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cpsc540project's Introduction

List of Papers

Stock-selection/prediction (room for improvements here..!):

[Support Vector Machine With Adaptive Parameters in Financial Time Series Forecasting](no link)

[SUPPORT VECTOR MACHINE FOR REGRESSION AND APPLICATIONS TO FINANCIAL FORECASTING](no link)

Financial time series forecasting using support vector machines Does just what it says. Potentially useful list of features. Discusses tuning the model parameters and risk minimization

A SVM Stock Selection Model within PCA Quite poorly written and not particularly interesting, but it's something. Main idea is to use PCA for dimensionality reduction/feature selection

Stock Price Forecasting by Hybrid Machine Learning Techniques A simple ensamble model consisting of Artificial Neural Nets and Decision Trees. Contains references to a plethora of related works. Attempts to rationalize prediction somewhat.

A multiple-kernel support vector regression approach for stock market price forecasting Not quite so relevant really. Technical. But interesting to see some of the optimization/kernel stuff we touched on in class being used in practice

__Machine Learning Techniques and Use of Event Information for Stock Market Prediction: A Survey and Evaluation A survey in the use of ML in predicting the stock market. A little bit outdated (2005) but still useful since it has good references and gives an idea of the state of the art in the use of ML 12 years ago.

Stock Market Forecasting Using Hidden Markov Model: A New Approach Some of the few papers I've found where they use somehow Markov Chains (The funny thing is that the way mathematicians model stocks is with first order Markov Chains!)

Portfolio Optimization:

Using genetic algorithm to support portfolio optimization for index fund management

Learning to Optimize Profits Beats Predicting Returns โ€” Comparing Techniques for Financial Portfolio Optimisation

Prediction-based portfolio optimization model using neural networks

These two look more like student papers like our own

Equity forecast: Predicting long term stock price movement using machine learning

Machine Learning in Stock Price Trend Forecasting

A few more market-theoretical papers on the fundamental question of prediction

Efficient Market Hypothesis and Forecasting

Forecasting stock market prices: Lessons for forecasters A well written and interesting survey รก la 1992 of what the deal is

A Theory of Power Law Distribution in Financial Market Fluctuations I think this is an interesting paper, since understing the statistical properties of outliers, might give us a more solid way to implement the algorithms.

How the Economic Machine Works A short video of how the economy works (according to the point of view of a guy that knows what he is talking about)

Buzzwords++

Captial Asset Pricing Model

"The Foundation of Economic Analysis" Apparently a classic of the field, I've got a copy booked at the library.

Adaptive Market Hypothesis

Mean Variance Optimization

The Efficient Frontier

cpsc540project's People

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cpsc540project's Issues

Legit?

Hi guys. Is the distribution of this book legit? I have deleted my download it so as not contravene any copyright in the mean time.

New complementary tool

My name is Luis, I'm a big-data machine-learning developer, I'm a fan of your work, and I usually check your updates.

I was afraid that my savings would be eaten by inflation. I have created a powerful tool that based on past technical patterns (volatility, moving averages, statistics, trends, candlesticks, support and resistance, stock index indicators).
All the ones you know (RSI, MACD, STOCH, Bolinger Bands, SMA, DEMARK, Japanese candlesticks, ichimoku, fibonacci, williansR, balance of power, murrey math, etc) and more than 200 others.

The tool creates prediction models of correct trading points (buy signal and sell signal, every stock is good traded in time and direction).
For this I have used big data tools like pandas python, stock market libraries like: tablib, TAcharts ,pandas_ta... For data collection and calculation.
And powerful machine-learning libraries such as: Sklearn.RandomForest , Sklearn.GradientBoosting, XGBoost, Google TensorFlow and Google TensorFlow LSTM.

With the models trained with the selection of the best technical indicators, the tool is able to predict trading points (where to buy, where to sell) and send real-time alerts to Telegram or Mail. The points are calculated based on the learning of the correct trading points of the last 2 years (including the change to bear market after the rate hike).

I think it could be useful to you, to improve, I would like to share it with you, and if you are interested in improving and collaborating I am also willing, and if not file it in the box.

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