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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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1answer
60 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 ...
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2answers
96 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 ...
2
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1answer
169 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 ...
2
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4answers
216 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 ...
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1answer
89 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 ...
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1answer
69 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 ...
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0answers
17 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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0answers
20 views

Isolating the portion of the market indexes' returns that are independent of the other market indexes

I am trying to find a way to isolate the portion of the market indexes' returns that are independent of the other market indexes. My dataset comprises of 10 sectors (Technology, Financials, Utilities, ...
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2answers
198 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 ...
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0answers
75 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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2answers
144 views

Seeking papers that deal with stock market analysis

I am sure there are a lot of papers that are related to stock market analysis.. but I haven't been able to find ones that fit my needs most. I want to read papers, replicate their analysis, and use ...
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0answers
52 views

Imposing qualitative views in Black -Litterman model

I'm trying to construct a ETF portfolio with various asset classes using Black Litterman model. To impose views, I'm considering only qualitative views like {strong bearish, bearish, bullish, strong ...
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1answer
55 views

How can I reproduce the experimental verification of the “False Strategy” theorem plot?

I recently came across the following blog post talking about the importance of back-testing overfitting, and a plot claiming to be an experimental verification of the False Strategy theorem. The ...
0
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1answer
56 views

Subset selection to identify independent variables that impact the market?

Given a lot of market-related features (~100 independent variables such as emerging market, developed market, s&p 500, tech sector returns, etc), I need to select a subset of them that are ideally ...
2
votes
1answer
149 views

approach on trading algorithm using machine learning [closed]

let's say I am supervising a algorithmic trading project using machine learning. I don't have involvement in the technical side but am involved in the high level planning. the style is likely ...
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0answers
257 views

Machine Learning usage in Q part of Quant Finance

Machine Learning algorithms is broadly used in trading strategies and in general when it comes to working with financial time series. The webpage Quantopian is a platform to see some of the ...
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0answers
24 views

Uniqueness of data metric [closed]

Is there a metric that calculates "uniqueness of data"? For example if i have two sets of 200 observations, DataSet 1 has 70 unique values but 4 values take up the next 130 observations. DataSet 2 ...
4
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1answer
421 views

Trading Strategy adapting to my trading frequency

We want to predict the direction towards which the price will change. In this work the term price is used to refer to the mid-price of a stock, which is defined as the mean between the best bid ...
0
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1answer
94 views

Find a reasonable h

The mid-price at time $t$ is denoted by $$p_t = \frac{s_t^{a,1} + s_t^{b,1}}{2}.$$ This mid-price can evolve in minimum increments of half a tick but is almost always observed to move at ...
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0answers
62 views

Unlabelled mid-price stock data

The mid-price at time $t$ is denoted by $$p_t = \frac{s_t^{a,1} + s_t^{b,1}}{2}.$$ This mid-price can evolve in minimum increments of half a tick but is almost always observed to move at ...
0
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2answers
114 views

Combining Quantitative data with fundamental data

These day, there is relatively new phenomena of combining quantitative data and fundamental data called 'Quantamentals'. In this regards, I was wondering how to combine Four Essential Types of ...
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3answers
213 views

Appropriate way to normalize Bollinger Bands?

I am playing around with using neural nets to make predictions on market trends. I am currently feeding in a portfolio of historical data of many stocks, and am now implementing several technical ...
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0answers
29 views

Model selection given return and variance?

Suppose out-of-sample testing of a model yields certain values of returns and variance for each varying values of hyperparameters, then what are some methods in choosing the right hyperparameter? ...
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0answers
28 views

Measure of stock price predictor's performance?

Just started on my thesis based in using machine learning in the area of price prediction. I'm wondering what would be considered 'good' measures of predictive performance? Some papers use RMSE, but ...
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2answers
528 views

What’s the derivative of the sharpe ratio for one asset? Trying to optimize on it for a model

It seems most Sharpe ratio derivations seem to be for portfolios but I am just tracking a single asset? $SR = (r_p - r_f) / \sigma_p$ but what would I derive with respect to for an optimization/ ...
1
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1answer
88 views

How can I 'quantize' a time-series in 'groups' exhibiting similar patterns? [closed]

In Signal processing, there is a topic of 'Quantization' (the process of mapping input values from a large set to output values in a (countable) smaller set ('states') ). I would like to construct a ...
0
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1answer
65 views

How to properly classify rate of change?

