size? Assuming I use a market making strategy and on a specific ...
0
votes
1answer
141 views

Market, Limit and Cancellation orders

From the paper https://web.stanford.edu/class/msande448/2017/Final/Reports/gr4.pdf page 8, I need at least the limit and market order. I can easily find the full depth from dxfeed or algoseek, but I ...
-1
votes
1answer
121 views

Separate market and limit orders from market depth/tick data

From the website https://www.algoseek.com/equities/, we can get a sample of the full depth market/tick data. From the paper https://arxiv.org/pdf/1710.03870.pdf page 8, I would like to extract the ...
1
vote
0answers
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 ...
0
votes
2answers
424 views

Can someone please share examples of machine learning in quantitative finance? [closed]

There has been a lot said about the application of AI, ML and Neural Networks in trading for predictive modelling. I was unable to find any relevant examples that prove a credible output based on ...
0
votes
0answers
296 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) ...