I am working in a Machine Learning Model for Bitcoin Price. I am attempting to predict how much the price changes in the next day. I am approaching this as a classification problem instead of ...
2
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1answer
217 views

Scaling (Data prep) & Feature selection for the financial Data for LSTM Models

Overview I'm training an index e.g. FTSE100, where I have 8 years of past data (daily). I also have a list of its constituents. For each stock, I have the following features: ...
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0answers
38 views

Sentiment classification - Commercial annotation services for market/trading/financial news sentiment

This question might well be off-topic (in that case I'd be grateful if someone could point me in the right direction). I'm looking for services (similar to Amazon Mechanical Turk) that could aid with ...
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0answers
53 views

How does HMM apply to Forex for example, when any imaginable state you can think of involving price is observable?

Intuitively it seems like you can add states to the transition probability matrix $A$ and use a learning process to figure out the new transitions. If that's correct then it answers my question as ...
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0answers
84 views

How can I estimate the “trending Ornstein-Uhlenbeck” parameters of some mean reverting data?

I am using trending OU process instead of normal OU process because my data is following the properties of trending OU process. So anyone can help me with this that how can i estimate the parameters ...
2
votes
1answer
471 views

CAPM and factor modeling: Machine learning

Excuse my ignorance with this I am still trying to wrap my head around the interpretation of the Fama French 1992 factor paper. I come from a computer science background but I am interested in ...
1
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0answers
85 views

What is a good algorithm to predict volatility in metals commodity markets? [closed]

I'm trying to create a script to predict major swings in the price of Aluminium. I am trying to implement a dynamic time warping algorithm for the same. Was wondering if this really is the best ...
2
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0answers
335 views

stochastic modeling and machine learning [closed]

For a little bit of background, I've been studying stochastic calc and a few of it's applications (currently I'm still at the early stages of learning applications) and have been curious as to whether ...
0
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1answer
116 views

Unsupervised learning and in out of sample

Assume we are given $N$ samples, let's say small timeseries of 1 hour resolution daily exchange rates - for the sake of argument. Each sample is a $24$ element vector $x$. Then we proceed to do ...
2
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0answers
210 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 ...
-4
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1answer
225 views

python: How use the S&P 500 index to predict japan stock,namely timezone issue

I want to use American stock index, such as S&P 500 index(open, close...) to predict japan stock daily close price or other with machine learning. I found that there is timezone between japan and ...
7
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3answers
1k views

Determine trends of data (direction detection or turning point detection)

I'm working on a model to determine trends (direction detection or turning point detection). Suppose that we have a stock trend which is illustrated below. Blue line is real trend of stock close ...
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votes
1answer
426 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 ...
-1
votes
1answer
260 views

z-score versus log standardisation of stock prices for calculating correlation; which to use (in ML clustering, distance measure)?

I need to compare (get correlation between) different financial instruments (stocks). The problem is that different stocks will have different price scales. I was thinking of using z-score ...
3
votes
2answers
487 views

Where I can find historical earnings dates for stocks?

I'm trying to find all of the historical earnings dates (just the dates is good enough) for certain stocks ranging back to their IPOs. I'm plan to use it for my machine learning project. Yahoo and ...
0
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0answers
47 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 ...
-1
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1answer
261 views

ML classification algorithms give random profit [closed]

I use backtrader python framework to backtest ML classification algorithms to make decision ...
2
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0answers
199 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 ...
0
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1answer
94 views

How to normalize various indicators into one column?

I've seen this video which talks about how to compress different indicators into a sin https://www.youtube.com/watch?v=sDu6CudKa0Q I tried to do the same by this way: ...
0
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2answers
159 views

Scaling the data to train, then how to scale the input data?

I'm somewhat new into the world of trading algo's, so bare with me. I've made a dataframe with 5 features say. I used preprocessing.scale to scale it. I checked the csv dump of it and it looks fine ...
3
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0answers
146 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 ...
0
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1answer
95 views

Markov chain downgrades in loan book modelling

I am working on a personal loan dataset. For each loan, we recorded its credit status monthly after it was drawn by the borrower. Let's say there were 6 status coded by A-F. My project is to use ...
1
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2answers
297 views

Use machine learning to find exercise boundary of American put option

I am working on using machine learning to obtain American Put's early exercise boundary. To train the model, I need an output label (known boundaries values). Is there a fast way to obtain the ...
2
votes
1answer
10k views

How do I use machine learning to build a credit scoring model? [closed]

There are currently a lot of ways for credit scoring. The most popular one is the FICO score, and its variants. For my masters thesis, I would like to work on making my own credit scoring system using ...